Use of datasets derived from time-series AVHRR imagery as surrogates for land cover maps in predicting species' distributions
Issue Date
2002-06Author
Egbert, Stephen L.
Martínez-Meyer, Enrique
Ortega-Huerta, Miguel
Peterson, A. Townsend
Publisher
IEEE
Type
Article
Article Version
Scholarly/refereed, publisher version
Published Version
http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=1026537Rights
© 2002 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
http://www.ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1026537
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Show full item recordAbstract
We hypothesized that NDVI time-series composite
imagery or clustered data derived from the NDVI time series could
serve as effective surrogates for land cover data in predictive
modeling of species’ ecological niches and potential geographic
distributions. Using two Mexican bird species, we examined our
hypothesis with GARP, the Genetic Algorithm for Rule-set
Prediction. Inputs included topographic and climate data, as well as
the NDVI and clustered NDVI datasets. We used a land cover map
previously derived from the NDVI dataset for comparison testing.
Considering only topographic factors, we found that the NDVI or
clustered NDVI data performed as well as or better than the land
cover data. When climate data were added, the land cover data
performed better than the NDVI data, but improvements were slight.
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Citation
Egbert, S. L., E. Martínez-Meyer, M. A. Ortega-Huerta, and A. T. Peterson. 2002. Use of datasets derived from time-series AVHRR imagery as surrogates for land cover maps in predicting species' distributions. Proceedings IEEE 2002 International Geoscience and Remote Sensing Symposium (IGARSS) 4:2337-2339.
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