thesis on land cover

Land use/land cover change in Orange County, North Carolina from 1955 to 2001

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• Land use land cover change in Orange County, North Carolina from 1955 to 2001
• March 21, 2019
• Affiliation: College of Arts and Sciences, Department of Geography
• Land use/land cover change (LULCC) has significant implications in terrestrial ecosystem goods and services. Current studies on LULCC are primarily based on space-borne satellite images. These studies are limited by the data availability. In this study, I extend the land-use land cover change analysis back to 1955 for Orange County, North Carolina based on historical aerial photos. I also analyzed the spatial configuration of the landscapes based on pattern matrix analysis and geospatial analysis. Results show that the urban area increased from 11.31 km2 to 128.15 km2 from; the agricultural area decreased from 335.53 km2 to 259.81 km2; the forest area decreased from 676.95 km2 to 632.45 km2. The LULCC change is associated with land configuration and composition changes. Pattern metrics analysis shows that in 2001, Orange County has more scattered land use patches and smaller patch sizes than in 1955 and 1975. Semi-vairograms generated for conifer and hardwoods are changing both in shape and the key characteristic parameters with time. These results can provide essential information for land management and planning to achieve sustainable development of Orange County in the future.
• August 2010
• https://doi.org/10.17615/awjy-zt84
• Masters Thesis
• In Copyright
• "... in partial fulfillment of the requirements for the degree of Master of Arts in the Department of Geography."
• Song, Conghe
• University of North Carolina at Chapel Hill
• Chapel Hill, NC
• Open access
• March 18, 2013

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ADDIS ABABA UNIVERSITY COLLEGE OF NATURAL AND COMPUTATIONAL SCIENCES SCHOOL OF EARTH SCIENCES

IMPACT OF LAND-USE/LAND-COVER CHANGES ON LAND SURFACE TEMPERATURE IN ADAMA ZURIA WOREDA, ETHIOPIA , USING GEOSPATIAL TOOLS

A thesis submitted to

The school of Graduate Studies of Addis Ababa University in partial fulfillment of the requirements for the Degree of Masters of Science in Remote Sensing and Geo- informatics

BELETE TAFESSE (GSR/0469/08)

Dr.K.V.SURYABHAGAVAN

Prof. M.BALAKRISHNAN

Addis Ababa University

IMPACT OF LAND-USE/LAND-COVER CHANGES ON LAND SURFACE TEMPERATURE IN ADAMA ZURIA WOREDA, ETHIOPIA, USING GEOSPATIAL TOOLS

A THESIS SUBMITTED TO THE SCHOOL OF GRADUATE STUDIES OFADDIS ABABA UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTERS OF SCIENCE IN REMOTE SENSING AND GEO- INFORMATICS

BELETE TAFESSE HABTEWOLD

(GSR/0469/08)

School of Graduate Studies

This is to certify the thesis prepared by Belete Tafesse entitled as “Impact of land-use/land- cover changes on land surface temperature in Adama Zuria Woreda, Ethiopia, using Geospatial tools” is submitted in partial fulfillment of the requirements for the Degree of Master of Science in Remote Sensing and Geo-informatics compiles with the regulations of the University and meets the accepted standards with respect to originality and quality.

Signed by the Examining Committee:

Dr. K.V.Suryabhagavan ______/______/______

Advisor Signature Date

Prof. M.Balakrishnan ______/______/______

Co-advisor Signature Date

Dr. Binyam Tesfaw ______/______/______

Chairman Signature Date

Dr. Ameha Atnafu ______/______/______

Examiner Signature Date

Head, School of Earth sciences

______/______/______

Signature Date

Acknowledgments

First and for most, I would like to thank the “Almighty God” who made it possible and for strength and patience that he gave me to complete my study successfully within the given short period of time. Next to “God”, many people and organizations deserve heartfelt thanks for their precious contributions to this study.

I express my heartfelt gratitude and indebtedness to my advisor Dr. K.V.Suryabhagavan and to my Co-advisor Prof. M.Balakrishnan, who helped me from the very beginning of the research to its compilation and for sharing me their time for valuable discussions and giving me their kind helpful guidance throughout this study.

Further, I would like to extend my thanks to all Earth Science Department staff members and Remote Sensing and Geo-informatics stream staff members for securing lab. facility and unreserved support.

I have a great gratitude to acknowledge the Ethiopian Mapping Agency, Central Statistical Agency, Ethiopian Geological Survey and Ethiopian Meteorology Agency for providing me the necessary data and information to carry out this study.

Special thanks go to all my family members and my classmates for their appreciation and continuous support. Finally, I want to thank all of those people who, once upon time, were my teachers, Friends and others, who helped me, to begin this long journey. Their names are too numerous to list, but many of them inspired me to learn and upgrade.

Table of Contents Acknowledgments ...... i

List of Tables ...... vi

List of Figures ...... vii

List of Appendices ...... ix

List of Acronyms ...... x

Abstract ...... xi

CHAPTER ONE ...... 1

1. INTRODUCTION...... 1

1.1. Background of the study ...... 1

1.2 Statement of the problem ...... 3

1.3. Objectives of the study ...... 3

1.3.1. General Objective ...... 4

1.3.2 Specific Objectives ...... 4

1.4. Research Questions ...... 4

1.5. Scope of the study ...... 4

1.6. Significance of the study ...... 5

1.7. Limitations of the study...... 5

1.8. Structure of the thesis ...... 5

CHAPTER TWO ...... 7

2. LITERATURE REVIEW ...... 7

2.1. Concept of land-use/land-cover change ...... 7

2.2. Causes of land-use and land-cover changes ...... 7

2.3. Land-use and land-cover change in Ethiopia ...... 8

2.4. Remote Sensing ...... 9

2.5. Geographic Information System (GIS) ...... 9

2.6. Role of Remote Sensing and GIS in Land-use and Land-cover change ...... 10

2.7. Land surface temperature ...... 11

2.8. Urban heat island ...... 11

2.9. The impact of land-use/land-cover change on land surface temperature ...... 12

2.10. Normalized Difference Vegetation Index ...... 12

CHAPTER THREE ...... 14

3. MATERIALS AND METHODS ...... 14

3.1. Description of the study area ...... 14

3.1.1. Location ...... 14

3.1.2. Topography ...... 15

3.1.4. Population ...... 18

3.2.1. Primary data ...... 19

3.2.2. Remote Sensing data acquisition ...... 19

3.2.3. Field data ...... 23

3.2.4. Secondary data ...... 23

3.2.5. Data description and source ...... 24

3.2.6. Software Packages used ...... 24

3.3. Methods...... 25

3.3.1. Data Preparation and Analyzing ...... 27

3.3.2. Digital Image Processing (DIP) ...... 27

3.3.3. Image enhancement ...... 27

3.3.4. Image classification ...... 28

3.3.5. Classification accuracy assessment ...... 29

3.3.6. Land-use/Land-cover change detection ...... 33

3.4. Derivation of Normalized Difference Vegetation Index and Land surface temperature ... 33

3.4.1. Derivation of Normalized Difference Vegetation Index ...... 33

3.4.2. Derivation of land surface temperature ...... 34

3.4.3. Radiometric correction ...... 35

3.4.4. Conversion at sensor spectral radiance ...... 35

3.4.5. Conversion to top of atmosphere (TOA) reflectance ...... 36

3.4.6. Conversion of radiance into brightness temperature ...... 37

CHAPTER FOUR ...... 42

4. RESULTS ...... 42

4.1. Land-use/land-cover in 1989, 1999 and 2016 ...... 42

4.2. Spatial extent of land-use/land-cover ...... 44

Table 4.3: Land transformation for Adama Zuria Woreda (1989––2016)...... 46

4.3. Settlement expansion during 1989–2016 ...... 46

4.4. Accuracy assessment ...... 47

4.5. Normalized difference vegetation index...... 48

4.6. Relationship between land-use/land-cover and Normalized difference vegetation index ...... 50

4.7. Spatial pattern of land surface temperature in Adama Zuria Woreda ...... 50

4.8. Impact of land-use/land-cover change on land surface temperature ...... 52

4.9. Relationship between land-use/land-cover and land surface temperature ...... 54

4.10. Relationship between normalized difference vegetation index and land surface temperature ...... 55

4.11. Comparisons of LST distribution during 1989, 1999 and 2016 ...... 57

4.12. Verification of the result for land surface temperature ...... 58

CHAPTR FIVE ...... 60

5. DISCUSSION ...... 60

5.1. Land-use/land-cover status of Adama Zuria Woreda ...... 60

5.2. Normalized difference vegetation index ...... 60

5.3. Land surface temperature ...... 60

CHAPTR SIX ...... 63

6. CONCLUSION AND RECOMMENDATIONS ...... 63

6.1. Conclusion ...... 63

6.2. Recommendations ...... 63

References ...... 65

Appendices ...... 71

List of Tables Table 3.1 Remote sensing data used in the study...... 19 Table 3.2: Landsat 4 Thematic Mapper sensor bands and description...... 21 Table 3.3: Landsat 7 Enhanced Thematic Mapper Plus bands and description...... 22 Table 3.4: Landsat 8 Operational Land Imager and Thermal infrared Sensor bands and description...... 23 Table 3.5: Data Description and source used in this study...... 24 Table 3.6: Land-use/land-cover classes and description of the study area...... 30 Table 3.7: Emissivity constant value of Landsat 8...... 35 Table 3.8: Thermal band calibration constant of Landsat 8...... 38 Table 3.9: Split window algorithm constant value...... 39 Table 3.10: Thematic mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) thermal band calibration constant...... 39 Table 4.1: Land-use/land-cover classes and area coverage of 1989, 1999 and 2016 in Adama Zuria Woreda, Ethiopia ...... 42 Table 4.2: Land-use/land-cover distribution and net changes during 1989–2016...... 45 Table 4.3: Land transformation for Adama Zuria Woreda (1989––2016)...... 46 Table 4.4: Statistical information of accuracy assessment for the year 1989, 1999 and 2016.. .. 48 Table 4.5: Statistical information of NDVI value for the years 1989, 1999 and 2016...... 48 Table 4.6: Zonal statistical description of LST in 1989, 1999 and 2016 over different LU/LC classes...... 52 Table 4.7: Mean temperature of 1989, 1999, 2016 and changes in temperature during 1989−2016 in the study area...... 54 Table 4.8: Trends of land surface temperature distribution during the study period in Adama Zuria Woreda during 1989–2016...... 57

List of Figures Figure 3.1: Location map of the study area...... 14 Figure 3. 2: (a) Elevation and (b) Slope map of the Study area...... 15 Figure 3.3: Monthly average rainfall distribution of the Study Area...... 16 Figure 3.4: Distribution of annual rainfall in the study area (1989–2016)...... 17 Figure 3.5: Maximum, minimum and mean monthly Temperature during 1989–2016...... 17 Figure 3.6: Population of Adama Zuria Woreda...... 18 Figure 3.7: Landsat Images of 1989 TM, 1999 ETM+ and 2016 OLI and TIRS...... 20 Figure 3.8: Methodological flow chart of the study…………………………………………….26

Figure 3.9: Interpretation of Landsat image for LU/LC classification...... 31 Figure 3.10: Steps and procedures followed to classify land-use/land-cover from a Landsat image...... 32 Figure 4.1: Land-use/land-cover maps of the study area of the years of 1989, 1999 and 2016. 43 Figure 4.2: Land-use/land-cover distribution and changes in the study area during the period 1989–2016...... 44 Figure 4.3: Land-use/land-cover changes during 1989–2016 in the Adama Zuria Woreda...... 45 Figure 4.4: The trend of expansion of settlement area from the year 1989 to 2016 in Adama Zuria Woreda...... 47 Figure 4.5: Normalized difference vegetation index maps of the study area (1989, 1999 and 2016)...... 49 Figure 4.6: Zonal statistical description of NDVI in 1989, 1999 and 2016 over different LU/LC classes in the study area...... 50 Figure 4.7: Land surface temperature maps of the study area of the years 1989, 1999 and 2016...... 51 Figure 4.8: Different views of land surface temperature of the study area for 2016...... 53 Figure 4.9: Comparisons of mean land surface temperature in different land-use/land-cover classes during the study period in Adama Zuria Woreda...... 54 Figure 4.10: Land surface temperature and normalized difference vegetation index correlation for the years 1989, 1999 and 2016 for the study area...... 56 Figure 4.11: Comparisons between LST distributions of the years 1989, 1999 and 2016 in Adama Zuria Woreda...... 57

Figure 4.12: a) Interpolated map of rainfall and b) Interpolated map of temperature of Adama Zuria Woreda...... 58

List of Appendices Appendix 1: Classification accuracy assessment report for the year 1989...... 71

Appendix 2: Classification accuracy assessment report for the year 1999...... 72

Appendix 3: Classification accuracy assessment report for the year 2016...... 73

Appendix 4.Plate 1: Sample of different LU/LC photographs...... 74

Appendix 5.map 1: Location of meteorological stations map...... 75

Appendix 6 map 2: GPS point data map...... 76

List of Acronyms CSA Central Statistical Agency

DEM Digital Elevation Model

DIP Digital Image Processing

DN Digital Number

ENVI Environment for Visualizing Images

ERDAS Earth Resources Data Analysis System

ETM+ Enhanced Thematic Mapper Plus

FAO Food and Agricultural Organization

GIS Geographic Information System

GPS Global Positioning System

LST Land Surface Temperature

LU/LC Land-Use/Land-Cover

LU/LCC Land-Use/Land-Cover Change

NDVI Normalized Difference Vegetation Index

OLI Operational Land Imager

QGIS Quantum GIS

TIRS Thermal Infrared Sensor

TM Thematic Mapper

TOA Top of Atmosphere

UHI Urban Heat Island

UNEP United Nations Environmental Program

USGS United States Geological Survey

UTM Universal Transverse Mercator

Abstract Land-use/land-cover change is one of the main environmental problems and challenges, which strongly influence the process of urbanization and agricultural development. The world has faced with the problem of overwhelming increase in land surface temperature (LST) as compared from year to year. The present study has investigated the impact of land-use/land-cover (LU/LC) change on LST. The research was conducted in Adama Zuria Woreda, located East Shewa Zone of Oromia region, in the main Ethiopian rift valley. Land-use/land-cover, LST and NDVI were extracted from Landsat TM (1989), Landsat ETM + (1999), Landsat 8 OLI/TIRS (2016) using GIS and remote sensing tools. Remote sensing and GIS were found to be a robust technique to quantify and map the LU/LC changes and its drawback on LST. Land surface temperature was done using split window algorism. Changes in LU/LC, which occurred between 1989 and 2016 in the study area was evaluated and analyzed using geospatial tools and verified by field data. The result of LU/LC indicated that farmland covered more than 60% during the study periods (1989–2016) and followed by shrub land covering more than 12%. The study indicated that most areas having lower LST in 1989 were changed to higher LST in 1999 and 2016.This happened due to the increased in different LU/LC changes especially the decreasing of vegetation cover in the study area. By linking the LU/LC classes and LST parameter using zonal statistics as table, it has been found that, LST has negative relationship with vegetation cover. Land surface temperature result showed that the northwestern, south (Wenji showa sugarcane plantation), lake Koka area and along Awash River have relatively low value ranged 9ºC–21ºC. This happened because of high NDVI value. While the eastern, Adama Town and western part had high LST value reached up to 42ºC. Therefore, the visual comparison of 1989, 1999 and 2016 images showed that the LU/LC type and NDVI status play a great role for variability of LST values. Land-use/land-cover change could not be stopped easily. However, different measures have to be taken by environmental experts and the concerned bodies to minimize the influence of changes on the LST and environments. At last, this study implies that the use of geospatial tools as time saving and cost effective methods for LST analysis and evaluation.

Key words: LU/LC, LST, NDVI, Landsat image, GIS, Remote sensing

M.Sc, Thesis on: Impact of land-use/land-cover changes on land surface temperature in Adama Zuria Woreda, Ethiopia using geospatial tools: may 2017

CHAPTER ONE

1. INTRODUCTION

1.1. Background of the study The planet earth has been in a state of continuous change since a long time ago and has faced with the problem of overwhelming increase in LST as compared from year to year. Land-use/land-cover (LU/LC) changes caused by natural and human processes have played a major role in global as well as regional scale patterns of the climate and other aspect of the earth (Ramachandra et al., 2012). Now a day’s global warming makes massive LU/LC changes, which affects many aspects of the natural environment. All over the world the change in LU/LC leads to environmental changes, rainy season fluctuation, sea level rise and increase land surface temperature (LST). Massive LU/LC changes are result of the need of land for settlement and agricultural in relation to the increasing human population. Land is a scarce (limited) natural resource, which cannot be changed when the population increases. Land-use should correspond to land capacity and it should respect the overall climate condition and the environment (FAO/UNEP, 1999). Human population is increasing and it causes transformation of natural setting/environment into human landscapes. Human settlements, the need for farmland, and especially industrial areas and large urban areas significantly modify their environment. Changing from permeable and moist land uses to impermeable and dry one with paving and building material can have a negative effect on LST and energy budget (Guo et al., 2012), and also, many other surface properties such as the surface infiltration, runoff rate, evaporation and drainage system. Therefore, it is critical to study to have detailed information, spatial-temporal LU/LC dynamics and the rate of changes.

One of the main environmental problems in both rural and urban areas is the increase of LST due to conversion of vegetated surfaces into a settlement, bare land and agricultural land. Land surface temperature is one of the main variables measured using thermal bands of different sensors such as AVHRR, MODIS, Landsat-5TM, Landsat-7ETM+ and Landsat-8TIRS (Gebrekidan, 2016). Land surface temperature is the temperature of the land derived from solar radiation (Kumar and Singh, 2016). It is also defined as the temperature of the skin of earth surface phenomena and the feeling of how much hot the surface of the earth as derived from direct measurements/ from remotely sensed information (Kayet et al., 2016).

AAU.Remote Sensing and Geo-informatics stream: By Belete Tafesse: [email protected] 1

According to Rajeshwari and Mani (2014), LST is the temperature emitted by the surface and measured in Kelvin/Celsius. In remote sensing language, LST is the surface radiometric temperatures emitted by the land surfaces and adhere/observed by a sensor at instance viewing angles (Prata and Caselles, 1995; Schmugge et al., 1998). It is highly influenced by increasing greenhouse gases in the atmosphere. The changes of LST integrated with many factors, such as dynamics in land-use, seasonal variation of rainfall, weather condition and socio-economic development (Jiang and Guangjin, 2010). Urban thermal is sway out by the physical characteristics of the earth surface and by human socio-economic activities. Also to the rural area, it influenced by way of land-use and agricultural activities (Yue et al., 2007). The thermal environment can be considered to be the main and significant indicator for describing the urban vegetation and environment (Yue et al., 2007).The total energy-balance of urban areas is similar to the rural areas; however, there is difference in the ratio of shortwave and long wave radiation (Orsolya et al., 2016).

Being a less developed society, the adhered dynamics of the land are rapid in Africa than in developed continents. Environmental degradation (deterioration) is a main phenomenon in Ethiopia since agricultural activities were begun (Orsolya et al., 2016). Increasing number of population or High population pressure, low production, decline land holding size, loss of soil fertility and increasing demand for fuel energy and construction are some of the expected challenges for Ethiopia for the future. Modification and conversion of environment have impact on ecology and foster pressure on the living standard of the society. As Asubonteng (2007), stated land-use and rapid modification of land-cover have adept or practiced implications for human survival.

Changes in the land-cover occurs predominantly because of fire (Nunes et al., 2005) and deforestation for agriculture and settlement (urban expansion) (Huang and Siegert, 2006). A Land- cover change further disturbs the biogeochemical cycling that induces global warming, erosion of soil and land-use patterns.

Remote sensing and GIS or geospatial tools are now providing new tools for advanced environmental management. Satellite data facilitate synoptic dissection of the earth system, patterning and changes from local to global scales over time. Therefore, attempt has been made in this study to evaluate, analyze and to map out the status of LU/LC and LST in Adama Zuria Woreda Ethiopia. Remote sensing and GIS also used for evaluating and processing meteorological data such

AAU.Remote Sensing and Geo-informatics stream: By Belete Tafesse: [email protected] 2

as rainfall and temperature and also used to generate important information for the concerned body to use as input in environmental management.

1.2 Statement of the problem Global climate appears to be changing at an alarming rate (Naissan and Lily, 2016). Both urban and rural areas are experiencing warm temperature condition and it is increasing from time to time. The earth’s environment is a dynamic system, including many interacting components (physical, chemical, biological and human) that are continuously varying (Emilio, 2008). Due to the increase of population, industrialization and other natural and human activities, its land-use/land-cover patterns are changing. One of the main factors that responsible for the increment of LST is land-use land-cover change. In the recent time, global warming and environmental problems are a headache for both within developing and developed countries. Practices such as overgrazing, deforestation, unplanned land-use for settlement and other activities lead our environment to warm temperature. In East Shewa (Misrak shewa) zone, Adama Zuria (surrounding) Woreda LST has been increasing from season to season. Therefore, what signifies the relevance of this study is a wide array of applications that gives information of LST and LU/LC status of the area as an input for planning and decision-making. According to Aires et al. (2001) LST is a main parameter in land surface processes, not only acting as an index of climate change, but also to control the upward terrestrial radiation, and consequently, the control of the surface sensible and latent heat flux exchange with the atmosphere.

In this study, RS and GIS approach or geospatial tools were used to examine and better understand LU/LC, the relationship between LU/LCC and LST of Adama Zuria Woreda. In addition, thermal infrared remote sensing is a part of the electromagnetic spectrum and one of the best observation tools for calculating and quantifying LST.

The basic argument why this study is proposed and done in Oromia national, regional estate, East Shewa Zone Adama Zuria Woreda is that, the area is characterized diverse vegetation, climate, and topographic pattern and different land-use/land-cover.

1.3. Objectives of the study This study was carried out in the Adama Zuria Woreda, Oromia national, regional estate, Ethiopia and was intended to meet the following general and specific objectives.

AAU.Remote Sensing and Geo-informatics stream: By Belete Tafesse: [email protected] 3

1.3.1. General Objective The general objective of this research was to investigate the impact of LU/LC dynamics on LST change in Adama Zuria Woreda, Ethiopia based on Landsat imagery using geospatial tools.

1.3.2 Specific Objectives To achieve the general objective, the following three specific objectives were formulated. These are:

 To examine temporal and spatial changes in the LST in relation to land-use/land-cover dynamics in the study area  To analyze the relationship between LST and NDVI over time.  To produce maps of land-use/land-cover, NDVI and LST from multi temporal Landsat satellite images.

1.4. Research Questions Understanding the impact of LU/LC and vegetation on LST could serve to be useful for land management and planning strategies focused on LST mitigation and the adaptation of the study area to the challenges of climate change. All the activities that are to be based on the above general and specific objectives should correlate with the following research question.

 What are the spatiotemporal patterns of LU/LC changes of the study area?  What is the major LU/LC class of the study area?  What is the role of NDVI in LST?  What is the correlation between LU/LC and LST in the study area?  What is the impact of LU/LC dynamics on LST change in the study area?

1.5. Scope of the study The present study was conducted in the Adama Zuria Woreda (Adama surroundings Woreda) Oromia Region; Ethiopia. The study areas have different land-use/land-cover patterns and the LU/LC has been changing from time to time. As a result, LST of the Woreda has been rising from time to time. However, the increase of LST is not supported by research rather than perception of local communities. In order to know and compute the trend of LU/LC change, to know the correlation between LST and Land-use/land-cover (LU/LC) and to analysis, the impact of LU/LC dynamics on LST change. For each land-use/land-cover classes the LST values assessed and LU/LC classification should be supported by field verification. Increasing LST leads to environmental

AAU.Remote Sensing and Geo-informatics stream: By Belete Tafesse: [email protected] 4

problem such as climate change and seasonal fluctuation. Unless land for Settlements and farmland properly managed, it can affect and have a negative impact on the environment. Therefore, to overcome this problem, the present study would contribute for decision makers as information and identifying different LU/LC classes and changes.

1.6. Significance of the study The significance of the study could be, 1) Give information about the trend of LU/LC and LST Change of the area. 2) It could be used as an input for government policy makers, urban and rural land management, natural resources managers, environmental experts and other concerned bodies for their decision making processes related to how LU/LC and LST changes through time. In addition to this, it can be a reference or initial step and use as input for coming researchers based on the analysis of the study. In addition, it helps to quantify the relationship between LST and LU/LC, and can be an important input to predict future land warming.

The output of the study would provide better information about the changes in urban areas and surrounding integrated application of GIS and remote sensing techniques or geospatial tools and its applicability, which is time and cost effective for analysis and impact of LU/LC dynamics on LST. It also provides the opportunity to understand the trends of changes and its driving factors.

1.7. Limitations of the study The present study was attempted with all possible efforts in acquiring required inputs in the form of primary and ancillary data collection, interpretation and analysis. However, the study has encountered certain limitations. One of the limitations was routine process to get data from different organizations.

1.8. Structure of the thesis The first Chapter introduces introduction, statement of problem, objectives of the study, research questions, the scope of the study, significance of the study and limitations of the study. Chapter two concentrates on literature review related to this study. This section presents a brief understanding of land-use/land-cover changes, LST and NDVI in general. The third chapter focuses on the general methodology followed, the data used in the study and detail explanation of the study area. Chapter four explains the results, which presents the detailed results from image classification and collected data. In this section, land-use/ land-cover maps generated using maximum likelihood classification,

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LST and NDVI result presented. Moreover, change analysis of LU/LC and LST, spatial map was prepared for comparison of changes in each year. Chapter five presents the discussion part and the last chapter six presents’ conclusions and recommendations. In this section, key findings and critical points that need further treatment has been forwarded as a recommendation for decision makers and for future research.

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CHAPTER TWO

2. LITERATURE REVIEW This chapter presents the basic concepts and meaning of LU/LC, LST, NDVI, RS and GIS. Furthermore, it tries to explore the findings of relevant studies that have been studied previously in different area.

2.1. Concept of land-use/land-cover change The earth’s surface has been changed considerably in the past decades by human’s as a result human induced factors of deforestation, agricultural activities and urbanization. Land is the ultimate resource of the biosphere and the definition LU/LC has been used as one in different research. However, these two terms explain two different issues and have different meanings. Land-cover refers to the observed biophysical cover on the earth’s surface, including water bodies, vegetation, soil and hard surfaces. Land-use is the exploitation/utilization of the land by human activities for the purpose of settlements, agriculture, forestry, and by pasture altering land surface processes including biogeochemistry, hydrology and biodiversity (Di Gregorio and Jansen, 2000). In this context, as variation in the surface component of the landscape and is only considered to occur if the surface has a different appearance when viewed on at least two successive occasions (Lemlem, 2007). The definition also given by FAO (1999) for land-use is as the arrangements, activities and inputs people undertake in a certain land cover type to produce change or to maintain it. According to Lambin and Meyfroidt (2010), transition in LU/LC can be caused negative socio-ecological feedback that comes from a rigorous (severe) degradation in ecosystem services/ as a result of from socio-economic changes and innovations.

2.2. Causes of land-use and land-cover changes Changes in the land-use reflect the history and, perhaps, the future of humankind. Such changes are influenced by a variety of factors related to human population growth, economic development, technology and environmental changes (Houghton, 1994). Land-cover changes, which is conversion of the land-cover from one type of to another and modification of the conditions within a category and land-use change occurs initially at the level of land parcels when land managers decide that a change towards another land utilization type is desirable (Meyer and Turner, 1992). Population growth is one of the major factors for LU/LC change. People are the most important natural resources, which is mutually inter-related and interdependent for their sustainable development

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(Santa, 2011). However, Land-use reflects the importance of land as a key and finite resource for most human activities such as forestry, agriculture, industry, energy production, recreation, settlement and water catchment and storage (http://www.ciesin.org/docs/002-105/002-105b.html ). During the past 3 centuries, the extent of earths cultivated land has grown by more than 45% increasing from 2.65 million km² to 15 million km² and at the same time, other natural resources (land-cover) such as forest has been shrinking due to agricultural land expansion and urbanization(Santa, 2011). High rate of deforestation in many developing countries is most commonly associated with population growth and poverty (Mather and Needle, 2000).

Land-use/land-cover changes have become major problems for the world, and it is a significant driving agent of global environmental changes (FAO, 1999). Such a large-scale land-use classes through the increase of agricultural land at rural area, deforestation (clearance of trees), urbanization and other natural phenomena and human activities are inducing changes in global systems and cycles. However, the major change in land-use, historically, has been the worldwide increase in agricultural land (Houghton, 1994). Climate change refers to long term or permanent shift in climate of the area. Some of the evidence for climate change includes increased frequency of the occurrence of drought, global temperature rise, tropical cyclones, flood, and reduced annual rainfall reduction in glacial cover over mountain and rising sea levels (Alemayehu, 2008).

United States Environmental Protection Agency (USEPA, 2004), identified the general causes of LU/LCCs are:

 Natural processes, such as climate and atmospheric changes, wildfire and pest infestation.  Direct effect of human activity such as road and illegal house construction and deforestation (clearance of trees).  Indirect effects of human activity is such as, water diversion leading to lowering of the water table.

2.3. Land-use and land-cover change in Ethiopia In Ethiopia, the availability of natural resources changes and management differs significantly from place to place. This variability is because difference in biogeography and topography climate. Land- use/land-cover changes are accelerating, by human actions, but also producing changes that affect humans (Agarwal et al., 2002). The dynamics of LU/LC alters the existence of different biophysical

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resources, including water, vegetation, soil, animal feed and other (Ali, 2009). Previous studies reported that, in Ethiopia there have been considerable LU/LC changes in different part of the country and the expansion of cultivated land at the expense of forestland and it were stretched into sloppy area due to scarcity of land. Especially in the highlands of Ethiopia, agricultural practices and human settlement have a long history and recently a highland population pressure including depletion of natural resources and unsustainable practices (Miheretu andYimer, 2017).

2.4. Remote Sensing Remote sensing is the capability to gather information without being in direct contact with it (Lillesand and Kiefer, 2000). The modern use of the term remote sensing has more to do with technical ways of collecting airborne and space born information. The earth observation from airborne platforms has 150 year of history, although the majority of the innovation and development has taken place in the recent decade’s years (Zubair, 2006). The first earth observation-using balloons in the 1860s were regarded as an important benchmark in the history of remote sensing (Lillesand and Kiefer, 2000).

Information is gathered by instruments at the natural level by our naked eyes, or by cameras (radiometric which measure radiation). Satellite based remote sensing provides valuable information that can be used in the assessment of the various aspect of atmospheric environment, climatology, meteorology, ecology, agronomy and environmental protection (Kern, 2011). The essence of remote sensing is measuring and recording of the electromagnetic radiation emitted or reflected from the earth’s surface (Hardegree, 2006). This technique enables us to investigate and know the tendency of LU/LC change through time.

2.5. Geographic Information System (GIS) Geographic Information System is a system designed to capture, store, manipulate, analyze, manage and present spatial or geographic data (Coppin and Bauer, 1996). The data type in GIS can be classified into two major groups as spatial and non-spatial data. The spatial data are the data that have location value and that non-spatial data are a data, which describe more the spatial data in the form of a table. According to Burrough (1990), data in GIS is composed of three dimensions that mean spatial (geographic), time and attribute. Some people believe that geographic information system as the system of hardware and software, which contribute to analyze applications or information processing (Maguire, 1991). Geographic information system is not only digital store of

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spatial objectives (areas, points and lines) but also capable of spatial analysis based on the relation between these objects, including the r/ship between objects defined by their location and geometry.

According to Foresman (1998), the combination of computer technology and cartography in the 1960s paved the way for the possibility of using techniques of superimposing and overlaying maps in fields other than cartography. The power multiplication, which results from the integration of climate, environment, terrain, agronomic, economic, social and institutional management data, makes available for managers and scientists alike a new and powerful modeling.

2.6. Role of Remote Sensing and GIS in Land-use and Land-cover change Remote sensing and geographic information system techniques have been widely used over the world for the study of historical changes in LU/LC and LST analysis. Remote sensing has been used to identify vegetation cover, air pollution, LST and other surface characteristics (Zha, 2012; Weng, 2004). Furthermore, understanding the correlation between LST and LU/LC is important to manage the land. It provides a large variety and amount of data about the earth’s surface for detailed analysis, change detection with the help of various airborne, and space born. With the availability of historical remote sensing data, the reduction in data cost and increased resolution from satellite platforms, remote sensing technology appears ready to make an even greater impact on monitoring land-cover change. Land-use/land-cover changes can be analyzed over a period using Landsat sensors such as Landsat Multi Scanner (MSS) data and Landsat Thematic Mapper (TM) data by image classification techniques (Gumindoga, 2010).

Since 1972, Landsat satellites have provided repetitive, synoptic, global coverage of high-resolution multispectral imageries. Their long history and reliability have made them a popular source for documenting changes in LU/LC over time(Turner et al., 2003) and their evolution is further marked by the launch of Landsat7 (Enhanced Thematic Mapper Plus sensors) by the United State in 1999.

According to Macleod and Congation (1998), there the following are four LU/LCs change detection (aspects of change detection), which are important when monitoring natural resources:

 Distinguishing the nature of the change  Detection/finding of the changes that have occurred  Measuring the area extent of the change  Assessing and investigating spatial pattern of the change

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The basis of using remote sensing data for change detection is that changes in land-cover result in changes in radiance values, which can be remotely sensed.

2.7. Land surface temperature Land surface temperature denotes the temperature on the surface of the earth or it is the skin temperature of the earth surface phenomena (Kayet et al., 2016). From the satellite’s point of view the ‘surface ‘looks different for different area at different times (Kumar and Singh, 2016). Remote Sensing and geospatial tools play crucial role in quantifying and estimating LST. Land surface temperature is derived from geometrically corrected Landsat Thermal Infrared (TIR) band 6 and Landsat 8 thermal infrared (TIR) band 10 and 11(Khin et al., 2012). Land surface temperature of a given area can be determined based on its brightness temperature and the land surface emissivity, which is calculated through applying the split window algorithm (Rajeshwari and Mani, 2014, Md Shahid, 2014). According to Kerr et al. (2004) land surface temperature gives information about the difference of the surface equilibrium state and vigorous/vital for many applications. LST also defined as, the monitoring of surface temperature based on pixel derived observation through remote sensing (Paramasivam, 2016). The characteristics of urban land surface temperature is depending up on its surface energy balance, which is governed by its properties such as orientation, sky and wind, openness to the sun and radiative ability to reflect solar and infrared and also ability to emit infrared availability of surface moisture to evaporate and roughness of the surface (Voogt, 2000). Land-use/land-cover changes due to changes in surface temperature (ST) which makes both urban and rural managers to estimate the urban surface temperature and its surrounding rural area for urban planning as well land management in general (Becker et al., 1990).

2.8. Urban heat island Urban heat island (UHI) is an urban area or metropolitan area that is significantly warmer than its surrounding rural areas due to human activities. The term heat island describes built up areas that are hotter than nearby rural areas (Sobrino et al., 2012). Urban heat island also defined as phenomenon/events that occurs when air and surface temperatures (ST) in urban areas became significantly greater than those experienced in nearby area and land-cover change has become a central component in current strategies for managing natural resources and monitoring and environmental changes (Sobrino et al., 2012). According to the Intergovernmental Panel on Climate Change (IPCC) Report, climate change has contributed to a significant increase in the global mean

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temperature (IPCC, 2014). There are number of contributor factors, which play significant role in creation of urban heat island such as low albedo materials, wind blocking, air pollutants, human gathering, distractions of trees, and increased use of air conditioner (Nuruzzaman, 2015).

