The Role of Remote Sensing Data in Habitat Suitability and Connectivity Modeling: Insights from the Cantabrian Brown Bear
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 contin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/6/1138 |
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| author | Pablo Cisneros-Araujo Teresa Goicolea María Cruz Mateo-Sánchez Juan Ignacio García-Viñás Miguel Marchamalo Audrey Mercier Aitor Gastón |
| author_facet | Pablo Cisneros-Araujo Teresa Goicolea María Cruz Mateo-Sánchez Juan Ignacio García-Viñás Miguel Marchamalo Audrey Mercier Aitor Gastón |
| author_sort | Pablo Cisneros-Araujo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T13:09:46Z |
| format | Article |
| id | doaj.art-7d2f7e2402804ed6b089d74f175bac91 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T13:09:46Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7d2f7e2402804ed6b089d74f175bac912023-11-21T10:49:49ZengMDPI AGRemote Sensing2072-42922021-03-01136113810.3390/rs13061138The Role of Remote Sensing Data in Habitat Suitability and Connectivity Modeling: Insights from the Cantabrian Brown BearPablo Cisneros-Araujo0Teresa Goicolea1María Cruz Mateo-Sánchez2Juan Ignacio García-Viñás3Miguel Marchamalo4Audrey Mercier5Aitor Gastón6EGOGESFOR Research Group, Universidad Politécnica de Madrid, ETSI Montes, Forestal y del Medio Natural, Ciudad Universitaria s/n, 28040 Madrid, SpainEGOGESFOR Research Group, Universidad Politécnica de Madrid, ETSI Montes, Forestal y del Medio Natural, Ciudad Universitaria s/n, 28040 Madrid, SpainEGOGESFOR Research Group, Universidad Politécnica de Madrid, ETSI Montes, Forestal y del Medio Natural, Ciudad Universitaria s/n, 28040 Madrid, SpainEGOGESFOR Research Group, Universidad Politécnica de Madrid, ETSI Montes, Forestal y del Medio Natural, Ciudad Universitaria s/n, 28040 Madrid, SpainDepartamento de Ingeniería y Morfología del Terreno, Universidad Politécnica de Madrid, 28040 Madrid, SpainCIRAD, Forêts et Sociétés, Univ Montpellier, 34398 Montpellier, FranceEGOGESFOR Research Group, Universidad Politécnica de Madrid, ETSI Montes, Forestal y del Medio Natural, Ciudad Universitaria s/n, 28040 Madrid, SpainEcological 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.https://www.mdpi.com/2072-4292/13/6/1138ecological modelinglandscape connectivityspecies–habitat relationshipsspatial resolutionthematic resolution<i>Ursus arctos</i> |
| spellingShingle | Pablo Cisneros-Araujo Teresa Goicolea María Cruz Mateo-Sánchez Juan Ignacio García-Viñás Miguel Marchamalo Audrey Mercier Aitor Gastón The Role of Remote Sensing Data in Habitat Suitability and Connectivity Modeling: Insights from the Cantabrian Brown Bear Remote Sensing ecological modeling landscape connectivity species–habitat relationships spatial resolution thematic resolution <i>Ursus arctos</i> |
| title | The Role of Remote Sensing Data in Habitat Suitability and Connectivity Modeling: Insights from the Cantabrian Brown Bear |
| title_full | The Role of Remote Sensing Data in Habitat Suitability and Connectivity Modeling: Insights from the Cantabrian Brown Bear |
| title_fullStr | The Role of Remote Sensing Data in Habitat Suitability and Connectivity Modeling: Insights from the Cantabrian Brown Bear |
| title_full_unstemmed | The Role of Remote Sensing Data in Habitat Suitability and Connectivity Modeling: Insights from the Cantabrian Brown Bear |
| title_short | The Role of Remote Sensing Data in Habitat Suitability and Connectivity Modeling: Insights from the Cantabrian Brown Bear |
| title_sort | role of remote sensing data in habitat suitability and connectivity modeling insights from the cantabrian brown bear |
| topic | ecological modeling landscape connectivity species–habitat relationships spatial resolution thematic resolution <i>Ursus arctos</i> |
| url | https://www.mdpi.com/2072-4292/13/6/1138 |
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