Assessing the effects of climate change on forest cover in dai tu district thai nguyen province

THAI NGUYEN UNIVERSITY UNIVERSITY OF AGRICULTURE AND FORESTRY KENNETH JOSHUA ZARATE KUA TOPIC TITLE: KENNETH JOSHUA Z KUA ASSESSING THE EFFECTS OF CLIMATE CHANGE ON FOREST KENNETH JOSHUA Z KUA COVER IN DAI TU DISTRICT, THAI NGUYEN PROVINCE KENNETH JOSHUA Z KUA BACHELOR THESIS REMOTE SENSING GLOBAL VARIATIONS: EFFECTS OF Study Mode: CLIMATE CHANGEFull-time PARAMETERS ON FOREST COVER AND Major: Environmental Science and Management VEGETATION IN DAI TU DISTRICT, THAI NGUYEN PROVINCE Faculty: Batch: International Programs Office K45 – AEP Thai Nguyen, 20/11/2017 REMOTE SENSING GLOBAL VARIATIONS: EFFECTS OF CLIMATE CHANGE PARAMETERS ON FOREST COVER AND VEGETATION IN DAI TU DISTRICT, THAI NGUYEN PROVINCE Thai Nguyen, 20/09/2017 DOCUMENTATION PAGE WITH ABSTRACT Thai Nguyen University of Agriculture and Forestry Degree Program Bachelor of Environmental Science and Management Student name Kenneth Joshua Zarate Kua Student ID DTN1454290056 Assessing the Effects of Climate Change on Forest Cover in Thesis Title Dai Tu District, Thai Nguyen Province Supervisor Abstract: Th.S Nguyễn Văn Hiểu List of Figures Varying temperature and precipitation patterns and rising concentrations of List of Tables (if necessary) atmospheric carbon dioxide (CO₂) are unquestionably urging noticeable changes List of Abbreviations in natural and modified forests Remote Sensing (RS) and Geographic Information PART I INTRODUCTION System (GIS) approaches for monitoring forest cover is one of the most prominent 1.1 Research rationale tool due to the increasing environmental problems that the Earth is facing The aim The unpredictable and changing environment of this thesis is to assess the effects of climate change on forest cover in Dai Tu awdawdawdawdawdawdawdawdawdawdawdawdawdawdawda district, Thai Nguyen province Landsat TM images of 10th June 1993 and 10th dddddddawdawdaawdadwawdawdawdawdawd been a serious June 2004, and Landsat OLI image of 6th June 2017 of Dai Tu district were topic all around the world, drawing the interests of intellectual utilized for supervised classification by using ArcGIS software Cross-tabulation humans to investigate its influence in different aspects change matrices were established to assess the land-cover changes for the 1st period (Ravindranath 2008, p 1) The effects of climate change are (1993 – 2004) and the 2nd period (2004 – 2017) The results from the land-cover predominated by rising temperatures, varying precipitation change analysis showed that, from the first period, the forest cover had decreased patterns and sea level increase, these impacts are capable to by 10.43% of the study area While, the second period had decreased by 12.53% of disturb different kinds of ecosystems and worst, damaging natural resources (such as forests, fertile lands and minerals) The inevitable losses of natural resources are most likely threat to human survival Scientific studies show proven prediction of ii the study area These changes were a byproduct from the expanding agricultural areas and some human interventions (such as urbanization and mining activities) that resulted to deforestation Moreover, regression analysis was performed to investigate the relationships between the mean values of vegetation indices (NDVI and FAPAR) and climate change parameters (SMI and LST) including the forest cover data that were extracted from the land-cover classification The result of the analysis proves that, climate change parameters have significant relationships to the changing forest cover (r² = < 0.80) of Dai Tu district Keywords: climate change; forest cover; remote sensing; Landsat; landuse/land-cover Number of pages: 56 Date of 20/11/17 Submission: iii ACKNOWLEDGEMENT Firstly, I humbly acknowledging my God, "Jesus Christ", who is the “Son of God” that I believe in Without His constant provision of love and grace, I might not have had the