The ability to detect ecological networks in landscapes is of utmost importance for managing biodiversity and planning corridors. In this study, 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. 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. In this study, we evaluated the information provided by a SAR image for landscape connectivity modeling compared to aerial photographs (AP) commonly used in landscape ecology studies. More precisely, 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 beetle species 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 bee