A comprehensive analysis of terrestrial surface features using remote sensing data
Using the remote sensing data, this study aims to enhance our understanding of land surface features, including ecosystem distribution in association with topographic controls and climatic controls, vegetation disturbance due to natural hazards, and surface temperature changes with consideration of...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Doctoral or Postdoctoral Thesis |
| Language: | English |
| Published: |
The University of Hong Kong (Pokfulam, Hong Kong)
2014
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| Subjects: | |
| Online Access: | https://doi.org/10.5353/th_b5351026 http://hdl.handle.net/10722/208044 |
| Summary: | Using the remote sensing data, this study aims to enhance our understanding of land surface features, including ecosystem distribution in association with topographic controls and climatic controls, vegetation disturbance due to natural hazards, and surface temperature changes with consideration of the influence of urbanization. In this study, the Global Inventory Monitoring and Modeling System (GIMMS) Normalized Difference Vegetation Index (NDVI) data sets from 1982 to 2006 were used to explore vegetation variation. A data mining method, Exhaustive Chi-squared Automatic Interaction Detector algorithm, was successfully applied to investigate the topographic influences on vegetation distribution in China. The study revealed that elevation is a predominant factor for controlling vegetation distribution among different topographic attributes (slope, aspect, Compound Topographic Index (CTI) and distance to the nearest river). Further, the study results indicated that solar radiation is the limited factor for plant growth in majority of the Northern Hemisphere in summer, and temperature is the main limitation for other seasons. Partial correlation coefficient (PCC) method was adopted to investigate the complex relationships of NDVI with weather variables (i.e., temperature, precipitation and solar radiation) and key climate indices (such as, El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Arctic Oscillation (AO), and Antarctic Oscillation (AAO)). The study indicated that AO is the most significant index in affecting the temperatures in spring and winter in the Northern Hemisphere. This study enhanced the understanding of vegetation responds to asymmetric daytime (Tmax) and nighttime (Tmin) warming in different seasons. The result revealed that asymmetric warming of Tmax and Tmin may influence vegetation photosynthesis and respiration in the plant growth in different periods across biomes. In spring and autumn, vegetation in boreal and wet temperate regions of the Northern Hemisphere is positively ... |
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