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Proceedings Paper

Estimation and seasonal monitoring of urban vegetation abundance based on remote sensing
Author(s): Ji Zhou; Yun H. Chen; Jing Li; Qi H. Weng; Yan Tang
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Paper 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.

Paper Details

Date Published: 26 July 2007
PDF: 13 pages
Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 67521W (26 July 2007); doi: 10.1117/12.760714
Show Author Affiliations
Ji Zhou, Beijing Normal Univ. (China)
Yun H. Chen, Beijing Normal Univ. (China)
Jing Li, Beijing Normal Univ. (China)
Qi H. Weng, Institute of Remote Sensing Applications (China)
Yan Tang, Beijing Normal Univ. (China)


Published in SPIE Proceedings Vol. 6752:
Geoinformatics 2007: Remotely Sensed Data and Information

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