2.9. The impact of land-use/land-cover change on land surface temperature One of the major factors that are responsible for the increase of land surface temperature is LU/LCC and Different researchers agree with that land-use change and unplanned use of land resources lead to increasing land surface temperature. Oluseyi et al., 2011 have studied that spatially there are correlations with changes as reflected in the characteristics of individual land-use classes or categories. This study emphasize the changes in LST of the various land-uses between 1995 and 2006 in the case of Anyigba Town; Kogi State, Nigeria. It shows that there was 1ºC variation and increase in surface temperature of vacant land, built-up area and stream, while cultivated land and vegetation also had increases of 0.95ºC ,respectively. The influence of LU/LC changes on LST is different at different latitude, for example in South Asia and East Asia tropical temperate regions (Shukla, 1990). The relationship between land-use changes, biodiversity and land degradation across East Africa shows that, land cover were transformed to grazing farmland and settlement area (Matimal et al., 2009).

According to Yue et al. (2007), the relationship between NDVI and LST with integrated remote sensing application to quantify Shanghai Landsat7 ETM+ data was used. The result shows that different LU/LC classes have significantly different impacts on land surface temperature and normalized difference vegetation index calculated by the Enhanced Thematic Mapper Plus sensor in the Shanghai urban environment.

2.10. Normalized Difference Vegetation Index Normalized Difference Vegetation Index is the difference of near infrared and visible red reflectance values normalized over reflectance and calculated from reflectance measurements in the near infrared (NIR) and red portion of the spectrum (Burgan and Hartford, 1993). To calculate the Normalized Difference Vegetation Index, subtracting the red band from near infrared and then dividing to near infrared plus red band. The value is ranging from -1 to1, the negative values are indicative of water, snow, clouds, non-reflective surface and other non-vegetated, while the positive value expresses reflective surfaces such as vegetated area (Burgan and Hartford 1993). Vegetation has a direct match/correspondence with thermal, moisture and radiative properties of the earth’s

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surface that determine LST (Weng, 2004). In addition to NDVI, Normalized Difference Moisture Index (NDMI) also used as an alternative indicator of surface urban heat island effects in Landsat imagery by investigating the r/ships between land surface temperature and NDVI. The index is expressed as NDMI= (NIR-IR)/ (NIR+IR), it evaluates the different content of humidity from the landscape elements, especially in soils, rocks and vegetations and it is an excellent indicator of dryness. Values greater than 0.1 are symbolized light colors and they signal high humidity level, whereas values close to -1 symbolized by dark colors represents low-level humidity level (Mihai, 2012).

Previously, different researchers outside Ethiopia did researches in relation to the impact of LU/LC changes on land surface temperature. However, in Ethiopia there are some papers related to the proposed title. For example, Gebrekidan,2016 studied modeling land surface temperature from satellite data, the case of Addis Ababa, which presented in Africa hall, United Nations conference centre Addis Ababa; Ethiopia (ESRI Eastern Africa Education GIS conference which held from 23−24 September, 2016). The study mainly focuses on modeling LST of Addis Ababa city, which acquired Landsat 5 and 8, from 1985 and 2015. Finally, the results show that negative correlation was found between Normalized difference vegetation index and Land Surface Temperature and the study indicates the need for urban greening and plans to increase vegetations covers to sustain the ecosystem of the city and to minimize urban heat island effect.

According to streutker (2003), one of the promising of studying urban surface temperature is using remote sensing or air born technology. Evaluation of land surface temperature from remotely sensed data is common and typically used in studies of evapotranspiration and desertification processes. Further, (Walsh et al., 2011) stated that urban area such as buildings and roads and infrastructures or anthropogenic factors contribute to increase atmospheric temperature. The wide use of land surface temperature for environmental studies, have made remote sensing of land surface temperature important academic issue during the last decades. Indeed, one of the most important parameters in all surface atmosphere interactions and fluxes between the land and the atmospheric is land surface temperature (Buyadi et al, 2013).

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CHAPTER THREE

3. MATERIALS AND METHODS

3.1. Description of the study area

3.1.1. Location Adama Zuria Woreda (Adama and surrounding Woreda) is one of the Woredas in the East Shewa Zone of the Oromia, Regional State of Ethiopia. The Administrative center of the Woreda is Adama, which is located southeast of Addis Ababa the capital city of Ethiopia approximately about 90 km at latitude and longitude of 8° 14′ 0″−8° 43′ 0″N and 39° 6′ 0″−39° 25′ 0″ E, covering a total area of 901.5 km2 (Fig.3.1). Altitude of the area range from 1415 to 2505 m above sea level and it is located within the Great Ethiopian Rift Valley.

Figure 3.1: Location map of the study area.

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3.1.2. Topography Topography is the study of the shape and feature of the surface of the earth and other related phenomenon or it is an integral part of the land surface. It includes such as landforms, elevation, latitude, longitude and topographic maps. The Adama Zuria Woreda has a broad flat area. However, in its northwest part there is relatively rugged topography. It also bordered on the south by the Arsi Zone , on the Southwest by Koka reservoir , which separates it from Dugda Bora on the west by Lome on the north by the Amhara Region , and on the East by Boset ; the Awash River, the only important river in this Woreda, defines the Woreda boundaries on the east and south. The elevation of the study area is indicated in (Fig. 3.2 a).

Slope of Adama Zuria Woreda shows that, 61.73% of the area is between 0 and 3 º. 26.55% of the area is between 3 and 7 º, 8% of the area between 7 and 14 º, 3% of the area 15 between 24 º and 0.62 % of the area is more than 24 º of slope. Slope of the area with greater than 24 º is located in the northern, northwest, southwest ridge part of the area. The slope of the area is indicated in (Fig.3.2 b).

Figure 3.2: (a) Elevation and (b) Slope map of the Study area.

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3.1.3. Climate and Vegetation A. Rainfall A long-term rainfall record from1989 to 2016 at the Adama meteorological station shows an average annual rainfall of 844. 20 mm and the maximum monthly average rainfall was 259.8 mm in the month of July (Fig.3.3). On the average, most rainfall or rainy season is June, July, August and September. Whereas, the dry month is in January, October, November and December. On average, the warmest month is May and coolest month is July. Among all month, the driest month is December.

100 Rainfall (mm) Rainfall

Figure 3.3: Monthly average rainfall distribution of the Study Area.

The average rainfall recorded between 1989 and 1999 was 709.9 mm, while 1999 and 2010; it was 833.24 mm and 745 for the years between 2010 and 2016. The distribution of annual rainfall from (1989–2016) is presented in (Fig.3.4).

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Rainfall (mm Rainfall 200

2002 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Figure 3.4: Distribution of annual rainfall in the study area (1989–2016).

B. Temperature The mean annual temperature of the Adama Zuria Woreda is 27.8 . It can be classified as semi- humid to semi-arid climate, which characterizes the altitude range between 1,500–2,400 m above mean sea level. In the study area, the hottest month with maximum mean temperature of 30.7ºC was May. The detailed information presented in (Fig. 3.5).

C) C) 30 ° 25 20 15 Temp Max

10 Temp Min Temperature ( Temperature 5 Mean

Figure 3.5: Maximum, minimum and mean monthly Temperature during 1989–2016.

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C. Vegetation The vegetation distribution of the area is mainly dependent on the climate condition of the area. The climate condition of the study area is characterized as tropical. On these types of climate, vegetation is scarce and typical example that is found in the area is shrub, Acacia and scattered trees of Eucalyptus. Eucalyptus trees, which is, grown by local communities in soil conservation program that is applied in the Main Ethiopian rift to protect soil from erosion. The local people cultivate, some types of crops cultivated in the area are Teff, wheat, Barley, maize and sorghum. The harvesting season is between October and December at which the rain is very low.

3.1.4. Population According to CSA (2007), the total human population of Adama Zuria Woreda including Adama Town is about 375,561. Out of this, 187,676 are women and 187,885 are men. While in 2014 CSA projected data (estimated population) the total population is about 473,385. From this, about 237,541 are women and 237844 are men. The detail information is presented in (Fig.3.6).

500,000 450,000 400,000 350,000 300,000 250,000

200,000 Population 150,000 100,000 50,000 0 Female Male Rural Urban Total population population population population population 2007 187,676 187,885 129,027 246,534 375,561 2014 237,541 237,844 153,501 319,884 473,385

Figure 3.1: Population of Adama Zuria Woreda.

3.2. Data and software

Data used for this research comprise both primary and secondary (Ancillary). The most important source was the primary data (satellite data). Ancillary source of data was collected from governmental and nongovernmental organization and published information.

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3.2.1. Primary data

The primary data includes Landsat satellite image (Landsat data): Thematic Mapper (TM) 1989, Enhanced Thematic Mapper Plus (ETM+) 1999 and Operational Land Imager/Thermal Infrared (OLI/TIRS) obtained from USGS. Digital Elevation Model (DEM) data with the resolution of 30*30m for mapping the elevation and slope of the area and field data, GPS point and photo of different classes of LULC were used.

3.2.2. Remote Sensing data acquisition One scene of Landsat 4 TM (1989), Landsat7 ETM+ (1999) and Landsat 8 OLI/TIRS (2016) cloud free image of the study area with the path of 168 and row 054 were acquired (downloaded) from the website of earth explorer. usgs.gov, United States Geological survey (USGS) and Landsat8 from https://libra.developmentseed.org.The-acquired data is world datum (WGS84) projection system.

Remote sensing images used to calculate LST, NDVI and LULC in Adama Zuria Woreda are shown in the following (Table 3.1) and multispectral band in (Fig.3.7).

Table 3.1: Remote sensing data used in the study.

Spatial Spectral Date of Multispectra Resolution Thermal range Acquisition Sensor Path Row l Band (Pixel Source Band (micromete Spacing) rs) 11/21/1989 TM 168 054 1 to5 and7 6 10.45-12.45 120

USGS ETM+ 168 054 1 to5 and7 6 10.45-12.51 60 12/3/1999 OLI 10 and 11/23/2016 and 168 054 1 to7 and9 10.60-12.51 100 11 TIRS

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Figure 3.7: Landsat Images of 1989 TM, 1999 ETM+ and 2016 OLI and TIRS.

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A. Landsat Thematic Mapper (TM)

The Landsat Thematic Mapper (TM) sensor was carried on-board four and five from July 1982 to May 2012 with 16-day repeat cycle. Thematic Mapper sensor has seven spectral bands; three in the visible range and four in the infrared range. Band 6 is specifically sensitive to thermal infrared radiation to measure surface temperature. Detailed information is shown in Table 3.2.

Table 3.2: Landsat 4 Thematic Mapper sensor bands and description.

Spatial Wavelength Repeating Bands Description resolution Source (Micrometers) time (m) Band 1 Blue 0.452-0.518 Band 2 Green 0.528-0.609 Landsat4 Band 3 Red 0.626-0.693 Thematic Near Infrared 30 Band 4 0.776-0.904 Mapper (NIR) 1 16 days USGS (TM) Near Infrared Band 5 1.567-1.784 (NIR) 2 Band 6 Thermal 10.45-12.45 120 Middle Infrared Band 7 2.097-2.349 30 (MIR)

B. Landsat 7 Enhanced Thematic Mapper plus (ETM+)

The Landsat Enhanced Thematic Mapper Plus sensor onboard the Landsat 7 satellite has acquired images of the Earth nearly continuously since July 1999,with a 16 day repeat cycle. Landsat 7 ETM+ images consist of eight spectral bands with a spatial resolution of 30m for bands 1-5 and 7 where as the thermal band 6 and panchromatic band 8 has a resolution of 60 and 15 m, respectively. Detail information is shown in Table 3.3.

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Table 3.3: Landsat 7 Enhanced Thematic Mapper Plus bands and description.

Spatial Repeati Satellite Bands Description Wavelength resolution (m) ng time Source /sensor (micrometer ) Band 1 Blue 0.452-0.514 Landsat 7 Band 2 Green 0.519-0.601 Enhanced Band 3 Red 0.631-0.692 30 Thematic Band 4 Near Infrared 0.772-0.898 Mapper (NIR) 1 Plus Band 5 Near Infrared 16 days USGS 1.547-1.748 (ETM+) (NIR) 2 Band 6 Thermal 10.31-12.36 60 Band 7 Middle 2.065-2.346 30 Infrared (MIR) Band 8 Panchromatic 0.515-0.896 15

C. Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS)

Images consist of nine spectral bands with a spatial resolution of 30 m for band 1-7, 9, and TIRS with two bands (band 10 and 11). New band 1 (ultra-blue) is useful for coastal and aerosols studies. Another new band 9 is useful for cirrus cloud detection. Landsat 8 was launched in February 11, 2013 (Table 3.4).

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Table 3.4: Landsat 8 Operational Land Imager and Thermal infrared Sensor bands and description.

Satellite/ Wavelength Resolutio Repeating Source Band Description sensor (micrometers) n (m) time Band 1 Coastal aerosol 0.43-0.45 Band 2 Blue 0.45-0.51 Band 3 Green 0.53-0.59 Band 4 Red 0.64-0.67 Near Infrared 30 Band 5 0.85-0.88 (NIR) Landsat Short -Wave Band 6 1.57-1.65 8 Infrared (SWIR) 1 16 days USGS OLI/TIR Short -Wave Band 7 2.11-2.29 S Infrared (SWIR) 2 Band 8 Panchromatic 0.50-0.68 15 Band 9 Cirrus 1.36-1.38 30 Thermal Infrared Band 10 10.60-11.19 (TIRS) 1 100 Thermal Infrared Band 11 11.50-12.51 (TIRS) 2

3.2.3. Field data Field data used for this research was from GPS data and used for accurately checking the LU/LC classified image into different classes. After Random point, generated GPS point was collected and pictures captured showing different LU/LC classes. Field data was used for validation accuracy assessment based on ground truth.

3.2.4. Secondary data Secondary data used in this study comprises meteorological data such as average monthly temperature (1989–2016) and average monthly rainfall (1989–2016).These meteorological data was used for evaluation and validation. In addition to this, the other data used in this study were

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geological map from Ethiopian Geological Survey to see the geological setting of the area and population data from CSA to understand the status of the population of the study area (CSA, 2007).

3.2.5. Data description and source Primary and ancillary data that used in this study was collected from different sources as indicated in Table 3.5.

Table 3.5: Data Description and source used in this study.

GIS Data layer Data Description Data source Vector (polygon) Woreda (Woreda) Boundary CSA,2008 Vector (line) River and Roads CSA,2008 Vector (point) Major Town Ethio-GIS Raster Elevation and Slope DEM 30m SRTM Attribute table Rainfall and Temperature National Metrological Agency Attribute table Population CSA,2007

Raster Satellite Image USGS 1989,1999 and 2016  1989 Landsat4 TM  1999 Landsat7ETM+  2016 Landsat 8 OLI and TIRS

Ground truth Ground truth 2016 Field survey

3.2.6. Software Packages used Software packages used for this study were ArcGIS 10.3 for image analysis; calculate LST, NDVI and map preparation, ERDAS (Earth Resources Data Analysis System) Imagine 2014 for RS application in order to process satellite images including image enhancement, preprocessing and for LU/LC classification.

PCI Geomatica 2015 was also used for top of atmospheric correction, change detection and export to Google earth. Google earth also used to check and compare results with ground truth, especially

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for the year 1989 and 1999 LU/LC verification. Quantum GIS (QGIS): open source software was used for raster manipulation include neighborhood analysis and map algebra. In addition to ERDAS Imagine software, ENVI software used for classification to compare and contrast the results. Microsoft office, Microsoft Excel and Microsoft power point was used to create word documents, analyze spreadsheets, to do graphs, manage databases and for presentation.

3.3. Methods In this study, the methods used to achieve the objectives of the research were begun with acquisition of Landsat imagery for the year 1989, 1999 and 2016 from website of earth explorer (USGS) and Landsat 8 from https://libra.developmentseed.org. The reason behind that these years chosen were because of the availability of the data. The image acquired or downloaded was during the dry season and was cloud free image. Figure 3.8 show the general methodological flow chart of the study.

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Landsat satellite imagery Ancillary L4 TM (1989), L7 ETM+ Data (1999), L8 OLI/TIRS 2016

Multispectral Thermal Band infrared Band Clipping of thermal bands Meteorologic Image Pre-processing: al data Geometric correction, subsetting,Enhancement

Band Rationing Conversion to at sensor Selecting Thematic Radiance classes and Training samples

NDVI using NIR At sensor Image &red band brightness classification Statistical temperature analysis& interpolation Maximum Supervised Likelihood classification method If not Land surface NDVI Emissivity Accuracy Ground assessment Truthing

If yes Spatial projected LST Map of Change metrological map 1989, 1999 & Detection NDVI 2016 1989, 1999&2016 LULC Map of Zonal 1989,1999 & Validation statistics 2016

Comparison of LST and LULC

Figure 3.8: Methodological flow chart of the study.

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3.3.1. Data Preparation and Analyzing Satellite image by its nature have some distortion, noise, haze and stripes. Therefore, before processing the data, image pre-processing activities were done. Preprocessing includes importing, layer stacking, and subsetting of the image based on the boundary of Adama Zuria Woreda, geometric correction, radiometric correction, and removal of stripes, pan sharpening and other image enhancement techniques. Radiometric correction is a removal of atmospheric noise to make more representatives of the ground truth conditions based on the sensors. These all previously mentioned activities done were to improve visible interpretability of an image by increasing apparent distinction between the features in the scene.

In addition, the image was also georeferenced-using boundary of the Woreda. During georeferencing and reprojecting process, Adindan UTM Zone 37N coordinate system was followed for raster and vector data in the study to maintain uniformity. Adindan UTM Zone 37N is local datum that Ethiopia used.

3.3.2. Digital Image Processing (DIP) A digital image is a numeric representation of (binary) a two dimensional image or digital image is a sampled quantized numeric representation of the scenes and made up of picture element called pixels. It involves the manipulation and interpretation of digital images with the aid of computers. In remote sensing digital image, processing historically stems from two principal application areas, the improvement of the information for human interpretation and the processing of image data for computer-assisted interpretation. The whole activities of DIP revolve around increasing spectral separability of the objects on the image.

3.3.3. Image enhancement Image enhancement is the procedure applied to image data in order to make more effectively display or record the data for subsequent visual interpretation. Normally, image enhancement involves techniques for increasing the visual distinction between features in the scene (Billah and Rahman, 2004).

The main purpose of image enhancement is to improve the interpretability of information in images for human viewers, or to provide better input for other automated image processing techniques.

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3.3.4. Image classification In remote sensing, Image classification is the task of extracting information classes from a multiband raster image or extracting information based on the reflectance of the object and it serves specific aims; which is converting image data into thematic data. Digital image classification techniques assemble pixels to represent LU/LC classes. Image classification uses the reflectance statistics for individual pixels. Pixels were grouped based on the reflectance properties of pixels called clusters. The users identify the number of clusters to generate and which bands to use. With this information, the image classification software generates clusters. In this research supervised classification techniques is used.

A. Supervised classification

Supervised classification is the techniques most often used for the quantitative analysis of RS image data depending on their reflectance properties. It uses the spectral signature obtained from training samples to classify an image. Image classification toolbar, can easily create training samples to represent classes. With supervised classification, it can be identified sample of information classes (any land-cover type) of interest in the image. The supervised classification image of each year involves pixel categorizations by taking training area for each class of LU/LC. After the training area assigned for each class classification activity was performed. For bare land, farmland, shrub land and settlement LU/LC types taken 20 training site, where as for plantation and water body LU/LC types was taken 15 training areas as sample. Areas in digital images were marked as signature of individual identity and the field truth verification was adapted to represent LU/LC class (Coppin and Bauer, 1996).

Using Multispectral Band from band 1 to 5 and 7 for TM 1989 and ETM+ 1999 and OLI 2016 1 to 7 Bands of the preprocessed images the land-use/ land-cover pattern mapped was by supervised classification with the likelihood classification algorithm of ERDAS Imagine 2014 software. In supervised classification, with the help of image processing techniques, the user specified type of the land-use land-cover classes. The six major classes studied in Adama Zuria Woreda were Farm Land, bare Land, settlement, shrub land, plantation and water body. The advantage of the supervised classification was development of information classes, self-assessment using training sites and training sites reusable. However, information classes may not match spectral classes, the signature homogeneity, uniformity of information classes may be varies.

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B. Maximum Likelihood classification

Maximum likelihood classification (MLC) is one of the most known methods of classification in remote sensing, in which a pixel with the MLC is classified into the matching classes/categories. It is a statistical decision measure to assist in the classification of overlapping signatures; pixels are assigned to the class (categories) of the highest probability. It was considered more accurate than parallelepiped classification. However, it is slower to extract computations. The MLC classification tool considers both the variances of the class signatures when assigning each cell to one of the classless represented in the signature file.

C. Ground Truthing

A ground truthing activity was carried out in the study area, in which different LU/LC classes were validated. The observed LU/LC includes: farmland (small scale agriculture),shrub land, sugarcane plantation (large scale agriculture),water body ,bare land and settlement (urban and rural settlement).These LU/LC classes were used in producing the map legends and with the assist of GPS, training set data used for image classification were acquired. During this ground truthing activities, photos of scenes of interest and coordinates from sampled LU/LC classes were captured.

3.3.5. Classification accuracy assessment Assessing classification accuracy requires the collection of some original data or a prior knowledge about some parts of the terrain, which can then be compared with the RS derived classification map. Thus, to assess classification accuracy, it is necessary to compare the following two-classification map:

 The remote sensing (RS) derived map  Assumed true map (it may contain some error).

The supposed true map may be derived from in situ examination or quite often from the interpretation of remotely sensed data obtained at a larger scale or higher resolution. Shortly, accuracy assessment is performed by comparing the created by RS analysis to a reference map based on a different information sources. Using hand held Global Positioning System (GPS) field survey was conducted in the study area and about 60 points identified. The field survey and Google Earth were used as a ground for evaluation the LU/LC classification accuracy. The final output of

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classification accuracy was calculated for the years 1989, 1999, 2016 Land-use/Land-cover Map. Land-use and Land-cover classes and its description are presented in Table 3.6.

Table 3.6: Land-use/land-cover classes and description of the study area.

LU/LC classes Description

Areas covered with shrubs, bushes and small trees, with little useful Shrubs land wood, mixed with some grasses and less dense than forests.

Areas used for crop cultivation, both annual perennials, and the Farm land scattered rural.

Area occupied by houses buildings including road network residential, Settlement commercial, industrial, transportation, roads, mixed urban and other facilities.

Areas covered by natural and manmade small dams, like pond, lake Water body and river.

Plantation agriculture is a form of commercial farming where crops Plantation were grown for profit. it includes sugarcane, sweet potato and tobacco.

Areas of has thin soil, sand/rocks and includes deserts, dry

Bare land Salt flats, beaches, sand dunes, exposed rocks, stripe mines, queries and gravel pits/non-vegetated area dominated by rock out crops, roads, eroded and degraded lands.

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To determine and classify LU/LC of the study area; prior knowledge about the area is important. During field observation, there were six major LU/LC class identified. Based on the coordinate point Sample of LU/LC class and LST values of each LU/LC of the study area was shown in figure 3.9.

Bare land Shrub land

Settlement Sugarcane Plantation

Water body Farmland

Figure 3.9: Interpretation of Landsat image for LU/LC classification.

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Steps or processes that were followed to classify land-use/land-cover from a Landsat image were presented in Fig 3.10.

Landsat imagery 1989 TM,

1999 ETM+ & 2016 OLI

Geometrically corrected

Land-use land-cover categories /class determined

Selecting Training area for ground truthing and visual interpretation Primary data

Cross check using Supervised classification Google earth on ERDAS Imagine 2014

environment Field data If not Field observation Edit/evaluate signatures Secondary/ancillary data If yes

Classify image Toposheet

Evaluate classification

Final data / map of land-use /land-cover 1989, 1999 & 2016

Figure 3.10: Steps and procedures followed to classify land-use/land-cover from a Landsat image.

AAU.Remote Sensing and Geo-informatics stream: By Belete Tafesse: [email protected] 32

3.3.6. Land-use/Land-cover change detection Land-use/land-cover change detection was done by involving images of 1989, 1999 and 1999 and 2016. Using GIS techniques thematic image was compared. The cross operation process of mapping LU/LCC over time began with mapping the recent 2016-satellite imagery, then looking back in time to map the 1989 imagery. Post classification is among the most widely used approach for change detection purpose (Chen, 2000). The analysis of LU/LCC maps involved technical procedures of integration using the ArcGIS software techniques. The first task was to develop a table indicating the area coverage in square kilometers and the percentage change for each year 1989, 1999 and 2016 measured against each LU/LC classes. Therefore, to calculate LU/LCC in percentage equation (eq.3.1) were used (Lambin et al, 2001).

Percentage change = ×100 Eq. (3.1)

3.4. Derivation of Normalized Difference Vegetation Index and Land surface temperature

3.4.1. Derivation of Normalized Difference Vegetation Index According to Farooq (2012), the reason NDVI relates to vegetation is that, the one which is well vegetated reflects better in the near infrared part of the spectrum. Green leaves have a reflectance of 20% or less in the 0.5 to 0.7 range and about 60% in the 0.7 to 1.0 micrometer range. The value of NDVI is between -1and 1. Normalized difference vegetation index (NDVI) was acquired from spectral reflectance measurements in the visible (RED) and near infrared regions (NIR) in the ArcGIS environment.

The index was defined by the following equation 3.2.

NDVI = Eq. (3.2)

NDVI=Normalized Difference Vegetation Index

NIR= is the near infrared band 4, R= is the red band 3. Equation 3.2 is used to calculate NDVI for the sensor TM 1989, ETM+ 1999 and OLI 2016.But in case of Landsat 8 NIR is band 5 and the red band is a band 4 (Weng et al, 2004).

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To calculate fractional vegetation cover (FVC) the following Eq. 3.3 was used.

FVC= Eq. (3.3)

FVC=Fractional Vegetation cover

NDVI= Normalized Difference Vegetation Index

The above equation (FVC) was used to get fraction an area with vegetation cover using NDVI value.

= is NDVI for soil and NDVI for vegetation

Equation 3.4 was used to calculate Proportion of Vegetation that helps in calculating Landsat 8 land surface emissivity (LSE).

Pv = (NDVI-NDVImin/NDVImax-NDVImin Eq. (3.4)

Pv =Proportion of Vegetation

NDVI min= Normalized Difference Vegetation Index minimum value

NDVImax= Normalized Difference Vegetation Index maximum value

3.4.2. Derivation of land surface temperature Calculating land surface temperature passes different steps.

To calculate the LSE, it is important know the inherent characteristics of the earth’s surface and change the thermal radiance energy during calculating LST (Sobrino et al., 2014). The emissivity constant values for vegetation and soil are stated in Table 3.7.

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Table 3.7: Emissivity constant value of Landsat 8.

Emissivity Band 10 Band 11

0.971 0.977

0.987 0.989

The following equation 3.5, 3.6 and 3.7 used to compute Land surface Emissivity, mean of LST and change of LSE of Band 10 and 11, respectively.

LSE=0.004Pv+0.986 Eq. (3.5)

Mean of LST= Eq. (3.6)

Difference of land surface emissivity (LSE) =LSE10-LSE11 Eq. (3.7)

3.4.3. Radiometric correction Radiometric correction requires converting a remote sensing digital number to spectral radiance values and data for comparisms. Image processing procedures that are used to correct errors, converting digital number (DN) values to radiance and then reflectance was categorized as a Radiometric correction (Parente, 2013). To perform the conversion of digital number to spectral radiance equation (3.8) was used.

L λ =Lmin+ (Lmax-Lmin)*DN/255 Eq. (3.8)

Where, L λ=spectral radiance Lmin=spectral radiance of DN value 1 Lmax=spectral radiance of DN value of 255 DN=Digital Number Equation 3.8 the above, was used to convert digital number into spectral radiance

3.4.4. Conversion at sensor spectral radiance In radiometric calibration, pixel values, which were represented by Q in remote sensing raw data

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and unprocessed image data, were changed into absolute radiance values.

The equation 3.9 was used to perform the conversion at sensor spectral radiance or satellite data scaled into 8 bits.

λ λ Lλ= ) (Qcal-Qcalmin) +LMINλ Eq. (3.9)

L λ=Spectral radiance at sensors aperture or the calculated radiance associated to the ground area enclosed in the pixel and referred to the λ wavelength range of specific band.

Lmaxλ=spectral at Sensor Radiance that is scaled to Qcalmax

Lminλ=Spectral at Sensor Radiance that is scaled to Qcalmin

Qcalmax=Maximum Quantized Calibrated Pixel values corresponding to Lmaxλ

Qcalmin=Minimum Quantized Calibrated Pixel values corresponding to Lminλ

Qcal=Quantized Calibration Pixel value (DN)

3.4.5. Conversion to top of atmosphere (TOA) reflectance Landsat Thematic Mapper top of atmosphere reflectance data must be corrected and processed .because of variation in solar zenith angle due to time difference between data acquisition. The gained output also calibrated to reflectance value. Equation 3.10 was used to compute TOA.

λ Pλ= Eq. (3.10) λ

Pλ=planetary top of atmosphere (TOA) reflectance, which has no unit or it is unit less

=mathematical constant approximately equal to 3.14159. It is also unit less

Lλ=spectral radiance at the sensors aperture

d=earth -sun distance/distance from the earth to the sun astronomical units

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ESUNλ=mean exoatmospheric solar irradiation (from meta data)

Solar zenith angle

3.4.6. Conversion of radiance into brightness temperature Thermal infrared data can be converted from atmosphere reflectance (Lλ) to effective sensor brightness temperature (TB) using thermal constants provided in the Meta data file.

Remote sensing data (Landsat imagery) thermal band that is band 6 on thematic mapper and enhanced thematic mapper plus needs to be converted from at sensor spectral radiance to effective at sensor brightness temperature. Brightness temperature is the radiance travelling upward from the top of earth atmosphere. To covert Lλ (spectral radiance) to TB (brightness temperature) equation 3.11 was used Rajeshwari andMani, 2014).

TB= Eq. (3.11)

TB=effective at satellite brightness temperature (unit in Kelvin)

K2=calibration constant 2

Ln=natural logarithm

K1=calibration constant 1

λ=spectral radiance at sensors aperture

Landsat 8 has two thermal bands (band 10 and band 11). Table.3.8 shows the calibration constant used during performing the formula for brightness temperature.

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Table 3.8: Thermal band calibration constant of Landsat 8.

Satellite sensors Categories Band 10 Band 11

K1 777.8853 480.8883

Landsat 8 OLI/TIRS K2 1321.0789 1202.1442

Radiance_ MULT_BAND 0.0003342 0.0003342

Radiance_ADD_BAND 0.01 0.01

LST was derived from Landsat TIRS using band 10 and band 11 based on split window algorithm, which was proposed for the first time by Mc Millin in1975. As follows (eq.3.12). LST=TB10+C1 (TB10-TB11) +C2 (TB10-TB11)2 +C0+ (C3+C4W) (1-m) + (C5+C6W) m

LST=Land Surface Temperature

C0 up to C6=Split Window Coefficient Value

TB10=Brightness Temperature of band 10

TB11=Brightness Temperature of band11

m=Mean Land Surface Emissivity of thermal infrared bands (mean of band 10 and band11)

W=Atmospheric water vapor content (0.005) from Earth science reference table (ESRT) Relative humidity table.

m=Difference in Land Surface Emissivity (LSE)

During performing/calculating LST of given area, using Landsat 8 thermal bands (Table 3.9) was used.

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Table 3.9: Split window algorithm constant value.

Constant Value

The calibration constant for Landsat 4 and Landsat 7 different from Landsat 8. For the case of Landsat 4 and Landsat 7 the following table 3.10 was used.

Table 3.10: Thematic mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) thermal band calibration constant.

satellite sensors constant value

TM K1 607.76 Landsat 4 K2 1260.56

K1 666.09 Landsat 7 ETM+ K2 1282.71

Source: Landsat7 handbook

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Temperature was converted into degree Celsius, by subtracting 272.15 from the result, which was in degree Kelvin. Therefore, to convert degree Kelvin into degree Celsius the following formula was used (eq.3.13).

C=K-272.15 Eq. (3.13)

C: result in degree Celsius

K: result in degree Kelvin

Generally, to calculate LST for the sensor TM and ETM+ subtracting 272.15 from the existed result that is performed from effective at satellite temperature formula in Kelvin.

Zonal statistics

A zone is defined as all areas in the input that have the same value. Zonal statistics function summarizes the value of a raster within the zones of another dataset (either raster or vector) and reports the results as a Table. Maps of LU/LC, NDVI and LST were prepared for the year 1989, 1999 and 2016. To examine the spatial difference of LST according to varies LU/LC; the result was summarized using the zonal statistics tool of the ArcGIS 10. 3. subsequently, summarized LU/LC, NDVI and LST map data were analyzed using excel. Zonal statistics as table is one of important methods that used to examine the correlation between LULC and LST.

Spatial interpolation

Spatial interpolation is the procedure of using points with known values to evaluate values at other unknown points. For instance, to map rainfall and temperature of given area based up on nearby weather station.

In present study, meteorological data was used to interpolate and see the result and compare with the LST values that were done from Landsat thermal infrared bands. To interpolate rainfall and temperature data Inverse Distance Weighted (IDW) interpolation method was used. Inverse distance weighted is one of the interpolation methods in which the sample points are weighted during interpolation such that the impact of one point to another with distance declines from the unknown

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point. Inverse Distance Weighted is the simplest interpolation method and deterministic models. Deterministic models include IDW, rectangular, natural neighbours and spine. Interpolation uses vector points with known values to estimate unknown locations to create raster surface covering an entire area (Legates and Wilmont, 1990).

The advantages of IDW interpolations are:

 Different distances are integrated in the estimation  The distance weighting is able to precisely regulate the impact of the distances  It allows very fast and simple calculation

Therefore, in the present study rainfall and temperature data was interpolated using sample points in ArcGIS 10.3 environment and finally the interpolated maps were used for validation.

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CHAPTER FOUR

4.1. Land-use/land-cover in 1989, 1999 and 2016

Land-use/land-cover classification result of 1989 showed that the dominant LU/LC classes were farmland and shrub land. These classes accounted for 82.7% of the overall area coverage. From the total area of 901.50 km2, farmland accounted for 556.65 km2 (61.7%) and shrub land accounted for 189.72 km2 (21%). The other LU/LC the bare land, plantation, settlement and water body together accounted for 155.13 km2 (17.3%) of the total area. The water bodies covered the smallest area than all than other classes. Analysis of 1999 image also showed that farmland constitutes the largest proportion of land in the study area with the value of 631 km2 (70%), followed by shrub land, which accounted 108.94 km2 (12.5%). Other LU/LC classes such as bare land, plantation, settlement and water body together accounted for 17.5% of the total area. In 1999, also water body covered the smallest area than all other classes. Land-use/land-cover classification results of 2016 image revealed that the dominant LU/LC categories were farmland and shrub land together accounted for 81.55% of the total area coverage. Farmland accounted for 623.4 km2 (69.15%) and shrub land accounted for 110.99 km2 (12.40%). Other LU/LC classes were bare land, plantation, settlement and water body, which together accounted 18.45% of the total area. However, the extent of farmland 2016 decreased by 0.85 from 1999. Generally, farmland is the major LU/LC of the area in relation to area coverage, followed by shrub land. Detailed statistical data for each of these classes and LU/LC map of the study period are shown in Table 4.1and Figure 4.1.

