The complex spatiotemporal vegetation variability in the subtropical mountain-hill region was investigated through a multi-level modeling framework. Three levels - parcel, landscape, and river basin levels- were selec...The complex spatiotemporal vegetation variability in the subtropical mountain-hill region was investigated through a multi-level modeling framework. Three levels - parcel, landscape, and river basin levels- were selected to discover the complex spatiotemporal vegetation variability induced by climatic, geomorphic and anthropogenic processes at different levels. The wavelet transform method was adopted to construct the annual maximum Enhanced Vegetation Index and the amplitude of the annual phenological cycle based on the 16-day time series of a5om Moderate Resolution Imaging Spectroradiometer Enhanced Vegetation Index datasets during 2OOl-2OlO. Results revealed that land use strongly influenced the overall vegetation greenness and magnitude of phenological cycles. Topographic variables also contributed considerably to the models, reflecting the positive influence from altitude and slope. Additionally, climate factors played an important role： precipitation had a considerable positive association with the vegetation greenness, whereas the temperature difference had strong positive influence on the magnitude of vegetation phenology. The multilevel approach leads to a better understanding of the complex interaction of the hierarchical ecosystem, human activities and climate change.展开更多
基金supported by the National Natural Science Foundation of China (NSFC) (Grant No. 41071267)Scientific Research Foundation for Returned Scholars ([2012]940)Ministry of Education of China, and the Science Foundation of Fujian Province (Grant Nos. 2012I0005, 2012J01167)
文摘The complex spatiotemporal vegetation variability in the subtropical mountain-hill region was investigated through a multi-level modeling framework. Three levels - parcel, landscape, and river basin levels- were selected to discover the complex spatiotemporal vegetation variability induced by climatic, geomorphic and anthropogenic processes at different levels. The wavelet transform method was adopted to construct the annual maximum Enhanced Vegetation Index and the amplitude of the annual phenological cycle based on the 16-day time series of a5om Moderate Resolution Imaging Spectroradiometer Enhanced Vegetation Index datasets during 2OOl-2OlO. Results revealed that land use strongly influenced the overall vegetation greenness and magnitude of phenological cycles. Topographic variables also contributed considerably to the models, reflecting the positive influence from altitude and slope. Additionally, climate factors played an important role： precipitation had a considerable positive association with the vegetation greenness, whereas the temperature difference had strong positive influence on the magnitude of vegetation phenology. The multilevel approach leads to a better understanding of the complex interaction of the hierarchical ecosystem, human activities and climate change.
