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Synthetic Aperture Radar (SAR) images improve habitat suitability models

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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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Acknowledgements

This work was supported by the DIVA 3 Program of the French Ministry of Environment AGRICONNECT Project, the CNES, the DLR by providing TerraSAR-X Imagery, and the Zone Atelier Armorique. We thank Jean-Luc Roger and Quentin Landais for field assistance, Santiago Saura for his help on connectivity modeling, and Eric Pottier for his helpful comments on SAR processing.

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Authors and Affiliations

  1. LETG, CNRS UMR 6554, Université Rennes 2, Place du recteur Henri Le Moal, 35043, Rennes Cedex, France

    Julie Betbeder, Marianne Laslier & Laurence Hubert-Moy

  2. ECOBIO, CNRS UMR 6553, Université de Rennes 1, Campus de Beaulieu, 35042, Rennes Cedex, France

    Françoise Burel

  3. INRA SAD-PAYSAGE, 65, rue de Saint Brieuc CS 84215, 35042, Rennes Cedex, France

    Jacques Baudry

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  1. Julie Betbeder
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  2. Marianne Laslier
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  4. Françoise Burel
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  5. Jacques Baudry
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Correspondence to Julie Betbeder.

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Betbeder, J., Laslier, M., Hubert-Moy, L. et al. Synthetic Aperture Radar (SAR) images improve habitat suitability models. Landscape Ecol 32, 1867–1879 (2017). https://doi.org/10.1007/s10980-017-0546-3

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