positive outlook to keep and press toward especially while working on with my thesis I am using this opportunity to consider everyone who supported me throughout my life and academics I may not include you all here, but I would like to say, “thank you very much!” This piece of work couldn’t be possible without the help and support of some dedicated and considerate people: I'd like to show my sincere gratitude and appreciation to my thesis supervisor Dr Nguyễn Văn Hiểu for offering his research center for me to work on Also for the immense support and valuable recommendations I am acknowledging the Advanced Education Program (AEP) of Thai Nguyen University of Agriculture and Forestry (TUAF) and staffs for building, teaching, encouraging and inspiring me throughout my college life, which helped me to have a brighter future Many thanks to my good friends (Anne, Katleen, Ekang, Tina, Carlo, Colleene, Jelo, Real, Nicole, Anh Kiet, and Kuya Jose) for the positive vibes that helped me a lot emotionally during the majority of my tiring days I greatly appreciate the members of GeoInformatic Research Center (GIRC) for the cares and concerns, which made me feel comfortable and special while doing my research I am deeply fascinated to mention my beloved brothers and sisters in Jesus Christ the Refiner’s Fire (JCRF) church and the Refiner’s Christian School (RCS) Thank you for all, without you, I might not have achieved a higher purpose Words can’t express my deepest thankfulness to Mishel Rañada, for the unceasing support and compliments that boost me to my best Many thanks, Mishel, for the insights, which you have shared for the betterment of my thesis I am grateful beyond reasonable doubt and willingly dedicating this thesis to my family (Mommy Vec, Daddy Bong, Kuya Kien, Kezia Baby, Ate April, Tita Cherry, Tita Ester, Tito Eddie, Tito Edison, Tita Lau, Tita Leoni, Lola Paking) for the support not merely financial but also in lots of different aspects The Researcher, Kenneth Joshua Zarate Kua iv TABLE OF CONTENTS List of Figures List of Tables List of Abbreviations PART I INTRODUCTION 1.1 Research Rationale 1.2 Research Objectives 1.2.1 Main Objective 1.2.1 Specific Objectives 1.3 Research Questions and Hypothesis 1.4 Scope and Limitations 10 1.5 Definition of Terms 11 PART II LITERATURE REVIEW 16 2.1 Land-Use and Land-Cover (LULC) 16 2.2 Land-use research studies 17 2.3 Remote sensing and GIS techniques for LULC change 18 2.4 Forest vegetation monitoring using RS and GIS techniques 19 2.5 Remote sensing climate change effects on forest vegetation 20 PART III METHODOLOGY 23 3.1 3.2 Materials 23 3.1.1 Time and place of research 23 3.1.2 Remotely sensed study area 23 3.1.3 Software used 23 3.1.4 Satellite data used 23 Methods 25 3.2.1 Satellite image pre-processing 25 3.2.2 Supervised classification 26 3.2.3 Accuracy assessment 26 3.2.4 Change rate analysis 27 3.2.5 Vegetation indices and climate change parameters 27 3.2.6 Establishing relationship 29 PART IV RESULTS 30 v 4.1 Study area 30 4.1.1 Geography 30 4.1.2 Topography 31 4.1.3 Hydrology 31 4.1.4 Climate and weather 31 4.1.5 Socio-economic activities 32 4.1.6 Population 32 4.2 Land-cover analysis 33 4.2.1 Land-cover classes 34 4.2.2 Land-cover maps 34 4.3 Land-cover area proportion 35 4.4 Accuracy Assessment results 38 4.5 Land-cover change analysis 38 4.5.1 Land cover change cross-tabulation 38 4.5.2 Land-cover gain-loss 40 4.6 Visualization of vegetation indices and climate change parameters 41 4.6.1 NDVI maps 41 4.6.2 FAPAR maps 42 4.6.3 SMI maps 43 4.6.4 LST maps 44 4.7 Linear relationships 45 Part V DISCUSSIONS AND CONCLUSIONS 47 Part VI RECOMMENDATIONS 49 Part VII REFERENCES 49 APPENDIX A 57 APPENDIX B 58 APPENDIX C 59 APPENDIX D 60 APPENDIX E 61 APPENDIX E 62 APPENDIX F 63 APPENDIX G 64 vi LIST OF FIGURES Figure 1: The overall methodological framework for assessing the effects of climate change on forest cover 25 Figure 2: Maps and locations for Dai Tu district, Thai