Table 4.1: Land-use/land-cover classes and area coverage of 1989, 1999 and 2016 in Adama Zuria Woreda, Ethiopia No LU/LC classes 1989 1999 2016 Area , Area, Area , Area, Area, Area, (%) (km2) (%) (km2) (%) (km2) 1 Farm land 556.65 61.7 631 70 623.4 69.15 2 Bare land 80.02 8.89 67.98 7.22 35.86 3.98 3 Plantation 66.21 7.41 68.97 7.57 72.90 8.00 4 Settlement 6.48 0.73 20.97 2.28 55.30 6.11 5 Shrub land 189.72 21 108.94 12.50 110.99 12.43 6 Water body 2.42 0.27 3.64 0.40 3.05 0.33

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Figure 4.1: Land-use/land-cover maps of the study area of the years of 1989, 1999 and 2016.

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Farmland habitat had the largest extent of all LU/LC in the study area during 1989–2016. The extent of farmland increased 1989 from 555.65 km2 to 631 km2 by 1999 but slightly decreased 623.4 km2

by 2016 (Fig 4.2).

1989 1999 2016

Area ( Area

3.05 2.42 0 Farm Land Bare Land Plantation Settlement Shrub Land Water Body

LU/LC catagories

Figure 4.2: Land-use/land-cover distribution and changes in the study area during the period 1989– 2016.

4.2. Spatial extent of land-use/land-cover

Land-use/land-cover patterns in the study area have indicated a significant change in between 1989 and 2016 years. Among the six major LU/LC classes, farmland and shrub land covered more than 80% of the area in all the study years. Settlement area has been increasing from 1989 to 2016. It has increased from 0.73% in 1989 to 2.28% by 1999 and 6.11% by 2016. Plantation also increased from 1989 to 2016. It increased from 7.41% by 1989 to 7.51% by 1999 and 8 % by 2016. Shrub land and water body showed fluctuations from 1989 to 2016. Shrub land was 21% in 1989, which was decreased to 12.0% by 1999 and then increased to 12.4% by 2016. Water body also has changed from 0.27% in 1989 to 0.40% by 1999 and then decreased to 0.33% by 2016. Farmland increased from 61.7% by 1989 to 70% by 1999 and decreased to 69.15% in 2016. Bare land showed continuous decrement from 1989–2016. It was 8.89% in 1989, which decreased to 7.20% by 1999 and 3.98% by 2016 (Table 4.2).

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Table 4.2: Land-use/land-cover distribution and net changes during 1989–2016.

1999 2016 LU/LC 1989 Net changes during N Classes 2 Area Area, Area, Area , Area , Area , 1989–2016, km o 2 2 ,km2 % km % km %

1 Farmland 556.65 61.7 631 70 623.6 69.15 +66.75 2 Bare land 80.02 8.89 67.98 7.22 35.86 3.98 -44.16 3 Plantation 66.21 7.41 68.97 7.57 72.90 8.00 +6.69 4 Settlement 6.48 0.73 20.97 2.28 55.30 6.11 +48.82 5 Shrub land 189.72 21 108.94 12.50 110.99 12.4 -78.73 6 Water body 2.42 0.27 3.64 0.40 3.05 0.33 +0.63 Total 901.5 100 901.5 100 901.5 100

The LU/LC change trends of the study area showed that shrub land and bare land have decreased by 78.73 km2 and 44.16 km2, respectively. Other classes showed increasing and fluctuating area coverage. Figure 4.3.illustrated the LU/LC changes in the study area.

700 1989 600 1999 500

2 400 Net change (1989–2016)

Area Area 200

0 Farm Land Bare Land Plantation Settlement Shrub Land Water Body -100 LU/LC catagories

Figure 4.3: Land-use/land-cover changes during 1989––2016 in the Adama Zuria Woreda.

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Table 4.3: Land transformation for Adama Zuria Woreda (1989––2016). 2016 LU/LC Farm Bare Shrub Class Total Plantation Settlement Water Classes land land land body

Farm land 423.6147 23.9958 14.6853 47.6505 39.3093 0.9207 550.1763

Bare land 61.7499 8.9118 1.9269 6.0228 2.673 0.0963 81.3807

Plantation 0.693 0.9522 43.1469 1.8711 19.5426 0.5148 66.7206

1989 Settlement 0.5499 0.2088 0.5031 4.2822 0.5967 0.1584 6.2991

Shrub land 98.6634 6.957 12.0681 14.4972 56.988 0.4311 190.6048

Water 0.3204 0.1521 0.2007 0.5472 0.3447 0.7245 2.2896 body Class Total 585.8451 41.202 72.5598 78.885 119.5173 2.8737 901.429

4.3. Settlement expansion during 1989–2016 An overlay analysis of settlement area detected from 1989, 1999 and 2016 satellite image of Adama Zuria Woreda revealed that the farmland, shrub land and bare land have been changed to settlement area in different direction of the Woreda. The spread of settlement area towards other LU/LC classes is shown in Figure 4.4.

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Figure 4.4: The trend of expansion of settlement area from the year 1989 to 2016 in Adama Zuria Woreda.

4.4. Accuracy assessment

The accuracy assessment of LU/LC for the years 1989, 1999 and 2016 recorded the overall classification accuracy of 88.33%, 90.00% and 88.33%, respectively. The classification Kappa statistics for 1989 is 0.85. For the years, 1999 and 2016 Kappa values are 0.87 and 0.84, respectively. The detailed information of producers and users accuracy is indicated in Table 4.4.

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Table 4.4: Statistical information of accuracy assessment for the year 1989, 1999 and 2016.

1989 1999 2016 class name producers users producers users producers users accuracy accuracy accuracy accuracy accuracy accuracy Plantation 87.50% 87.50% 77.78% 87.50% 100.00% 80.00% Settlement 81.82% 90.00% 100.00% 90.00% 93.75% 88.24% Water body 90.91% 90.91% 100.00% 90.91% 75.00% 100.00% Farm land 92.31% 85.71% 92.86% 92.86% 89.47% 94.44% Shrub land 90.00% 90.00% 90.00% 90.00% 66.67% 80.00% Bare land 85.71% 85.71% 75.00% 85.71% 90.91% 83.33% Overall

classification 88.33% 90.00% 88.33% accuracy Overall

Kappa 0.8588 0.8790 0.8496 statistics

4.5. Normalized difference vegetation index

In this study, it has been observed that the vegetation cover was more in 1989 and 1999 with maximum NDVI values of 0.66 and 0.55, respectively. The highest value shows healthy vegetation. Vegetation cover has decreased for the year 2016 with the NDVI value of 0.48. Normalized difference vegetation index result for the years 1989, 1999 and 2016 are presented in Figure 4.5.

The normalized difference vegetation index result of 1989, 1999 and 2016 showed that the northern and northwest part of the study area resulted higher NDVI value and the area of Boku ridge and Wonji sugarcane plantation showed highest NDVI value. However, settlement area along Awash River and most parts in eastern region have relatively low NDVI values. As indicated in figure 4.5, vegetation cover has decreased and non-vegetated area has been increasing gradually over the study period. However, in 2016 sugarcane plantation, area has slightly increased due to the expansion of Wenji Shewa Sugarcane Plantation. Table 4.5 showed statistical information of NDVI value for the year 1989, 1999 and 2016.

Table 4.5: Statistical information of NDVI value for the years 1989, 1999 and 2016.

Year Min Max Mean Std 1989 -0.40 0.66 0.159 0.123 1999 -0.61 0.55 -0.099 0.138 2016 -0.14 0.48 0.140 0.069 Where, Min = Minimum, Max = Maximum, Std = Standard deviation

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Figure 4.5: Normalized difference vegetation index maps of the study area (1989, 1999 and 2016).

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4.6. Relationship between land-use/land-cover and Normalized difference vegetation index

Normalized difference vegetation index maps extracted from near infrared and red-bands of the study periods indicated that different LU/LC has different NDVI values. As NDVI is related with the vegetation condition, the value varies from area to area based on vegetation intensity of the sites. Plantation and shrub land have the highest NDVI value than other classes (Fig. 4.6).

Farm Land Shrub Land Settlement Plantation Bare Land Water Body NDVI -0.2

-0.8 LU/LC catagories

Figure 4.6: Zonal statistical description of NDVI in 1989, 1999 and 2016 over different LU/LC classes in the study area.

4.7. Spatial pattern of land surface temperature in Adama Zuria Woreda

The spatial distribution of LST of the study area was extracted and quantified using Landsat TM of 1989, ETM+ of 1999 and OLI/TIRS thermal bands. The analysis from such images indicated that the LST value of the study period ranged from 9°C to 42°C. As it was observed from the processed thermal images, the east, northeast and southeast parts of the study area exhibited relatively high temperature. This is mainly due to altitude, slope and LU/LC types. On the other hand, Northwest part and Wenji Shewa sugarcane plantation area exhibited relatively low LST values. The Northwestern part of the study area had high altitude ranges from 2,118 to 2,505 meters a.s.l, and the southwestern part had altitude ranges from 1758 to 2,118 meters a.s.l. For each period, LST distribution is shown in Figure.4.7.

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Figure 4.7: Land surface temperature maps of the study area of the years 1989, 1999 and 2016.

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4.8. Impact of land-use/land-cover change on land surface temperature

Land-use/land-cover dynamics has an impact on land surface temperature. The changes LU/LC resulted in alteration of land surface temperature, particularly in the settlement, farmland (after harvested) and bare land. The reason behind farmland LU/LC classes of high LST was that the image used for the analysis was taken during harvesting time and dry weather condition. For each of LU/LC classes LST value varies from place to place because of vegetation and climate condition. The conversions of farmland and other types of LU into settlement area also contributed to the increase of LST. As observed in Figure 4.4 (Expansion of settlement area from the year 1989 to 2016), the settlement area has highly increased especially the northern and southern parts of Adama town and along the main gate of town (Addis Ababa, Wenji Shewa, Asella and Metehara). In addition to that, settlement area has increased in small towns of the Woreda (Table 4.6).

Table 4.6: Zonal statistical description of LST in 1989, 1999 and 2016 over different LU/LC classes.

LU/LC classes Min Max Mean Std Min Max Mean Std Min Max Mean Std

10.4 14.9 38.3 21.2 Farm land 32.82 25.49 2.32 31.11 2.36 41.86 35.55 2.12 4 6 7 4

13.6 35.0 19.8 Shrub land 9.02 32.12 22.55 3.30 26.47 2.95 39.75 31.79 3.27 2 0 3

12.7 17.6 38.7 20.8 Settlement 31.76 23.05 2.43 28.46 2.46 41.17 31.01 2.48 8 0 3 6

14.0 32.7 19.7 Plantation 9.78 28.55 18.55 2.65 22.73 1.34 38.00 31.48 3.03 4 1 1

16.4 18.9 38.3 20.9 Bare land 32.47 26.15 1.80 31.34 1.74 41.18 33.68 2.50 3 0 7 3

Water body 16.7 28.8 19.9 9.96 23.39 15.72 1.98 21.23 2.15 39.91 29.20 3.54 3 1 0 Where, Min = Minimum, Max = Maximum, Std = Standard deviation.

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Most of the study area was under farmland cover, it covered more than 61% in all study period. The LST value has increased from the mean of 25.49ºC in 1989 to 35.55ºC in 2016. Figure 4.8 showed how LU/LCs changes affect the variability of LST.

Figure 4.8: Different views of land surface temperature of the study area for 2016.

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4.9. Relationship between land-use/land-cover and land surface temperature

Land-use/land-cover and LST map of the study area revealed how the categories and corresponding land surface temperature have changed during the study period. Land-use/land-cover categories that received minimum temperature was water body, plantation and shrub land, respectively. Water body and Vegetated area played crucial role in minimizing the LST. Vegetated area and trees in different part of the area provide shade, which help lower land surface temperatures and reduce air temperature through evapotranspiration, in which plant release water to the surrounding air. Relatively, the highest LST value was recorded in farmland, settlement and bare land. The LST value was high in farmland during the acquisition time of the image.

From 1989 to 2016, the mean LST of Adama Zuria Woreda has increased by 9.66 °C. Table 4.5 indicates that all major LU/LC categories have recorded increase in the LST over the study period. The highest mean LST was recorded in farmland, bare land and settlement area. Whereas, the lowest mean LST was recorded in water body, plantation and shrub land area. Comparisons of mean LST with LU/LC classes for the study period are indicated in Figure 4.9. The land surface temperature in the study area has increased considerably from 1989 to 2016 (Table 4.7).

30 25 20 1989 15 1999 10 2016

Mean LST (ºC) LST Mean 5 0 Farm Land Shrub Settlement Plantation Bare Land Water Land Body LU/LC categories

Figure 4.9: Comparisons of mean land surface temperature in different land-use/land-cover classes during the study period in Adama Zuria Woreda.

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Table 4.7: Mean temperature of 1989, 1999, 2016 and changes in temperature during 1989−2016 in the study area.

1989–1999 1999–2016 LU/LC 1989 1999 2016 1989–2016 Change in Change in Classes Mean Mean Mean Change in (ºC) (ºC) (ºC) Farm land 25.49 31.11 35.15 5.62 4.04 9.66

Shrub land 22.55 26.47 31.52 3.92 5.05 8.97

Settlement 23.05 28.46 31.67 5.41 3.21 8.62

Plantation 18.55 22.73 31.48 4.18 8.75 12.93

Bare land 26.15 31.34 33.68 5.19 2.34 7.53

Water body 15.72 21.23 29.20 5.41 7.97 13.48

4.10. Relationship between normalized difference vegetation index and land surface temperature The analyzed Landsat images of 1989, 1999 and 2016 indicated that NDVI and LST have indirect relationships. A Low NDVI value has high LST and high NDVI values have low LST. However, the relationship between NDVI and LST were direct/positive relationship for the case of water body, because both NDVI and LST value was less for water body. The R² values of the study period between NDVI and LST correlations indicated 96.08%, 97.52% and 98.58%, respectively. These results show that, in (1989) 96.08% value of LST variability was due to the difference in NDVI value, the coefficient variation values are also the same for 1999 and 2016. Generally, negative correlation was found between NDVI values with LST. Land surface temperature and NDVI correlation for the years 1989, 1999 and 2016 are shown in Figure 4.10.

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Figure 4.10: Land surface temperature and normalized difference vegetation index correlation for the years 1989, 1999 and 2016 for the study area.

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4.11. Comparisons of LST distribution during 1989, 1999 and 2016 The results of the study indicated that throughout all the study period the LST value increased drastically. Land surface temperatures were classified into six groups ranging from 9 ºC –42ºC.The LST distribution statuses during study period are given in Table 4.8.

Table 4.8: Trends of land surface temperature distribution during the study period in Adama Zuria Woreda during 1989–2016.

Area coverage during study period (km2) LST 1989 1999 2016 36 0.347769 117.7 137.6997 Total 901.5 901.5 901.5

The trend of increase in LST shows that in Adama Zuria Woreda LST was increasing from year to year. A comparison between LST distributions of the selected years are given in Figure 4.11.

400 350 LST 1989

300 LST 1999

250 LST 2016 200

150 Area (km²) Area 100 50 0 36 LST (ºC)

Figure 4.11: Comparisons between LST distributions of the years 1989, 1999 and 2016 in Adama Zuria Woreda.

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4.12. Verification of the result for land surface temperature The land surface temperature extracted from Landsat thermal band of the study area and the interpolated atmospheric temperature have shown the direct relationships. However, LST trend attained in this study was from Landsat imagery acquired during dry weather condition and harvesting time. Considering the existing metrological and acquired time of Landsat, the spatial distribution of the LST was validated. Land surface temperature was verified using meteorological data and LU/LC classes of the study area, which validated with GPS point collected from field and Google Earth. Interpolation of rainfall and temperature were done based on the meteorological data collected from five stations within and nearby the study area. These stations were; Mojo, Koka Dam, Nazareth, Sodere and Welenchiti . There is strong relationship between LST and atmospheric temperature, which was interpolated from five stations. Remotely sensed LST is most valuable in characterization and prediction of spatial temporal patterns of atmospheric temperature. Therefore, LST can be used as indicator of atmospheric temperature. Interpolated map of rainfall and atmospheric temperature are given in Figure 4.12a and 4. 12b.

Figure 4.12: a) Interpolated map of rainfall and b) Interpolated map of temperature of Adama Zuria Woreda.

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As observed from the interpolated map of the study area, both rainfall and temperature results was low at the eastern part. However, rainfall map of the northern and western parts have high values than eastern and southern parts. Interpolated map of temperature result showed that the southern and the northern part have higher value in ºC than east and western relatively.

The result of LST extracted from Landsat thermal bands, also revealed that the eastern part had the highest value in each of the study periods, where as the northern part had low value. However, in the area of Wenji Shewa sugarcane plantation, LST was low. For instance, Wenji Sugarcane plantation area; the LST was very low but in the interpolated map it shows relatively medium temperature. The mean LST, which was extracted from thermal bands of the study period, was ranged from 25.49ºC to 35.15 ºC. Whereas, the interpolated atmospheric temperature result ranged from 23.35 ºC – 33.82 ºC.

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CHAPTR FIVE

5. DISCUSSION 5.1. Land-use/land-cover status of Adama Zuria Woreda The LU/LC changes experienced across Adama Zuria Woreda shows as a sign of population growth and the increase of needs of land for settlement and agriculture. These changes have brought about a series of adverse impacts on the environment. Houghton (1994) revealed that human population growth, economic development, technology and environmental changes are major factor responsible for LU/LC variability. According to FAO (1999), LU/LC changes have become major problems and it is a significant driving agent of environmental changes. Alemayehu (2008) also showed some of the evidence for climate change including occurrence of drought, rising temperature, flood, reduced annual rainfall and rising sea levels. The present study revealed that during the study period there were drastic changes in LU/LC. In 1989, the settlement area was 6.48 km2 and then it reached 55.30 km2 in 2016. It is one of the evidences for the increasing needs of settlement. The extent of sugarcane plantation also increased from 66.21 km2 in 1989 km2 to 72.90 km2 in 2016 and bare land decreased from 80.02 km2 in 1989 to 35.86 km2 in 2016. Farmland, shrub land and water body showed fluctuation. In general, the classified satellite image indicates that there is a change in LU/LC.

5.2. Normalized difference vegetation index Normalized difference vegetation index is a measure of vegetation condition and used to determine LST. According to Mihai (2012) and Weng (2004), NDVI is found as an acceptable indicator of LST and dryness. The findings of the present study also revealed that NDVI varied from south towards to the north of the Woreda. As farmland and shrub land classes dominate the present study area, the vegetation cover also varied. The NDVI and LST correlation coefficient was evaluated in this study for 2016 was R2=0.98, which indicate how the vegetation condition determine the LST of the area and NDVI accounts 98.58% in distribution of LST. In line with this, the present study revealed that NDVI have strong relation with LST (with the exception of water body class).

5.3. Land surface temperature A thermal band of Landsat image was important in quantifying the LST of the area. In the present study farmland, settlement and bare land have high LST. The main reason behind less NDVI and high LST values in the area of farmland LU/LC class is that the image was taken during the dry

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season and during harvesting time November and December. In Ethiopia, the main harvesting season is November, December and January. Due to this, farmland, bare land and settlement classes had high surface reflectance. After harvest of crops, the land was very dry and at the same time, land reflectance high. Normalized Difference Vegetation Index value is less for water body, bare land, settlement and farmland classes. However, in the case of plantation and shrub land, the values were relatively high. To evaluate the thermal status of land surface by satellite, it is important to find the relation between LU/LC type and LST. Normalized difference vegetation index play a great role in assessing and investigating of LST. Land surface temperature and NDVI have indirect relationships in different LU/LC categories except in the case of water body. Water body LU/LC class has low NDVI and LST value. Vegetation condition (NDVI value) and types of LU/LC have impacts on LST value. However, it does not mean that the type of LU/LC is the only factor for the increase of LST. There are a number of contributing factors for increasing or decreasing the LST value. For instance, types of soil, geothermal energy, geological setting, altitude and climate condition in the area.

Different researchers used thermal bands to study LST. Gebrekidan Worku (2016) used Landsat satellite imagery to study LST situation of Addis Ababa city. The study investigated the relationship between LU/LC, LST and NDVI. Gebrekidan Worku (2016) found negative linkage in NDVI and LST values. However, in this case, water body should be considered as exception, as water body has low NDVI and LST value. The result of present study shows that different LU/LC has different NDVI and LST values during the study period.

Maitima et al. (2009), Khin et al. (2012) and Kerr et al. (2004) agree with that the main factor for increase of LST in rural as well as urban areas land-cover changes and unplanned use of land resources. In less developed areas the relation between Land-cover change, biodiversity and land deterioration is very high, because of that land-cover are transformed to human settlements, farmlands and grazing lands at the expense of forest and vegetation (Maitima et al. 2009). With regard to LU/LC changes in the study area, with the expansion of settlements, plantations (sugarcane plantation) and farmland the shrub land and open space have highly declined. This is clearly observed, especially at Adama town area. In this area, due to the expansion of settlement area, small-scale farmers moved and start to settled in open space and shrub land. In addition to this, Wonji Shewa sugarcane plantation also extended its area towards the previously open space, shrub

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land and farmland. In 1989, at the Boku ridge area, there were some sparsely vegetated area but through time, this area was totally changed into farmland and settlement. Thus, the study area is highly degraded and exposed to natural disasters such as soil erosion. As evident from the classified satellite images. However, sometimes LU/LC has a positive impact on environment. For instance, the conversion of open space/bare land/ to plantation LU/LC type. This might help to control increasing LST.

Normalized difference vegetation index and LST have indirect/negative/ relationship. However, that could be a positive/direct/ relationship, especially in water body and high potential geothermal areas. According to Burgan and Hartford (1993), the negative values are indicative of water, snow, clouds, non-reflective surface and other non-vegetated areas while positive values expresses reflective surfaces such as vegetated area. However, it does not mean that all negative values of NDVI have high LST value. Land surface temperature depends up on the reflective conditions of the surface of the earth. In general, area of non-vegetated and non-reflective surface, have high LST than those of vegetated and water body.

According to the result of this study, there are six major LU/LC classes and for each class LST value varies. Water body, plantation and shrub land have relatively low LST than settlement, farmland and bare land LU/LC class. Spatial and temporal distribution of LST during 1989,1999 and 2016 revealed that during the study period there was drastic changes took place in the area. In 1989, the distribution of LST was 9ºC and 33ºC, minimum and maximum, respectively, where as in 1999 and 2016, the minimum and maximum LST ranges were 13ºC–39ºC and 19ºC–42ºC, respectively.

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6. CONCLUSION AND RECOMMENDATIONS

6.1. Conclusion Satellite data are useful for different applications such as LU/LC change detection and environmental management programs. Landsat imageries have the potential to examine the LU/LC dynamics and to analyze the spatio-temporal distribution of LST. The present study has used Landsat TM 4, ETM+7 and Landsat OLI/TIRS images to evaluate and analyze the impact of LU/LC changes on LST. Land surface temperature and LU/LC derived from such data provides important information to monitor human activities and environmental changes. It was reveals that Landsat images are very useful in quantifying and mapping LU/LC change, NDVI and LST in Adama Zuria Woreda. The LU/LC pattern of the study area has attributed to socio-economic and natural factors and their exploitation in time and space. Water body and vegetation classes (Plantation and shrub land) have lowest LST value where as settlement, bare land and farmland class have highest LST. Land-use/land-cover has been changing from time to time in the present study area. Land-use/land- cover change has an impact on LST. For each year, the minimum and maximum LST has increased. Hence, Calculated LST acts an important role or functions of LU/LC and alters temperature condition of the area.

The findings of this study showed that LSTs of Adama Zuria Woreda has increased during period of 1989, 1999 and 2016. This was because of LU/LC changes, human activities and climate variability of the area. The land surface temperature of the Woreda indicated a high variation in LST between different LU/LC types. The derived LST values from satellite data were found to be in fine harmony with interpolated temperature values from the weather stations used. The derived NDVI values from satellite data also somehow a good indicator of LST status of the study area. Therefore, the use of GIS remote sensing data to investigate the differences in LST and different LU/LC class pattern in the study area illustrates that it suggest a quicker and cost effective technique with the advantage of covering large area.

6.2. Recommendations

The focus of this study was to assess and investigate the impact of LU/LC changes on LST in Adama Zuria Woreda based on Landsat imagery of 1989, 1999 and 2016. The study showed that the

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conversion of LU/LC types to another type of LU/LC types such as from vegetation area to farmland, farmland to settlement and the value of LST was different for each LU/LC class. However, there is case in which the change could make a positive impact on LST. For instance, Wenji sugarcane plantation had a positive contribution for the low LST value. Therefore, based on the result of this study, the following recommendations have drawn.

 Land-use/land-cover change becomes a central component in current strategies for managing resources and monitoring environmental changes. Therefore, the governmental and non-governmental bodies should give high attention for proper land-use management and ecological effect of each LU/LC.  Conservation activities have to be taken on both rural and urban green areas of the study area and recommended to plant trees and delineate green area, especially at high settlement and bare land areas.  Land managers and environmental experts should control LU/LC changes through different measures to minimize the influence on the environments.  The output of this research can serve as an input for future researchers who want to study the impact of LU/LC changes on environmental issues.

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References Agarwal, C., Green, G. M., Grove, J. M., Evans, T. P., and Schweik, C. M. (2002). A review and assessment of land-use change models: dynamics of space, time, and human choice.

Aires, F., Prigent, C., Rossow, W. B., and Rothstein, M. (2001). A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations. Journal of Geophysical Research: Atmospheres, 106(D14), 14887–14907.

Alemayehu Mengistu (2008). Climate Variability and change. Ethiopian journal of Animal production.8 (1), 94–98.

Ali, H. (2009). Land use and land cover change, drivers and its impact: A comparative study from Kuhar Michael and Lenche Dima of Blue Nile and Awash Basins of Ethiopia (Doctoral dissertation, Cornell University).

Asubonteng, K. O. (2007). Identification of land use/cover transfer hotspots in the Ejisu-Juabeng Woreda, Ghana. International Institute for Geo-information Science and Earth Observation Enschede, Netherlands.

Becker, F., and Li, Z. L. (1990). Towards a local split window method over land surfaces. Remote Sensing, 11(3), 369–393.

Billah, M., and Rahman, G. A. (2004). Land-cover mapping of Khulna city applying remote sensing technique. In Proceedings of the 12th International Conference on Geoinformatics (Sweden: Geospatial Information Research: Bridging the Pacific and Atlantic, University of Gävle) pp. 707– 714.

Bonini, M., Corti, G., Innocenti, F., Manetti, P., Mazzarini, F., Abebe, T., and Pecskay, Z. (2005). Evolution of the Main Ethiopian Rift in the frame of Afar and Kenya rifts propagation. Tectonics 24, TC1007.

Burgan, R. E., and Hartford, R. A. (1993). Monitoring vegetation greenness with satellite data.

Burrough, P.A., (1990). Principles of Geographic information systems.Pp.333.Oxford University press, UK.

Buyadi, S. N. A., Mohd, W. M. N. W., and Misni, A. (2013). Impact of land use changes on the surface temperature distribution of area surrounding the National Botanic Garden, Shah Alam. Procedia-Social and Behavioral Sciences, 101, 516–525.

CSA. (2007) Central Statistical Agency. Summary and statistical report of the population and housing census. Addis Ababa, Ethiopia.

Chen, X., (2000). Using RS and GIS to analyze land-cover change and its impacts on the Regional Sustainable Development. International Journal Remote Sensing. (23), 107–113.

AAU.Remote Sensing and Geo-informatics stream: By Belete Tafesse: [email protected] 65

Coppin, P. R., and Bauer, M. E. (1996). Digital change detection in forest ecosystems with remote sensing imagery. Remote sensing reviews, 13(3-4), 207–234.

Di Gregorio, A., and Jansen L.J., (2000). Land-cover classification: classification concepts and user manual, Food and Agriculture Organization (FAO) of the United Nations Rome.

Emilio, C. (2008). The role of satellite RS in monitoring global environment “department of Geography University of Alkala Spain, PP. 1–2.

FAO/UNEP., (1999).Terminology for integrated resources planning and management. Food and agriculture organization/United Nations Environmental program, Rome, Italy and Nairobi, Kenya.

Farooq, A. (2012). Landsat ETM+ and MODIS EVI/NDVI data products for climate variation and Agricultural Measurements in Holliston Desert. Global Journal of Humanities, social science: Geography and Environmental Geosciences, 12 :( 13).111.

Foresman, T. W. (Ed.). (1998). The history of geographic information systems: perspectives from the pioneers. Prentice Hall.

Gebrekidan, Werku (2016). Modeling land surface temperature from satellite data, the case of Addis Ababa. ES RI, Eastern Africa Education GIS conference. Held at Africa hall, United Nations conference center from23-24 sept. 2016, Addis Ababa.

Gumindoga, W. (2010). Hydrologic Impact of Land-Use Change in The Upper Gilgel Abay River Basin, Ethiopia; TOPMODEL Application (Doctoral dissertation, M. Sc. Thesis, ITC Netherlands).

Guo, Z., Wang, S. D., Cheng, M. M., and Shu, Y. (2012). Assess the effect of different degrees of urbanization on land surface temperature using remote sensing images. Procedia Environmental Sciences, 13, 935–942.

Hardegree, L. C. (2006). Spatial Characteristics Of The Remotely-sensed Surface Urban Heat Island in Baton Rouge, LA: 1988-2003 (Doctoral dissertation).

Houghton, R. A. (1994). The worldwide extent of land-use change. BioScience, 44(5), 305-313.

Huang, S., and Siegert, F. (2006). Land cover classification optimized to detect areas at risk of desertification in North China based on SPOT VEGETATION imagery. Journal of Arid Environments, 67(2), 308–327.

IPCC. (2014). Climate Change 2014–Impacts, Adaptation and Vulnerability: Regional Aspects. Cambridge University Press.

Jiang, J., and Guangjin, T. (2010). Analysis of the impact of land use/land cover change on land surface temperature with remote sensing. Procedia environmental sciences, 2, 571–575.

AAU.Remote Sensing and Geo-informatics stream: By Belete Tafesse: [email protected] 66

Kayet, N., Pathak, K., Chakrabarty, A., and Sahoo, S. (2016). Spatial impact of land use/land cover change on surface temperature distribution in Saranda Forest, Jharkhand. Modeling Earth Systems and Environment, 2(3), 1–10.

Kazmin, V., and Seifemichael, B. (1978). Ethiopian institution of geological survey; Geology and Development of the Nazret area, Northern Ethiopian Rift, Addis Ababa.

Kern, A. (2011). Deriving Normalized Difference Vegetation Index based on remotely sensed AVHRR and MODIS data (Doctoral dissertation, Eötvös Loránd University).

Kerr, Y.H., Lagouarde, J.P., Nerry, F., Ottle, C. (2004). Land surface retrieval techniques and applications: case of AVHRR, in: Quatrocci, D.A., Luvall, J. C. Thermal remote sensing in land surface processes. CRC press, Florida, USA, 33–109.

Khin, M.Y., Shin D.Y., Park, J.J., Choi C.C., (2012). Land surface temperature changes by LU changes of Inlay Lake area, Myanmar. Department of Spatial Information Engineering, Pukyong National University, South Korea.

Kumar, R., and Singh, S. (2016). Case Study for Change Detection Analysis and Land Surface Temperature Retrieval in Uttrakhand Region and their correlation. Imperial Journal of Interdisciplinary Research, 2(9).

Lambin, E. F., Turner, B. L., Geist, H. J., Agbola, S. B., Angelsen, A., Bruce, J. W., ... and George, P. (2001). The causes of land-use and land-cover change: moving beyond the myths. Global environmental change, 11(4), 261–269.

Lambin, E.F., and Meyfroidt, P. (2010). Land-use transitions: socio-ecological feedback versus socioeconomic change, land-use policy, 27,108–118.

Legates, D. R., and Willmott, C. J. (1990). Mean seasonal and spatial variability in global surface air temperature. Theoretical and applied climatology, 41(1), 11–21.

Lemlem Abraha (2007). Assessing the Impact of Land Use and Land Cover Change on Groundwater Recharge using Rs and GIS; a Case of Awassa Catchement, Southern Ethiopia (Doctoral dissertation, AAU), Addis Ababa.

Lillesand, T., Kiefer, R. W., and Chipman, J. (2014). Remote sensing and image interpretation. John Wiley and Sons, New Delhi.

Macleod, R. D., and Congalton, R. G. (1998). A quantitative comparison of change-detection algorithms for monitoring eelgrass from remotely sensed data. Photogrammetric engineering and remote sensing, 64(3), 207–216.

Maguire, D.J., (1991). An overview and definition of Geographic information system. In geography information systems: principles and applications. London Longman, Vol.1,9–20.

AAU.Remote Sensing and Geo-informatics stream: By Belete Tafesse: [email protected] 67

Maitima,J.M.,Mgatha,S.M.,Reid,R.S.,Gachimbi,L.N.,Majule,A.,Lyaru,H.,Pomery,D.,Mathai,S.,and Mugisha,S.,(2009). The linkage between land-use change, land degradation and biodiversity across East Africa. Africa Journal of environmental science and technology, 3 (10), 310–326.

Mather, A. S., and Needle, C. L. (2000). The relationships of population and forest trends. TheGeographical Journal, 166(1), 2–13.

Md Shahid, L., (2014).Land Surface Temperature Retrieval of Landsat 8 Data using split window Algorithm a case study Ranchi Woreda. International Journal of Engineering Development and Research, 2(4), 3840–3849.

Meyer, W. B., and Turner, B. L. (1992). Human population growth and global land-use/cover change. Annual review of ecology and systematics, 23(1), 39-61.

Mihai,V.H.(2012).Using Satellite Images Landsat TM For calculating Normalized Difference Indexes for the landscape of Parang mountains.Lect.Ph.D.Eng.Banat University of Agricultural sciences and Veterinary medicine, Romania.

Miheretu Birhan and Assefa Abegaz (2017). Land use/land cover changes and their environmental implications in the Gelana sub-watershed of Northern highlands of Ethiopia. Environmental Systems Research, 6(1), 7.

Naissan, O.W., and Lily,T.(2016). Aesthetc Dermatology,effects of climate change on skin,Dermatology news USA, May 16, 2016.

Nunes, M. C., Vasconcelos, M. J., Pereira, J. M., Dasgupta, N., Alldredge, R. J., and Rego, F. C. (2005). Land cover type and fire in Portugal: do fires burn land cover selectively?. Landscape Ecology, 20(6), 661–673.

Nuruzzaman, M. (2015). Urban Heat Island: Causes, Effects and Mitigation Measures-A Review. International Journal of Environmental Monitoring and Analysis, 3(2), 67.

Oluseyi, I. O., Danlami, M. S., and Olusegun, A. J. (2011). Managing Land Use Transformation and Land Surface Temperature Change in Anyigba Town, Kogi State, Nigeria. Journal of Geography and Geology, 3(1), 77.

Orsolya, G., Zalan T., Boudewijn V.L. (2016). Satellite based Analysis of surface UHI intensity. Journal of Environmental Geography, 9(2), 23–30.

