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Can remote sensing of land cover improve species distribution modelling?

Can remote sensing of land cover improve species distribution modelling? Remote sensing has been used as a tool for mapping land cover since sources of data became readily available in the 1970s. Spectral, temporal, and textural differences among satellite images allow users to distinguish among broad classes of vegetation. However, the applicability of remote sensing to classification breaks down at the species level. General categories of vegetation, such as deciduous and coniferous forests, can be separated, provided patches are relatively homogenous, but species with similar growth forms, for example pine and fir, are problematic. Hence, there is a gap between what an ecologist would like from remote sensing – a map of tree species – and what can be delivered – a map of forest types. Land cover maps derived from remote sensing often are not detailed enough to improve predictions of species distributions based on ecological niche modelling or similar approaches. In addition, land cover classification yields a fairly small number of nominal variables (e.g. deciduous forest, coniferous forest, mixed forest, grassland). By contrast, climatic and topographic data typically have a greater range of continuous values, and are more often used for predicting species distributions ( Guisan & Zimmermann, 2000 ). This is especially true across large http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Biogeography Wiley

Can remote sensing of land cover improve species distribution modelling?

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References (12)

Publisher
Wiley
Copyright
© 2008 The Authors. Journal compilation © 2008 Blackwell Publishing Ltd
ISSN
0305-0270
eISSN
1365-2699
DOI
10.1111/j.1365-2699.2008.01928.x
Publisher site
See Article on Publisher Site

Abstract

Remote sensing has been used as a tool for mapping land cover since sources of data became readily available in the 1970s. Spectral, temporal, and textural differences among satellite images allow users to distinguish among broad classes of vegetation. However, the applicability of remote sensing to classification breaks down at the species level. General categories of vegetation, such as deciduous and coniferous forests, can be separated, provided patches are relatively homogenous, but species with similar growth forms, for example pine and fir, are problematic. Hence, there is a gap between what an ecologist would like from remote sensing – a map of tree species – and what can be delivered – a map of forest types. Land cover maps derived from remote sensing often are not detailed enough to improve predictions of species distributions based on ecological niche modelling or similar approaches. In addition, land cover classification yields a fairly small number of nominal variables (e.g. deciduous forest, coniferous forest, mixed forest, grassland). By contrast, climatic and topographic data typically have a greater range of continuous values, and are more often used for predicting species distributions ( Guisan & Zimmermann, 2000 ). This is especially true across large

Journal

Journal of BiogeographyWiley

Published: Jul 1, 2008

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