Synthetic Aperture Radar (SAR) images improve habitat suitability models
The ability to detect ecological networks in landscapes is of utmost importance for managing biodiversity and planning corridors.
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).
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.
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.
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.
KeywordsBiodiversity Remote sensing TerraSAR-X Hedgerow networks Forest carabid beetles Canopy cover density Landscape connectivity Graph theory Habitat suitability
- Aksoy S, Akcay G, Cinbis G, Wassenaar T (2008) Automatic mapping of linear woody vegetation features in agricultural landscapes. In: IGARSS 2008—2008 IEEE international geoscience and remote sensing symposium, p IV-403–IV-406Google Scholar
- Baudry J, Jouin A (2003) De la haie aux bocages. Organisation, dynamique et gestion (Editions Quae)Google Scholar
- Blaschke T, Strobl J (2001) What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS. GeoBIT/GIS 6:12–17Google Scholar
- Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. Chapman and Hall/CRC, New YorkGoogle Scholar
- Burnham KP, Anderson DR (2002) Model selection and multimodel inference: A practical information-theoretic approach, Second edn. Springer, New York, USAGoogle Scholar
- Definiens (2004) eCognition professional: user guide 4. Definiens Imaging Gmbh, MunichGoogle Scholar
- Esmenjand M, Esteoule J, Guyader J (1976) Étude pédologique des différents types de talus: considérations sur la différenciation des profils; essai de systématique. Les bocages: histoire, écologie, économie. Inra ENSA Univ. Rennes, Rennes, p 167–175Google Scholar
- Legendre P, Legendre LFJ (2012) Numerical ecology. Elsevier, AmsterdamGoogle Scholar
- Loreau M, Nolf C-L (1993) Occupation of space by the carabid beetle Abax ater. Acta Oecol 14:247–258Google Scholar
- Pottier E, Ferro-Famil L (2012) PolSARPro V5.0: an ESA educational toolbox used for self-education in the field of POLSAR and POL-INSAR data analysis. In: 2012 IEEE international geoscience and remote sensing symposium (IGARSS), p 7377–7380Google Scholar
- Thiele HU (1977). Carabid beetles in their environments. A study on habitat selection by adaptation in physiology and behaviour. Springer, BerlinGoogle Scholar
- Wade A, McKelvey KS, Schwartz M (2015) Resistance-surface-based wildlife conservation connectivity modeling: summary of efforts in the United States and guide for practitioners. Gen. Tech. Rep. RMRS-GTR-333. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort CollinsGoogle Scholar