Paramasivam, C.R. (2016). Remote Sensing and spectral studies of magnesite mining area, Salem, India. For the award of the Degree of Doctor of Philosophy in Geology thesis submitted to Periyar University, Salem, India. Prata, A.J., and Caselles, C., (1995). Thermal remote sensing of land surface temperature from satellites: status and future prospects. Remote sensing rev.12, 175–223.

AAU.Remote Sensing and Geo-informatics stream: By Belete Tafesse: [email protected] 68

Rajeshwari, A., and Mani, N. D. (2014). Estimation of land surface temperature of Dindigul Woreda using Landsat 8 data. International Journal of Research in Engineering and Technology, 3(5), 122– 126.

Ramachandra, T. V., Aithal, B. H., and Sanna, D. (2012). Land Surface Temperature Analysis in an Urbanising Landscape through Multi-Resolution Data. Research and Reviews: Journal of Space Science and Technology, 1(1), 1–10.

Santa, P. (2011). Forest cover and land-use changes: a study of Laljihadi forest (corridor), far western development region, Nepal.MSc Thesis Tribhuvan University.

Schmugge, T., Hook, S. J., and Coll, C. (1998). Recovering surface temperature and emissivity from thermal infrared multispectral data. Remote Sensing of Environment, 65(2), 121–131.

Shukla, J., Nobre, C., and Sellers, P. (1990). Amazon deforestation and climate change. Science (Washington), 247(4948), 1322–1325.

Sobrino, J.A., Rosa, O., Guillen, S., Juan, C.J., Belen.F. Victoria.H., Cristian, M., Yves,J.,Juan,C.,Mireia,R.,Antonio,G.J.,Eduardo,D.M.,Remo,B.,andMarc,p.(2012).Evaluation of the surface UHI effect in the city of Madrid by thermal RS. International journal of remote sensing, 34, 910.

Streutker, D. R. (2003). Satellite-measured growth of the urban heat island of Houston, Texas. Remote Sensing of Environment, 85(3), 282–289.

Turner, W., Spector, S., Gardiner, N., Fladeland, M., Sterling, E., and Steininger, M. (2003). Remote sensing for biodiversity science and conservation. Trends in ecology and evolution, 18(6), 306–314.

USAPA, (2004). A Summary of Models for assessing the effects of Community Growth and change on land-use patterns.EPA.600.R-00.098.U.S.Environmental protection Agency office of research and development , pp. 260.

Voogt, J., (2000). How researchers measure UHI. Glossary of Meteorology, 2nd edition, Canada.

Walsh, C. L., Dawson, R. J., Hall, J. W., Barr, S. L., Batty, M., Bristow, A. L., and Tight, M. R. (2011). Assessment of climate change mitigation and adaptation in cities. Proceedings of the Institution of Civil Engineers-Urban Design and Planning, 164(2), 75–84.

Weng, Q.H. (2004). A remote sensing and GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China. International journal of remote sensing 22(10), 1999-2014. Weng, Q., Lu, D., and Schubring, J. (2004). Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote sensing of Environment, 89(4), 467– 483.

AAU.Remote Sensing and Geo-informatics stream: By Belete Tafesse: [email protected] 69

Yue, W., Xu, J., Tan, W., and Xu, L. (2007). The relationship between land surface temperature and NDVI with remote sensing: application to Shanghai Landsat 7 ETM+ data. International Journal of Remote Sensing, 28(15), 3205–3226.

Zha, Y., Gao, J., Jiang, J., Lu, H., and Huang, J. (2012). Normalized difference haze index: a new spectral index for monitoring urban air pollution. International journal of remote sensing, 33(1), 309–321.

Zubair, A.O. (2006). Change detection in LU/LC using RS data and GIS a case study of Ilorin and its environments in Kwara State. Department of geography, University of Ibadan.

AAU.Remote Sensing and Geo-informatics stream: By Belete Tafesse: [email protected] 70

Appendix 1: Classification accuracy assessment report for the year 1989.

AAU.Remote Sensing and Geo-informatics stream: By Belete Tafesse: [email protected] 71

Appendix 2: Classification accuracy assessment report for the year 1999.

AAU.Remote Sensing and Geo-informatics stream: By Belete Tafesse: [email protected] 72

Appendix 3: Classification accuracy assessment report for the year 2016.

AAU.Remote Sensing and Geo-informatics stream: By Belete Tafesse: [email protected] 73

Appendix 4. Plate 1: Sample of different LU/LC photographs.

Plantation Settlement

Water body Bare land

Farmland Shrub land

AAU.Remote Sensing and Geo-informatics stream: By Belete Tafesse: [email protected] 74

Appendix 5. map 1: Location of meteorological stations map.

AAU.Remote Sensing and Geo-informatics stream: By Belete Tafesse: [email protected] 75

Appendix 6.map 2: GPS point data map.

AAU.Remote Sensing and Geo-informatics stream: By Belete Tafesse: [email protected] 76

Declaration of originality

I, undersigned, declare that this thesis entitled “IMPACT OF LAND-USE/LAND-COVER CHANGES ON LAND SURFACE TEMPERATURE IN ADAMA ZURIA WOREDA, ETHIOPIA, USING GEOSPATIAL TOOLS” is my original work and has not been presented for a degree in any other university and that all sources of materials used for this thesis have been interestingly acknowledged.

Belete Tafesse Habtewold

Signature ______

Date ______

Addis Ababa

This is certified that the thesis entitled “Impact of land-use/land-cover changes on land surface temperature in Adama Zuria Woreda, Ethiopia, using geospatial tools” is original work of Mr. Belete Tafesse Habtewold for the partial fulfillment of the Degree of Masters of science in remote sensing and Geo-informatics from Addis Ababa University under our guidance and supervision.

Dr.K.V.Suryabhagavan Prof. M. Balakrishnan

Signature ______Signature ______

Date ______Date ______

School of earth science

FOURTH NATIONAL CLIMATE ASSESSMENT

Chapter 5: land cover and land-use change, key message 1 land-cover changes influence weather and climate.

Changes in land cover continue to impact local- to global-scale weather and climate by altering the flow of energy, water, and greenhouse gases between the land and the atmosphere. Reforestation can foster localized cooling, while in urban areas, continued warming is expected to exacerbate urban heat island effects.

Key Message 2 Climate Impacts on Land and Ecosystems

Climate change affects land use and ecosystems. Climate change is expected to directly and indirectly impact land use and cover by altering disturbance patterns, species distributions, and the suitability of land for specific uses. The composition of the natural and human landscapes, and how society uses the land, affects the ability of the Nation’s ecosystems to provide essential goods and services.

Key Message 1

Key message 2, confidence level.

Documenting Uncertainty: This assessment relies on two metrics to communicate the degree of certainty in Key Findings. See Guide to this Report for more on assessments of likelihood and confidence.

VIEW THE EXECUTIVE SUMMARY

EXECUTIVE SUMMARY: Chapter 5: Land Cover and Land-Use Change

Executive summary.

Climate can affect and be affected by changes in land cover (the physical features that cover the land such as trees or pavement) and land use (human management and activities on land, such as mining or recreation). A forest, for instance, would likely include tree cover but could also include areas of recent tree removals currently covered by open grass areas. Land cover and use are inherently coupled: changes in land-use practices can change land cover, and land cover enables specific land uses. Understanding how land cover, use, condition, and management vary in space and time is challenging.

Changes in land cover can occur in response to both human and climate drivers. For example, demand for new settlements often results in the permanent loss of natural and working lands, which can result in localized changes in weather patterns, temperature, and precipitation. Aggregated over large areas, these changes have the potential to influence Earth’s climate by altering regional and global circulation patterns, changing the albedo (reflectivity) of Earth’s surface, and changing the amount of carbon dioxide (CO 2 ) in the atmosphere. Conversely, climate change can also influence land cover, resulting in a loss of forest cover from climate-related increases in disturbances, the expansion of woody vegetation into grasslands, and the loss of beaches due to coastal erosion amplified by rises in sea level.

Land use is also changed by both human and climate drivers. Land-use decisions are traditionally based on short-term economic factors. Land-use changes are increasingly being influenced by distant forces due to the globalization of many markets. Land use can also change due to local, state, and national policies, such as programs designed to remove cultivation from highly erodible land to mitigate degradation, 1 legislation to address sea level rise in local comprehensive plans, or policies that reduce the rate of timber harvest on federal lands. Technological innovation has also influenced land-use change, with the expansion of cultivated lands from the development of irrigation technologies and, more recently, decreases in demand for agricultural land due to increases in crop productivity. The recent expansion of oil and gas extraction activities throughout large areas of the United States demonstrates how policy, economics, and technology can collectively influence and change land use and land cover.

Decisions about land use, cover, and management can help determine society’s ability to mitigate and adapt to climate change.

Changes in Land Cover by Region

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CHAPTER 5 Land Cover and Land-Use Change

Introduction, contributors, recommended citation.

<b>Sleeter</b>, B.M., T. Loveland, G. Domke, N. Herold, J. Wickham, and N. Wood, 2018: Land Cover and Land-Use Change. In <i>Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment, Volume II</i> [Reidmiller, D.R., C.W. Avery, D.R. Easterling, K.E. Kunkel, K.L.M. Lewis, T.K. Maycock, and B.C. Stewart (eds.)]. U.S. Global Change Research Program, Washington, DC, USA, pp. 202–231. doi: 10.7930/NCA4.2018.CH5

Related Links

• Climate Science Special Report

Climate can affect and be affected by changes in land cover (the physical features that cover the land, such as trees or pavement) and land use (human management and activities on land, such as mining or recreation). A forest, for instance, would likely include tree cover but could also include areas of recent tree removals currently covered by open grass areas. Land cover and use are inherently coupled: changes in land-use practices can change land cover, and land cover enables specific land uses. Understanding how land cover, use, condition, and management vary in space and time is challenging, because while land cover and condition can be estimated using remote sensing techniques, land use and management typically require more local information, such as field inventories. Identifying, quantifying, and comparing estimates of land use and land cover are further complicated by factors such as consistency and the correct application of terminology and definitions, time, scale, data sources, and methods. While each approach may produce land-use or land-cover classifications, each method may provide different types of information at various scales, so choosing appropriate data sources and clearly defining what is being measured and reported are essential.

Changes in land cover can occur in response to both human and climate drivers. For example, the demand for new settlements often results in the permanent loss of natural and working lands, which can result in localized changes in weather patterns, 4 , 5 temperature, 6 , 7 and precipitation. 8 Aggregated over large areas, these changes have the potential to influence Earth’s climate by altering regional and global circulation patterns, 9 , 10 , 11 changing the albedo (reflectivity) of Earth’s surface, 12 , 13 and changing the amount of carbon dioxide (CO 2 ) in the atmosphere. 14 , 15 Conversely, climate change can also influence land cover, resulting in a loss of forest cover from climate-related increases in disturbances, 16 , 17 , 18 the expansion of woody vegetation into grasslands, 19 and the loss of coastal wetlands and beaches due to increased inundation and coastal erosion amplified by rises in sea level. 20

Changes in land use can also occur in response to both human and climate drivers. Land-use decisions are often based on economic factors. 21 , 22 , 23 Land-use changes are increasingly being influenced by distant forces due to the globalization of many markets. 21 , 24 , 25 , 26 Land use can also change due to local, state, and national policies, such as programs designed to remove cultivation from highly erodible land to mitigate degradation, 1 legislation to address sea level rise in local comprehensive plans, 27 and policies that reduce the rate of timber harvest on federal lands 28 , 29 or promote the expansion of cultivated lands for energy production. 30 Technological innovation has also influenced land-use change, with the expansion of cultivated lands from the development of irrigation technologies 31 , 32 and, more recently, decreases in demand for agricultural land due to increases in crop productivity. 33 The recent expansion of oil and gas extraction activities throughout large areas of the United States demonstrates how policy, economics, and technology can collectively influence and change land use and land cover. 34

Land use also responds to changes in climate and weather. For example, arable land (land that is suitable for growing crops) may be fallowed (left uncultivated) or abandoned completely during periods of episodic drought 35 , 36 or converted to open water during periods of above-normal precipitation. 37 Increased temperatures have also been shown to have a negative effect on agricultural yields ( Ch. 10: Ag & Rural, KM 1 ) . 38 Climate change can also have positive impacts on land use, such as increases in the length of growing seasons, particularly in northern latitudes. 39 , 40 , 41 Forest land use is also susceptible to changes in weather and climate ( Ch. 6: Forests ) . For example, the recent historical drought in California has resulted in a significant forest die-off event, 42 , 43 which has implications for commercial timber production. Similarly, insect outbreaks across large expanses of western North American forests have been linked to changes in weather and climate, 17 which in turn may result in important feedbacks on the climate system. 44 Sea level rise associated with climate change will likely require changes in coastal land use, as development and infrastructure are increasingly impacted by coastal flooding. 27 , 45 , 46 , 47 As sea levels rise, many coastal areas will likely experience increased frequency and duration of flooding events, and impacts may be felt in areas that have not experienced coastal flooding in the past ( Ch. 8: Coastal, KM 1 ) .

Decisions about land use, cover, and management can help determine society’s ability to mitigate and adapt to climate change. Reducing atmospheric greenhouse gas (GHG) concentrations can, in part, be achieved by increasing the land-based carbon storage. 48 Increasing this carbon storage can be achieved by increasing the area of forests, stabilizing or increasing carbon stored in soils 49 , 50 and forests ( Ch. 6: Forests ) , 51 avoiding the release of stored carbon due to disturbances (such as wildfire) through forest management practices ( Ch. 6: Forests, KM 3 ) , 52 , 53 and increasing the carbon stored in wood products. 54 However, there are large uncertainties about what choices will be made in the future and the net effects of the resulting changes in land use and land cover. 55 , 56 , 57

State of the Sector

Humans have had a far-reaching impact on land cover within the contiguous United States. Of the approximately 3.1 million square miles of land area, approximately 28% has been significantly altered by humans for use as cultivated cropland and pastures (22%) or settlements (6%; Figure 5.1a). 3 Land uses associated with resource production (such as grazing, cropland, timber production, and mining) account for more than half of the land area of the contiguous United States, 58 followed by land that is conserved (16%), built-up areas (13%), and recreational land (10%; Figure 5.1b). Between 2001 and 2011, developed land cover increased by 5% and agriculture declined by 1%. Urbanization was greater between 2001 and 2006 than between 2006 and 2011, which may be attributable to the 2007–2009 economic recession. 59 , 60 The relative stability in agricultural land use between 2001 and 2011 masks widespread fluctuations brought about by the abandonment and expansion of agricultural lands (see Figure 5.2 for more detail).

Figure 5.1: Land-Use and Land-Cover Composition

Figure 5.1.a: land cover composition of the united states.

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Land area as

Bars viewed as

Figure 5.1.b: Land Use Composition of the United States

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Table 5.1: Estimates of Land-Use Area (Square Miles) by NCA Region

Vegetated land cover, including grasslands, shrublands, forests, and wetlands, accounted for approximately two-thirds of the contiguous U.S. land area and experienced a net decline of approximately 5,150 square miles between 2001 and 2011. However, many of these areas are also used for the production of ecosystem goods and services, such as timber and grazing, which lead to changes in land cover but may not necessarily result in a land-use change. Between 2001 and 2011, forest land cover had the largest net decline of any class (25,730 square miles) 3 but forest land use increased by an estimated 3,200 square miles over a similar period ( Ch. 6: Forests ) . 61 The increase in forest land use is due, in large part, to the conversion of abandoned croplands to forestland 62 and the reversion to and expansion of trees in grassland ecosystems in the Great Plains and western United States. 61 There have also been losses in forest land use over the past 25 years, predominantly to grasslands and settlements, with grasslands and shrublands increasing in area by nearly 20,460 square miles. Collectively, non-vegetated areas, including water, barren areas, and snow and ice, account for approximately 6% of the total land area.

Figure 5.2: Changes in Land Cover by Region

Coastal regions, as mapped within the National Oceanic and Atmospheric Administration’s (NOAA) Coastal Change Analysis Program (C-CAP), account for 23% of the contiguous U.S. land area and have been particularly dynamic in terms of change, accounting for approximately 50% of all land-cover change and 43% of all urbanization in the contiguous United States. Approximately 8% of the coastal region changed between 1996 and 2010, which included about 16,500 square miles of forest loss and about 5,700 square miles of gain in urban land, a rate three times higher than that of the interior of the United States. Additionally, nearly 1,550 square miles of wetlands were lost in coastal regions, a trend counter to that of the Nation as a whole. A majority of this wetland loss has occurred in the northern Gulf of Mexico ( Ch. 8: Coastal ; Ch. 19: Southeast ) . 63 Coastal shoreline counties comprise approximately 10% of the United States in terms of land cover (excluding Alaska and the U.S. Caribbean) yet represent 39% of the U.S. population (2010 estimates), with population densities six times higher than in non-coastal areas. 64 Between 1970 and 2010, the population in coastal areas increased by nearly 40% and is projected to increase by an additional 10 million people over 2010–2020 (Figure 5.3). 64 Increases in the frequency of high tide flooding and extreme weather events (such as hurricanes and nor’easters), wetland loss, and beach loss from sea level rise present potential threats to people and property in the coastal zone ( Ch. 8: Coastal, KM 1 ; Ch. 18: Northeast ; Ch. 19: Southeast, KM 2 ) .

Figure 5.3: Development in the Houston Area

Disturbance events (such as wildfire and timber harvest) are important factors that influence land cover. For example, forest disturbances can initiate a succession from forest to herbaceous grasslands to shrublands before forest reestablishment, with each successional stage having a different set of feedbacks with the climate. The length of an entire successional stage varies based on local environmental characteristics. 65 Permanent transitions to new cover types after a disturbance are also possible for many reasons, including the establishment of invasive or introduced species that are able to quickly establish and outcompete native vegetation. 66 , 67 Data from the North American Forest Dynamics dataset indicate that forest disturbances affected an average of approximately 11,200 square miles per year in the contiguous United States from 1985 to 2010 (an area greater than the entire state of Massachusetts). Between 2006 and 2010, the rate of forest disturbance declined by about one-third. 68 Although these data include a wide range of disturbance agents, including fire, insects, storms, and harvest, the sharp decline likely corresponds to a reduction in timber harvest activities resulting from a drop in demand for construction materials following the 2007–2009 economic recession.

Wildland fires provide a good example of how ecosystem disturbance, climate change, and land management can interact. Between 1979 and 2013, the number of days with weather conditions conducive to fire has increased globally, including in the United States. 69 At the same time, human activities have expanded into areas of uninhabited forests, shrublands, and grasslands, 70 exposing these human activities to greater risk of property and life loss at this wildland–urban interface. 71 , 72 Over the last two decades, the amount of forest area burned and the expansion of human activity into forests and other wildland areas have increased. 73 These changes in climate and patterns of human activity have led in part to the development of a national strategy for wildland fire management for the United States. The strategy, published in 2014, was one outcome of the Federal Land Assistance, Management, and Enhancement (FLAME) Act of 2009. An important component of the national strategy 74 is a classification of U.S. counties based on their geographic context; fire history; amount of urban, forest, and range land; and other factors. The land-use, land-cover, and other components of the classification model are used to guide management actions.

Future Changes

Representative Concentration Pathways (RCPs) were developed to improve society’s understanding of plausible climate and socioeconomic futures. 75 U.S. projections of land-use and land-cover change (LULCC) developed for the RCPs span a wide range of future climate conditions, including a higher scenario (RCP8.5) 76 and three mitigation scenarios (RCP2.6, RCP4.5, and RCP6.0) (for more on RCPs, see Front Matter and the Scenario Products section in App. 3 ) . 77 , 78 , 79 Projected changes in land use within each scenario were harmonized with historical data 80 and include a broad range of assumptions, from aggressive afforestation (the establishment of a forest where there was no previous tree cover) in the Midwest and Southeast (RCP4.5) to large-scale expansion of agricultural lands to meet biofuel production levels (RCP2.6; see Hibbard et al. 2017 81 ).

The Shared Socioeconomic Pathways (SSPs) have been developed to explore how future scenarios of climate change interact with alternative scenarios of socioeconomic development (in terms of population, economic growth, and education) to understand climate change impacts, adaptation and mitigation, and vulnerability. 82 , 83 In a scenario with medium barriers to climate mitigation and adaptation (SSP2) and a scenario with high barriers to climate mitigation (SSP5), the amount of land devoted to developed use (for example, urban and suburban areas) is projected to increase by 50% and 80%, respectively, from 2010 levels by the year 2100. These changes represent a potential loss of between 500,000 and 620,000 square miles of agricultural or other vegetated lands (for more on SSPs, see the Scenario Products section of App. 3 ) . 84

Future changes in land use are likely to have far-reaching impacts on other sectors. For example, by mid-century, water use in California is projected to increase by 1.5 million acre-feet, driven almost entirely by a near 60% increase in developed water-use demand. 85 Research in Hawai‘i projects a steady reduction in the strength of the state’s annual ecosystem carbon sink, resulting primarily from a combination of urbanization and a shift toward drier, less productive ecosystems by mid-century. 86

Land-Cover Changes Influence Weather and Climate

The influence of land-use and land-cover change (LULCC) on climate and weather is complex, and specific effects depend on the type of change, the scale of the assessment (local, regional, or global), the size of the area under consideration, the aspect of climate and weather being evaluated (such as temperature, precipitation, or seasonal trends), and the region where the change occurs. 87 , 88

Recent studies suggest that forests tend to be cooler than herbaceous croplands throughout much of the temperate region. 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 These studies suggest that reforestation in the temperate forest region would promote cooling, with the magnitude of cooling decreasing with increasing latitude. 90 , 94 , 95 , 96 , 97 The scale of the cooling from reforestation would depend on its extent and location. Biogeophysical (albedo, surface roughness, and transpiration) changes arising from land-cover change tend to result in more localized changes, whereas biogeochemical changes (such as carbon sequestration) tend to have a more global reach. Reforestation in the temperate forest region is an effective climate mitigation and adaptation strategy. 90 , 94

Fires in forests, grasslands, shrublands, and agricultural lands affect climate in two ways: 1) transporting carbon from the land to the atmosphere in the form of carbon dioxide and other greenhouse gases, and 2) increasing the concentration of small particles (aerosols) in the atmosphere that tend to reduce the amount of solar energy reaching the surface of Earth by increasing (although often temporarily) the reflectivity of the atmosphere. 98 Climate is also a principal determinant of an area’s fire regime, 99 which refers to the pattern in which fires occur within ecosystems based on factors such as size, severity, and frequency. Studies suggest that most aspects of the fire regime are increasing in the United States ( Ch. 6: Forests, KM 1 ; Ch. 26: Alaska ) . 18 , 99 , 100 , 101 However, the true extent of an altered fire regime’s influence on climate is unclear, because the warming attributable to carbon releases to the atmosphere and decreases in surface albedo (at least temporarily) may be offset by increased reflectivity of the atmosphere from the increased concentration of small particles and the enhanced storage of carbon due to forest regrowth. 99

Urban regions include several characteristics that can influence climate, 102 including construction materials that absorb more heat than vegetation and soils do, impervious cover that minimizes the cooling effect of evapotranspiration, the canyon-like architecture of buildings that tends to trap heat, and heat generation from vehicle and building emissions. 103 , 104 These factors make urban areas warmer than their surroundings, a phenomenon referred to as the urban heat island (UHI) effect. Urbanization has a small effect on global temperatures, with more dramatic effects evident regionally where urbanization is extensive. 105 , 106 , 107 The local-scale UHI impact is relative to the regional climate such that its effect tends to be more severe in the eastern United States and declines westward. 10 , 108 , 109 , 110 , 111 Although the evidence is not conclusive, urbanization may also increase downwind precipitation. 112 , 113 , 114 Further, climate change may act synergistically with future urbanization (that is, an increase in impervious cover), resulting in increased likelihoods and magnitudes of flood events (e.g., Hamdi et al. 2011, Huong and Pathirana 2013 115 , 116 ).

Water transport and application to cropland also impact climate. Between 2002 and 2007, irrigated lands expanded by approximately 1.3 million acres in the United States, with much of the change occurring in the Great Plains regions. 117 Approximately 88.5 million acre-feet of water were applied to approximately 55 million acres of irrigated agriculture in the United States in 2012. 118 Globally, the amount of water transported to the atmosphere through irrigated agriculture is roughly equivalent to the amount of water not transported to the atmosphere from deforestation. 119 Studies have shown reductions in surface air temperatures in the vicinity of irrigation due to both evaporation effects 120 , 121 , 122 and increases in downwind precipitation as a result of increased atmospheric moisture. 123 These potentially local-to-regional cooling effects are also counterbalanced by constraints on the availability of water for irrigation. 124

Climate Impacts on Land and Ecosystems

Climate can drive changes in land cover and land use in several ways, including changes in the suitability of agriculture ( Ch. 10: Ag & Rural ) , 125 , 126 increases in fire frequency and extent ( Ch. 6: Forests ) , 18 , 101 the loss or migration of coastal wetlands, 127 and the spatial relocation of natural vegetation. The extent of the climate influence is often difficult to determine, given that changes occur within interconnected physical and socioeconomic systems, and there is a lack of comprehensive observational evidence to support the development of predictive models, leaving a large degree of uncertainty related to these future changes ( Ch. 17: Complex Systems ) . Models can be used to demonstrate how climate change may impact the production of a given agricultural commodity and/or suggest a change in land use (for example, econometric models, global gridded crop models, and integrated assessment models). However, the true impact may be mitigated by the influence of global economic markets, a shift to a different crop that is better suited to the new climate pattern, technological innovations, policy incentives, or capital improvement projects. This area of integrated, multidisciplinary scientific research is just emerging.

Important feedbacks with agriculture are anticipated under changing climate conditions. Recent trends show a shift from dryland farming to irrigated agriculture throughout much of the Great Plains region ( Ch. 22: N. Great Plains ; Ch. 23: S. Great Plains ) . 117 Future projections suggest that cropland suitability may increase at higher latitudes 128 and that croplands could shift to livestock grazing southward. 126 For high-latitude regions, climate change could result in a large-scale transformation from naturally vegetated ecosystems to agronomy-dominated systems. Climate warming also could result in a shift from higher-productivity systems (such as irrigated agriculture) to lower-productivity systems (such as dryland farming). 129 Due to the globally interconnected nature of agricultural systems, climate change has broad implications for food security ( Ch. 16: International ) . 130 Energy policies have also influenced the type and location of agricultural activities; for example, nearly two-thirds of recent land area converted for energy use was due to biofuel expansion 34 , 131 mandated by the Energy Independence and Security Act of 2007. 30 , 131 By 2040, the total new land area impacted by energy development could exceed an area the size of Texas—2,700 square miles per year, 34 which is more than two times higher than the historical rate of urbanization. 2

Natural disturbances such as wildfires can trigger changes in land cover that have the potential to result in a permanent land-cover conversion. Over the past several decades, drought, 132 climate warming, and earlier spring snowmelt have led to an increase in fire activity across the United States ( Ch. 6: Forests ) , 18 , 133 although the burnt area increase may be partly due to changes in fire suppression policies. 134 Under future warming scenarios (that is, A1B, as described here ), the burnt area in southwestern California could double by 2050 and increase by 35% in the Sierra Nevada due to an increase in the length of the fire season and an increase in warmer and drier days. 135 Human activity will continue to play an important role in wildfire frequency and intensity. Hot spots of fire activity were identified at the wildland–urban interface, 136 and urbanization is expected to increase fire hazard exposure to people and property. Land management strategies, such as prescribed burning, fuel reduction and clearing, invasive species management, and forest thinning, have the potential to mitigate wildland fire and its associated consequences, 137 but more research is needed to evaluate their efficacy across a range of spatial and temporal scales.

Current relationships between plant species and climate variables 138 have been used to estimate potential changes in the geographic distribution of species and vegetation under future climate conditions. 12 , 139 , 140 , 141 , 142 , 143 Studies have projected the conversion of forests to shrubland and grassland across some areas of the western United States due to increasing aridity, pest outbreaks, and fire, resulting in a substantial transfer of carbon from the biosphere to the atmosphere. 144 , 145 For example, increases in mountainous forests and grasslands at the expense of alpine and subalpine communities have been projected. 146 Across North America, projected changes include an expansion of tropical dry deciduous forests and desert shrub/scrub biomes, a poleward migration of deciduous and boreal forests, and an expansion of grasslands at the expense of high-latitude taiga and tundra communities. 12 , 144 , 146 , 147 , 148 , 149 However, it is important to note that projecting the future distributions of vegetation and land cover is highly complex, driven not only by changes in climate but also land-use changes, shifts in disturbance regimes, interactions between species, and evolutionary changes. 150

TRACEABLE ACCOUNTS

Process description.

Chapter authors developed the chapter through technical discussions, literature review, and expert deliberation via email and phone discussions. The authors considered feedback from the general public, the National Academies of Sciences, Engineering, and Medicine, and federal agencies. For additional information about the overall process for developing the report, see Appendix 1: Process .

The topic of land-use and land-cover change (LULCC) overlaps with numerous other national sectoral chapters (for example, Ch. 6: Forests ; Ch. 10: Ag & Rural ; Ch. 11: Urban ) and is a fundamental characteristic of all regional chapters in this National Climate Assessment. This national sectoral chapter thus focuses on the dynamic interactions between land change and the climate system. The primary focus is to review our current understanding of land change and climate interactions by examining how land change drives changes in local- to global-scale weather and climate and how, in turn, the climate drives changes in land cover and land use through both biophysical and socioeconomic responses. Where possible, the literature cited in this chapter is specific to changes in the United States.

KEY MESSAGES

Key message 1: land-cover changes influence weather and climate.

Changes in land cover continue to impact local- to global-scale weather and climate by altering the flow of energy, water, and greenhouse gases between the land and the atmosphere (high confidence) . Reforestation can foster localized cooling (medium confidence) , while in urban areas, continued warming is expected to exacerbate urban heat island effects (high confidence) .

Description of evidence base

The Land-Use and Climate, IDentification of robust impacts (LUCID) project 88 , 151 evaluated climate response to LULCC using seven coupled land surface models (LSMs) and global climate models (GCMs) to determine effects that were larger than model variability and consistent across all seven models. Results showed significant discrepancies in the effect of LULCC (principally, the conversion of forest to cropland and grassland at temperate and higher latitudes) on near-surface air temperatures; the discrepancies were mainly attributable to the modeling of turbulent flux (sensible heat [the energy required to change temperature] and latent heat [the energy needed to change the phase of a substance, such as from a liquid to a gas]). Land surface models need to be subjected to more rigorous evaluations 151 , 152 and evaluate more than turbulent fluxes and net ecosystem exchange. 152 Rigorous evaluations should extend to the parameterization of albedo, 153 including the effect of canopy density on the albedo of snow-covered land; 154 the seasonal cycle of albedo related to the extent, timing, and persistence of snow; 155 and the benchmarking of the effect of present-day land cover change on albedo. 156 More recently, there is consistent modeling and empirical evidence that forests tend to be cooler than nearby croplands and grasslands. 91 , 92 , 93 , 95 , 96 , 156

The study of the influence of wildland fire on climate is at its advent and lacks a significant knowledge base. 98 , 99 Improved understanding would require more research on the detection of fire characteristics; 157 fire emissions; 158 and the relative roles of greenhouse gas (GHG) emissions, aerosol emissions, and surface albedo changes in climate forcing. 98

The urban heat island (UHI) is perhaps the most unambiguous documentation of anthropogenic modification of climate. 159 Two studies have found that the stunning rate of urbanization in China has led to regional warming, 105 , 106 which is consistent with the observation that land-use and land-cover changes must be extensive for their effects to be realized. 87 Research on the effects of urbanization on precipitation patterns has not produced consistent results. 113 , 114 Uncertainties related to the effect of urban areas on precipitation arise from the interactions among the UHI, increased surface roughness (for example, tall buildings), and increased aerosol concentrations. 160 In general, UHIs produce updrafts that lead to enhanced precipitation either in or downwind of urban areas, whereas urban surface roughness and urban aerosol concentrations can either further contribute to or dampen the updrafts that arise from the UHI. 160

Major uncertainties

Land use and land cover are dynamic; therefore, climate is influenced by a constantly changing land surface. Considerable uncertainties are associated with land-cover and land-use monitoring and projection. 161 , 162 , 163 , 164 Land-cover maps can be derived from remote sensing approaches, but comprehensive approaches are typically characterized by coarse temporal resolution. 2 , 3 , 59 , 60 More recently, remote sensing has enabled annual classification over large areas (national and global), though these efforts have been centered on a single land cover or disturbance type. 68 , 165 , 166 Comprehensive multitemporal mapping of land use is even more limited and is a source of considerable uncertainty in understanding land change and feedbacks with the climate system. Deforestation, urbanization, wildland fire, and irrigated agriculture are the main land-use and land-cover changes that influence climate locally and regionally throughout the United States. Deforestation is likely to behave as a warming agent throughout most of the United States, but higher confidence in this finding would require more research on how to treat sensible and latent heat fluxes in coupled GCM–LSM models; the relationship of albedo to forest density in the presence of snow; the timing, persistence, and extent of snow cover; and real-world comparisons of the response of albedo to land-cover change. Urbanization constitutes a continued expansion of the UHI effect, increasing warming at local scales. Determining the effect of urbanization on precipitation patterns and storm tracks would require extensive, additional research. Tabular irrigation water volume estimates, such as those provided by the U.S. Department of Agriculture’s (USDA) Farm and Ranch Irrigation Survey, must be translated into maps so that the data can be input in GCMs and LSMs to determine the impact of irrigation on climate. Current translation schemes do not provide consistent model output. 124 The effect of wildland fires on climate processes is an emerging issue for which there is little research. Fire releases carbon dioxide (CO 2 ) and other GHGs to the atmosphere, which, along with a decreased albedo, should promote warming. These warming effects, however, may be counterbalanced by the release of aerosols to the atmosphere and enhanced carbon sequestration by forest regrowth. 99

Description of confidence and likelihood

There is medium confidence that deforestation throughout much of the continental United States promotes climate warming through a decrease in carbon sequestration and reduced transpiration. There is low confidence that wildland fires will impact climate, because many of the associated processes and characteristics produce counteracting effects. There is high confidence that urbanization produces local-scale climate change, but there is low confidence in its influence on precipitation patterns. There is high confidence that surface air temperature is reduced near areas of irrigated agriculture and medium confidence that downwind precipitation is increased.

Key Message 2: Climate Impacts on Land and Ecosystems

Climate change affects land use and ecosystems. Climate change is expected to directly and indirectly impact land use and cover by altering disturbance patterns ( medium confidence) , species distributions ( medium confidence ), and the suitability of land for specific uses ( low confidence ). The composition of the natural and human landscapes, and how society uses the land, affects the ability of the Nation’s ecosystems to provide essential goods and services ( high confidence ).

