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7 November 2008 Remote sensing-based study on the relationship between land brightness temperature and vegetation abundance in Wuhan city
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Proceedings Volume 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images; 71471E (2008) https://doi.org/10.1117/12.813251
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China
Abstract
Vegetation abundance is an important indicator of urban heat island (UHI), because it influences the partitioning of sensible and latent heat fluxes. In order to reveal the effect of vegetation abundance on UHI of Wuhan city, one of the fast changing urban area in China, we classified land use/land cover types and calculated land brightness temperature (LBT) from a Landsat Enhanced Thematic Mapper Plus (ETM +) image acquired on July 9, 2002. The vegetation fraction derived from a linear spectral mixture analysis (LSMA) model was used as an alternative indicator of vegetation abundance. The fractal analysis of LBT and vegetation abundance was also conducted on 20 transects. Results showed that the spatial pattern of LBT changed with vegetation abundance and higher temperature was located in the area of lower vegetation abundance. Unmixed vegetation fraction was more negatively correlated with UHI than NDVI for most land cover types, except for water. Fractal analysis of image texture showed that transects comprised of larger number of different land cover types exhibited higher fractal dimension. On the contrary, the fractal dimension was lower in transects that covered mostly by built-up land. In addition, the fractal dimension correlation between LBT and vegetation abundance was higher than that between LBT and NDVI.
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Chunling Zhang, Hua Yu, Peng Gong, Weimin Ju, and Huan Pei "Remote sensing-based study on the relationship between land brightness temperature and vegetation abundance in Wuhan city", Proc. SPIE 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images, 71471E (7 November 2008); https://doi.org/10.1117/12.813251
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