Estimation and seasonal monitoring of urban vegetation abundance based on remote sensing

Ji Zhou; Yun H. Chen; Jing Li; Qi H. Weng; Yan Tang

doi:10.1117/12.760714

26 July 2007 Estimation and seasonal monitoring of urban vegetation abundance based on remote sensing

Ji Zhou, Yun H. Chen, Jing Li, Qi H. Weng, Yan Tang

Proceedings Volume 6752, Geoinformatics 2007: Remotely Sensed Data and Information; 67521W (2007) https://doi.org/10.1117/12.760714
Event: Geoinformatics 2007, 2007, Nanjing, China

Abstract

Vegetation is a fundamental component of urban environment and its abundance is determinant of urban climate and urban ground energy fluxes. Based on the radiometric normalization of multitemporal ASTER imageries, the objectives of this study are: firstly, to estimate the vegetation abundance based on linear spectral mixture model (LSMM), and to compare it with NDVI and SDVI; secondly, to analyze the spatial distribution patterns of urban vegetation abundance in different seasons combined with some landscape metrics. The result indicates that both the vegetation abundance estimation based on LSMM and SDVI can reach high accuracy; however, NDVI is not a robust parameter for vegetation abundance estimation because there is significant non-linear effect between NDVI and vegetation abundance. This study reveals that the landscape characteristics of vegetation abundance is most complicated in summer, with spring and autumn less complicated and simplest in winter. This provides valuable information for urban vegetation abundance estimation and its seasonal change monitoring using remote sensing data.

Citation Download Citation

Ji Zhou, Yun H. Chen, Jing Li, Qi H. Weng, and Yan Tang "Estimation and seasonal monitoring of urban vegetation abundance based on remote sensing", Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 67521W (26 July 2007); https://doi.org/10.1117/12.760714

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KEYWORDS

Vegetation

Spectral models

Remote sensing

Spatial resolution

Data modeling

Climatology

Reflectivity

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