Much of the research assessing the impact of climate change on agriculture has been undertaken as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP), 128 which has been understandably focused on productivity and food security. 128 , 129 , 167 , 168 , 169 Less effort has been devoted to understanding the impact of climate change on the spatial distribution of agriculture. Deryng et al. (2011) 170 used one of the AgMIP crop models (PEGASUS) to show poleward and westward shifts in areas devoted to corn, soybean, and wheat production. Parker and Abatzoglou (2016) 130 have reported a poleward migration of the USDA’s cold hardiness zones as a result of a warming climate. Several empirical studies have found an increase in wildland fires in the western United States over the last several decades, 18 , 101 , 171 in which indicators of aridity correlate positively with the amount of area burned. Several studies have reported a decline in forest cover throughout the western United States and project future declines due to a warming climate and increasing aridity, as well as the concomitant likely increase in pest outbreaks and fire. 144 , 145 , 172 , 173 , 174 Several studies have also reported a poleward shift in the forest communities of the eastern United States, resulting primarily from CO 2 enrichment in a warming and wetter environment. 12 , 144 , 147 , 148 , 149 , 175

Determining the impact of climate change on agriculture requires the integration of climate, crop, and economic models, 176 each with its own sources of uncertainty that can propagate through the three models. Sources of uncertainty include the response of crops to the intermingled factors of CO 2 fertilization, temperature, water, and nitrogen availability; species-specific responses; model parameterization; spatial location of irrigated areas; and other factors. 129 , 169 , 177 The projection of recent empirical fire–climate relationships 18 , 101 , 171 into the future introduces uncertainty, as the empirical results cannot account for future anthropogenic influences (for example, fire suppression management) and vegetation response to future fires. 171 , 178 Similarly, process-based models must account for vegetation response to fire, uncertainty in precipitation predictions from climate models, and spatiotemporal nonuniformity in human interactions with fire and vegetation. 178 Many of the studies on climate-induced spatial migration of vegetation are based on dynamic global vegetation models, which are commonly based only on climate and soil inputs. These models aggregate species characteristics that are not uniform across all species represented and are generally lacking ecological processes that would influence a species’ range shift. 179 , 180 , 181 , 182 , 183 Considerable uncertainties are associated with land-cover and land-use monitoring and projection. 161 , 162 , 163 , 164 Land-cover maps can be derived from remote sensing approaches; however, comprehensive approaches are typically characterized by coarse temporal resolution. 2 , 3 , 59 , 60 More recently, remote sensing has enabled annual classification over large areas (at national and global scales), but these efforts have been centered on a single land cover or disturbance type. 68 , 165 , 166 Comprehensive multitemporal mapping of land use is even more limited and is a source of considerable uncertainty in understanding land change and feedbacks with the climate system.

There is high confidence that climate change will contribute to changes in agricultural land use; however, there is low confidence in the direction and magnitude of change due to uncertainties in the capacity to adapt to climate change. There is high confidence that climate change will impact urbanization in coastal areas, where sea level rise will continue to have direct effects. There is medium confidence that climate change will alter natural disturbance regimes; however, land management activities, such as fire suppression strategies, are likely to be of equal or greater importance. There is low confidence that climate change will result in changes to land cover resulting from changes in species distribution environmental suitability.

• 2008: Carbon flux to the atmosphere from land-use changes 1850-2005. In TRENDS: A Compendium of Data on Global Change. URL . ↩
• 2015: NACP NAFD Project: Forest Disturbance History from Landsat, 1986–2010. doi: 10.3334/ORNLDAAC/1290 . ↩
• Abatzoglou, J. T., and A. P. Williams, 2016: Impact of anthropogenic climate change on wildfire across western US forests. Proceedings of the National Academy of Sciences of the United States of America , 113 (42), 11770–11775. doi: 10.1073/pnas.1607171113 . ↩
• Abramowitz, G., R. Leuning, M. Clark, and A. Pitman, 2008: Evaluating the performance of land surface models. Journal of Climate , 21 (21), 5468–5481. doi: 10.1175/2008jcli2378.1 . ↩
• Ackerly, D. D., W. K. Cornwell, S. B. Weiss, L. E. Flint, and A. L. Flint, 2015: A geographic mosaic of climate change impacts on terrestrial vegetation: Which areas are most at risk? PLOS ONE , 10 (6), e0130629. doi: 10.1371/journal.pone.0130629 . ↩
• Ajaaj, A. A., A. K. Mishra, and A. A. Khan, 2017: Urban and peri-urban precipitation and air temperature trends in mega cities of the world using multiple trend analysis methods. Theoretical and Applied Climatology , doi: 10.1007/s00704-017-2096-7 . ↩
• Alexander, P., R. Prestele, P. H. Verburg, A. Arneth, C. Baranzelli, F. Batista e Silva, C. Brown, A. Butler, K. Calvin, N. Dendoncker, J. C. Doelman, R. Dunford, K. Engström, D. Eitelberg, S. Fujimori, P. A. Harrison, T. Hasegawa, P. Havlik, S. Holzhauer, F. Humpenöder, C. Jacobs-Crisioni, A. K. Jain, T. Krisztin, P. Kyle, C. Lavalle, T. Lenton, J. Liu, P. Meiyappan, A. Popp, T. Powell, R. D. Sands, R. Schaldach, E. Stehfest, J. Steinbuks, A. Tabeau, H. van Meijl, M. A. Wise, and M. D. A. Rounsevell, 2017: Assessing uncertainties in land cover projections. Global Change Biology , 23 (2), 767–781. doi: 10.1111/gcb.13447 . ↩
• Allen, C. D., A. K. Macalady, H. Chenchouni, D. Bachelet, N. McDowell, M. Vennetier, T. Kitzberger, A. Rigling, D. D. Breshears, E. H. Hogg, P. Gonzalez, R. Fensham, Z. Zhang, J. Castro, N. Demidova, J.-H. Lim, G. Allard, S. W. Running, A. Semerci, and N. Cobb, 2010: A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecology and Management , 259 (4), 660–684. doi: 10.1016/j.foreco.2009.09.001 . ↩
• Anderson, R. G., J. G. Canadell, J. T. Randerson, R. B. Jackson, B. A. Hungate, D. D. Baldocchi, G. A. Ban-Weiss, G. B. Bonan, K. Caldeira, L. Cao, N. S. Diffenbaugh, K. R. Gurney, L. M. Kueppers, B. E. Law, S. Luyssaert, and T. L. O'Halloran, 2011: Biophysical considerations in forestry for climate protection. Frontiers in Ecology and the Environment , 9 (3), 174–182. doi: 10.1890/090179 . ↩
• Argüeso, D., J. P. Evans, L. Fita, and K. J. Bormann, 2014: Temperature response to future urbanization and climate change. Climate Dynamics , 42 (7), 2183–2199. doi: 10.1007/s00382-013-1789-6 . ↩
• Arnfield, A. J., 2003: Two decades of urban climate research: A review of turbulence, exchanges of energy and water, and the urban heat island. International Journal of Climatology , 23 (1), 1–26. doi: 10.1002/joc.859 . ↩
• Asner, G. P., P. G. Brodrick, C. B. Anderson, N. Vaughn, D. E. Knapp, and R. E. Martin, 2016: Progressive forest canopy water loss during the 2012–2015 California drought. Proceedings of the National Academy of Sciences of the United States of America , 113 (2), E249–E255. doi: 10.1073/pnas.1523397113 . ↩
• Barnes, C. A., and D. P. Roy, 2008: Radiative forcing over the conterminous United States due to contemporary land cover land use albedo change. Geophysical Research Letters , 35 (9), L09706. doi: 10.1029/2008GL033567 . ↩
• Bauer, A., R. Farrell, and D. Goldblum, 2016: The geography of forest diversity and community changes under future climate conditions in the eastern United States. Ecoscience , 23 (1), 41–53. doi: 10.1080/11956860.2016.1213107 . ↩
• Bellassen, V., and S. Luyssaert, 2014: Carbon sequestration: Managing forests in uncertain times. Nature , 503 (7487), 153–155. URL . ↩
• Bentz, B. J., J. Régnière, C. J. Fettig, E. M. Hansen, J. L. Hayes, J. A. Hicke, R. G. Kelsey, J. F. Negrón, and S. J. Seybold, 2010: Climate change and bark beetles of the Western United States and Canada: Direct and indirect effects. BioScience , 60 (8), 602–613. doi: 10.1525/Bio.2010.60.8.6 . ↩
• Betts, R. A., N. Golding, P. Gonzalez, J. Gornall, R. Kahana, G. Kay, L. Mitchell, and A. Wiltshire, 2015: Climate and land use change impacts on global terrestrial ecosystems and river flows in the HadGEM2-ES Earth system model using the representative concentration pathways. Biogeosciences , 12 (5), 1317–1338. doi: 10.5194/bg-12-1317-2015 . ↩
• Blake, R., A. Grimm, T. Ichinose, R. Horton, S. Gaffin, S. Jiong, D. Bader, and L. Cecil, 2011: Urban climate: Processes, trends, and projections. Climate Change and Cities: First Assessment Report of the Urban Climate Change Research Network . Rosenzweig, C., W. D. Solecki, S. A. Hammer, and S. Mehrotra, Eds., Cambridge University Press, Cambridge, UK, 43–81. URL . ↩
• Bounoua, L., P. Zhang, G. Mostovoy, K. Thome, J. Masek, M. Imhoff, M. Shepherd, D. Quattrochi, J. Santanello, J. Silva, R. Wolfe, and A. M. Toure, 2015: Impact of urbanization on US surface climate. Environmental Research Letters , 10 (8), 084010. doi: 10.1088/1748-9326/10/8/084010 . ↩
• Bowman, D. M. J. S., J. K. Balch, P. Artaxo, W. J. Bond, J. M. Carlson, M. A. Cochrane, C. M. D'Antonio, R. S. DeFries, J. C. Doyle, S. P. Harrison, F. H. Johnston, J. E. Keeley, M. A. Krawchuk, C. A. Kull, J. B. Marston, M. A. Moritz, I. C. Prentice, C. I. Roos, A. C. Scott, T. W. Swetnam, G. R. van der Werf, and S. J. Pyne, 2009: Fire in the Earth system. Science , 324 (5926), 481–484. doi: 10.1126/science.1163886 . ↩
• Brandão, M., A. Levasseur, M. U. F. Kirschbaum, B. P. Weidema, A. L. Cowie, S. V. Jørgensen, M. Z. Hauschild, D. W. Pennington, and K. Chomkhamsri, 2013: Key issues and options in accounting for carbon sequestration and temporary storage in life cycle assessment and carbon footprinting. International Journal of Life Cycle Assessment , 18 (1), 230–240. doi: 10.1007/s11367-012-0451-6 . ↩
• Brown, J. F., and M. S. Pervez, 2014: Merging remote sensing data and national agricultural statistics to model change in irrigated agriculture. Agricultural Systems , 127 , 28–40. doi: 10.1016/j.agsy.2014.01.004 . ↩
• Cheaib, A., V. Badeau, J. Boe, I. Chuine, C. Delire, E. Dufrêne, C. François, E. S. Gritti, M. Legay, and C. Pagé, 2012: Climate change impacts on tree ranges: Model intercomparison facilitates understanding and quantification of uncertainty. Ecology Letters , 15 (6), 533–544. doi: 10.1111/j.1461-0248.2012.01764.x . ↩
• Cohen, J. D., 2000: Preventing disaster: Home ignitability in the wildland-urban interface. Journal of Forestry , 98 (3), 15–21. doi: 10.1093/jof/98.3.15 . ↩
• Congalton, R. G., J. Gu, K. Yadav, P. Thenkabail, and M. Ozdogan, 2014: Global land cover mapping: A review and uncertainty analysis. Remote Sensing , 6 (12), 12070–12093. doi: 10.3390/rs61212070 . ↩
• Cotton, W. R., and R. A. Pielke Sr., 2007: Human Impacts on Weather and Climate. Cambridge University Press, Cambridge; New York, doi: 10.1017/CBO9780511808319 . ↩
• Crossett, K., B. Ache, P. Pacheco, and K. Haber, 2013: National Coastal Population Report: Population Trends from 1970 to 2020. NOAA Office for Coastal Management, Silver Spring, MD, 19 pp. URL . ↩
• Daehler, C. C., 2003: Performance comparisons of co-occurring native and alien invasive plants: Implications for conservation and restoration. Annual Review of Ecology, Evolution, and Systematics , 34 (1), 183–211. doi: 10.1146/annurev.ecolsys.34.011802.132403 . ↩
• Dale, V. H., 1997: The relationship between land‐use change and climate change. Ecological Applications , 7 (3), 753–769. doi: 10.1890/1051-0761(1997)007[0753:TRBLUC]2.0.CO;2 . ↩
• Daniels, J. M., 2005: The rise and fall of the Pacific Northwest log export market. General  Technical Report PNW-GTR-624. USDA, Pacific Northwest Research Station, Portland, OR, 80 pp. URL . ↩
• Davidson, A., and S. Wang, 2004: The effects of sampling resolution on the surface albedos of dominant land cover types in the North American boreal region. Remote Sensing of Environment , 93 (1), 211–224. doi: 10.1016/j.rse.2004.07.005 . ↩
• de Noblet-Ducoudré, N., J.-P. Boisier, A. Pitman, G. B. Bonan, V. Brovkin, F. Cruz, C. Delire, V. Gayler, B. J. J. M. van den Hurk, P. J. Lawrence, M. K. van der Molen, C. Müller, C. H. Reick, B. J. Strengers, and A. Voldoire, 2012: Determining robust impacts of land-use-induced land cover changes on surface climate over North America and Eurasia: Results from the first set of LUCID experiments. Journal of Climate , 25 (9), 3261–3281. doi: 10.1175/JCLI-D-11-00338.1 . ↩
• DeAngelis, A., F. Dominguez, Y. Fan, A. Robock, M. D. Kustu, and D. Robinson, 2010: Evidence of enhanced precipitation due to irrigation over the Great Plains of the United States. Journal of Geophysical Research , 115 , D15115. doi: 10.1029/2010JD013892 . ↩
• Deryng, D., W. J. Sacks, C. C. Barford, and N. Ramankutty, 2011: Simulating the effects of climate and agricultural management practices on global crop yield. Global Biogeochemical Cycles , 25 (2), GB2006. doi: 10.1029/2009GB003765 . ↩
• Diffenbaugh, N. S., 2009: Influence of modern land cover on the climate of the United States. Climate Dynamics , 33 (7–8), 945–958. doi: 10.1007/s00382-009-0566-z . ↩
• Drummond, M. A., 2007: Regional dynamics of grassland change in the western Great Plains. Great Plains Research , 17 , 133–144. URL . ↩
• Drummond, M. A., R. F. Auch, K. A. Karstensen, K. L. Sayler, J. L. Taylor, and T. R. Loveland, 2012: Land change variability and human–environment dynamics in the United States Great Plains. Land Use Policy , 29 (3), 710–723. doi: 10.1016/j.landusepol.2011.11.007 . ↩
• Eidenshink, J., B. Schwind, K. Brewer, Z. Zhu, B. Quayle, and S. Howard, 2007: A project for monitoring trends in burn severity. Fire Ecology , 3 (1), 3–21. URL . ↩
• Elliott, J., D. Deryng, C. Muller, K. Frieler, M. Konzmann, D. Gerten, M. Glotter, M. Florke, Y. Wada, N. Best, S. Eisner, B. M. Fekete, C. Folberth, I. Foster, S. N. Gosling, I. Haddeland, N. Khabarov, F. Ludwig, Y. Masaki, S. Olin, C. Rosenzweig, A. C. Ruane, Y. Satoh, E. Schmid, T. Stacke, Q. Tang, and D. Wisser, 2014: Constraints and potentials of future irrigation water availability on agricultural production under climate change. Proceedings of the National Academy of Sciences of the United States of America , 111 (9), 3239–3244. doi: 10.1073/pnas.1222474110 . ↩
• Energy Independence and Security Act of 2007. Pub. L. No. 110-140, 121 Stat 1492, December 19 2007. URL . ↩
• EPA, 2017: Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015. EPA 430-P-17-001. U.S. Environmental Protection Agency, Washington, D.C., 633 pp. URL . ↩
• EPA, 2017: Updates to the Demographic and Spatial Allocation Models to Produce Integrated Climate and Land Use Scenarios (ICLUS) Version 2. EPA/600/R-16/366F. U.S. Environmental Protection Agency, National Center for Environmental Assessment, Washington, DC, various pp. URL . ↩
• Flannigan, M., B. Stocks, M. Turetsky, and M. Wotton, 2009: Impacts of climate change on fire activity and fire management in the circumboreal forest. Global Change Biology , 15 (3), 549–560. doi: 10.1111/j.1365-2486.2008.01660.x . ↩
• Foley, J. A., N. Ramankutty, K. A. Brauman, E. S. Cassidy, J. S. Gerber, M. Johnston, N. D. Mueller, C. O'Connell, D. K. Ray, P. C. West, C. Balzer, E. M. Bennett, S. R. Carpenter, J. Hill, C. Monfreda, S. Polasky, J. Rockstrom, J. Sheehan, S. Siebert, D. Tilman, and D. P. M. Zaks, 2011: Solutions for a cultivated planet. Nature , 478 (7369), 337–342. doi: 10.1038/nature10452 . ↩
• Food Security Act of 1985. Pub. L. No. 99-198, 99 Stat 1504, December 23 1985. URL . ↩
• Forest Service and Bureau of Land Management, 1994: Record of decision for: Amendments to Forest Service and Bureau of Land Management planning documents within the range of the northern spotted owl ; and, Standards and guidelines for management of habitat for late-successional and old-growth forest related species within the range of the northern spotted owl. USDA, Forest Service; U.S. Dept. of Interior, Bureau of Land Management, Washington, DC, various pp. URL . ↩
• Forkel, M., N. Carvalhais, C. Rödenbeck, R. Keeling, M. Heimann, K. Thonicke, S. Zaehle, and M. Reichstein, 2016: Enhanced seasonal CO2 exchange caused by amplified plant productivity in northern ecosystems. Science , 351 (6274), 696–699. doi: 10.1126/science.aac4971 . ↩
• Friedl, M. A., J. M. Gray, E. K. Melaas, A. D. Richardson, K. Hufkens, T. F. Keenan, A. Bailey, and J. O'Keefe, 2014: A tale of two springs: Using recent climate anomalies to characterize the sensitivity of temperate forest phenology to climate change. Environmental Research Letters , 9 (5), 054006. doi: 10.1088/1748-9326/9/5/054006 . ↩
• Fry, J. A., G. Xian, S. Jin, J. A. Dewitz, C. G. Homer, Y. Limin, C. A. Barnes, N. D. Herold, and J. D. Wickham, 2011: Completion of the 2006 national land cover database for the conterminous United States. Photogrammetric Engineering & Remote Sensing , 77 (9), 858–864. . ↩
• Gelfand, I., R. Sahajpal, X. Zhang, R. C. Izaurralde, K. L. Gross, and G. P. Robertson, 2013: Sustainable bioenergy production from marginal lands in the US Midwest. Nature , 493 (7433), 514–517. doi: 10.1038/nature11811 . ↩
• Gerrard, M. B., and E. McTiernan, 2017: New York's new sea level rise projections will affect land use, infrastructure. New York Law Journal , 257 (45), 3. URL . ↩
• Gonzalez, P., R. P. Neilson, J. M. Lenihan, and R. J. Drapek, 2010: Global patterns in the vulnerability of ecosystems to vegetation shifts due to climate change. Global Ecology and Biogeography , 19 (6), 755–768. doi: 10.1111/j.1466-8238.2010.00558.x . ↩
• Gordon, L. J., W. Steffen, B. F. Jönsson, C. Folke, M. Falkenmark, and Å. Johannessen, 2005: Human modification of global water vapor flows from the land surface. Proceedings of the National Academy of Sciences of the United States of America , 102 (21), 7612–7617. doi: 10.1016/j.landusepol.2011.11.007 . ↩
• Hale, R. C., K. P. Gallo, T. W. Owen, and T. R. Loveland, 2006: Land use/land cover change effects on temperature trends at U.S. Climate Normals stations. Geophysical Research Letters , 33 (11), L11703. doi: 10.1029/2006gl026358 . ↩
• Halofsky, J. E., M. A. Hemstrom, D. R. Conklin, J. S. Halofsky, B. K. Kerns, and D. Bachelet, 2013: Assessing potential climate change effects on vegetation using a linked model approach. Ecological Modelling , 266 , 131–143. doi: 10.1016/j.ecolmodel.2013.07.003 . ↩
• Hamdi, R., P. Termonia, and P. Baguis, 2011: Effects of urbanization and climate change on surface runoff of the Brussels Capital Region: A case study using an urban soil–vegetation–atmosphere‐transfer model. International Journal of Climatology , 31 (13), 1959–1974. doi: 10.1002/joc.2207 . ↩
• Han, J.-Y., J.-J. Baik, and H. Lee, 2014: Urban impacts on precipitation. Asia-Pacific Journal of Atmospheric Sciences , 50 (1), 17–30. doi: 10.1007/s13143-014-0016-7 . ↩
• Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend, 2013: High-resolution global maps of 21st-century forest cover change. Science , 342 (6160), 850–853. doi: 10.1126/science.1244693 . ↩
• Hao, W. M., and N. K. Larkin, 2014: Wildland fire emissions, carbon, and climate: Wildland fire detection and burned area in the United States. Forest Ecology and Management , 317 , 20–25. doi: 10.1016/j.foreco.2013.09.029 . ↩
• Harrison, S. P., J. R. Marlon, and P. J. Bartlein, 2010: Fire in the Earth system. Changing Climates, Earth Systems and Society . Dodson, J., Ed., Springer, Dordrecht, 21–48. doi: 10.1007/978-90-481-8716-4_3 . ↩
• Hartmann, D. L., A. M. G. Klein Tank, M. Rusticucci, L. V. Alexander, S. Brönnimann, Y. Charabi, F. J. Dentener, E. J. Dlugokencky, D. R. Easterling, A. Kaplan, B. J. Soden, P. W. Thorne, M. Wild, and P. M. Zhai, 2013: Observations: Atmosphere and surface. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change . Stocker, T. F., D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P. M. Midgley, Eds., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 159–254. URL . ↩
• Hauer, M. E., J. M. Evans, and D. R. Mishra, 2016: Millions projected to be at risk from sea-level rise in the continental United States. Nature Climate Change , 6 (7), 691–695. doi: 10.1038/nclimate2961 . ↩
• Hawbaker, T. J., V. C. Radeloff, R. B. Hammer, and M. K. Clayton, 2005: Road density and landscape pattern in relation to housing density, and ownership, land cover, and soils. Landscape ecology , 20 (5), 609–625. doi: 10.1007/s10980-004-5647-0 . ↩
• Hibbard, K. A., F. M. Hoffman, D. Huntzinger, and T. O. West, 2017: Changes in Land Cover and Terrestrial Biogeochemistry. Climate Science Special Report: Fourth National Climate Assessment, Volume I . Wuebbles, D. J., D. W. Fahey, K. A. Hibbard, D. J. Dokken, B. C. Stewart, and T. K. Maycock, Eds., U.S. Global Change Research Program, Washington, DC, USA, 277–302. doi: 10.7930/J0416V6X . ↩
• Higuera, P. E., J. T. Abatzoglou, J. S. Littell, and P. Morgan, 2015: The changing strength and nature of fire-climate relationships in the northern Rocky Mountains, U.S.A., 1902-2008. PLoS ONE , 10 (6), e0127563. doi: 10.1371/journal.pone.0127563 . ↩
• Hinkel, J., D. Lincke, A. T. Vafeidis, M. Perrette, R. J. Nicholls, R. S. Tol, B. Marzeion, X. Fettweis, C. Ionescu, and A. Levermann, 2014: Coastal flood damage and adaptation costs under 21st century sea-level rise. Proceedings of the National Academy of Sciences of the United States of America , 111 (9), 3292–3297. doi: 10.1073/pnas.1222469111 . ↩
• Homer, C. G., J. A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N. D. Herold, J. Wickham, and K. Megown, 2015: Completion of the 2011 National Land Cover Database for the conterminous United States—Representing a decade of land cover change information. Photogrammetric Engineering & Remote Sensing , 81 (5), 345–354. doi: 10.14358/PERS.81.5.345 . ↩
• Homer, C., C. Huang, L. Yang, B. Wylie, and M. Coan, 2007: Development of a 2001 National Land-Cover Database for the United States. Photogrammetric Engineering & Remote Sensing , 70 (7), 829–840. doi: 10.14358/PERS.70.7.829 . ↩
• Houghton, R. A., J. L. Hackler, and K. T. Lawrence, 1999: The U.S. carbon budget: Contributions from land-use change. Science , 285 (5427), 574–578. doi: 10.1126/science.285.5427.574 . ↩
• Huong, H. T. L., and A. Pathirana, 2013: Urbanization and climate change impacts on future urban flooding in Can Tho City, Vietnam. Hydrology Earth System Sciences , 17 (1), 379–394. doi: 10.5194/hess-17-379-2013 . ↩
• Hurteau, M. D., J. B. Bradford, P. Z. Fulé, A. H. Taylor, and K. L. Martin, 2014: Climate change, fire management, and ecological services in the southwestern US. Forest Ecology and Management , 327 , 280–289. doi: 10.1016/j.foreco.2013.08.007 . ↩
• Hurtt, G. C., L. P. Chini, S. Frolking, R. A. Betts, J. Feddema, G. Fischer, J. P. Fisk, K. Hibbard, R. A. Houghton, A. Janetos, C. D. Jones, G. Kindermann, T. Kinoshita, K. Klein Goldewijk, K. Riahi, E. Shevliakova, S. Smith, E. Stehfest, A. Thomson, P. Thornton, D. P. van Vuuren, and Y. P. Wang, 2011: Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Climatic Change , 109 (1), 117. doi: 10.1007/s10584-011-0153-2 . ↩
• IPCC, 2014: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Intergovernmental Panel on Climate Change (IPCC), Geneva, Switzerland, 151 pp. URL . ↩
• Iverson, L. R., A. M. Prasad, S. N. Matthews, and M. Peters, 2008: Estimating potential habitat for 134 eastern US tree species under six climate scenarios. Forest Ecology and Management , 254 (3), 390–406. doi: 10.1016/j.foreco.2007.07.023 . ↩
• Jankovic, V., and M. Hebbert, 2012: Hidden climate change—Urban meteorology and the scales of real weather. Climatic Change , 113 (1), 23–33. doi: 10.1007/s10584-012-0429-1 . ↩
• Jiang, X., S. A. Rauscher, T. D. Ringler, D. M. Lawrence, A. P. Williams, C. D. Allen, A. L. Steiner, D. M. Cai, and N. G. McDowell, 2013: Projected future changes in vegetation in western North America in the twenty-first century. Journal of Climate , 26 (11), 3671–3687. doi: 10.1175/jcli-d-12-00430.1 . ↩
• Jolly, W. M., M. A. Cochrane, P. H. Freeborn, Z. A. Holden, T. J. Brown, G. J. Williamson, and D. M. J. S. Bowman, 2015: Climate-induced variations in global wildfire danger from 1979 to 2013. Nature Communications , 6 , 7537. doi: 10.1038/ncomms8537 . ↩
• Kalnay, E., and M. Cai, 2003: Impact of urbanization and land-use change on climate. Nature , 423 (6939), 528–531. doi: 10.1038/nature01675 . ↩
• Keeley, J., and A. Syphard, 2016: Climate change and future fire regimes: Examples from California. Geosciences , 6 (3), 37. doi: 10.3390/geosciences6030037 . ↩
• Kirwan, M. L., and J. P. Megonigal, 2013: Tidal wetland stability in the face of human impacts and sea-level rise. Nature , 504 (7478), 53–60. doi: 10.1038/nature12856 . ↩
• Kriegler, E., J. Edmonds, S. Hallegatte, K. L. Ebi, T. Kram, K. Riahi, H. Winkler, and D. P. van Vuuren, 2014: A new scenario framework for climate change research: The concept of shared climate policy assumptions. Climatic Change , 122 (3), 401–414. doi: 10.1007/s10584-013-0971-5 . ↩
• Kulmatiski, A., and K. H. Beard, 2013: Woody plant encroachment facilitated by increased precipitation intensity. Nature Climate Change , 3 (9), 833–837. doi: 10.1038/nclimate1904 . ↩
• Kuppinger, D. M., M. A. Jenkins, and P. S. White, 2010: Predicting the post-fire establishment and persistence of an invasive tree species across a complex landscape. Biological Invasions , 12 (10), 3473–3484. doi: 10.1007/s10530-010-9745-4 . ↩
• Kurz, W. A., C. C. Dymond, G. Stinson, G. J. Rampley, E. T. Neilson, A. L. Carroll, T. Ebata, and L. Safranyik, 2008: Mountain pine beetle and forest carbon feedback to climate change. Nature , 452 (7190), 987–990. doi: 10.1038/nature06777 . ↩
• Lambin, E. F., and P. Meyfroidt, 2011: Global land use change, economic globalization, and the looming land scarcity. Proceedings of the National Academy of Sciences of the United States of America , 108 (9), 3465–3472. doi: 10.1073/pnas.1100480108 . ↩
• Lambin, E. F., B. L. Turner, H. J. Geist, S. B. Agbola, A. Angelsen, J. W. Bruce, O. T. Coomes, R. Dirzo, G. Fischer, C. Folke, P.S.George, K. Homewood, J. Imbernon, R. Leemans, X. Li, E. F. Moran, M. Mortimore, P. S. Ramakrishnan, J. F. Richards, H. Skånes, W. Steffen, G. D. Stone, U. Svedin, T. A. Veldkamp, C. Vogel, and J. Xu, 2001: The causes of land-use and land-cover change: Moving beyond the myths. Global Environmental Change , 11 (4), 261–269. doi: 10.1016/S0959-3780(01)00007-3 . ↩
• Lawrence, P. J., and T. N. Chase, 2007: Representing a new MODIS consistent land surface in the Community Land Model (CLM 3.0). Journal of Geophysical Research , 112 (G1), G01023. doi: 10.1029/2006JG000168 . ↩
• Leng, G., M. Huang, Q. Tang, W. J. Sacks, H. Lei, and L. R. Leung, 2013: Modeling the effects of irrigation on land surface fluxes and states over the conterminous United States: Sensitivity to input data and model parameters. Journal of Geophysical Research Atmospheres , 118 (17), 9789–9803. doi: 10.1002/jgrd.50792 . ↩
• Li, Y., M. Zhao, S. Motesharrei, Q. Mu, E. Kalnay, and S. Li, 2015: Local cooling and warming effects of forests based on satellite observations. Nature communications , 6 , 6603. doi: 10.1038/ncomms7603 . ↩
• Lobell, D. B. F., Christopher B., 2007: Global scale climate - Crop yield relationships and the impacts of recent warming. Environmental Research Letters , 2 (1) doi: 10.1088/1748-9326/2/1/014002 . ↩
• Lobell, D., G. Bala, A. Mirin, T. Phillips, R. Maxwell, and D. Rotman, 2009: Regional differences in the influence of irrigation on climate. Journal of Climate , 22 (8), 2248–2255. doi: 10.1175/2008JCLI2703.1 . ↩
• Loehman, R. A., E. Reinhardt, and K. L. Riley, 2014: Wildland fire emissions, carbon, and climate: Seeing the forest and the trees—A cross-scale assessment of wildfire and carbon dynamics in fire-prone, forested ecosystems. Forest Ecology and Management , 317 , 9–19. doi: 10.1016/j.foreco.2013.04.014 . ↩
• Lutz, J. A., J. W. van Wagtendonk, and J. F. Franklin, 2010: Climatic water deficit, tree species ranges, and climate change in Yosemite National Park. Journal of Biogeography , 37 (5), 936–950. doi: 10.1111/j.1365-2699.2009.02268.x . ↩
• Mahmood, R., R. A. Pielke, K. G. Hubbard, D. Niyogi, P. A. Dirmeyer, C. McAlpine, A. M. Carleton, R. Hale, S. Gameda, A. Beltrán-Przekurat, B. Baker, R. McNider, D. R. Legates, M. Shepherd, J. Du, P. D. Blanken, O. W. Frauenfeld, U. S. Nair, and S. Fall, 2014: Land cover changes and their biogeophysical effects on climate. International Journal of Climatology , 34 , 929–953. doi: 10.1002/joc.3736 . ↩
• Markell, D. L., 2016: Emerging legal and institutional responses to sea-level rise in Florida and beyond. Columbia Journal of Environmental Law , 42 (1), 1–58. URL . ↩
• Marshall, C. H., R. A. Pielke Sr., L. T. Steyaert, and D. A. Willard, 2004: The impact of anthropogenic land-cover change on the Florida peninsula sea breezes and warm season sensible weather. Monthly Weather Review , 132 (1), 28–52. doi: 2.0.CO;2'>10.1175/1520-0493(2004)132 2.0.CO;2 . ↩
• Masui, T., K. Matsumoto, Y. Hijioka, T. Kinoshita, T. Nozawa, S. Ishiwatari, E. Kato, P. R. Shukla, Y. Yamagata, and M. Kainuma, 2011: An emission pathway for stabilization at 6 Wm −2  radiative forcing. Climatic Change , 109 (1), 59–76. doi: 10.1007/s10584-011-0150-5 . ↩
• McGrath, J. M., and D. B. Lobell, 2013: Regional disparities in the CO2 fertilization effect and implications for crop yields. Environmental Research Letters , 8 (1), 014054. doi: 10.1088/1748-9326/8/1/014054 . ↩
• Melillo, J. M., J. M. Reilly, D. W. Kicklighter, A. C. Gurgel, T. W. Cronin, S. Paltsev, B. S. Felzer, X. Wang, A. P. Sokolov, and C. A. Schlosser, 2009: Indirect emissions from biofuels: How important? Science , 326 (5958), 1397–1399. doi: 10.1126/science.1180251 . ↩
• Melton, F., C. Rosevelt, A. Guzman, L. Johnson, I. Zaragoza, J. Verdin, P. Thenkabail, C. Wallace, R. Mueller, P. Willis, and J. Jones, 2015: Fallowed area mapping for drought impact reporting: 2015 assessment of conditions in the California Central Valley. NASA Ames Research Center, 13 pp. URL . ↩
• Meyfroidt, P., E. F. Lambin, K.-H. Erb, and T. W. Hertel, 2013: Globalization of land use: Distant drivers of land change and geographic displacement of land use. Current Opinion in Environmental Sustainability , 5 (5), 438–444. doi: 10.1016/j.cosust.2013.04.003 . ↩
• Mildrexler, D. J., M. Zhao, and S. W. Running, 2011: A global comparison between station air temperatures and MODIS land surface temperatures reveals the cooling role of forests. Journal of Geophysical Research , 116 (G3), G03025. doi: 10.1029/2010JG001486 . ↩
• Montenegro, A., M. Eby, Q. Mu, M. Mulligan, A. J. Weaver, E. C. Wiebe, and M. Zhao, 2009: The net carbon drawdown of small scale afforestation from satellite observations. Global and Planetary Change , 69 (4), 195–204. doi: 10.1016/j.gloplacha.2009.08.005 . ↩
• Moss, R. H., J. A. Edmonds, K. A. Hibbard, M. R. Manning, S. K. Rose, D. P. van Vuuren, T. R. Carter, S. Emori, M. Kainuma, T. Kram, G. A. Meehl, J. F. B. Mitchell, N. Nakicenovic, K. Riahi, S. J. Smith, R. J. Stouffer, A. M. Thomson, J. P. Weyant, and T. J. Wilbanks, 2010: The next generation of scenarios for climate change research and assessment. Nature , 463 , 747–756. doi: 10.1038/nature08823 . ↩
• Mu, J. E., B. M. Sleeter, J. T. Abatzoglou, and J. M. Antle, 2017: Climate impacts on agricultural land use in the United States: The role of socio-economic scenarios. Climatic Change , 144 (2), 329–345. doi: 10.1007/s10584-017-2033-x . ↩
• National Agricultural Statistics Service, 2014: 2012 Census of Agriculture: 2013 Farm and Ranch Irrigation Survey. AC-12-SS-1. U.S. Department of Agriculture, 249 pp. URL . ↩
• Nelson, G. C., D. Mensbrugghe, H. Ahammad, E. Blanc, K. Calvin, T. Hasegawa, P. Havlik, E. Heyhoe, P. Kyle, H. Lotze‐Campen, M. von Lampe, D. Mason d'Croz, H. van Meijl, C. Müller, J. Reilly, R. Robertson, R. D. Sands, C. Schmitz, A. Tabeau, K. Takahashi, H. Valin, and D. Willenbockel, 2014: Agriculture and climate change in global scenarios: Why don't the models agree. Agricultural Economics , 45 (1), 85–101. doi: 10.1111/agec.12091 . ↩
• Nelson, G. C., H. Valin, R. D. Sands, P. Havlík, H. Ahammad, D. Deryng, J. Elliott, S. Fujimori, T. Hasegawa, E. Heyhoe, P. Kyle, M. V. Lampe, H. Lotze-Campen, D. M. d'Croz, H. van Meijl, D. van der Mensbrugghe, C. Müller, A. Popp, R. Robertson, S. Robinson, E. Schmid, C. Schmitz, A. Tabeau, and D. Willenbockelo, 2014: Climate change effects on agriculture: Economic responses to biophysical shocks. Proceedings of the National Academy of Sciences of the United States of America , 111 (9), 3274–3279. doi: 10.1073/pnas.1222465110 . ↩
• Notaro, M., A. Mauss, and J. W. Williams, 2012: Projected vegetation changes for the American Southwest: Combined dynamic modeling and bioclimatic‐envelope approach. Ecological Applications , 22 (4), 1365–1388. doi: 10.1890/11-1269.1 . ↩
• O'Neill, B. C., E. Kriegler, K. Riahi, K. L. Ebi, S. Hallegatte, T. R. Carter, R. Mathur, and D. P. van Vuuren, 2014: A new scenario framework for climate change research: The concept of shared socioeconomic pathways. Climatic Change , 122 (3), 387–400. doi: 10.1007/s10584-013-0905-2 . ↩
• Office for Coastal Management, 2018: C-CAP FTP Tool: An interface for downloading land cover data. NOAA National Ocean Service, Silver Spring, MD. URL . ↩
• Oleson, K. W., A. Monaghan, O. Wilhelmi, M. Barlage, N. Brunsell, J. Feddema, L. Hu, and D. F. Steinhoff, 2015: Interactions between urbanization, heat stress, and climate change. Climatic Change , 129 (3–4), 525–541. doi: 10.1007/s10584-013-0936-8 . ↩
• Park, C.-E., S.-J. Jeong, C.-H. Ho, and J. Kim, 2015: Regional variations in potential plant habitat changes in response to multiple global warming scenarios. Journal of Climate , 28 (7), 2884–2899. doi: 10.1175/JCLI-D-13-00753.1 . ↩
• Park, T., S. Ganguly, H. Tømmervik, E. S. Euskirchen, K.-A. Høgda, S. R. Karlsen, V. Brovkin, R. R. Nemani, and R. B. Myneni, 2016: Changes in growing season duration and productivity of northern vegetation inferred from long-term remote sensing data. Environmental Research Letters , 11 (8), 084001. doi: 10.1088/1748-9326/11/8/084001 . ↩
• Parker, L. E., and J. T. Abatzoglou, 2016: Projected changes in cold hardiness zones and suitable overwinter ranges of perennial crops over the United States. Environmental Research Letters , 11 (3), 034001. doi: 10.1088/1748-9326/11/3/034001 . ↩
• Paustian, K., J. Lehmann, S. Ogle, D. Reay, G. P. Robertson, and P. Smith, 2016: Climate-smart soils. Nature , 532 , 49–57. doi: 10.1038/nature17174 . ↩
• Pearson, R. G., and T. P. Dawson, 2003: Predicting the impacts of climate change on the distribution of species: Are bioclimate envelope models useful? Global Ecology and Biogeography , 12 (5), 361–371. doi: 10.1046/j.1466-822X.2003.00042.x . ↩
• Pielke Sr., R. A., A. Pitman, D. Niyogi, R. Mahmood, C. McAlpine, F. Hossain, K. K. Goldewijk, U. Nair, R. Betts, S. Fall, M. Reichstein, P. Kabat, and N. de Noblet, 2011: Land use/land cover changes and climate: Modeling analysis and observational evidence. Wiley Interdisciplinary Reviews: Climate Change , 2 (6), 828–850. doi: 10.1002/wcc.144 . ↩
• Pielke, R. A., Sr., 2005: Land use and climate change. Science , 310 (5754), 1625–1626. doi: 10.1126/science.1120529 . ↩
• Pielke, R. A., Sr., J. Adegoke, A. BeltráN-Przekurat, C. A. Hiemstra, J. Lin, U. S. Nair, D. Niyogi, and T. E. Nobis, 2007: An overview of regional land-use and land-cover impacts on rainfall. Tellus B , 59 (3), 587–601. doi: 10.1111/j.1600-0889.2007.00251.x . ↩
• Pitman, A. J., F. B. Avila, G. Abramowitz, Y. P. Wang, S. J. Phipps, and N. de Noblet-Ducoudré, 2011: Importance of background climate in determining impact of land-cover change on regional climate. Nature Climate Change , 1 (9), 472–475. doi: 10.1038/nclimate1294 . ↩
• Plevin, R. J., M. O'Hare, A. D. Jones, M. S. Torn, and H. K. Gibbs, 2010: Greenhouse gas emissions from biofuels' indirect land use change are uncertain but may be much greater than previously estimated. Environmental Science & Technology , 44 (21), 8015–8021. doi: 10.1021/es101946t . ↩
• Radeloff, V. C., R. B. Hammer, S. I. Stewart, J. S. Fried, S. S. Holcomb, and J. F. McKeefry, 2005: The wildland-urban interface in the United States. Ecological Applications , 15 (3), 799–805. doi: 10.1890/04-1413 . ↩
• Rehfeldt, G. E., N. L. Crookston, C. Sáenz-Romero, and E. M. Campbell, 2012: North American vegetation model for land-use planning in a changing climate: A solution to large classification problems. Ecological Applications , 22 (1), 119–141. doi: 10.1890/11-0495.1 . ↩
• Rehfeldt, G. E., N. L. Crookston, M. V. Warwell, and J. S. Evans, 2006: Empirical analyses of plant-climate relationships for the western United States. International Journal of Plant Sciences , 167 (6), 1123–1150. . ↩
• Renwick, K. M., and M. E. Rocca, 2015: Temporal context affects the observed rate of climate‐driven range shifts in tree species. Global Ecology and Biogeography , 24 (1), 44–51. doi: 10.1111/geb.12240 . ↩
• Revi, A., D. E. Satterthwaite, F. Aragón-Durand, J. Corfee-Morlot, R. B. R. Kiunsi, M. Pelling, D. C. Roberts, and W. Solecki, 2014: Urban areas. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel of Climate Change . Field, C. B., V. R. Barros, D. J. Dokken, K. J. Mach, M. D. Mastrandrea, T. E. Bilir, M. Chatterjee, K. L. Ebi, Y. O. Estrada, R. C. Genova, B. Girma, E. S. Kissel, A. N. Levy, S. MacCracken, P. R. Mastrandrea, and L. L. White, Eds., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 535–612. ↩
• Riahi, K., S. Rao, V. Krey, C. Cho, V. Chirkov, G. Fischer, G. Kindermann, N. Nakicenovic, and P. Rafaj, 2011: RCP 8.5—A scenario of comparatively high greenhouse gas emissions. Climatic Change , 109 (1–2), 33–57. doi: 10.1007/s10584-011-0149-y . ↩
• Rosenzweig, C., J. Elliott, D. Deryng, A. C. Ruane, C. Müller, A. Arneth, K. J. Boote, C. Folberth, M. Glotter, N. Khabarov, K. Neumann, F. Piontek, T. A. M. Pugh, E. Schmid, E. Stehfest, H. Yang, and J. W. Jones, 2014: Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proceedings of the National Academy of Sciences of the United States of America , 111 (9), 3268–3273. doi: 10.1073/pnas.1222463110 . ↩
• Rosenzweig, C., J. W. Jones, J. Antle, jerry Hatfield, A. Ruane, S. McDermid, K. Boote, P. Thorburn, K. Descheemaeker, R. Valdivia, C. Porter, S. Janssen, W.-L. Bartels, A. Sullivan, and C. Mutter, 2016: Protocols for AgMIP Regional Integrated Assessments, Version 6.1. Agricultural Model Intercomparison and Improvement Project (AgMIP), New York, NY, 65 pp. URL . ↩
• Ryals, R., M. Kaiser, M. S. Torn, A. A. Berhe, and W. L. Silver, 2014: Impacts of organic matter amendments on carbon and nitrogen dynamics in grassland soils. Soil Biology and Biochemistry , 68 , 52–61. doi: 10.1016/j.soilbio.2013.09.011 . ↩
• Sacks, W. J., B. I. Cook, N. Buenning, S. Levis, and J. H. Helkowski, 2009: Effects of global irrigation on the near-surface climate. Climate Dynamics , 33 (2–3), 159–175. doi: 10.1007/s00382-008-0445-z . ↩
• Salazar, A., G. Baldi, M. Hirota, J. Syktus, and C. McAlpine, 2015: Land use and land cover change impacts on the regional climate of non-Amazonian South America: A review. Global and Planetary Change , 128 , 103–119. doi: 10.1016/j.gloplacha.2015.02.009 . ↩
• Scheiter, S., L. Langan, and S. I. Higgins, 2013: Next‐generation dynamic global vegetation models: Learning from community ecology. New Phytologist , 198 (3), 957–969. doi: 10.1111/nph.12210 . ↩
• Schoennagel, T., C. R. Nelson, D. M. Theobald, G. C. Carnwath, and T. B. Chapman, 2009: Implementation of National Fire Plan treatments near the wildland–urban interface in the western United States. Proceedings of the National Academy of Sciences of the United States of America , 106 (26), 10706–10711. doi: 10.1073/pnas.0900991106 . ↩
• Searchinger, T., R. Edwards, D. Mulligan, R. Heimlich, and R. Plevin, 2015: Do biofuel policies seek to cut emissions by cutting food? Science , 347 (6229), 1420–1422. doi: 10.1126/science.1261221 . ↩
• Searchinger, T., R. Heimlich, R. A. Houghton, F. Dong, A. Elobeid, J. Fabiosa, S. Tokgoz, D. Hayes, and T.-H. Yu, 2008: Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change. Science , 319 (5867), 1238–1240. doi: 10.1126/science.1151861 . ↩
• Shafer, S. L., P. J. Bartlein, and R. S. Thompson, 2001: Potential changes in the distributions of western North America tree and shrub taxa under future climate scenarios. Ecosystems , 4 (3), 200–215. doi: 10.1007/s10021-001-0004-5 . ↩
• Shepherd, J. M., 2005: A review of current investigations of urban-induced rainfall and recommendations for the future. Earth Interactions , 9 (12), 1–27. doi: 10.1175/EI156.1 . ↩
• Sleeter, B. M., J. Liu, C. J. Daniel, T. J. Hawbaker, T. S. Wilson, L. B. Fortini, J. D. Jacobi, P. C. Selmants, C. P. Giardina, C. M. Litton, and R. F. Hughes, 2017: Projected future carbon storage and carbon fluxes in terrestrial ecosystems of Hawai‘i from changes in climate, land use, and disturbance. Baseline and Projected Future Carbon Storage and Carbon Fluxes in Ecosystems of Hawaiʻi . Selmants, P. C., C. P. Giardina, J. D. Jacobi, and Z. Zhu, Eds., U.S. Geological Survey, Reston, VA, 107–128. doi: 10.3133/pp1834 . ↩
• Sleeter, B. M., T. L. Sohl, T. R. Loveland, R. F. Auch, W. Acevedo, M. A. Drummond, K. L. Sayler, and S. V. Stehman, 2013: Land-cover change in the conterminous United States from 1973 to 2000. Global Environmental Change , 23 (4), 733–748. doi: 10.1016/j.gloenvcha.2013.03.006 . ↩
• Smyth, C. E., G. Stinson, E. Neilson, T. C. Lemprière, M. Hafer, G. J. Rampley, and W. A. Kurz, 2014: Quantifying the biophysical climate change mitigation potential of Canada's forest sector. Biogeosciences , 11 (13), 3515–3529. doi: 10.5194/bg-11-3515-2014 . ↩
• Sohl, T. L., M. C. Wimberly, V. C. Radeloff, D. M. Theobald, and B. M. Sleeter, 2016: Divergent projections of future land use in the United States arising from different models and scenarios. Ecological Modelling , 337 , 281–297. doi: 10.1016/j.ecolmodel.2016.07.016 . ↩
• Sommers, W. T., R. A. Loehman, and C. C. Hardy, 2014: Wildland fire emissions, carbon, and climate: Science overview and knowledge needs. Forest Ecology and Management , 317 , 1–8. doi: 10.1016/j.foreco.2013.12.014 . ↩
• Soulard, C. E., and T. S. Wilson, 2013: Recent land-use/land-cover change in the Central California Valley. Journal of Land Use Science , 10 (1), 59–80. doi: 10.1080/1747423x.2013.841297 . ↩
• Stephens, S. L., J. D. McIver, R. E. Boerner, C. J. Fettig, J. B. Fontaine, B. R. Hartsough, P. L. Kennedy, and D. W. Schwilk, 2012: The effects of forest fuel-reduction treatments in the United States. BioScience , 62 (6), 549–560. doi: 10.1525/bio.2012.62.6.6 . ↩
• Swanson, M. E., J. F. Franklin, R. L. Beschta, C. M. Crisafulli, D. A. DellaSala, R. L. Hutto, D. B. Lindenmayer, and F. J. Swanson, 2011: The forgotten stage of forest succession: Early-successional ecosystems on forest sites. Frontiers in Ecology and the Environment , 9 (2), 117–125. doi: 10.1890/090157 . ↩
• Taylor, J. L., W. Acevedo, R. F. Auch, and M. A. Drummond, 2015: Status and trends of land change in the Great Plains of the United States—1973 to 2000. USGS Professional Paper 1794-B. U.S. Geological Survey, Reston, VA, 179 pp. doi: 10.3133/pp1794B . ↩
• The National Strategy, 2014: The National Strategy: The Final Phase in the Development of the National Cohesive Wildland Fire Management Strategy. U.S. Departments of the Interior and Agriculture, Washington, DC, 93 pp. URL . ↩
• Theobald, D. M., 2014: Development and applications of a comprehensive land use classification and map for the US. PLOS ONE , 9 (4), e94628. doi: 10.1371/journal.pone.0094628 . ↩
• Theobald, D. M., and W. H. Romme, 2007: Expansion of the US wildland-urban interface. Landscape and Urban Planning , 83 (4), 340–354. doi: 10.1016/j.landurbplan.2007.06.002 . ↩
• Thompson, R. S., 1988: Western North America: Vegetation dynamics in the western United States: Modes of response to climatic fluctuations. Vegetation History . Huntley, B., and T. Webb III, Eds., Kluwer Academic, Dordrecht, Netherlands, 415–458. ↩
• Thompson, R. S., K. H. Anderson, R. T. Pelltier, L. E. Strickland, S. L. Shafer, and P. J. Bartlein, 2012: Atlas of relations between climatic parameters and distributions of important trees and shrubs in North America—Modern data for climatic estimation from vegetation inventories. USGS Professional Paper 1650-F. U.S. Geological Survey, Reston, VA, various pp. URL . ↩
• Thomson, A. M., K. V. Calvin, S. J. Smith, G. P. Kyle, A. Volke, P. Patel, S. Delgado-Arias, B. Bond-Lamberty, M. A. Wise, and L. E. Clarke, 2011: RCP4.5: A pathway for stabilization of radiative forcing by 2100. Climatic Change , 109 (1–2), 77–94. doi: 10.1007/s10584-011-0151-4 . ↩
• Trainor, A. M., R. I. McDonald, and J. Fargione, 2016: Energy sprawl is the largest driver of land use change in United States. PLOS ONE , 11 (9), e0162269. doi: 10.1371/journal.pone.0162269 . ↩
• USGCRP, 2017: Climate Science Special Report: Fourth National Climate Assessment, Volume I. Wuebbles, D. J., D. W. Fahey, K. A. Hibbard, D. J. Dokken, B. C. Stewart, and T. K. Maycock, Eds. U.S. Global Change Research Program, Washington, DC, USA, 470 pp. doi: 10.7930/J0J964J6 . ↩
• USGCRP, 2018: Second State of the Carbon Cycle Report (SOCCR2): A Sustained Assessment Report. U.S. Global Change Research Program, Washington, DC, 877 pp. URL . ↩
• van Vuuren, D. P., S. Deetman, M. G. J. den Elzen, A. Hof, M. Isaac, K. Klein Goldewijk, T. Kram, A. Mendoza Beltran, E. Stehfest, and J. van Vliet, 2011: RCP2.6: Exploring the possibility to keep global mean temperature increase below 2° C. Climatic Change , 109 (1–2), 95–116. doi: 10.1007/s10584-011-0152-3 . ↩
• Verburg, P. H., K. Neumann, and L. Nol, 2011: Challenges in using land use and land cover data for global change studies. Global Change Biology , 17 (2), 974–989. doi: 10.1111/j.1365-2486.2010.02307.x . ↩
• Vitousek, S., P. L. Barnard, C. H. Fletcher, N. Frazer, L. Erikson, and C. D. Storlazzi, 2017: Doubling of coastal flooding frequency within decades due to sea-level rise. Scientific Reports , 7 (1), 1399. doi: 10.1038/s41598-017-01362-7 . ↩
• Westerling, A. L., 2016: Increasing western US forest wildfire activity: Sensitivity to changes in the timing of spring. Philosophical Transactions of the Royal Society B: Biological Sciences , 371 , 20150178. doi: 10.1098/rstb.2015.0178 . ↩
• Westerling, A. L., H. G. Hidalgo, D. R. Cayan, and T. W. Swetnam, 2006: Warming and earlier spring increase western U.S. forest wildfire activity. Science , 313 (5789), 940–943. doi: 10.1126/science.1128834 . ↩
• Wheeler, T., and J. von Braun, 2013: Climate change impacts on global food security. Science , 341 (6145), 508–513. doi: 10.1126/science.1239402 . ↩
• Wickham, J. D., T. G. Wade, and K. H. Riitters, 2012: Comparison of cropland and forest surface temperatures across the conterminous United States. Agricultural and Forest Meteorology , 166 , 137–143. doi: 10.1016/j.agrformet.2012.07.002 . ↩
• Wickham, J. D., T. G. Wade, and K. H. Riitters, 2013: Empirical analysis of the influence of forest extent on annual and seasonal surface temperatures for the continental United States. Global Ecology and Biogeography , 22 (5), 620–629. doi: 10.1111/geb.12013 . ↩
• Wickham, J., C. Barnes, M. Nash, and T. Wade, 2015: Combining NLCD and MODIS to create a land cover-albedo database for the continental United States. Remote Sensing of Environment , 170 , 143–152. doi: 10.1016/j.rse.2015.09.012 . ↩
• Wickham, J., M. Nash, and C. A. Barnes, 2016: Effect of land cover change on snow free surface albedo across the continental United States. Global and Planetary Change , 146 , 1–9. doi: 10.1016/j.gloplacha.2016.09.005 . ↩
• Wickham, J., T. Wade, and K. Riitters, 2014: An isoline separating relatively warm from relatively cool wintertime forest surface temperatures for the southeastern United States. Global and Planetary Change , 120 , 46–53. doi: 10.1016/j.gloplacha.2014.05.012 . ↩
• Williams, A. P., and J. T. Abatzoglou, 2016: Recent advances and remaining uncertainties in resolving past and future climate effects on global fire activity. Current Climate Change Reports , 2 (1), 1–14. doi: 10.1007/s40641-016-0031-0 . ↩
• Williams, A. P., C. D. Allen, C. I. Millar, T. W. Swetnam, J. Michaelsen, C. J. Still, and S. W. Leavitt, 2010: Forest responses to increasing aridity and warmth in the southwestern United States. Proceedings of the National Academy of Sciences of the United States of America , 107 (50), 21289–21294. doi: 10.1073/pnas.0914211107 . ↩
• Williams, A. P., R. Seager, J. T. Abatzoglou, B. I. Cook, J. E. Smerdon, and E. R. Cook, 2015: Contribution of anthropogenic warming to California drought during 2012–2014. Geophysical Research Letters , 42 (16), 6819–6828. doi: 10.1002/2015GL064924 . ↩
• Wilson, T. S., B. M. Sleeter, and D. R. Cameron, 2016: Future land-use related water demand in California. Environmental Research Letters , 11 (5), 054018. doi: 10.1088/1748-9326/11/5/054018 . ↩
• Woodall, C. W., B. F. Walters, M. B. Russell, J. W. Coulston, G. M. Domke, A. W. D'Amato, and P. A. Sowers, 2016: A tale of two forest carbon assessments in the eastern United States: Forest use versus cover as a metric of change. Ecosystems , 19 (8), 1401–1417. doi: 10.1007/s10021-016-0012-0 . ↩
• Wright, C. K., and M. C. Wimberly, 2013: Recent land use change in the Western Corn Belt threatens grasslands and wetlands. Proceedings of the National Academy of Sciences of the United States of America , 110 (10), 4134–4139. doi: 10.1073/pnas.1215404110 . ↩
• Xu, C., G. Z. Gertner, and R. M. Scheller, 2007: Potential effects of interaction between CO2 and temperature on forest landscape response to global warming. Global Change Biology , 13 (7), 1469–1483. doi: 10.1111/j.1365-2486.2007.01387.x . ↩
• Yang, X., Y. Hou, and B. Chen, 2011: Observed surface warming induced by urbanization in east China. Journal of Geophysical Research , 116 (D14), D14113. doi: 10.1029/2010JD015452 . ↩
• Young, D. J., J. T. Stevens, J. M. Earles, J. Moore, A. Ellis, A. L. Jirka, and A. M. Latimer, 2017: Long‐term climate and competition explain forest mortality patterns under extreme drought. Ecology Letters , 20 (1), 78–86. doi: 10.1111/ele.12711 . ↩
• Yue, X., L. J. Mickley, and J. A. Logan, 2014: Projection of wildfire activity in Southern California in the mid-twenty-first century. Climate Dynamics , 43 (7–8), 1973–1991. doi: 10.1007/s00382-013-2022-3 . ↩
• Zabel, F., B. Putzenlechner, and W. Mauser, 2014: Global agricultural land resources—A high resolution suitability evaluation and its perspectives until 2100 under climate change conditions. PLOS ONE , 9 (9), e107522. doi: 10.1371/journal.pone.0107522 . ↩
• Zhao, K., and R. B. Jackson, 2014: Biophysical forcings of land‐use changes from potential forestry activities in North America. Ecological Monographs , 84 (2), 329–353. doi: 10.1890/12-1705.1 . ↩
• Zhao, L., X. Lee, R. B. Smith, and K. Oleson, 2014: Strong contributions of local background climate to urban heat islands. Nature , 511 (7508), 216–219. doi: 10.1038/nature13462 . ↩
• Zhao, M., A. J. Pitman, and T. Chase, 2001: The impact of land cover change on the atmospheric circulation. Climate Dynamics , 17 (5), 467–477. doi: 10.1007/pl00013740 . ↩
• Zhou, L., R. E. Dickinson, Y. Tian, J. Fang, Q. Li, R. K. Kaufmann, C. J. Tucker, and R. B. Myneni, 2004: Evidence for a significant urbanization effect on climate in China. Proceedings of the National Academy of Sciences of the United States of America , 101 (26), 9540–9544. doi: 10.1890/12-1705.1 . ↩