Nguyen province, Vietnam 30 Figure 3: Land-cover classification maps for years 1993; 2004; and 2017 34 Figure 4: Illustrates the proportion of land-cover classes by area (km²) and percentage (%), in year 1993 35 Figure 5: Illustrates the proportion of land-cover classes by area (km²) and percentage (%), in year 2004 36 Figure 6: Illustrates the proportion of land-cover classes by area (km²) and percentage (%), in year 2017 36 Figure 7: Comparison of land-cover proportion by percentage (%) years 1993; 2004; and 2017 37 Figure 8: Land-cover gain – loss in km² for the 1st period (1993 – 2004) and 2nd period (2004 – 2017) 40 Figure 9: NDVI maps of Dai Tu district in years 1993; 2004; and 2017 41 Figure 10: FAPAR maps of Dai Tu district in years 1993; 2004; and 2017 42 Figure 11: SMI maps of Dai Tu district in years 1993; 2004; and 2017 43 Figure 12: LST maps of Dai Tu district in years 1993; 2004; and 2017 44 Figure 13: Graphical relationship between (a) FC and SMI, (b) FC and LST, (c) NDVI and SMI, (d) NDVI and LST, (e) FAPAR and SMI, (f) FAPAR and LST 46 LIST OF TABLES Table Details of the satellite data used in the study 24 Table Illustrates the characteristics of Landsat bands that were used for calculating vegetation indices and climate change parameters 28 Table Land-cover classes definitions and the criteria used to identify classes 33 Table 4: Land-cover classes conversion in area (km²) from 1993 – 2004 period 38 Table 5: Land-cover classes conversion in area (km²) from 2004 – 2017 period 39 Table 6: Statistical relationship between vegetation indices and climate change parameters in Dai Tu district in years 1993; 2004; and 2017 45 LIST OF ABBREVIATIONS AEV Area of Ephemeral Vegetation AVHRR Advanced Very High-Resolution Radiometer CO₂ Carbon Dioxide DEM Digital Elevation Model ETM Enhanced Thematic Mapper FAO Forest and Agriculture Organization FAPAR Fraction of Absorbed Photosynthetically Active Radiation GCP Ground Control Points GIS Geographic Information System LST Land Surface Temperature LULC Land-use and Land-Cover MODIS Moderate Resolution Imaging Spectrometer NDVI Normalized Difference Vegetation Index NFI National Forest Inventory NOAA National Oceanic and Atmospheric Administration REDD Reducing Emissions from Deforestation and forest Degradation RS Remote Sensing SMI Soil Moisture Index SPOT Système Pour l'Observation de la Terre TM Thematic Mapper UK United Kingdom UNFCCC United Nations Framework Convention on Climate Change USGS United States Geological Survey UTM Universal Transverse Mercator WGS World Geodetic System Lambin, E.F., Geist, H., Rindfus, R.R (2006) Introduction: local processes with global impacts In: Lambin EF, Geist H (eds) Land-use and land-cover change: local processes and global impacts Springer, Berlin pp 1-8 Mas, J F (1999) "Monitoring land-cover changes: a comparison of change detection techniques." 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2017 58 APPENDIX C Classified Image Land-Cover Class Reference Image Forest Agriculture Land/BuiltMining Waterbody up area Row Totals Forest 38 42 Agriculture 35 0 36 Land/Built-up area 36 39 Mining 0 36 36 Waterbody 0 38 39 Column Totals 40 40 40 40 40 200 User's accuracy 90.48 97.22 92.31 100 97.44 Producer's accuracy 95 87.5 90 90 95 Overall accuracy = 91.5% Kappa coefficient = 89.48% Confusion matrix table of classified vs reference image for year 1993 59 APPENDIX D Land-Cover Type Forest Agriculture Land/Builtup area Mining Waterbody Row Totals Forest 38 1 41 Agriculture 38 0 40 Land/Builtup area 1 39 42 Mining 0 38 38 Waterbody 0 38 39 Column Totals 40 40 40 40 40 200 User's accuracy 90.48 97.22 92.31 94.74 97.44 Producer's accuracy 95 95 97.5 95 95 Overall accuracy = 95.50% Kappa coefficient = 94.38% Confusion matrix table of classified vs reference image for year 2004 60 APPENDIX E Classified Image Reference Image Land-Cover Class Forest Agriculture Land/BuiltRow Mining Waterbody up area Totals Forest 39 42 