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Use of datasets derived from time-series AVHRR imagery as surrogates for land cover maps in predicting species' distributions
Egbert, Stephen L. Martínez-Meyer, Enrique Ortega-Huerta, Miguel Peterson, A. Townsend
Egbert, Stephen L.
Martínez-Meyer, Enrique
Ortega-Huerta, Miguel
Peterson, A. Townsend
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Abstract
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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2002-06
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IEEE
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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.