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Resettlement and its impacts on land use land cover change in Nansebo district, Ethiopia

• Published: 24 November 2021
• Volume 87 , pages 5067–5085, ( 2022 )

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• Ifa Lencho Roba 1 ,
• Engida Esayas Dube   ORCID: orcid.org/0000-0001-9736-6153 2 &
• Dereje Likissa Beyene   ORCID: orcid.org/0000-0002-4257-0840 3  

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Understanding the land use land cover dynamics and its drivers is essential to generate evidences for making appropriate interventions to safeguard the environment. The study assesses the impacts of resettlement on the Land use land cover (LULC) change in Nensebo district of Ethiopia using remote sensing and in situe field surveys over a period of 33 years (1986–2019). Four years’ temporal satellite images 1986, 2000, 2011 and 2019 were used to assess the LULCC. Supervised maximum likelihood classification algorithm was used to classify the images. A multi-stage sampling procedure was used to select the study district, three kebeles and respondents. From a total of 987 households, 285(29%) household heads were selected using simple random sampling. Questionnaire survey, key informant interviews, and focus group discussions were employed to address the drivers. The result showed that there was a rise of farmland from 9.27% in 1986 to 31.55% in 2019 with a rate of 7.30% per annum. Conversely, forest cover was shrinking from 84.40% in 1986 to 57.87% in 2019 with a rate of 0.95% per annum. The study also revealed a significant conversion of shrubland and forest cover to farmland with 58.2% and 29% respectively over the study period. The study revealed that the rapid rate of deforestation and LULCC were attributed to proximate drivers (commercial farmland expansion, resettlement, forest fire, population growth, illegal logging, charcoal and firewood production) and underlying causes (population pressure, poverty, land scarcity, and weak law enforcement). Thus, making appropriate interventions aimed at ensuring sustainability of environments by protecting forests and grasslands and minimizing livelihood dependence on the environmental resources is necessary.

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Abera, A., Yirgu, T., & Uncha, A. (2020). Impacts of Resettlement Scheme on Vegetation Cover and its implications on Conservation in Chewaka district of Ethiopia. Environmental Systems Research . https://doi.org/10.1186/s40068-020-00164-7

Article   Google Scholar  

Adeoye, N., & O. & Ayeni, B. (2011). Assessment of deforestation, biodiversity loss and the associated factors: Case study of Ijesa-Ekiti region of Southwestern Nigeria. GeoJournal, 76 , 229–243. https://doi.org/10.1007/s10708-009-9336-z

Ahmed, M. (2005). Resettlement, Socio-Economic and Environmental Impact Evaluation: The Case of HaroTatessa Resettlement Site.Forum for Social Studies (FSS). Addis Ababa.

Belay, S., Amsalu, A., & Abebe, E. (2014). Land Use and Land Cover Changes in Awash National Park, Ethiopia: Impact of Decentralization on the Use and Management of Resources. Open Journal of Ecology, 4 , 950–960.

Berhanu,G.(2007). The impact of resettlement on woodland vegetation: The case of Chewaka resettlement area, southwestern Ethiopia. MSc thesis; Addis Ababa University, Ethiopia.

Beyene, D,L. (2014). Assessing the Impact of UNESCO Biosphere Reserves on Forest Cover Change, MSc Thesis, Wagengen University.

Congalton R., & Green K.(2008).Assessing the Accuracy of Remotely Sensed Data:Principles and Practices.Retrieved from https://Books.Google.Com/ .

CSA [Central Statistical Agency]. (2007). Population and Housing Census of Ethiopia 2007: Statistical Report . Federal Democratic Republic of Ethiopia.

Google Scholar  

Dejenie, A. (2011). Impact of Resettlement on Woody Plant Species and Local Livelihood:The Case of Guraferda Woreda in Bench Maji Zone, Southwestern, Ethiopia. Msc Thesis; Addis Ababa University, Ethiopia.

Dereje,T.(2007). Forest Cover Change and Socio-Economic Drivers in Southwest Ethiopia. M.sc.Thesis Center of Land Management and Land Tenure. Technische University Munich, Germany.

Dibaba, W., & T;Demisse, T, A;Miegel, K. (2020). Drivers and Implications of Land Use/Land Cover Dynamics in Finchaa Catchment. Northwestern Ethiopia, Land, 9 (4), 113. https://doi.org/10.3390/land9040113

EFAP.(1994). Guidelines for Social,Environmental Ecological Impact Assessment in Resettlement Areas. Addis Ababa, Ethiopia.

FAO. (2005). State of the world’s forests :Food and Agriculture Organization of the United Nations,Rome.

FAO. (2010). Global Forest Resources Assessment : Food and Agriculture Organization of the United Nations, Rome.

FAO. (2011). State of the world’s forests :Food and Agriculture Organization of the United Nations,Rome.

FAO. (2016). State of the world’s forests :Food and Agriculture Organization of the United Nations,Rome

FAO and UNEP. (2020). The State of World’s Forests: Forests. Biodiversity and People, Rome, . https://doi.org/10.4060/ca8642en

FDRE (2011). Ethiopia’s Climate-Resilient Green Economy:Green economy strategy. Addis Ababa, Ethiopia.

Gadisa, C. (2017). The Political Economy of Deforestation and Forest Degradation in Ethiopia: A Review Article. Journal of Resources Development and Management, 29 , 38–43.

Geist, H., & J. & Lambin, E, F. (2002). Proximate Causes and Underlying Driving Forces of Tropical Deforestation. BioScience, 52 (2), 143–150.

Getachew, W. (1989). The consequences of resettlement in Ethiopia. African Affairs, 88 (352), 359–374.

Getahun, K., & Poesen., J & Rompaey, A, V. (2017). Impacts of Resettlement Programs on Deforestation of Moist Evergreen Afromontane Forests in Southwest Ethiopia. Mountain Research & Development, 37 (4), 474–486. https://doi.org/10.1659/MRD-JOURNAL-D-15-00034-1

Haileab, Z. (2017). Major Drivers and Consequences of Deforestation In Ethiopia: Implications for Forest Conservation. Asian Journal of Science and Techology, 08 , 5166–5175.

Hailu, A., Mammo, S., & Kidane, M. (2020). Dynamics of land use, land cover change trends and its drivers in Jimma Geneti District, Western Ethiopia. Land Use Policy, 99 , 105011. https://doi.org/10.1016/j.landusepol.2020.105011

Inter-governmental Panel on Climate Change. (IPCC). (2000).Summary for policy makers: Emission Scenarios A Special Report of the IPCC Working Group III.

Kabanza,A., Dondeyne, S., Tenga, J.,Kimaro, D.,Poesen, J., Kafiriti,E., & Deckers,J. (2013).More people, more trees in Eastern Tanzania: local and global drivers of land use/cover changes, African Geographical Review , https:dx.doi.org/ https://doi.org/10.1080/19376812.746093

Kleemann, J., Baysal, G., Bulley, H., & N, N., Christine Fürst, C. (2017). Assessing driving forces of land use and land cover change by a mixed-method approach in north-eastern Ghana, West Africa. Journal of Environmental Management, 196 , 411–442. https://doi.org/10.1016/j.jenvman.2017.01.053

Kloos, H. (1989). Settler Migration during the 1984/85 Resettlement Programme in Ethiopia. GeoJournal, 19 (2), 113–127.

MEFCC. (2016). Ethiopian Forest Reference Level Submission to UNFCC Main Report 1–49.

Mekuria,A.(2005). Forest Conversion,SoilDegradation,Farmers'PerceptionNexus:Implications for Sustainable Land Use in the Southwest of Ethiopia Doctoral Thesis. ZEF-Ecology and Development Series University of Bonn, Germany.

Messay, M. (2009). Challenges and opportunities of resettlers in Ethiopia: A case from Jiru Gamachu resettlement scheme, West Shewa. Journal of Sustainable Development in Africa, 11 , 83–102.

Messay, M., & Bekure, W. (2011). The Impact of Resettlement Schemes on Land-Use/Land-Cover Changes in Ethiopia: A Case Study from Nonno Resettlement Sites. Central Ethiopia, Journal of Sustainable Development in Africa, 13 (2), 269–293.

Million, B. (2011). Forest plantations and woodlots in Ethiopia African Forest Forum. Working Paper Series 1, 11–15.

Mussa, M., Hashim, H., & Teha, M. (2016). Rangeland degradation: Extent, impacts, and alternative restoration techniques in the rangeland of Ethiopia. Tropical and Subtropical Agroecosystems, 19 , 305–318.

Nensebo District office of Environment, Forest and Climate Change Authority. (2019). Official Document on forest Condition of the District . Unpublished.

Norovsuren, B., Batchuluum, T., & Batomunkuev., V. (2019). Land cover classification using maximum likilihood method (2000 amd 2019) at Khandgait valley in Mongolia. IOP Conference Series Earth and Environmental Science, 381 , 012054.

Nune, S., Soromessa, T., & Teketay, D. (2015). Non-carbon benefits for effective implementation of REDD+: The case of Bale Mountains Eco-Region, Southeastern Ethiopian. African Journal of Environmental Science and Technology, 9 , 747–764.

NWoANR.(2019).Official Document on Socio-Economic Baseline Survey Condition of the District, West Arsi, Oromia, Ethiopia. Unpublished.

OFWE, FARM Africa, SOS Sahel Ethiopia. (2015). Bale Mountains Eco-Region Reduction of Emission from Deforestation and Forest Degradation (REDD+) Project-Ethiopia. Addis Ababa,Ethiopia: FARM Africa and SOS Sahel.

Oshore.(2015). Impacts of Resettlement Schemes on Land Use and Land Cover Changes in Gumgumuta Settlement Village Southern Regional State of Ethiopia,M.A thesis, Haramaya University unpublished.

Ouedraogo, I., & S. (2009). Is rural migration a threat to environmental sustainability in Southern Burkina Faso? Land Degradation & Development, 20 (2), 217–230.

Rahmato, D. (2003). Resettlement in Ethiopia. The Tragedy of Population Relocation in the 1980s. FSS Discussion Paper No. 11 . Addis Ababa, Ethiopia: Forum for Social Studies.

SOS Sahel Ethiopia. (2010). Annual Accomplishment Report for 2009. Submitted to Charities and Societies Agency (CSA), Addis Ababa, Ethiopia.

Senbeta, T., & W. (2007). Floristic Diversity and Composition of Sheko ForestSouthwest Ethiopia. Ethiopian Journal of Biological Sciences, 6 (1), 11–42.

Tadesse, G., Zavaleta, E., Shennan, C., & FitzSimmons, M. (2014). Policy and demographic factors shape deforestation patterns and socio-ecological processes in South West Ethiopian Cofee Ecosystems. Applied Geography, 54 , 149–159.

Tan, Y., & Yao, F. (2006). Three Gorges Project: Effects of Resettlement on the Environment in the Reserviour Area and Countermeasures. Population and Environment, 27 , 351–371. https://doi.org/10.1007/s11111-006-0027-0

Tesfaye,T.(2004). The Migration, Environment, Conflict Nexus: A Case Study from Easten Wellega. Forum for Social Studies on Poverty No 4, Addis Ababa.

Tolessa,T., Dechassa,C., Simane, B., Alaminrew, B.,& Kidane, M.(2019). Land use/Land Cover dynamics in response to various driving forces in Didessa sub-basin Ethiopia. Geojournal , 85(3), 747-760

Woube, M. (1995). Southward-Northward Resettlement in Ethiopia. Northeast African Studies, 2 (1), 85–106. https://doi.org/10.1353/nas.1995.0001

Yamane, T. (1967). Statistics. Introductory Analysis (2nd ed.). New York.

Yntiso, G. (2003). Resettlement and the unnoticed losers: Impoverishment disasters among the Gumz in Ethiopia. Human Organization, 62 (1), 50–61.

