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Proceedings Paper

Remote sensing monitoring mechanism model for heavy metal Cd pollution in rice farmland based on hyperspectral data
Author(s): Li Guan; Chengqi Cheng
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Paper Abstract

As one of the significant ecological environment problems, heavy metal pollution associates closely with environment quality, human existence and security of food supplies. The remote sensing pollution mechanism in soil pollution-Cd is discussed by researching into the status of rice leaf polluted-Cd in this paper. The response relationships between remote sensing information parameters, which reflected the vegetation structure, physicochemical properties and biologic parameters of soil-vegetation system, and soil polluted degree by Cd element are analyzed based on Hyperion satellite data and a great number of ground experiment data. To extract remote sensing parameter to Cd pollution, multiple discriminant analysis (MDA) was applied over the data, which is sensitive to rice chlorophyll, rice leaf moisture, rice cell structure and rice LAI. The remote sensing mechanism models of Cd pollution in rice soil are established, including MCARI-NDWI model, MCARI-RVSI model, MCARI-RVI model, NDWI-RVSI model, NDWIRVI model and RVSI-RVI model. The research results indicated that the pollution monitoring of soil Cd element in large scale might carry on initially according to these models, because different Cd pollution degrees are in different positions of these models, however, the precision of pollution models need be further improved.

Paper Details

Date Published: 3 November 2008
PDF: 11 pages
Proc. SPIE 7145, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Monitoring and Assessment of Natural Resources and Environments, 71450R (3 November 2008); doi: 10.1117/12.813004
Show Author Affiliations
Li Guan, Peking Univ. (China)
Chengqi Cheng, Peking Univ. (China)


Published in SPIE Proceedings Vol. 7145:
Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Monitoring and Assessment of Natural Resources and Environments
Lin Liu; Xia Li; Kai Liu; Xinchang Zhang; Yong Lao, Editor(s)

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