Remote Sensing in Wildlife Corridor Analysis (Zoological Archives: An International Journal, 2023)
Wildlife corridors -- linear or patchy habitat features connecting otherwise isolated populations across fragmented landscapes -- are among the most widely advocated structural interventions in conservation planning, yet their design, monitoring, and effectiveness evaluation have historically relied on coarse-resolution land cover data and species distribution models that inadequately represent the fine-scale habitat features determining actual wildlife movement. The integration of multi-resolution remote sensing data -- spanning Sentinel-2 multispectral imagery, LiDAR-derived canopy structure metrics, and synthetic aperture radar (SAR) for landscape structure characterisation -- with machine learning-based habitat classification and GPS animal tracking datasets now enables corridor analysis at spatial resolutions and thematic richness far exceeding previous approaches. This study presents the first systematic multi-species, multi-region comparison of remote sensing-based corridor analyses validated against animal GPS tracking data, applying integrated Sentinel-2 + LiDAR + SAR habitat classification (10 m resolution; 18 land cover classes) to 247 wildlife corridor planning areas across 18 European and African landscapes. RS-based corridor models outperformed conventional coarse-resolution models in predicting GPS-tracked animal movement paths: mean corridor overlap with GPS tracks increased from 57.4% (conventional CORINE-based models) to 84.7% (RS-integrated models). Random forest and gradient boosting classifiers achieved mean land cover accuracy of 91.4% and 89.7% respectively; LiDAR canopy height and density metrics were the most important predictors of animal habitat use for 14 of 18 focal species. The approach identified 247 additional high-quality corridor segments not visible at 100 m resolution -- including 84 segments through agroforestry and tree-lined riparian margins that would be classified as agricultural matrix in coarse resolution data.