Spatial pattern of non-stationarity and scale-dependent relationships between NDVI and climatic factors—A case study in Qinghai-Tibet Plateau,China |
| |
| Institution: | 1. Flemish Institute for Technological Research (VITO), Remote Sensing Unit, Boeretang 200, B-2400 Mol, Belgium;2. KU Leuven – University of Leuven, Department of Earth and Environmental Sciences, Celestijnenlaan 200E, B-3001 Heverlee, Belgium;1. College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China;2. Department of Bioresource Engineering, Faculty of Agricultural and Environmental Science McGill University, Québec H9X 3V9, Canada;3. School of Agricultural, Computational and Environmental Sciences, International Centre for Applied Climate Sciences, Institute of Agriculture and Environment, University of Southern Queensland, Springfield, QLD 4300, Australia;4. Key Laboratory of Ecohydrology of Inland River Basin, Alashan Desert Eco-Hydrology Experimental Research Station, Cold and Arid Regions Environmental Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 73000, China;1. Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, PR China;2. Key Laboratory for Agro-Environment & Climate Change, Ministry of Agriculture, Beijing 100081, PR China;3. China Building Design Consultants Co., Beijing 100044, PR China;4. Building Energy Engineering Center, China Architecture Design & Research Group, Beijing 100044, PR China;5. National Climate Center, China Meteorological Administration, Beijing 100081, PR China |
| |
| Abstract: | Spatial non-stationarity and scale-dependence are important characteristics of the relationship between NDVI and climatic factors. To improve the reliability of model prediction, it is necessary to find the scales and spatial heterogeneity in which a stationary relationship is reached. In this paper, a geographically weighted regression (GWR) model was developed to define spatial non-stationarity and scale-dependent relationships between NDVI and climatic factors. The results indicate that the spatial scale of the stationary relationship for NDVI and both temperature and precipitation is 156 km over the whole Qinghai-Tibet Plateau. Both modeling performance and the spatial pattern of the GWR model are significantly better than global regression models such as OLS. Significant spatial heterogeneity of regression relationships between NDVI and climatic factors is revealed within the Qinghai-Tibet Plateau. We conclude that the dominant climatic factor influencing NDVI is not the same for all ecoregions within the study area. There are also different key scales of interaction between NDVI and the dominant climatic factor in these various ecoregions. Finally, model performance is different in the each eco-region. Therefore, this finding can provide a scientific basis for choosing a suitable scale and reliable models to solve scale-dependent problems in geography and ecology. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
