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Proceedings Paper

Using hyperspectral data to detect the responses of masson pine's needle spectral reflectance to acid stress
Author(s): Xiaodong Song; Hong Jiang; Shuquan Yu; Guomo Zhou
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Paper Abstract

Acid rain has been a worldwide environmental problem for decades. China is one of the most serious acid deposition polluted regions in the world. How to effectively monitor acid deposition's severity and spatial distribution has constituted a great challenge to the traditionally chemistry methodology used to monitor acid rain. Long-term acid stress will change foliar internal structure and the content of pigments (such as chlorophyll a and b). Generally, such changes of foliar attributes will result increased reflectance in the visible and near-infrared wavelength regions. In this study, field and greenhouse experiments were performed separately to illustrate the influence of both natural and simulated acid rain to the spectra reflectance and chlorophyll content of masson pine (Pinus Massoniana). As measured with a portable spectroradiometer and a portable chlorophyll meter, spectra reflectance was a more sensitive indicator than chlorophyll content to indicate the severity of acid stress. In most of our cases, the reflectance of masson pine (both natural and greenhouse) was increasing with the severity of acid stress in part or in the whole wavelength regions ranged from 400 to 800nm. Vegetation indices computed using simulated Landsat Thematic Mapper (TM) bands showed that light acid stress often caused higher indices' values, and it was suggested that multispectral image data might be used to monitor acid stress from a canopy level.

Paper Details

Date Published: 26 July 2007
PDF: 8 pages
Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 675226 (26 July 2007); doi: 10.1117/12.760773
Show Author Affiliations
Xiaodong Song, Nanjing Univ. (China)
Hong Jiang, Nanjing Univ. (China)
Shuquan Yu, Zhejiang Forestry Univ. (China)
Guomo Zhou, Zhejiang Forestry Univ. (China)

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

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