The promising combination of a remote sensing approach and landscape connectivity modelling at a fine scale in urban planning
Urban landscapes are rapid changing ecosystems with diverse urban forms that impede the movement of organisms. Therefore, designing and modelling ecological networks to identify biodiversity reservoirs and their corridors are crucial aspects of land management in terms of population persistence and...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-06-01
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| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X22004010 |
| _version_ | 1811255767055990784 |
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| author | Elie Morin Pierre-Alexis Herrault Yvonnick Guinard Frédéric Grandjean Nicolas Bech |
| author_facet | Elie Morin Pierre-Alexis Herrault Yvonnick Guinard Frédéric Grandjean Nicolas Bech |
| author_sort | Elie Morin |
| collection | DOAJ |
| description | Urban landscapes are rapid changing ecosystems with diverse urban forms that impede the movement of organisms. Therefore, designing and modelling ecological networks to identify biodiversity reservoirs and their corridors are crucial aspects of land management in terms of population persistence and survival. However, the land cover/use maps used for landscape connectivity modelling can lack information in such a highly complex environment. In this context, remote sensing approaches are gaining interest for the development of accurate land cover/use maps. We tested the efficiency of an object-based classification using open-source projects and free images to identify vegetation strata at a very fine scale and evaluated its contribution to landscape connectivity modelling. We compared different spatial and thematic resolutions from existing databases and object-based image analyses in three French cities. Our results suggested that this remote sensing approach produced reliable land cover maps to differentiate artificial areas, tree vegetation and herbaceous vegetation. Land cover maps enhanced with the remote sensing products substantially changed the structural connectivity indices, showing an improvement up to four times the proportion of herbaceous and tree vegetation. In addition, functional connectivity indices evaluated for several forest species were mainly impacted for medium dispersers in quantitative (metrics) and qualitative (corridors) estimations. Thus, the combination of this reproductible remote sensing approach and landscape connectivity modelling at a very fine scale provides new insights into the characterisation of ecological networks for conservation planning. |
| first_indexed | 2024-04-12T17:29:56Z |
| format | Article |
| id | doaj.art-2e650209e42f4421a958dd7cf59225c7 |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-04-12T17:29:56Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-2e650209e42f4421a958dd7cf59225c72022-12-22T03:23:10ZengElsevierEcological Indicators1470-160X2022-06-01139108930The promising combination of a remote sensing approach and landscape connectivity modelling at a fine scale in urban planningElie Morin0Pierre-Alexis Herrault1Yvonnick Guinard2Frédéric Grandjean3Nicolas Bech4Université de Poitiers, Laboratoire Ecologie et Biologie des Interactions (UMR CNRS 7267), 5 Rue Albert Turpin, 86022 Poitiers, France; Corresponding author.Université de Strasbourg, Laboratoire Image, Ville, Environnement (UMR CNRS 7362), 3 Rue de l'Argonne, 67000 Strasbourg, FranceGrand Poitiers Communauté Urbaine, 86000 Poitiers, FranceUniversité de Poitiers, Laboratoire Ecologie et Biologie des Interactions (UMR CNRS 7267), 5 Rue Albert Turpin, 86022 Poitiers, FranceUniversité de Poitiers, Laboratoire Ecologie et Biologie des Interactions (UMR CNRS 7267), 5 Rue Albert Turpin, 86022 Poitiers, FranceUrban landscapes are rapid changing ecosystems with diverse urban forms that impede the movement of organisms. Therefore, designing and modelling ecological networks to identify biodiversity reservoirs and their corridors are crucial aspects of land management in terms of population persistence and survival. However, the land cover/use maps used for landscape connectivity modelling can lack information in such a highly complex environment. In this context, remote sensing approaches are gaining interest for the development of accurate land cover/use maps. We tested the efficiency of an object-based classification using open-source projects and free images to identify vegetation strata at a very fine scale and evaluated its contribution to landscape connectivity modelling. We compared different spatial and thematic resolutions from existing databases and object-based image analyses in three French cities. Our results suggested that this remote sensing approach produced reliable land cover maps to differentiate artificial areas, tree vegetation and herbaceous vegetation. Land cover maps enhanced with the remote sensing products substantially changed the structural connectivity indices, showing an improvement up to four times the proportion of herbaceous and tree vegetation. In addition, functional connectivity indices evaluated for several forest species were mainly impacted for medium dispersers in quantitative (metrics) and qualitative (corridors) estimations. Thus, the combination of this reproductible remote sensing approach and landscape connectivity modelling at a very fine scale provides new insights into the characterisation of ecological networks for conservation planning.http://www.sciencedirect.com/science/article/pii/S1470160X22004010Object-based classificationLandscape connectivity modellingGraph theoryUrbanDispersalVery high resolution |
| spellingShingle | Elie Morin Pierre-Alexis Herrault Yvonnick Guinard Frédéric Grandjean Nicolas Bech The promising combination of a remote sensing approach and landscape connectivity modelling at a fine scale in urban planning Ecological Indicators Object-based classification Landscape connectivity modelling Graph theory Urban Dispersal Very high resolution |
| title | The promising combination of a remote sensing approach and landscape connectivity modelling at a fine scale in urban planning |
| title_full | The promising combination of a remote sensing approach and landscape connectivity modelling at a fine scale in urban planning |
| title_fullStr | The promising combination of a remote sensing approach and landscape connectivity modelling at a fine scale in urban planning |
| title_full_unstemmed | The promising combination of a remote sensing approach and landscape connectivity modelling at a fine scale in urban planning |
| title_short | The promising combination of a remote sensing approach and landscape connectivity modelling at a fine scale in urban planning |
| title_sort | promising combination of a remote sensing approach and landscape connectivity modelling at a fine scale in urban planning |
| topic | Object-based classification Landscape connectivity modelling Graph theory Urban Dispersal Very high resolution |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X22004010 |
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