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 BACHELOR Z. KUA THESIS REMOTE SENSING GLOBAL VARIATIONS: EFFECTS OF Study Mode: CLIMATE CHANGEFull-time PARAMETERS ON FOREST COVER AND Major: Environmental VEGETATION IN DAI TU DISTRICT, Science THAI and Management NGUYEN PROVINCE Faculty: International Programs Office Batch: K45 – AEP Thai Nguyen, REMOTE SENSING GLOBAL 20/11/2017 VARIATIONS: EFFECTS OF CLIMATE CHANGE PARAMETERS ON FOREST COVER AND VEGETATION IN DAI TU DISTRICT, THAI NGUYEN PROVINCE 1 c 20/09/2017 Thai Nguyen, 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 Thesis Title Assessing the Effects of Climate Change on Forest Cover in Dai Tu District, Thai Nguyen Province Supervisor Th.
Nguyễn Văn Hiểu Abstract: List of Figures 1 Varying temperature and precipitation patterns and rising concentrations of List of Tables (if necessary) 2 atmospheric carbon dioxide (CO₂) are unquestionably urging noticeable changes List of Abbreviations 3 in natural and modified forests. Remote Sensing (RS) and Geographic Information PART I. INTRODUCTION 4 System (GIS) approaches for monitoring forest cover is one of the most prominent 1. Research rationale 4 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 5 TM images of 10th June 1993 and 10th dddddddawdawdaawdadwawdawdawdawdawd been a serious June 2004, and Landsat 8 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. 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). ii The inevitable losses of natural resources are most likely threat c to human survival. Scientific studies show proven prediction of 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; land- use/land-cover Number of pages: 56 Date of 20/11/17 Submission: iii c 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 do 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 c TABLE OF CONTENTS List of Figures. 1 List of Tables.
2 List of Abbreviations. Research Questions and Hypothesis. Scope and Limitations. Definition of Terms.
Land-Use and Land-Cover (LULC). Land-use research studies. Remote sensing and GIS techniques for LULC change. Forest vegetation monitoring using RS and GIS techniques.
Remote sensing climate change effects on forest vegetation .1 Time and place of research .2 Remotely sensed study area .4 Satellite data used .1 Satellite image pre-processing .4 Change rate analysis .5 Vegetation indices and climate change parameters. Climate and weather. Socio-economic activities. Land-cover analysis.
Land-cover classes. Land-cover maps. Land-cover area proportion. Accuracy Assessment results.
Land-cover change analysis. Land cover change cross-tabulation. Land-cover gain-loss. Visualization of vegetation indices and climate change parameters.
DISCUSSIONS AND CONCLUSIONS. 64 vi c 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 1 c LIST OF TABLES Table 1. Details of the satellite data used in the study.
Illustrates the characteristics of Landsat bands that were used for calculating vegetation indices and climate change parameters. 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 2 c 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 3 c 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 4 c PART I. Research Rationale The unpredictable and changing environment has been a serious topic all around the world, drawing the interests of various scientists, citizens, and policymakers to investigate its influence on different aspects (Ravindranath and Ostwald, 2008). Shako (2015) reportedly demonstrated the climate change parameters, such as temperature, precipitation, rainfall, soil moisture, vegetation cover, sea level, sunshine hours, atmospheric pressure, wind velocity, etc.
Slight changes in these parameters affect each other directly or indirectly (Palmate et al. These effects are capable to disturb different kinds of ecosystems and worst, damaging natural resources (e. forests, fertile lands, minerals, etc. The inevitable losses of natural resources are unquestionably a threat to human survival.
According to the United Nations Framework Convention on Climate Change (UNFCCC, 2006), demonstrates proven prediction of some catastrophic events of climate change, which are subsequent droughts and heavy rainfall conditions, decreased in the terrestrial forest, loss of biodiversity, food and water scarcity that can result in increased risk of hunger. Forest occupies one-third of the Earth’s surface and serves as an essential resource for Earth’s inhabitants. Furthermore, Forests give protection for the natural disasters (e. floods, landslides, tsunamis, etc.), preserve the quality of the soil, provide habitats for animals, increase the biodiversity, progress the economic growth (producing raw materials such as woods and medicines), and functions globally as a prevention for climate change 5 c by means of lessening global warming through carbon sequestration (Baumann et al., 2014; Kim et al.
Unfortunately, according to Food and Agriculture Organization (FAO, 2012), forests have been continuously and rapidly depleting worldwide. Recent studies claim that forest depletion has been a serious issue regarding global variations. To prove that, recent report from Chakravarty et al. (2012), demonstrates that world forest cover lost from 1990 to 2000 was approximately 0.20% and from 2000 to 2010 was approximately 0.
She also outlines that North and South Africa were leading countries that had the highest rates of deforestation from 1990 to 2010 with average approximately to 0. Moreover, FAO has shown that since 1990, the total amount of forest that had been lost was equivalent to 129 million hectares, which are approximately the size of South Africa. It is widely known that deforestation described as clearing out massive Earth’s forests and potentially damages the quality of the land. Deforestation has a lot of negative impacts on the environment and to the diverse ecosystems.
It is the primary cause of soil erosion that leads to loss of habitats for many species and sedimentation of water bodies (Chakravarty et al. For many years until now, degradation of the forest has been widespread due to human interventions (intentional) and natural factors (unintentional) (FAO, 2012).