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Proceedings Paper

Principal component analysis of TM images for monitoring inland water quality
Author(s): Xiaozhou Shu; Yin Qiu; Dingbo Kuang
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Paper Abstract

TM data have been used by many researchers for remote sensing of monitoring chlorophyll-a concentration, which is strongly correlated with trophic state of surface water. In this paper, principal component analysis (PCA) is employed for extracting alga production in lake water from TM data. Input images are chosen as radiance ratios TM2/TM1, TM2/TM3 and TM4/TM3, instead of four independent band data. Regression analysis gives relationship between the first principal component (PCA1) and chlorophyll-a concentrations as: Chl((mu) g/L) equals -56.69+135.68X(PCA1), where PCA1 equals 0.146 X(TM2/TM1)-0.274X(TM4/TM2)+0.951X(TM4/TM 3). Chlorophyll-a concentrations estimated from TM image are compared with field measurements. The chlorophyll-a algorithm is also applied to TM images of the same lake acquired at different time. Multi-temporal chlorophyll-a distributions in the lake are mapped.

Paper Details

Date Published: 17 December 1999
PDF: 5 pages
Proc. SPIE 3868, Remote Sensing for Earth Science, Ocean, and Sea Ice Applications, (17 December 1999); doi: 10.1117/12.373102
Show Author Affiliations
Xiaozhou Shu, Shanghai Institute of Technical Physics (China)
Yin Qiu, Shanghai Institute of Technical Physics (China)
Dingbo Kuang, Shanghai Institute of Technical Physics (China)


Published in SPIE Proceedings Vol. 3868:
Remote Sensing for Earth Science, Ocean, and Sea Ice Applications
Giovanna Cecchi; Edwin T. Engman; Eugenio Zilioli, Editor(s)

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