The scale-location specific control on vegetation distribution was investigated through continuous wavelet transforms approaches in subtropical mountain-hill region, Fujian, China. The Normalized Difference Vegetation...The scale-location specific control on vegetation distribution was investigated through continuous wavelet transforms approaches in subtropical mountain-hill region, Fujian, China. The Normalized Difference Vegetation Index (NDVI) was calculated as an indicator of vegetation greenness using Chinese Environmental Disaster Reduction Satellite images along latitudinal and longitudinal transects. Four scales of variations were identified from the local wavelet spectrum of NDVI, with much stronger wavelet variances observed at larger scales. The characteristic scale of vegetation distribution within mountainous and hilly regions in Southeast China was around 20 km. Significantly strong wavelet coherency was generally examined in regions with very diverse topography, typically characterized as small mountains and hills fractured by rivers and residents. The continuous wavelet based approaches provided valuable insight on the hierarchical structure and its corresponding characteristic scales of ecosystems, which might be applied in defining proper levels in multilevel models and optimal bandwidths in Geographically Weighted Regression.展开更多
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 select...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 of250m Moderate Resolution Imaging Spectroradiometer Enhanced Vegetation Index datasets during 2001-2010. 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,Ministry of Education of China(Grant No.[2012]940)the Science & Technology Department of Fujian Province,China(Grant Nos.2012I0005,2012J01167)
文摘The scale-location specific control on vegetation distribution was investigated through continuous wavelet transforms approaches in subtropical mountain-hill region, Fujian, China. The Normalized Difference Vegetation Index (NDVI) was calculated as an indicator of vegetation greenness using Chinese Environmental Disaster Reduction Satellite images along latitudinal and longitudinal transects. Four scales of variations were identified from the local wavelet spectrum of NDVI, with much stronger wavelet variances observed at larger scales. The characteristic scale of vegetation distribution within mountainous and hilly regions in Southeast China was around 20 km. Significantly strong wavelet coherency was generally examined in regions with very diverse topography, typically characterized as small mountains and hills fractured by rivers and residents. The continuous wavelet based approaches provided valuable insight on the hierarchical structure and its corresponding characteristic scales of ecosystems, which might be applied in defining proper levels in multilevel models and optimal bandwidths in Geographically Weighted Regression.
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 of250m Moderate Resolution Imaging Spectroradiometer Enhanced Vegetation Index datasets during 2001-2010. 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.