Yonas, B., Beyene, F., Negatu, L., & Angassa, A. (2013). Influence of resettlement on pastoral land use and local livelihoods in southwest Ethiopia. Tropical and Subtropical Agro Ecosystems, 16 (1), 103–117.

Zemenu, A., Bogale, T., & Teferi, M. (2016). Impacts of Resettlement on Woodland Resource Utilization in Abobo Wereda. World Jour of Pharmaceutical and Life Sciences, 2 , 60–77.

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Roba, I.L., Dube, E.E. & Beyene, D.L. Resettlement and its impacts on land use land cover change in Nansebo district, Ethiopia. GeoJournal 87 , 5067–5085 (2022). https://doi.org/10.1007/s10708-021-10551-x

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Accepted : 10 November 2021

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DOI : https://doi.org/10.1007/s10708-021-10551-x

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Research Article

Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, Jiangle, China

Roles Data curation, Formal analysis, Methodology, Software, Validation, Writing – original draft, Writing – review & editing

Affiliation State Forestry Administration Key Laboratory of Forest Resources & Environmental Management, Beijing Forestry University, Beijing, China

Roles Funding acquisition, Investigation, Project administration, Supervision, Visualization, Writing – review & editing

* E-mail: [email protected]

Roles Writing – original draft, Writing – review & editing

• Chen Liping, 
• Sun Yujun, 
• Sajjad Saeed

• Published: July 13, 2018
• https://doi.org/10.1371/journal.pone.0200493
• Reader Comments

Land use and land cover change research has been applied to landslides, erosion, land planning and global change. Based on the CA-Markov model, this study predicts the spatial patterns of land use in 2025 and 2036 based on the dynamic changes in land use patterns using remote sensing and geographic information system. CA-Markov integrates the advantages of cellular automata and Markov chain analysis to predict future land use trends based on studies of land use changes in the past. Based on Landsat 5 TM images from 1992 and 2003 and Landsat 8 OLI images from 2014, this study obtained a land use classification map for each year. Then, the genetic transition probability from 1992 to 2003 was obtained by IDRISI software. Based on the CA-Markov model, a predicted land use map for 2014 was obtained, and it was validated by the actual land use results of 2014 with a Kappa index of 0.8128. Finally, the land use patterns of 2025 and 2036 in Jiangle County were determined. This study can provide suggestions and a basis for urban development planning in Jiangle County.

Citation: Liping C, Yujun S, Saeed S (2018) Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, Jiangle, China. PLoS ONE 13(7): e0200493. https://doi.org/10.1371/journal.pone.0200493

Editor: Andreas Westergaard-Nielsen, University of Copenhagen, DENMARK

Received: November 4, 2017; Accepted: June 27, 2018; Published: July 13, 2018

Copyright: © 2018 Liping et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Remote sensing data is available from the USGS ( http://glovis.usgs.gov ) for free. ASTER GDEM is available from the Geospatial Data Cloud ( http://www.gscloud.cn/ ) for free.

Funding: This study was supported by the Introduce Project of Forest Multifunction Management Science and Technology of Forplan System (No.2015-4-31). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Land use research programs at a global scale have become central to international climate and environmental change research since the launch of land use and land cover (LULC) change project[ 1 ]. LULC has two separate terminologies that are often used interchangeably[ 2 ]. Land cover refers to the biophysical characteristics of earth’s surface, including the distribution of vegetation, water, soil, and other physical features of the land. While land use refers to the way in which land has been used by humans and their habitat, usually with an emphasis on the functional role of land for economic activities[ 3 – 5 ]. For instance, in terms of urbanization, a large amount of agricultural / forestry land has been transformed into urban land, and mining activities / oil exploitation have occurred worldwide to meet the demands of people and can directly and obviously lead to the LUCC[ 6 , 7 ]. In past studies, global environmental changes such as emissions of greenhouse gases, global climate change, loss of biodiversity, and loss of soil resources have been closely linked to LULC changes[ 8 ]. Land use and land cover change (LULCC) is the conversion of different land use types and is the result of complex interactions between humans and the physical environment[ 9 ]. LULCC is a major driver of global change and has a significant impact on ecosystem processes, biological cycles and biodiversity[ 7 , 10 , 11 ]. Moreover, LULCC is also closely related to the sustainable development of the social economy[ 12 , 13 ]. Vast areas of the earth’s terrestrial surface have undergone LULCC[ 14 – 16 ]. With rapid economic development, land uses change more rapidly, and the contrast among land use types also increases[ 17 ].

Various techniques of LULC change detection analysis were discussed by Lu et al [ 18 ]. It is possible to establish a model to predict the trends in land uses in a certain period of time through the study of past land use changes, which could provide some basis for scientific and effective land use planning, management and ecological restoration in a study area and guidance for regional socio-economic development. Therefore, accurate and up-to-date land cover change information is necessary for understanding and assessing LULC changes. Remote sensing (RS) and geographic information system (GIS) are essential tools in obtaining accurate and timely spatial data of land use and land cover, as well as analyzing the changes in a study area[ 19 – 21 ]. Remote sensing images can effectively record land use situations and provide an excellent source of data, from which updated LULC information and changes can be extracted, analyzed and simulated efficiently through certain means[ 22 , 23 ]. Therefore, remote sensing is widely used in the detection and monitoring of land use at different scales[ 24 – 27 ]. GIS provides a flexible environment for collecting, storing, displaying and analyzing digital data necessary for change detection[ 19 , 28 , 29 ].

Land cover change modeling means time interpolation or extrapolation when the modeling exceeds the known period[ 30 ]. Commonly used models for estimating land cover changes are analytical equation-based models[ 31 ], statistical models[ 32 ], evolutionary models[ 33 ], cellular models[ 34 ], Markov models[ 35 ], hybrid models[ 36 ], expert system models[ 37 ] and multi-agent models[ 38 ]. At present, the most widely used models in land use change monitoring and prediction are cellular and agent-based models or the mixed model based on these two types of models[ 39 – 42 ]. The Markov chain and Cellular Automata (CA-Markov) model, one of a mixed models, is the hybrid of the Cellular Automata and Markov models. This model effectively combines the advantages of the long-term predictions of the Markov model and the ability of the Cellular Automata (CA) model to simulate the spatial variation in a complex system, and this mixed model can effectively simulate land cover change[ 43 ]. A CA model is a dynamic model with local interactions that reflect the evolution of a system, where space and time are considered as discrete units, and space is often represented as a regular lattice of two dimensions[ 44 ]. CA-based models have a strong ability to represent nonlinear, spatial and stochastic processes. However, CA model cannot represent macro-scale social, economic and cultural driving forces that influence urban expansion well. Thus, an integration of agents into CA models results in the improved CA-Markov model[ 40 ]. In the Markov model, the change in an area is summarized by a series of transition probabilities from one state to another over a specified period of time. These probabilities can be subsequently used to predict the land use properties at specific future time points[ 45 ]. The use of the CA-Markov model in LULCC studies has advantages such as its dynamic simulation capability; high efficiency with data, scarcity and simple calibration; and ability to simulate multiple land covers and complex patterns[ 17 , 46 ]. Many researchers have applied the CA-Markov model to monitor land use and landscape changes and predictions[ 23 , 36 , 47 , 48 ]. Therefore, we adopted this method to obtain reliable results for Jiangle. In this study, the 2025 and 2036 LULCs were predicted based on the state of 2003 and 2014 LULCs.

In recent decades, rapid population migration from rural to urban regions and improved economic conditions in China have resulted in unprecedented LULC changes and urban expansion rates[ 6 ]. Drivers of urbanization and changes in urban planning should be taken into account[ 48 ]. Many studies have focused on the LULCC at the scale of large cities to provinces in terms of surface runoff, urban impervious surface, surface urban heat islands, etc.[ 1 , 6 , 8 , 49 , 50 ]. While there are few studies on small cities such as Jiangle, a county that is west of Fujian Province. Fujian Province, one of the most economically developed provinces in China and located in the southeastern hilly area, plays an important role in China. Moreover, Fujian Province is the core area of the “the Belt and Road Initiative” policy[ 51 ]. Jiangle is the representative county of the hilly area and owns a state-owned forest farm. In the “National Wood Strategic Reserve Production Base Construction Plan (2013–2020)”, Jiangle is one of the bases. Therefore, understanding the LULC in this area and predicting future LULC can be of great importance.

This study seeks to utilize remotely sensed data and GIS tools to analyze the LULCC in Jiangle County in Fujian, China for the purpose of detecting changes in the area by comparing images between two years. Based on the Markov model, the transfer probability was established based on the data from 1992 and 2003, and the predicted data of 2014 was processed using the transfer probability and suitability maps in the CA model. After validation, the land use and land cover in 2025 and 2036 were predicted. Finally, a scientific basis for decision-making for the region's ecological protection and optimal allocation of resources is provided.

Jiangle, located in the western part of Fujian Province, has a latitude between "26°25’ 31”~27°4’8” N and a longitude between 117°5’2”~117°39’56”E ( Fig 1 ). The study area is in the subtropical monsoon climate zone, with marine and continental climate characteristics. The annual average temperature is 18.7°C. The annual average rainfall during 2011 to 2015 was 1802.16 mm, and the frost-free period is approximately 273 days[ 52 ]. The precipitation during April—August accounts for more than 60% of the annual precipitation. The study area is in the middle of the main section of the Wuyi Mountains, with an average elevation of 540 m, and its highest peak in the southwest is Longxi Mountain with a height of 1620 m. The altitude in the center of the study area is lower than the altitude around the edge of the area. The terrain is tilted from northwest to southeast. The terrain is complex, and 90% of the region is the mountain hilly landform. The Jinxi River runs across the county. The main soil type in the study area is red soil. The main vegetation types are natural secondary forest, artificial fir, Pinus massoniana and Phyllostachys pubescens .

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https://doi.org/10.1371/journal.pone.0200493.g001

Data collection and research methods

Fig 2 illustrates the framework of the prediction process. The major steps include (1) data preparation; (2) determination of the classification results of three years; and (3) the application of the CA-Markov model to obtain the predictions the LULCs in the years 2025 and 2036.

https://doi.org/10.1371/journal.pone.0200493.g002

Data collection

In this study, Landsat satellite remote sensing images from 1992, 2003 and 2014 are used, with a resolution of 30 m and track numbers of 120 / 41, 120 / 42. The detailed data are shown in Table 1 . Documents such as the "Land use status classification" from the national standards and "Fujian Environmental Bulletin" and "Fujian Statistical Yearbook" are used.

https://doi.org/10.1371/journal.pone.0200493.t001

Remote sensing image preprocessing and accuracy verification

The remote sensing image data of the years 1992, 2003 and 2014 were radially calibrated and atmospherically corrected. The relative geometric corrections of the three images were conducted to remove geometric distortion caused by the sensor or the Earth rotation. Due to the differences between the TM and OLI sensors, the geometric correction of the year 2002 was based on the DEM data of the study area. Then, the 1992 and 2014 images were georeferenced to the 2002 image[ 48 ]. The errors were less than 1 pixel. Finally, terrain correction and image stitching were conducted. Considering the "China Land Classification System" and the goal of this study, the land use types were divided into five categories: farmland (including dry land and paddy field), woodland (the forest area), water, construction land (including settlements and roads) and bare land. Based on Google Earth images, Forest Management Inventory data and Landsat data of different periods, the training samples and the validation data of different periods were selected. With the help of the maximum likelihood method, classification was carried out on these three images. Precision testing was conducted using the Kappa index and the overall accuracy for the classification[ 53 , 54 ]. Image processing was based on the UTM WGS 1984 (50N) projection system. The software ENVI 5.1 and ArcGIS 10.2 were used.

Prediction of future LULC dynamics

Markov chain analysis..

Cellular automata model.

CA-Markov model.

There are no spatial variables in the Markov model, while the status for cells in the CA model is closely related to the spatial variables. The CA-Markov model integrates the CA model's ability to simulate the spatial variation in complex systems and the advantages of the long-term predictions of the Markov model. The Markov chain model component controls the temporal dynamics among the LULC classes based on transition probabilities, while the spatial dynamics are controlled by local rules determined either by the CA spatial filter or transition potential maps. The transition probabilities matrix produced by the Markov chain model is one of the inputs of the CA model[ 23 ]. The CA Markov model effectively combines the advantages of the Markov model and the CA model. The spatial prediction accuracy can be effectively simulated at the same time[ 60 ]. The process of prediction with the CA-Markov model is 1) building the suitability atlas based on the MCE, 2) generating the transfer matrix and the state transition probability matrix using the Markov model, and 3) predicting the future LULC using the CA model.

Suitability maps preparation.

To use Cellular Automata, a suitability atlas for all the classes is considered as a prerequirement[ 17 ]. The suitability atlas contains a series of suitability maps, which are usually built through the multi-criteria evaluation (MCE). The basic point of MCE is to integrate different rules to derive a single index of evaluation[ 61 ]. The MCE includes two parts: the constraints (the hard rules) and the factors (the soft rules). The constraints are the criteria that limit the expansion of classes. The constraints are expressed in the form of Boolean maps where the areas that are not suitable will be set a value of 0, while the suitable areas will be set a value of 1. The factors give a degree of suitability for an area to change (mostly on distance basis) [ 62 ]. The process of data preparation is outlined in Fig 2 . There are 3 steps: (1) Identification and development of the criteria, (2) standardization of the criteria and (3) aggregation of the criteria to obtain the suitability map for each class.

Due to the complexity of the terrain, social development and the Soil and Water Conservation Work Regulations, the existing slope, road, construction area, water are adopted to build the rules. The water and construction area were derived from the LULC maps. The road was downloaded from the OpenStreetMap and was checked according to the image data of each year. The slope was derived from the DEM data of study area with a resolution of 30×30 m. Factor and constraint images were first prepared in ArcGIS 10.0. Then, the images were imported into the IDRISI 17.0 for further processing. Using the Decision Wizard module in IDRISI, the suitability maps (Water, Construction, Bare land, Woodland and Farm land) were derived ( Fig 3 ). First, the constraints for the year 2014 were standardized into Boolean maps. Here, we utilized two constraints, the water and existing construction, because no changes can take place in the waterbodies and existing construction areas. Second, the Fuzzy function combined with the Weighted Linear Combination (WLC) was used to process the standard factors. During the standardization, the factors were stretched from 0 to 255 using different fuzzy functions and control points. For different type of factors, the fuzzy functions can be Sigmoidal, J-shaped and Linear, with monotonically increasing/decreasing or symmetric. For the control points, we set them according to the statistical results or regulations from the government. The weights of the factors were derived with the AHP function in the WLC module. Third, the suitability map of a certain class was processed in the MCE module with the constraints, factors, and weights. Finally, the suitability atlas was obtained using Collection Editor in IDRISI.

Construction (b), Bare land (c), Woodland (d), Farmland(e) and Water (a) are suitability maps. Slope (f), Elevation (g), Road (h) and Construction (i) are the input data.

https://doi.org/10.1371/journal.pone.0200493.g003

LULC change prediction.

In this study, the cell is the image grid cell, the unit size is 30×30 mm, and the whole land use spatial pattern is the cell space. The interval time is 11 years, so the number of cycles for the cellular automaton is 11. First, the land use transfer matrix and state transition probability matrix from 1992 to 2003 and 2003 to 2014 are calculated using the Markov module of IDRISI 17.0. Based on the MCE suitability map, the multi-objective decision-making module (multicriteria evaluation, MCE) in IDRISI is used to determine the suitability[ 63 ]. After comparing the results derived under the 3×3 filter with the 5×5 filter in CA model process, we adopted the 5×5 filter, meaning that the change in status for a central cell will be affected by the 5×5 neighbor cells. Finally, the land use predictions for 2025 and 2036 based on the data in 2003 and 2014 were carried out using the CA-Markov module integrated in IDRISI.

CA-Markov model validation.

Model calibration and validation is an important step in the process of model prediction. The usefulness of a model depends on the output of the validation model. The Kappa index is one of the most popular used methods for quantifying the predictive power of a model[ 23 ]. That is to compare the predicted data with reference data using the VALIDATE module. Using the CROSSTAB module in IDRISI, the predicted LULC of 2014 is compared with the 2014 observed results to obtain the Kappa index. When the Kappa index is acceptable, the land use and land cover in 2025 and 2036 can be predicted.

Analysis on dynamic change rate of land use

The rate of land use change reflects the severity of land use change in the study area in a given time period. The standard of measurement is divided into a single land use dynamic degree and a comprehensive land use dynamic degree[ 64 – 66 ]. In this study, we adopt a single land use dynamic degree and a comprehensive land use dynamic degree. In addition, a spatial analysis model of land use dynamic change on the basis of the dynamic degree proposed by Liu Shenghe and Shu Jin is also used to compare the differences between these two dynamic evaluation methods[ 67 ]. The formulas are as follows:

Results and discussion

Results of classification and analysis.

Classification results of the preprocessed images in 1992, 2003 and 2014 are presented in Fig 4 . According to the classification results, the statistics for the three years of different types of land use areas and their proportions are shown in Table 2 . It can be drawn from Table 2 and Fig 4 that the area of woodland is the largest in the study area. The woodland area are 2012.78 km 2 , 2020.76 km 2, and 1997.88 km 2 in 1992, 2003 and 2014, respectively. Construction land increases gradually during the 22 years from 1992 to 2014, which is characteristic of the Chinese urbanization process. The water area shows a trend of decreasing first and then increasing. According to the images of the years and relevant data, water bodies were developed and dredged. Substantial sand excavation equipment existed in the early stage and sediment built up. The river width was narrower in 2014 than 1992, so the water area decreased sharply. Woodland and bare land changes correlate with urban expansion and wood cutting. The Jiangle state-owned forest farm is dominated by Chinese Fir, which is cut and planted for a fixed period.

https://doi.org/10.1371/journal.pone.0200493.g004

https://doi.org/10.1371/journal.pone.0200493.t002

In this study, 163 polygons for Landsat TM and 172 polygons for Landsat OLI are randomly selected to assess classification accuracy. The validation data are randomly and manually chosen based on Google Maps and the Forest Management Inventory. Table 3 contains the evaluation results of the three periods of images. Producer’s accuracy and user’s accuracy are obtained by a confusion matrix. Overall classification accuracy in 1992, 2003 and 2014 are 94.94%, 92.12% and 92.33%, respectively, with Kappa indexes of 0.9254, 0.8964 and 0.8746, respectively.

https://doi.org/10.1371/journal.pone.0200493.t003

Analysis of land use change

Fig 5 is the schematic diagram of each land use in the periods of 1992–2003 ( Fig 5(A) ), 2003–2014 ( Fig 5(B) ) and 1992–2014 ( Fig 5(C) ), and the diagrams show the increase and decrease of each land use. Table 4 is the statistical table of land use change in Jiangle. Considering Fig 5 and Table 4 , it can be concluded that land use change is obvious in the three periods. Fig 6 is a sketch map of the increases and decreases in different land use types from 1992 to 2014. For a certain land use and land cover type, the green cells mean during that period, the LULC type of a cell is transferred from another LULC type into that specific LULC type. In contrast, the red cells mean that the certain land use type of LULC transferred out to other types. Fig 7 demonstrates the mutual transformation of different land use types from 1992 to 2014. In Fig 7 , each color represents one kind of transformation.

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https://doi.org/10.1371/journal.pone.0200493.g006

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https://doi.org/10.1371/journal.pone.0200493.t004

From 1992–2003, the net changes in water area and farmland area are relatively large, at more than 30%. The amount of water area and farmland area that converts to other land use types are 6.32 km 2 and 30.20 km 2 , accounting for 44.58% and 53.50%, respectively. The percent of other land use types that transfer into water and farmland are 19.35% and 32.77%, respectively. According to the remote sensing images and the material, sand excavation and mine exploration existed around Jinxi River, which flowed through Jiangle County; therefore, Jinxi River became narrower. Farmland transfers into construction land and bare land under the affection of urbanization process. According to the Table 4 , construction land area has a net increase of 9.21 km 2 , accounting for 17.05%. In addition, the percent of this construction land that transfers to other land use types is 57.55%, and the amount of area that transfers into construction land is 63.74%. The net increase in bare land is 19.33 km 2 , accounting for 27.08% of the area, and the percent of area that transfers into and from bare land are 76.26% and 69.93%, respectively. The net increase in area of woodland is 7.98 km 2 , accounting for 0.4% of the area. The amount of area that transfers into and from woodland are 3.26% and 2.87%, respectively. Because of forest maturity and economy development, the increasing percent of the bare land area is relatively high in this period.

Of the area that changes during 2003 to 2014, water has the largest change, at 45.34%, with a net increase in area of 6.29 km 2 . The percent of area that transfers from water into other land use type is 9.42%, and the percent of area that transfers into water is 37.68%. According to Jinagle County annals, the water area increases sharply because of a series of improvements, sand cleaning, a decrease in sand excavation, etc., which were part of dredging the Jinxi River. The area of farmland increases by 28.93% compared with that of 2003, with a net area increase of 19.6 km 2 . The percent of area that transfers into farmland and from farmland are 65.08% and 55.08%, respectively. Construction land area increased slowly, with a net increase of 5.86 km 2 , accounting for 9.27%. The percent of area that transfers into and from construction land are 56.31% and 55.08%, respectively. The area of bare land and woodland both decrease. Bare land area decreases 8.86 km 2 , accounting for 9.77%. The percent of area that transfers into and from bare land area are 66.80% and 70.08%, respectively. Woodland area decreases 22.88 km 2 , accounting for 1.13%. The percent of area that transfers into and from woodland area are 2.72% and 3.81%, respectively.

According to the changes from 1992 to 2014, the water area is flat, and the changes from 1992 to 2003 and from 2003 to 2014 demonstrate dynamic changes due to improvements to the Jinxi River. Generally, the construction area increased from 54.01 km 2 to 69.08 km 2 , accounting for 27.09% of the increasing area. The percent of the area that transfers into and from the construction are 66.06% and 56.57%, respectively. The bare land area increases from 71.36 km 2 to 81.83 km 2 , accounting for 14.67%, and the percent of area that changed into and from bare land are 87.72% and 85.98%, respectively, and these changes are the result of economic development and forest cutting. The farmland area decreases from 97.94 km 2 to 87.34 km 2 , accounting for 10.82% of the decreasing area. The percent of area that transfers into and from farmland area are 54.13% and 59.18%, respectively, and these changes are the comprehensive results of forest cutting and economic development. The percent of the woodland area changed little at 0.74%.

Analysis of change rate between two different models

Table 5 and Table 6 are the statistical tables of the land use dynamic changes in Jiangle in 1992–2003 and 2003–2014. The fastest change rate from 1992 to 2003 is bare land, which is 15.17 km 2 .a -1 , and its transfer rate and gain rate are 6.36 km 2 .a -1 and 8.82 km 2 .a -1 , respectively. The bare land dynamic index is 6.36 km 2 .a -1 . The second fastest change rate is construction land area, which is 12.01 km 2 .a -1 . Construction land area transfer rate and gain rate are 5.23 km 2 .a -1 and 6.78 km 2 .a -1 , respectively, and the construction land dynamic rate is 5.23 km 2 .a -1 . Change rates for water and farmland are low, namely, 5.26 km 2 .a -1 and 6.92 km 2 .a -1 , respectively. However, their dynamic rates are the highest, namely, 4.05 km 2 .a -1 and 4.86 km 2 .a -1 , respectively. The change rate and dynamic rate of woodland are lowest, namely, 0.56 km 2 .a -1 and 0.26 km 2 .a -1 , respectively. For the period of 2003–2014, farmland has the fastest change rate, which is 12.64 km 2 .a -1 , and its transfer rate and gain rate are 5.01 km 2 .a -1 and 7.64 km 2 .a -1 , respectively. The farmland dynamic rate is 5.01 km 2 .a -1 . The next fastest change rate is for bare land, which is 11.86 km 2 .a -1 . The transfer rate and gain rate of bare land are 6.37 km 2 .a -1 and 5.48 km 2 .a -1 , respectively. The bare land dynamic rate is 6.37 km 2 .a -1 . The construction land change rate is 10.34 km 2 .a -1 , and its dynamic rate is 4.75 km 2 .a -1 . The change rates of water and woodland are lower, which are 5.84 km 2 .a -1 and 0.59 km 2 .a -1 , respectively. Their dynamic rates are 0.86 km 2 .a -1 and 0.35 km 2 .a -1 , respectively.

The following conclusions can be drawn: the rate of change is larger than the dynamic rate. During these two periods, the transfer rate, gain rate, rate of change and the dynamic rate are all relatively large, meaning that the LULC change is intense. However, the rate of change is significantly larger than dynamic rate, so the single land use dynamic rate cannot properly describe the dynamic changes of the LULCC. For the whole area, the dynamic rate is the same as the rate of change.

https://doi.org/10.1371/journal.pone.0200493.t005

https://doi.org/10.1371/journal.pone.0200493.t006

Analysis of land use change matrices

Table 7 and Table 8 are the land use change matrix and transfer matrix, respectively. Markov’s transfer matrix reveals different types of transfer probabilities while quantitatively demonstrating the land use transfer process. The rows of the table signify the land use status and transferring out situation in the preliminary state t1 of land use change, while the columns of the table represent the land use status and transferring in situation in the final state. As is shown in the tables, the highest proportion of net increase in area is bare land, whose net increase area is 19.33 km 2 , and its net increase area accounts for 27.08%. The main reason for the increase is the transfer from woodland and farmland, and the amounts of their transfer areas are 35.51 km 2 and 23.93 km 2 , respectively, with state transition probabilities of 0.1065 and 0.2761, respectively. The second highest proportion of net increase in area is construction, which accounts for 17.05% of the net increasing area, with a net increased area of 9.21 km 2 . The net increase area of construction land is mainly from woodland transfer-in and farmland transfer-in, and their transfer-in areas are 19.61 km 2 and 12.99 km 2 , respectively, with state transition probabilities of 0.2263 and 0.0393, respectively. The woodland net increase area is 7.98 km 2 , and it has the lowest net increase percent at 0.40%. The woodland net increase area is mainly from bare land transfer-in, whose transfer-in area and transfer probability are 43.37 km 2 and 0.6472, respectively. Water and farmland areas both decrease. Water area decreased 6.32 km 2 and has a decrease proportion of 31.29%. Water mainly transfers into construction land, whose transfer-out area is 4.52 km 2 with a probability of 0.0266. Farmland transfer-out area is 30.20 km 2 , accounting for 30.83%. Farmland mainly transfers into bare land and construction land, whose areas are 23.93 km 2 and 19.6 km 2 , respectively, with state transition probabilities of 0.1065 and 0.0393, respectively.

https://doi.org/10.1371/journal.pone.0200493.t007

https://doi.org/10.1371/journal.pone.0200493.t008

Table 9 and Table 10 are the land use change transfer matrix and transition probability matrix, respectively. The preliminary stage and the final state of changes are 2003 and 2014 with an 11-year interval. As can be seen from the table, water has the highest net area percent for the land use changes during 2003–2014. The net increase area is 6.29 km 2 , with a proportion of 45.34%. The net increase area mainly comes from construction, which is 3.77 km 2 , and the state transition probability is 0.0677. The second highest net area percent is farmland, which accounts for 29.93%. The farmland increase area is 19.60 km 2 , which mainly comes from woodland and bareland with transferring areas of 26.47 km 2 and 18.27 km 2 , respectively, and with transition probabilities of 0.0628 and 0.2127, respectively. Construction land has a net increase proportion of 9.27% and its net increase area is 5.86 km 2 . Construction land increases mainly come from the transfer-in of woodland and farmland, and their transfer-in areas are 21.14 km 2 and 10.51 km 2 , respectively, with state transition probabilities of 0.0501 and 0.1744, respectively. For the bare land, the net decrease proportion is 9.77%, with a net decrease of 8.86 km 2 . Bare land mainly transfers into woodland, farmland and construction land, with transfer-out areas of 38.57 km 2 , 18.27 km 2 and 6.10 km 2 , respectively, and transition probabilities of 0.4538, 0.2127 and 0.0718, respectively. The woodland net area change proportion is the lowest, with a decrease proportion of 1.13%. However, woodland net area change amount is the highest, at 22.88 km 2 . Woodland mainly transfers into bare land, farmland and construction land, whose areas are 28.01, 26.47 and 21.14 km 2 , respectively. The corresponding transition probabilities are 0.0657, 0.0628 and 0.0501, respectively.

https://doi.org/10.1371/journal.pone.0200493.t009

https://doi.org/10.1371/journal.pone.0200493.t010

Results validation (observed LULC of 2014, simulated LULC of 2014)

The state transition area matrix and state transition probability matrix are created according to land use maps in 1992 and 2003, which can be obtained by running the CA-Markov model in IDRISI software based on the suitability atlas that has already been created. The predictive results map for 2014 is obtained with a 5×5 contiguity filter, whose running cycle is 11 years. Fig 8 is the predicted map of 2014. The next step is to use the CROSSTAB module in IDRISI. Predictive results are analyzed by overlaying the land use map of 2014 that is truly classified. Commonly, if the Kappa index is less than or equal to 0.4, then the land uses changed greatly and with poor consistency between the two images. If the Kappa index is 0.4–0.75, then there are general consistencies and obvious changes between the two images. Otherwise, there is high consistency between two images[ 58 ]. Usually, the Kappa values range from 0 to 1. Values of 0.61–0.80 means substantial, while 0.81–1 means almost perfect[ 68 , 69 ]. Here, the Kappa index between the predicted map and the observed map of 2014 is 0.8128, which is above 0.75, illustrating that the results are reliable. There is high consistency between the actual observed results and predictive results. The precision for correct predictions is relatively high; therefore, this method can be used to predict the results in 2025 and 2036.

https://doi.org/10.1371/journal.pone.0200493.g008

Results prediction

The state transition area matrix and state transition probability matrix are created by land use maps for 2003 and 2014, and the results for 2025 and 2036 are predicted by the same method ( Fig 9 ). For the prediction of 2025, the interval time is 11 years, while for the year of 2036, the interval time is 22 years. Table 11 is the statistical table based on the predictive results in 2025 and 2036. In general, in 2025 and 2036, the woodland area decreased greatly, and the remaining land area increased by a certain amount, especially in 2036, and the area of woodland decreased significantly. Therefore, to consider ecology, the protection of woodland is necessary in planning.

Predicted results of 2025 (left) and 2036 (right).

https://doi.org/10.1371/journal.pone.0200493.g009

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In recent years, with economic development and the impact of human activities, the county's land use has experienced substantial changes since the 1990s. In this study, Landsat5 TM and Landsat8 OLI image data were used to obtain land use maps for 1992, 2003 and 2014. Then, the land use structure of the study area was simulated and predicted based on the CA-Markov model.

According to the results of the classification, the forest coverage rate of Jiangle County was high, and the forest areas in 1992, 2003 and 2014 were 2012.78, 2020.76 and 977.88 km 2 , respectively. Construction land increased from 1992 to 2014 year by year. Water, bare land, and farmland area changes were closely related to human activities.

Under the influence of human activities, the land use changes in Jiangle County from 1992 to 2014 were obvious. The water area decreased first and then increased. In 1992–2003, a large amount of sand mining equipment was built in the Jinxi River, and a large amount of sediment was deposited on the river bank, so that the water area was drastically reduced. In 2003–2014, river sand mining equipment had reduced significantly, and the river was cleared, which led to the gradual restoration of the water area. The woodland area is large in size, although the changed area is large, and the proportion is small. Changes in the woodland area are mainly related to timber harvesting and urban expansion. The results showed that in 2025 and 2036, the area of woodland decreased drastically. Taking into account of the ecological functions of woodland, we should pay attention to the amount of woodland harvest in planning.

In the experimental process, there were some points that affected the prediction results. First, there were some difficulties in data acquisition due to the location of the study area. In addition, the study area is in the hilly area, where the ground changes in altitude, which has a certain impact on the image pixel value and ultimately leads to inaccuracy of the classification results. Second, the setting of the suitability data set had some influence on the LULC predictions. Finally, there was some human impacts on the land use types, especially the construction land changes. Therefore, through improving the quality of the input data and the setting of related parameters, the accuracy of the predicted LULC scenarios can be increased.

Acknowledgments

The authors thank the editors and the anonymous reviewers for their useful input. In addition, thanks to Sydney M. Greenfield for English language revision and editing.

• View Article
• Google Scholar
• 3. McConnell WJ. Land Change: The Merger of Land Cover and Land use Dynamics A2—Wright, James D. International Encyclopedia of the Social & Behavioral Sciences (Second Edition). Oxford: Elsevier; 2015. p. 220–3.
• 5. Arsanjani JJ. Dynamic Land Use / Cover Change Modelling : Geosimulation and Agent-Based Modelling. Vienna: University of Vienna; 2011.
• PubMed/NCBI

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Monitoring land use land cover transformations and its effects on land surface temperature using geospatial approach in Jharsuguda District, Odisha, India

2024, Ecology, Environment and Conservation

The aim of the work was to analyse Land use land cover (LU/LC) changes and their correlation with the increased Land surface temperature (LST) in Jharsuguda district, Odisha using geospatial techniques and transformation analysis in ArcGIS 10.4 software. Remotely sensed data from Landsat 8 operational land imager (OLI) for March 2013 and Landsat 9 OLI for March 2023 were utilized to investigate LU/LC and LST changes. The satellite data was classified using the maximum likelihood supervised classification algorithm (MLSC) to derive LULC maps. The overall accuracy of these classified LULC maps was determined to be more than 85% in both years. In order to obtain LST information from the satellite images, the spectral radiance model was utilized. The findings of the study revealed a clear correlation between the loss of vegetation cover (VC) and the expansion of built-up areas, which consequently contributed to an increase in the urban heat islands (UHI). The LU/LC estimation indicates substantial changes in the landscape over the past ten years. Specifically, there was a notable net increase in urban area (UA) by 55.12%, while very dense forest (VDF) experienced a reduction of 49.28%, moderately dense forest (MDF) decreased by 18.60%, and open forest (OF) by 42.58% as well as non-forest (NF) by 1.76% between 2013 and 2023. Furthermore, the study observed that the maximum temperature of the city rises from 46.8°C in 2013 to 48.3°C in 2023. So, the municipal authority can take new decision policies and management to reduce the effects of LST for sustainable development in the further future.

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built-up area in the periphery (Chadchan and Shankar, Int J Sustainable Built Environ 1:36–49, 2012; Pandey and Seto, J Environ Manag 148:53–66, 2015). With these physical changes, i.e. decrease in green cover and increase in built-up, the land surface temperature (LST) is bound to increase. The green area is a basic need of any city because it is a must for a healthy life and also maintains the aesthetic and ecological beauty in the urban areas (Low et al. 2007). The present study aims to analyse the association between built-up, green cover and land surface temperature for which district-level analysis of the normalised differential built-up index (NDBI), normalised differential vegetation index (NDVI) and land surface temperature (LST) has been done over the urban area of Delhi. In this study, Landsat 7 (ETM+ SLC) for 2003, Landsat 5 (TM) for 2010, and Landsat 8 (OLI/TIRS) for 2017 have been used together with Survey of India (SOI) toposheet of Delhi at 1:25,000. Indices like NDBI, NDVI and LST are calculated for 2003, 2010 and 2017 using the spectral radiance model (SRM), the mono-window algorithm (MWA) and the split window algorithm (SWA). Thereafter, district-wise NDBI, NDVI and LST are extracted by using clip tools in ArcGIS 10.5 software. To analyse the relationship between built-up and green cover with LST, correlation is done in SPSS software and a scatter diagram is made to assess the correlation amongst the variables. The further surface temperature profile is created to know which part of the Delhi has the highest and lowest temperatures on a particular surface. The study shows that NDVI and LST are negatively correlated with each other as vegetation has a cooling effect on the land surface temperature whereas NDBI and LST are positively correlated with each other. The studies show a change in the distribution of vegetation cover and gradually increase in the built-up land which results in the increase in land surface temperature to about 3.31 °C in the last 14 years. The result shows thatMWA give the most accurate result in this study since RMSE ofMWA is the lowest (0.71 °C) amongst the three algorithms used in the study. Temporal analysis of land surface temperature by all the three algorithms shows the increase in land surface temperature of Delhi between 2003 and 2017.

