1 September 2005 Monitoring spatiotemporal changes of urban vegetation abundance by normalized spectral mixture analysis (NSMA): the case study of Nanjing, China
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Abstract
Nowadays, urban vegetation abundance estimation using medium spatial resolution satellite images presents great challenges due to the intensive heterogeneity of urban landscapes. NSMA based on spectral mixture analysis (SMA) highlights the spectral shape information and minimizes the effects of absolute pixel reflectance values. It can effectively solve the spectral diversity problem of the same urban biophysical composition. In this study, Nanjing City, East China was chosen as study area. A Landsat TM image acquired in 1988 and a Landsat ETM+ image acquired in 2000 were used to extract vegetation fraction maps. Base on the idea of vegetation-impervious surface-soil (V-I-S) model and the actual condition of the study area, three endmembers (i.g. vegetation, impervious surface, and shade/water) were selected from the normalized images of the two periods. Urban vegetation fraction maps were acquired through applying a fully constrained SMA to the normalized images. Urban vegetation abundance was expressed by vegetation fraction maps. The spatiotemporal dynamics of urban vegetation abundance changes was analyzed systematically using vegetation fraction maps of the two periods. The relation between urban vegetation abundance and urban land use was analyzed, too. The accuracy of the vegetation fraction maps was validated using IKONOS images of the study area. Results indicate that NSMA method is a more powerful tool for estimating urban vegetation abundance. Furthermore, urban vegetation fraction maps may provide a reference for analyzing and monitoring urban environments and urban development.
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Su Li, Su Li, Dafang Zhuang, Dafang Zhuang, Zhiqiang Gao, Zhiqiang Gao, Shunyan Hou, Shunyan Hou, } "Monitoring spatiotemporal changes of urban vegetation abundance by normalized spectral mixture analysis (NSMA): the case study of Nanjing, China", Proc. SPIE 5884, Remote Sensing and Modeling of Ecosystems for Sustainability II, 588419 (1 September 2005); doi: 10.1117/12.614717; https://doi.org/10.1117/12.614717
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