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Synthetic Aperture Radar (SAR) images improve habitat suitability models

Abstract : Context The ability to detect ecological networks in landscapes is of utmost importance for managing biodiversity and planning corridors. Objectives The objective of this study was to evaluate the information provided by a synthetic aperture radar (SAR) image for landscape connectivity modeling compared to aerial photographs (APs). Methods We present a novel method that integrates habitat suitability derived from remote sensing imagery into a connectivity model to explain species abundance. More precisely, we compared how two resistance maps constructed using landscape and/or local metrics derived from AP or SAR imagery yield different connectivity values (based on graph theory), considering hedgerow networks and forest carabid beetle species as a model. Results We found that resistance maps using landscape and local metrics derived from SAR imagery improve landscape connectivity measures. The SAR model is the most informative, explaining 58% of the variance in forest carabid beetle abundance. This model calculates resistance values associated with homogeneous patches within hedgerows according to their suitability (canopy cover density and landscape grain) for the model species. Conclusions Our approach combines two important methods in landscape ecology: the construction of resistance maps and the use of buffers around sampling points to determine the importance of landscape factors. This study was carried out through an interdisciplinary approach involving remote sensing scientists and landscape ecologists. This study is a step forward in developing landscape metrics from satellites to monitor biodiversity.
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Contributor : Laurent Jonchère <>
Submitted on : Monday, August 28, 2017 - 3:27:11 PM
Last modification on : Wednesday, October 14, 2020 - 3:50:53 AM



Julie Betbeder, Marianne Laslier, Laurence Hubert-Moy, Francoise Burel, Jacques Baudry. Synthetic Aperture Radar (SAR) images improve habitat suitability models. Landscape Ecology, Springer Verlag, 2017, 32 (9), pp.1867--1879. ⟨10.1007/s10980-017-0546-3⟩. ⟨hal-01577991⟩



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