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Proceedings Paper

Study on species invasion warning modeling using GIS and data mining
Author(s): Hao Chen; Lijun Chen; Jiatian Li; Thomas P. Albright; Qinfeng Guo
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Paper Abstract

Biological invasion has been one of the most dramatic ecological even in human history that threatens our economy, public health and ecological integrity. GIS and Remote Sensing technology should be integrated with spatial data mining to recognize the patterns of invasive species over space and time and predict the distribution at the large-scale. Presented with the challenge of problems during the prediction modeling including the uncertainty in biodiversity data, the uncertainty in model selection, and the uncertainty in niche cross the geographic space, this paper used information-theoretic approaches based on a set of GIS/RS environment layers to generate two kinds of species invasion warning models: global species invasion warning model (G-SIWM) and local species invasion warning model (L-SIWM) and illustrated the approach through a habitat-suitability analysis of ragweed (Ambrosia artemisiifolia L.).

Paper Details

Date Published: 14 November 2007
PDF: 7 pages
Proc. SPIE 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications, 67903F (14 November 2007); doi: 10.1117/12.751358
Show Author Affiliations
Hao Chen, Wuhan Univ. (China)
National Geomatics Ctr. of China (China)
Lijun Chen, National Geomatics Ctr. of China (China)
Jiatian Li, Chinese Univ. of Mining and Technology (China)
Thomas P. Albright, Univ. of Wisconsin, Madison (United States)
Qinfeng Guo, United States Department of Agriculture (United States)


Published in SPIE Proceedings Vol. 6790:
MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications

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