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Materials and methods, result and discussion, acknowledgements, funding sources, data availability statement, conflict of interest, assessments of the impacts of land use/land cover change on water resources: tana sub-basin, ethiopia.

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Bewuketu Abebe Tesfaw , Bloodless Dzwairo , Dejene Sahlu; Assessments of the impacts of land use/land cover change on water resources: Tana Sub-Basin, Ethiopia. Journal of Water and Climate Change 1 February 2023; 14 (2): 421–441. doi: https://doi.org/10.2166/wcc.2023.303

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Change detecting land use/land cover helps assess and quantify its impact on water resources. This study aims to assess the impacts of land-use/land-cover change on water resources using the SWAT model in the Tana sub-basin, Ethiopia. The research detects and presents the changes between three LULC maps (1986, 2000, and 2014). The results suggest that over the last 28 years, the water body is the least disturbed and sub-afroalpine vegetation is the most transformed in terms of coverage. Cultivated lands gained a large area of cover from the other types. Most of the vegetation cover showed a decreased trend. Forest land and grassland decreased continuously while wetland showed a small variation compared to the other cover types. On the other hand, bush and shrubland recorded about a 1% increase in the total area and an unexpected fast decline in the second period. LULC changes would have an impact on water resources in the study area. The average annual water yield increased by 14.88 and 12.6%, baseflow increased by 18.4% and decreased by 7.16%, surface runoff increased by 12 and 16.16%, evapotranspiration decreased by 18.39 and 13.49%, for 2000 and 2014, respectively, compared to baseline 1986.

The impact of land-use/land-cover change on water resources is assessed in the Tana sub-basin.

The study analysis indicated that the changes in LULC have implicated effects on the water balance components using SWAT.

The approach used has accredited contributions and provides perceptible information that will allow stakeholders and decision-makers to make prominent choices regarding natural resource planning and management.

Graphical Abstract

Land-use changes are altering the hydrologic system and have potentially large impacts on water resources ( Wagner 2014 ). Ethiopia's natural resources are influenced by several interlinked factors such as agricultural expansion, population pressure, rapid urbanization, migration and resettlement, climate change, and environmental pollution ( Wassie 2020 ). There is, therefore, a meaningful change in land use/land cover. Most of the study area population lives in rural areas and their livelihood depends on agriculture ( McCartney et al. 2010 ; Abera et al. 2020 ). As cited in the study of Nile Transboundary Environmental Action Project of Nile Basin Initiative, the author explained that the land-use trend of the Tana sub-basin describes an area that is heavily cultivated and populated ( Alemayhu 2006 ). The expansion of Bahir Dar and other cities in the Amhara Region is putting pressure on natural resources and land with resultant compromises for biodiversity conservation and food security. The status of land-cover and continued land-use changes as a result of uncontrolled land fragmentation and the intensive use of sub-land division and deforestation have long been encouraging massive soil erosion rates almost in all parts of the watershed ( Bogale 2020 ; Tewabe & Fentahun 2020 ). Poor agricultural practices and improper grazing land management, overexploitation of natural resources, and climate change and variability will result in increasing pressure on the limited water resources. In the future, there is significant potential for further socio-economic development based on increased utilization of water in the catchment ( McCartney et al. 2010 ).

Land-use/land-cover (LULC) changes have potentially large impacts on water resources ( Stonestrom et al. 2009 ). The spatial distribution of land cover information is important for different purposes. The trend of LULC of an area can be used as one component in the determination of a planning unit for its actual and potential appraisal of predefined land utilization types. Land-use and -cover maps are frequently used as a tool for natural resources management and urban planning, and they can assist in targeting and prioritizing risk mitigation activities ( Cleve et al. 2008 ). In general, identifying LULC and area can be used as input for viable decisions in resource allocation and sustainable use of the available resources. However, a proper plan is required to ensure that such development is sustainable and does not adversely impact those communities that depend on the natural resources of Lake Tana. Several schemes are under development and planned for the future in the study area. Hence, assessment of LULC and knowing its trend in the study area is fundamental to assessing its impact on the water resources. As Woldesenbet et al. (2017 , 2018 ) showed the change in LULC has an impact on the hydrology components of the Lake Tana basin. It is obvious that changes in LULC such as the expansion of cultivation land, and reduction of bush and shrubland, grassland, and forest land help its response on water balance components such as increased surface runoff, water yield, and reduce evapotranspiration (ET) and baseflow. This could be due to more land being left unprotected by deforestation by natural such as fire and anthropogenic causes such as urbanization, settlement, industry, and cropland, the more it will be prone to erosion and thus increasing the runoff. The LULC of the study area was changed over time, and this could have an impact on water resources. Therefore, it is very crucial to assess and update the impact of LULC change on the water resources in the Tana sub-basin.

This research aimed to assess the impact of LULC change on water resources in the Tana sub-basin using the Soil and Water Assessment Tool (SWAT). The application of the SWAT model in the study area was tested and validated by the scholar Setegn et al. (2009) . The existing LULC and its changes were assessed and the effect of LULC change on the sub-basin water resources was analyzed.

Description of the study area

Location of the Tana Sub-Basin from Ethiopia and Abbay Basin.

Drainage, major watersheds, and slope distribution in the Tana sub-basin . *MG: Megech, RB: Rib, GU: Gumara, AB: Abbay, and GA: Gilgel Abay.

One of the hydrological response units (HRU) in the SWAT model is the slope. The distribution of slope in watersheds will directly or indirectly affect the generated response related to water resources and the runoff derived from the catchment. Accordingly, out of the total drainage area, 63.18% has a slope of 0–8%. The remaining 36.82% of the drainage area has a slope above 8%; out of which 15.65% has a slope of 8–15% ( Figure 2 ).

Hydrological model

The SWAT model was developed by the United States Department of Agricultural (USDA) Agriculture Research Service to model the hydrology of a given watershed ( Arnold et al. 1998 ). SWAT is a widely used tool in the world to evaluate and assess the influences of environmental and ecological alterations and hydrological responses at different watershed scales, even with limited data ( Fu et al. 2009 ; Liu et al. 2018 ). In this study to assess the impact of LULC change on water resources in the Tana sub-basin, the SWAT hydrological model was developed and used. The application of the SWAT model in the study area was tested and the result showed that the model was suitable for the analysis of hydrological response in the Tana sub-basin ( Setegn et al. 2009 ). The SWAT tool requires a digital elevation model (DEM), LULC, and soil data for the delineation of watersheds and generating HRU. ArcGIS 10.3 tool was used to analyze the LULC change in the Tana sub-basin. The areas covered by each LULC type for the various periods were compared. The flow data in the study area were available up to the year 2013 only and used for calibration. Then the directions of the changes in each LULC type between 1986 and 2000, 2000 and 2014, and 1986 and 2014 were determined.

The available data such as weather data (rainfall, maximum and minimum temperature, wind speed, sunshine hour, and relative humidity) were collected and converted into the usable format for the receiving convenient models to be applied in the study; SWAT Calibration and Uncertainty Problems (SWAT-CUP). The quality of time-series data and its outliers were checked. Then, time-series data were organized and converted into a usable format for the model as an input: weather data from 1987 to 2013 and flow data from 1990 to 2013 which were used to calibrate the SWAT model setup.

Data collection and analysis

The summary of LULC assessment: the years 1986, 2000, and 2014

Summary of transformed versus unchanged land use land cover.

Transformed versus unchanged LULC classes: 1986–2000, 2000–2014, and 1986–2014.

The slope data that were derived from the DEM were also reclassified to correspond with the SWAT database requirement and adopted from Food and Agricultural Organization (FAO) system ( Yimer et al. 2006 ; Dagnachew et al. 2020 ). Accordingly, the study area slope was calculated from the DEM and classified into flat to very gently sloping (<3%), gently to sloppy sloping (3–8%), strongly sloping (8–15%), moderately steep (15–30%), and steep to extremely steep (>30%).

LULC change of four major watersheds in the Tana Sub-basin.

Measured weather data for the period from 1987 to 2013 was used. The full data set of daily time-series measured rainfall, maximum and minimum temperature, relative humidity, sunshine hour, and wind speed data were available from Amhara Metrological Agency for six weather stations. The rest of the stations were used to correct the bias of Climate Forecast System Reanalysis (CFSR) rainfall data. Since we have limited meteorological station spatial distribution and a short period of records, this study used additional meteorological data from the CFSR ( https://globalweather.tamu.edu/ ) sourced from National Centers for Environmental Prediction (NCEP). This study, therefore, includes 42 rainfall grid points covering the extent of the Tana sub-basin (one point contains 6 meteorological variables including rainfall, minimum temperature, maximum temperature, relative humidity, wind speed, and solar radiation). These CFSR data were used after bias correcting it using ground observations. Data analysis was done, filling in missed data, data quality check, and interpretation of hydrology and meteorology data were done. This study used the method of arithmetic mean, normal ratio, and linear regression to estimate the missing observation of the station, and these methods were tested and applied in this area by the scholar Mesfin et al. (2021) . The study adopted arithmetic mean techniques to estimate the missing observation data if the annual rainfall data at surrounding gauges are within the range of 10% of the annual rainfall of the considered station, while it exceeds 10% of the normal ratio method applied ( Caldera et al. 2016 ). The linear regression method was applied if the correlation of the annual rainfall of data missing stations with an annual rainfall of the same years at nearby stations is good enough to estimate the missing observation of the station ( Caldera et al. 2016 ). The missed data were computed from records of several stations at the same time. Screening the precipitation data requires careful examination of large time-series files, graphical tools, and standard reports of precipitation data were applied to facilitate this process.

The corrected CFSR data with nearby observed data were tested by performance evaluation criteria such as Nash Sutcliffe efficiency (NSE), coefficient of determination ( R 2 ), and Percent Bias (PBIAS). NSE indicates how well the simulation matches the observation and it ranges from the negative infinitive to 1. The higher the NSE value, the more reliable the model is in comparison to the mean ( Nash & Sutcliffe 1970 ). PBIAS value shows the average tendency of the simulated to their observed data counterparts ( Gupta et al. 1999 ). Positive values indicate an overestimation of observation, while negative values indicate an underestimation. The optimal value of PBIAS is 0.0, with low-magnitude values indicating accurate model simulations. MAE demonstrates the average model prediction error with less sensitivity to large errors. R 2 indicates how much the observed and corrected data fit ( Van Liew et al. 2003 ).

Three stations' rainfall data were used to represent the climate variability of the study area. The daily maximum and minimum air temperature, solar radiation, wind speed, and 1-h rainfall (1-h rainfall is one-third of the daily rainfall of the station) were available for the representative three stations. The statistical parameters for precipitation were computed using the tools of weather generator maker and precipitation (WGNmaker and PCP stat) and then used by the weather generator of the SWAT model. Daily dewpoint was computed using the formula expressed below and for verification, Dew02.exe: was used to calculate it using minimum and maximum daily temperature and the average daily humidity. WGEN user were developed using daily rainfall, daily maximum and minimum temperature, 1-h rainfall, daily solar radiation, daily wind speed, and daily dew point temperature. Bahir Dar, Dangla, and Gonder weather stations in the study area were used to generate weather generator (WGEN) user.

The flow data were collected from Abbay Basin Development Office and used for calibration, and flow data generation for missed values and ungauged stations. Available flow data were used for Gilegl Abbay near Merawi, Megech near Azezo, Rib near Addis Zemen, and Gumara near Bahir Dar from 1987 to 2013. In this study, the flow data were used for model simulation for sensitivity analysis, calibration, and validation.

Three model setups were developed for the years 1986, 2000, and 2014 LULC maps. For the three model setups, all data were the same except for the LULC. The SWAT database files were adapted for local conditions. The model has been implemented in the 69 watersheds/sub-basins. The simulation covered 27 years (from 1987 to 2013) where the first three years (1987–1989) were used as model warm-up periods, 16 years (1990–2005) for calibration, and the last 8 years (2006–2013) for validation. The model was simulated and calibrated at a monthly time scale against observed discharge series at the four stations of the major rivers of the Tana sub-basin namely Gilgel Abbay, Megech, Rib, and Gumara.

Model performance indices/statistical analysis

The model performance is satisfactory and applied for further analysis if the NSE > 0.5 ( Nash & Sutcliffe 1970 ), PBIAS < 25% ( Gupta et al. 1999 ), and RSR ≤ 0.7 ( Moriasi et al. 2007 ) for a monthly time step variable/flow. The values of R 2 greater than 0.5 are considered acceptable based on the previous criteria reported by Santhi et al. (2001) and Van Liew et al. (2003) while Setegn et al. (2009) also stated on the model performance can be judged as satisfactory if R 2 is greater than 0.6 and NSE is greater than 0.5. The value NSE shows the level of reliability of the model in comparison to the mean and the value of R 2 indicates how much the observed and simulated streamflow fit. The comparison between the observed and simulated streamflow indicated that there is a good agreement between the simulated and observed discharge which was verified by lower values of RSR and PBIAS and higher values of R 2 and NSE. RSR ranges from zero to a large positive value. The lower the RSR, the lower the RMSE and the better the model simulation performance.

LULC changes: 1986, 2000, and 2014

The LULC map for 1986, 2000, and 2014 is shown in Figure 5 . In the year 1986, cultivated land accounted for about 36.64% of the study area while water bodies, grassland, and bush/shrubland collectively represented about 56.48%. The built-up area, forest land, Afroalpine, and wetland collectively covered only 6.88% as shown in Table 1 . In the year 2000, cultivated land covered 40.71% of the study area while water body, grassland, and bush/shrubland collectively represented about 55.95% as shown in Table 1 . The built-up area, forest land, Afroalpine, and wetland covered only 3.35% of the Tana sub-basin. For the year 2014, cultivated land accounted 48.82% of the study area while water body, grassland, and bush/shrubland collectively represented about 48.02%. The built-up area, forest land, Afroalpine, and wetland covered only 3.15% of the Tana sub-basin as shown in Table 1 .

Change detection

The LULC of the study area for the last 28 years (1986, 2000, and 2014) were assessed ( Table 1 ). Most of the vegetation covers showed a decrease in the last 28 years. Forestland and grassland have decreased continuously in these years. Sub-afroalpine vegetation showed a dramatic decrease in the second period of assessment. On the other hand, bushes and shrubs recorded about a 1% increase in the total area and an unexpectedly fast decline in the second period. The forest land showed a continuous reduction while water bodies and wetlands showed a small variation as compared to the other cover types in the study area. Waterbody occupies 20.29, 20.37, and 20.62% of the Tana sub-basin in the years 1986, 2000, and 2014, respectively. From the total area of the sub-basin, wetlands decreased to 0.67 from 0.72% in the first assessment period and increased to 0.80% in the second period.

Another good expectation was cultivated land increment. It progressed from 36.64 to 40.71% in the first and then, to 48.82% in the second period of assessment, considering the total area of the sub-basin. The study area is one of the areas in Ethiopia influenced by population pressure dominantly engaged in agriculture ( Abera et al. 2020 ). Therefore, there was continuous pressure on the surrounding vegetation cover in need of expanding the cultivated lands.

Transformed versus unchanged LULC: 1986–2000, 2000–2014, and 1986–2014

Due to the complex influx and the heavy agrarian population pressure in Tana sub-basin, the amount of LULC change was more than 25%. As indicated in Figure 3 , the amount of land cover remain unchanged in the first period (1986–2000) was 73.47% and it was 71.54% in the next period (2000–2014). However, the overall (1986–2014) changes recorded increased a little bit more than 30%, which pushed the unchanged down to 66.06%. In the first period (1986–2000), the water body keeps by far the highest percentage of survival coverage, having 99.7% unchanged. There is also no significant cover transformation on cultivated land. More than half of bushes, shrubs, and grasslands remain unchanged in this period. On the other hand, sub-afroalpine vegetation (4.09%) followed by built-up areas (8.24%) have the lowest unchanged coverage. In other words, these land covers are the most disturbing cover types and strongly transformed into other cover types in the last 28 years.

In the second period (2000–2014), still water bodies preserved 99.7% of coverage in the first period, 99.47% in the second, and 99.87% in the whole assessment years. Similarly, built-up areas were also the least maintained (9.24%) cover type in the period. Wetlands were the second least preserved; only maintain 24.65% of their previous wetland areas ( Figure 4 ).

Despite the increased pressure of cultivated land expansion, the Tana sub-basin still has a large area of grassland (grazing land) coverage. Grassland is the fourth largest in the amount of coverage with 1,824.30 km 2 followed by cultivation land, water body, and bush, respectively. The recent conservation and rehabilitation program of the government resulted in significant coverage of bushes and shrubs inland, and the current socio-economic pressures may influence it for the last year of assessments. The built-up areas of the study area are highly transformed and converted to other land cover types ( Figure 4 ). However, the transition statistics of the built-up area are not a good indicator of the change expected actually on the ground.

Over the last 28 years, the waterbody, including Lake Tana, has been the least disturbed in terms of coverage. On the other hand, sub-afroalpine vegetation is the most transformed cover. Cultivated lands gained a large area of cover from the other types.

LULC change of four major watersheds

Percentage of unchanged and transformed LULC for major Tana sub-basin watershed

Afroalpine, wetland, and forest land were the most highly disturbed cover types and strongly transformed to other cover types in the last 28 years and decreased their coverage whereas cultivated land increased over time in all watersheds. Bush and shrubland coverage decreased within 28 years for the major watersheds while it increased for Gilgel Abbay. Grassland increased for Gumara and Rib watersheds and decreased for Gilgel Abbay and Megech watersheds (1986–2000). There was also no significant cover conversion observed on the water body as all eight major LULC types ( Figure 5 ). This transformed LULC shows a significant impact on the water balance components ( Figures 8 , 9 10 – 11 ).

Sensitivity analysis, calibration, and validation

SWAT streamflow sensitive parameters and fitted values after calibration for four rivers

The symbol R indicates multiple the existing values, A add on existing values, and V replaces the existing values.

Performance of simulated versus observed flow for calibration and validation periods

Streamflow hydrographs of watersheds in the Tana sub-basin during calibration and validation: (a) Rib (b) Gumara, (c) Megech, and (d) Gilgel Abay.

In the calibration period, the monthly flow hydrograph of observed and simulated streamflow for the Tana sub-basin major watersheds showed that the simulated streamflow replicated the observed streamflow ( Figure 6 ). In the streamflow hydrographs, the model overestimated the observed streamflow for the calibration period except for the Megech watershed. In addition to this, peaks and baseflow of the hydrographs were not well predicted compared to the rising and falling limb. The different trend observed during the calibration and validation period for the Megech watershed indicates that there are uncertainties in simulated flow due to errors in input data such as temperature and rainfall, errors in the type of soil and the corresponding soil characteristics such as infiltration capacity, and/or other unknown activities in the watershed. As the model does not simulate certain input data, the predictions can be uncertain. This result is more or less similar to other hydrological studies done by Setegn et al. (2009) .

As compared to the calibration period, the observed streamflow was not replicated by the simulated flow for the validation period. However, the result was satisfactory to use the calibrated model for further analysis. The hydrograph of the Megech and Gilgel Abbay watershed during the validation period showed that the observed streamflow was poorly replicated by the simulated one. This is similar to the previous studies done on the Megech watershed by Halefom et al. (2018) and on the Gilgel Abay watershed ( Worqlul et al. 2018 ).

Impacts of LULC change on water resources

The LULC changes during the past 28 years (1986, 2000, and 2014) in the Tana sub-basins are shown in Table 1 . The assessment of LULC maps for the years 1986, 2000, and 2014 indicates that the most significant changes occurred in LULC classes in the Tana sub-basins, namely built-up area, grassland, Afroalpine, bush and shrubland, cultivated land, and forest land. Cultivation land expanded continuously while forest land and grassland declined throughout the study period of the study area. This study shows the change in land use/land cover has an impact on the water resources in the study area. The study considered baseflow (BF) as the sum of lateral flow and groundwater contribution to the streamflow for further analysis.

Proportional LULC extent, changes of LULCs, the annual average value of hydrological components, and changes in hydrological components for the Tana sub-basin

ET, Evapotranspiration; SURF, Surface runoff; WYLD, Water yield and BF, Baseflow.

Monthly average response of evapotranspiration, surface runoff, water yield, and baseflow to the different LULC scenarios and the change between each scenario.

Monthly average response of evapotranspiration to different LULC scenarios (a) Megech, (b) Rib, (c) Gumara, and (d) Gilgel Abbay.

Monthly average response of surface runoff to different LULC scenarios (a) Megech, (b) Rib, (c) Gumara, and (d) Gilgel Abbay.

Monthly average response of water yield to different LULC scenarios (a) Megech, (b) Rib, (c) Gumara, and (d) Gilgel Abbay.

The monthly average response of baseflow to different LULC scenarios (a) Megech, (b) Rib, (c) Gumara, and (d) Gilgel Abbay.

The change in the average monthly ET, SURQ, WYLD, and BF of the study area could be mainly ascribed to the LULC changes from 1986 to 2014 ( Table 1 ; Figure 7 ). It is obvious that the expansion of cultivation land and reduction of bush and shrubland, grassland, and forest help increase surface runoff, and water yield, and reduce ET and baseflow. This could be because more and more land is left unprotected by deforestation by natural such as fire and anthropogenic causes such as urbanization, settlement, industry, and cropland, the more it will be prone to erosion and thus increasing the runoff. As Woldesenbet et al. (2017) stated very strong positive correlation with the Pearson correlation factor was observed between the proportional extent of cultivation land and surface runoff components. On the other hand, strong negative correlations were found between shrub and surface runoff components. In contrast to the surface runoff component, the groundwater component is strongly negatively correlated with the expansion of cultivation and is strongly positively correlated with the percentage of bush and shrubland ( Woldesenbet et al. 2017 ). This study also showed similar increments in average monthly and annual surface runoff due to the expansion of cultivation land and reduction of shrubland within 27 years whereas baseflow declined on average annual values from 1986 to 2000 and increment from 2000 to 2014. Increased average monthly/annual sub-basin surface runoff associated with expanding cultivation areas probably is due to the decrease in the infiltration of soil ( Franczyk & Chang 2009 ). The increase in surface runoff and water yield in the study area corresponds to sub-basins with a reduction in forest cover and shows an effect on ET. High forest cover will respond to a high rate of transpiration, and this will increase the value of ET while more or less undisturbed water bodies of the sub-basin do not show significant changes in evaporation. The rate of transpiration within the forest will decline due to the reduction of forest cover in the study area. Due to decreasing transpiration rates related to forest cover reduction and unchanged water bodies, ET values will decrease over time in the study area, Tana sub-basin. This was affirmed by the high negative correlation between open forests, surface runoff, and water yield ( Awotwi et al. 2019 ). An association between the decrease in ET and cultivation expansion from 1986 to 2014 can be inferred from the comparison between the variations of average monthly/annual sub-basin ET and changes in LULC from 1986 to 2014. As shown in Table 5 , ET decreased from 1986 to 2014, and baseflow increased from 1986 to 2014 and decreased from 2000 to 2014.

In the Tana sub-basin, the expansion of cultivation land replaced the grassland and/or bush and shrubland. Cultivation land decreases soil infiltration rate/percolation/baseflow and increases surface runoff compared to grassland and shrubland. As an expansion of cultivation land, the change in grassland and bush and shrub can also change the water balance component of the basin. Further comparison between changes in water yield and changes in LULCs in the Tana sub-basin ( Table 5 ) indicates that the increase in water yield from 1986 to 2014 is due to the gradual increase in cultivation land and a simultaneous decrease in the bush and shrubland. This study shows that the LULC change has significant impacts on infiltration rates, runoff production, total simulation flow, interflow, base flow, water yield, ET, and water retention capacity of the soil or change in storage of the soil; hence, it affects the water balance of the study area.

Impact of LULC change on ET of major watersheds

The average monthly values of ET simulated from each LULC map and its changes between each scenario for four major watersheds of the Tana sub-basin are shown in Figure 8 . ET values increased with time in each watershed except for Megech. The average monthly ET values were high from June to September (wet season) of the year. The average annual ET was 9.6 and 24.45 mm in 2000 and 2014, respectively (i.e. decreased by 5.42 and 2.13%) for Megech, 18.41 mm lower in 2000, 367.9 mm above in 2014 (i.e. decreased by 3.39% and increased by 67.76%) for Rib, 90.67 and 123.36 mm above in 2000 and 2014, respectively (i.e. increased by 16.71 and 22.73%) for Gumara, and 39.39 and 46.86 mm above in 2000 and 2014, respectively (i.e. increased by 5.71 and 6.79%) for Gilgel Abbay as compared to the baseline year of 1986. This study shows as the variation of LULC changes has a significant impact on the ET of the study area as shown in Figure 8 . In the watersheds, significant expansion of cultivation land and reduction of forest and shrub land coverage, and relatively unchanged water bodies were observed and could cause decreasing the amount of ET because of transpiration declines. There will be a change in the leaf area index (LAI). Therefore, changes in LAI can lead to changes in ET, which controls the soil moisture content. Additionally, vegetation growth with taller canopy height and more abundant leaves becomes possible when sufficient soil moisture is available ( Wang & Qu 2009 ).

Impact of LULC change on surface runoff of major watersheds

Average monthly values of surface runoff simulated from each LULC map and its changes within the three scenarios for the four major watersheds of the Tana sub-basin are shown in Figure 9 . Surface runoff increased with time for four major watersheds and the response was significantly high in the wet season (June to September) of the year while in the dry season, there was an increment, but the values were small ( Figure 9 ). The average annual surface runoff was 17.13 mm lower in 2000, 31.76 mm above in 2014 (i.e. decreased by 9.63% and increased by 15.99%), 41.22 mm above in 2000, 239.82 mm above in 2014 (i.e. increased by 16.08 and 93.54%), 38.98 mm above in 2000, 49.32 mm above in 2014 (i.e. increased by 8.74 and 11.05%), and 132.42 mm above in 2000, 180.91 mm above in 2014 (i.e. increased by 15.14 and 20.69%) as compared to the baseline year of 1986 for Megech, Rib, Gumara, and Gilgel Abbay, respectively. Continuous increment of surface runoff was observed in all scenarios except for Megech. From 1986 to 2000, there was a reduction in surface runoff value observed and for the whole period, the value averagely increased. This may be uncertainties due to errors in input data such as temperature and rainfall, errors in the type of soil and the corresponding soil characteristics such as infiltration capacity, and/or other unknown activities in the watershed. In the Tana sub-basin, the average annual surface runoff also increased by 12 and 16.16% in 2000 and 2014, respectively. As Figure 5 shows, cultivation lands were expanding, and shrub and forest land were reducing over time. The surface runoff response was positively correlated with the expansion of cultivation land and negatively correlated with the reduction of forest and shrubland coverage in the study area. Settlement/urbanization creates more impervious surfaces that do not allow the percolation of water down through the soil to the aquifer which leads to increased surface runoff. The LULC change in a watershed determines to what degree water infiltrates, accumulates, or flows over the land, and influences the runoff characteristics to a significant extent, which in turn, affects the surface and groundwater availability of the area. As a result, these LULC changes showed an effect on surface runoff responses for the major watersheds of the study ( Figure 9 ). The study outcomes are consistent with the conclusions from studies by Woldesenbet et al. (2017) and Woldesenbet et al. (2018) in the Upper Blue Nile Basin, Ethiopia, where an increase in surface runoff was identified as the result of increasing settlement/built-up and agriculture/cultivation land at the expense of closed and open forests.

Impact of LULC change on water yield of major watersheds

As Figure 10 shows, the average monthly water yield simulated from each LULC map and its changes within the three scenarios for the four major watersheds of the Tana sub-basin. Water yield values increased with time for four major watersheds and the value of seasonal changes was significantly high in the wet season (June to September) of the year while in the dry season, there was an increment but small ( Figure 10 ). The average annual water yield was 229.24 and 56.13 mm lower in 2000 and 2014, respectively (i.e. decreased by 46.63 and 11.42%) for Megech, 14.8 and 288.13 mm above in 2000 and 2014, respectively (i.e. increased by 4.2 and 81.68%) for Rib, 204.25 and 232.01 mm above in 2000 and 2014, respectively (i.e. increased by 27.09 and 30.79%) for Gumara and 253.58 and 153.62 mm above in 2000 and 2014, respectively (i.e. increased by 23.43 and 14.19%) for Gilgel Abbay as compared to the baseline year of 1986. A continuous increment of water yield was observed in all scenarios while it declined in the Megech watershed.

In the Tana sub-basin, the average annual water yield also increased by 14.88 and 12.6% in 2000 and 2014, respectively. In Figure 7 , the water yield of the sub-basin was shown an increased trend monthly. This was due to the expansion of cultivation lands and reduction of shrub and forest land coverage over time and the result was similar to the previous studies by Awotwi et al. (2019) in Pra River Basin, Ghana, and Sead et al. (2010) in Upper Blue Nile River. The response of water yield was observed for LULC variability in the study area, Tana sub-basin.

Impact of LULC change on baseflow of major watersheds

Average baseflow values simulated monthly from each LULC map and their changes within the three scenarios for the four major watersheds of the Tana sub-basin are shown in Figure 11 . As indicated in the above graph, the variation of the flow was higher in the wet season than in the dry season. Increasing monthly values of baseflow observed in Rib and Gumara whereas for Megech and Gilgel Abbay, the values were declined with time. In Rib and Gumara, both cultivation and grassland increased, and shrubland was reduced whereas in Megech cultivation land increased and grassland and shrubland reduced. For the Gilgel Abbay watershed, the coverage of cultivation and shrublands increased but grassland declined ( Figure 5 ). As Woldesenbet et al. (2017) stated that the cultivation land expansion will decrease soil infiltration rate/percolation/baseflow, and increases surface runoff compared to grass and shrubland. But the changes LULC in each cover category will have a response on hydrology components with different scales. This study also shows the variation of land use/land cover has an impact on water balance components at different proportions in the study area. The average annual baseflow was 24.55 mm lower in 2000 and 27.55 mm lower in 2014 (i.e. decreased by 36.59 and 41.06%), 0.44 mm lower in 2000 and 40.66 mm above in 2014 (i.e. decreased by 1.27% and increased by 116.97%), 90.72 mm above in 2000 and 105.40 mm above in 2014 (i.e. increased by 31.12 and 36.15%), and 121.51 mm above in 2000 and 26.44 mm lower in 2014 (i.e. increased by 63.15% and decreased by 13.74%) as compared to the baseline year of 1986 for Megech, Rib, Gumara, and Gilgel Abbay, respectively. In the Tana sub-basin, the average annual baseflow also increased by 18.4%, and 9.93% in 2000 and 2014, respectively. In Figure 7 , the baseflow show continuously increasing trends between scenario 2000 and 1986 and 2014 and 1986 while it is declining between 2014 and 2000. This will be the result of variation of LULC changes and its proportional changes of LULC types. As Woldesenbet et al. (2017) stated, the groundwater component of water balance is strongly negatively correlated with the expansion of cultivation and is strongly positively correlated with the percentage of bush and shrubland. The LULC change in a watershed determines to what degree water infiltrates, accumulates, or flows over the land, and influences the baseflow characteristics of a drainage basin, soil infiltration, and percolation to a significant extent, which in turn, affects the surface and groundwater availability of the area. Averagely, a high deviation of baseflow in each scenario was observed starting from the month of June to November due to high rainfall intensity and variability, and agricultural practice or expansion of cultivation land. As a result, these LULC changes showed an impact on the magnitude of annual and monthly baseflow for the major watersheds of the study ( Figure 11 ).

Change in LULC is one of the factors responsible for changing the water balance of the sub-basin by altering the magnitude of surface runoff, base flow, water yield, and ET. The SWAT tool was used in this study to assess the impact of LULC change on water resources in the Tana sub-basin. The study included SWAT model evaluation, detection of LULC changes, and impact assessment. In this study, the impact of LULC changes on water resources was successfully assessed within the available data. The model has generated 942, 886, and 869 HRUs for 1986, 2000, and 2014 scenarios and 69 watersheds/sub-basin for all. The overall performance of the model was satisfactory. In four calibration stations where observational data were available, the values of NSE were above 0.72, which is above the minimum requirement to employ the model for further analysis. The study was simulated with the available flow data and lacks to calibrate with recent data to show the current impact of LULC changes on water balance components. Model uncertainty may also be the factor that affects the result of the research.

This study analysis indicated that the changes in LULC have an impact on the water resources of the study area. LULC changes have implicated effects on hydrological response and will continue to have consequences on natural resources management and development. The amount of land cover that remained unchanged in the first period (1986–2000) was 73.47% and it was 71.54% in the next period (2000–2014). However, the overall changes, for the period from 1986 to 2014, were recorded as 33.94%, which pushed the unchanged land cover down to 66.06%.

The water balance components of the study area showed positive and negative responses due to LULC changes in the Tana sub-basin. The expected reduction of surface runoff during the dry season may affect agriculture/irrigation and water-oriented activities while its increments during the wet/rainy season may lead to flooding. The rise in soil erosion and sedimentation of water resources structures and Lake Tana is also likely to occur due to the possible surface runoff increments. Overall, this could affect the livelihood in the study area. Further analysis using the latest flow dataset and LULC are required to show the overall effect of LULC change on the water resources in the study area.

Immediate measures must be taken to mitigate this by ensuring and enforcing land use plan preparation and implementation, and this may minimize illegal expansion trends of cultivation land and prevent forest cover from continuous reduction resulting in the normalization of water balance components. The approach used in this study has accredited contributions of changes in LULCs to water resources, providing perceptible information that will allow stakeholders and decision-makers to make prominent choices regarding natural resource planning and management. This approach could be applied to a variety of river basins to predict the consequences of LULC changes on water resources. The research methods used in this study can serve as a guide for other similar studies aiming at evaluating and computing hydrological responses to LULC variations.

The authors are thankful to the Ministry of Water, Irrigation and Energy, Abbay Basin Development Office, Amhara Design and Supervision Work Enterprise and Amhara Meteorology Agency for providing valuable data and information used in this study. The BRICS multilateral R&D project (BRICS2017-144) Team is sincerely acknowledged. The Durban University of Technology is sincerely acknowledged for hosting the grant. University of Limpopo and South Africa's Agricultural Research Council-Natural Resources & Engineering are most sincerely acknowledged as co-investigators in this NRF-BRICS research project.

The financial assistance of the South Africa National Research Foundation (NRF) is hereby acknowledged. Opinions expressed and conclusions arrived at, are those of the authors and are not necessarily to be attributed to the NRF. The research is under the grant BRICS multilateral R&D project (BRICS2017-144), the NRF UID number 116021 and the Durban University of Technology UCDG Water Research Focus Area grant.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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