Agriculture 39 0 39 Land/Builtup area 1 38 41 Mining 0 40 40 Waterbody 0 0 38 38 Column Totals 40 40 40 40 40 200 User's accuracy 92.85714 100 92.68293 100 100 Producer's accuracy 97.5 97.5 95 100 95 Overall accuracy = 97% Kappa coefficient = 96.25% Confusion matrix table of classified vs reference image for year 2017 61 1993 Land Cover APPENDIX E 2004 2017 Area (km²) (%) Area (km²) (%) Area (km²) Forest 435.14 77.35 376.46 66.92 305.99 54.39 Agricultural 106.11 18.86 131.11 23.3 182.6 32.46 Land/Built- 13.86 2.46 43.22 7.68 61.63 10.96 Mining 0.67 0.12 1.2015 0.21 8.52 1.51 Waterbody 6.82 1.21 10.6 1.88 3.85 0.68 (%) Class up area The land-cover proportion (percentage and area) for years 1993, 2004, and 2017 62 APPENDIX F 500 450 400 Area change in km² 350 300 250 200 150 100 50 1993 2004 2017 Year Forest Agricultural Land/Built-up area Mining Waterbody Land-cover change stacked graph for years 1993, 2004 and 2017 63 APPENDIX G Equation 1: Kappa Coefficient calculation: - - - - - - - - - - - - - - - - - - - - - - - - (1) Where, N is the total number of sites in the matrix, r is the number of rows in the matrix, 𝑥𝑖𝑖 is the number in row i and column i, 𝑥+𝑖 is the total for row i, and 𝑥𝑖+ is the total for column i Kappa interpretation: • Poor agreement = Less than 0.20 • Fair agreement = 0.20 to 0.40 • Moderate agreement = 0.40 to 0.60 • Good agreement = 0.60 to 0.80 • Very good agreement = 0.80 to 1.00 64 Equation 2: NDVI calculation: NDVI = 𝑁𝐼𝑅−𝑅𝐸𝐷 𝑁𝐼𝑅+𝑅𝐸𝐷 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -(2) Where; NIR = the spectral reflectance measurements acquired in the near- infrared region (band) R = the spectral reflectance measurements acquired in the red region (band) NDVI interpretation: In general, a high NDVI value involves considerably more healthy vegetation covers Alternatively, a low NDVI value signifies poor or no vegetation at all The standard range of NDVI is usually between -0.35 (water) to (bare soil) to +0.6 (for an area with dense vegetation) Equation 3: FAPAR calculation: FAPAR = NDVI * 1.25-0.025 - - - - - - - - - - - - - - - - - - - - - - - - - - - - -(3) Where NDVI can be calculated by using the equation (2) Equation 4: LST calculation: 𝐿𝑆𝑇 = 𝑇𝐵 (1 + (𝜆 𝑇𝐵/ 𝜌) 𝑙𝑛𝜀 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - (4) Where, 65 LST is land surface temperature (in Kelvin); TB is radiant surface temperature (in Kelvin); λ is the wavelength of emitted radiance (11.5 μm); ρ is h×c/σ (1.438×10−2 m K); h is Planck’s constant (6.26×10-34J s); c is the velocity of light (2.998×108 m/sec); σ is Stefan Boltzmann’s constant (1.38×10-23J K-1); and ε is emissivity the LSTs was converted into Celsius simply by subtracting 273.15 from the value determined in Equation (4) Equation 4: SMI calculation: SMI = (𝐿𝑆𝑇𝑚𝑎𝑥 – 𝐿𝑆𝑇) (𝐿𝑆𝑇𝑚𝑎𝑥 – 𝐿𝑆𝑇𝑚𝑖𝑛) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -(5) Where, LSTmax and LSTmin are the maximum and minimum surface temperature for a given NDVI and LST LSTmax and LSTmin are calculated using following Equation (5) and (6), correspondingly: LSTmax = a1 * NDVI + b1 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - (6) 66 LSTmin = a2 * NDVI + b2- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - (7) Where a1, a2, b1 and b2 are the scientific factors acquired based on linear regression identifying both dry and wet (warm and cold) corners of the data 67 ... DTN1454290056 Assessing the Effects of Climate Change on Forest Cover in Thesis Title Dai Tu District, Thai Nguyen Province Supervisor Abstract: Th.S Nguyễn Văn Hiểu List of Figures Varying temperature... Does mining activities in Dai Tu district expanded? Does expansion in agricultural areas had caused deforestation? Is there a reduction or expansion of forest coverage in the study area within the. .. 1.2.1 Main Objective The primary objective of this study was to assess the effects of climate change on forest cover in Dai Tu district, Thai Nguyen province by using remote sensing and GIS techniques