Ecological habitat quality assessed from remote sensing images improves landscape connectivity measures

The ability to detect ecological networks in landscapes is of utmost importance for managing biodiversity and planning corridors. We present a novel method that integrates habitat suitability derived from remote sensing imagery into a connectivity model to explain species abundance.
In recent years, the use of remotely sensed data, particularly optical imagery, has increased in ecological applications (Pettorelli et al 2014). Vegetation is often classified in broad categories using red and infrared spectral bands and/or vegetation indices, e.g. the Normalized Difference Vegetation Index. However, new remote sensing data, such as Synthetic Aperture Radar (SAR) images, offer important opportunities to characterize vegetation structure over an entire landscape.
We evaluated the information provided by a SAR image for landscape connectivity modeling compared to aerial photographs (AP) commonly used in landscape ecology studies. We compared how two resistance maps constructed using landscape and/or local metrics derived from aerial photographs or SAR imagery yield different connectivity values, considering hedgerow networks and forest carabid beetles as a model.
Two maps of the studied hedgerow network were produced: i) the first one in which each hedgerow is represented by a line, derived from AP ii) the second one derived from the SAR image that integrates the internal structure of the canopy (canopy cover) and in which hedgerows are represented by the projection of tree cover on the ground.
Then, two resistance maps were constructed using metrics derived from AP or a SAR image. The first one integrates a landscape metric; the landscape grain that traduces the hedgerow network structure, the second one considers also the landscape grain and a metric calculated at two scales (i.e. local and landscape): the canopy cover derived from SAR imagery. Finally, connectivity modeling, based on graph theory, was performed to explain forest carabid beetle abundance.
Results showed that resistance maps using landscape and local metrics derived from SAR imagery improves 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 and landscape grain) for the model species.
Our approach is a step forward in the use of remote sensing data for ecological applications, particularly in developing landscape metrics from satellites to monitor biodiversity. SAR sensors acquire novel information about vegetation cover, which facilitates mapping habitat structure and suitability. New interdisciplinary collaboration between remote sensing and ecology communities are fully emerging, which could enhance our understanding of the functionality of ecological patterns.