The Role of Remote Sensing Data in Habitat Suitability and Connectivity Modeling: Insights from the Cantabrian Brown Bear

• Published in: Remote Sensing
• Main Authors: Pablo Cisneros-Araujo, Teresa Goicolea, María Cruz Mateo-Sánchez, Juan Ignacio García-Viñás, Miguel Marchamalo, Audrey Mercier, Aitor Gastón
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2021
• Subjects: ecological modeling; landscape connectivity; species–habitat relationships; spatial resolution; thematic resolution; Ursus arctos; Science; Q
• Online Access: https://doi.org/10.3390/rs13061138; https://doaj.org/article/7d2f7e2402804ed6b089d74f175bac91

Ecological modeling requires sufficient spatial resolution and a careful selection of environmental variables to achieve good predictive performance. Although national and international administrations offer fine-scale environmental data, they usually have limited spatial coverage (country or continent). Alternatively, optical and radar satellite imagery is available with high resolutions, global coverage and frequent revisit intervals. Here, we compared the performance of ecological models trained with free satellite data with models fitted using regionally restricted spatial datasets. We developed brown bear habitat suitability and connectivity models from three datasets with different spatial coverage and accessibility. These datasets comprised (1) a Sentinel-1 and 2 land cover map (global coverage); (2) pan-European vegetation and land cover layers (continental coverage); and (3) LiDAR data and the Forest Map of Spain (national coverage). Results show that Sentinel imagery and pan-European datasets are powerful sources to estimate vegetation variables for habitat and connectivity modeling. However, Sentinel data could be limited for understanding precise habitat–species associations if the derived discrete variables do not distinguish a wide range of vegetation types. Therefore, more effort should be taken to improving the thematic resolution of satellite-derived vegetation variables. Our findings support the application of ecological modeling worldwide and can help select spatial datasets according to their coverage and resolution for habitat suitability and connectivity modeling.