Satellite imagery as a single source of predictor variables for habitat suitability modelling: how Landsat can inform the conservation of a critically endangered lemur

Lahoz-Monfort, Jose´ J., Guillera-Arroita, Gurutzeta, Milner-Gulland, E. J., Young, Richard P. and Nicholson, Emily 2010, Satellite imagery as a single source of predictor variables for habitat suitability modelling: how Landsat can inform the conservation of a critically endangered lemur, Journal of applied ecology, vol. 47, no. 5, pp. 1094-1102, doi: 10.1111/j.1365-2664.2010.01854.x.

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Title Satellite imagery as a single source of predictor variables for habitat suitability modelling: how Landsat can inform the conservation of a critically endangered lemur
Author(s) Lahoz-Monfort, Jose´ J.
Guillera-Arroita, Gurutzeta
Milner-Gulland, E. J.
Young, Richard P.
Nicholson, EmilyORCID iD for Nicholson, Emily orcid.org/0000-0003-2199-3446
Journal name Journal of applied ecology
Volume number 47
Issue number 5
Start page 1094
End page 1102
Total pages 9
Publisher Wiley
Place of publication London, Eng.
Publication date 2010-10
ISSN 0021-8901
1365-2664
Keyword(s) Alaotran gentle lemur
Habitat heterogeneity
Hapalemur alaotrensis
Image textures
Landsat
Maxent
Remote sensing
Scale
Vegetation index
Summary 1. Statistical modelling of habitat suitability is an important tool for planning conservation interventions, particularly for areas where species distribution data are expensive or hard to collect. Sometimes however the predictor variables typically used in habitat suitability modelling are themselves difficult to obtain or not meaningful at the geographical extent of the study, as is the case for the Alaotran gentle lemur Hapalemur alaotrensis, a critically endangered lemur confined to the marshes of Lake Alaotra in Madagascar.2. We developed a habitat suitability model where all predictor variables, including vegetation indices and image texture measures at different scales (as surrogates for habitat structure), were derived from Landsat7 satellite imagery. Using relatively few presence records, the maximum entropy (Maxent) approach and AUC were used to assess the performance of candidate predictor variables, for studying the effect of scale, model selection and mapping suitable habitat.3. This study demonstrated the utility of satellite imagery as a single source of predictor variables for a Maxent habitat suitability model at the landscape level, within a restricted geographical extent and with a fine grain, in a case where predictor variables typically used at the macro-scale level (e.g. climatic and topographic) were not applicable.4. In the case of H. alaotrensis, the methodology generated a habitat suitability map to inform conservation management in Lake Alaotra and a replicable protocol to allow rapid updates to habitat suitability maps in the future. The exploration of candidate predictor variables allowed the identification of scales that appear ecologically relevant for the species.5. Synthesis and applications. This study presents a cost-effective combination of maximum entropy habitat suitability modelling and satellite imagery, where all predictor variables are derived solely from Landsat7 images. With a habitat modelling method like Maxent that shows good performance with few presence samples and Landsat images now freely available, the methodology can play an important role in rapid assessments of the status of species at the landscape level in data-poor regions, when typical macro-scale environmental predictors are of little use or difficult to obtain.
Language eng
DOI 10.1111/j.1365-2664.2010.01854.x
Field of Research 050202 Conservation and Biodiversity
Socio Economic Objective 960805 Flora
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2010, Wiley
Persistent URL http://hdl.handle.net/10536/DRO/DU:30073640

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