Object-based image analysis for operational fine-scale regional mapping of land cover within river corridors from multispectral imagery and thematic data
Accurate mapping of land-cover diversity within riparian areas at a regional scale is a major challenge for better understanding the influence of riparian landscapes and related natural and anthropogenic pressures on river ecological status. As the structure (composition and spatial organization) of riparian area land cover (RALC) is generally not accessible using moderate-scale satellite imagery, finer spatial resolution imagery and specific mapping techniques are needed. For this purpose, we developed a classification procedure based on a specific multiscale object-based image analysis (OBIA) scheme dedicated to producing fine-scale and reliable RALC maps in different geographical contexts (relief, climate and geology). This OBIA scheme combines information from very high spatial resolution multispectral imagery (satellite or airborne) and available spatial thematic data using fuzzy expert knowledge classification rules. It was tested over the Hérault River watershed (southern France), which presents contrasting landscapes and a total stream length of 1150 km, using the combination of SPOT (Système Probatoire d'Observation de la Terre) 5 XS imagery (10 m pixels), aerial photography (0.5 m pixels) and several national spatial thematic data. A RALC map was produced (22 classes) with an overall accuracy of 89% and a kappa index of 83%, according to a targeted land-cover pressures typology (six categories of pressures). The results of this experimentation demonstrate that the application of OBIA to multisource spatial data provides an efficient approach for the mapping and monitoring of RALC that can be implemented operationally at a regional or national scale. We further analysed the influence of map resolution on the quantification of riparian spatial indicators to highlight the importance of such data for studying the influence of landscapes on river ecological status at the riparian scale.
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dig-cirad-fr-563161 |
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Francia |
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Europa del Oeste |
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Biblioteca del CIRAD Francia |
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P31 - Levés et cartographie des sols B10 - Géographie U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques cartographie analyse d'image télédétection bassin versant couverture végétale couverture du sol paysage classification modèle imagerie imagerie par satellite image spot photographie aérienne http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_36762 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_8334 http://aims.fao.org/aos/agrovoc/c_25409 http://aims.fao.org/aos/agrovoc/c_37897 http://aims.fao.org/aos/agrovoc/c_4185 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_4881 http://aims.fao.org/aos/agrovoc/c_36760 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_16343 http://aims.fao.org/aos/agrovoc/c_8634 http://aims.fao.org/aos/agrovoc/c_4188 http://aims.fao.org/aos/agrovoc/c_3081 P31 - Levés et cartographie des sols B10 - Géographie U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques cartographie analyse d'image télédétection bassin versant couverture végétale couverture du sol paysage classification modèle imagerie imagerie par satellite image spot photographie aérienne http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_36762 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_8334 http://aims.fao.org/aos/agrovoc/c_25409 http://aims.fao.org/aos/agrovoc/c_37897 http://aims.fao.org/aos/agrovoc/c_4185 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_4881 http://aims.fao.org/aos/agrovoc/c_36760 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_16343 http://aims.fao.org/aos/agrovoc/c_8634 http://aims.fao.org/aos/agrovoc/c_4188 http://aims.fao.org/aos/agrovoc/c_3081 |
spellingShingle |
P31 - Levés et cartographie des sols B10 - Géographie U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques cartographie analyse d'image télédétection bassin versant couverture végétale couverture du sol paysage classification modèle imagerie imagerie par satellite image spot photographie aérienne http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_36762 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_8334 http://aims.fao.org/aos/agrovoc/c_25409 http://aims.fao.org/aos/agrovoc/c_37897 http://aims.fao.org/aos/agrovoc/c_4185 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_4881 http://aims.fao.org/aos/agrovoc/c_36760 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_16343 http://aims.fao.org/aos/agrovoc/c_8634 http://aims.fao.org/aos/agrovoc/c_4188 http://aims.fao.org/aos/agrovoc/c_3081 P31 - Levés et cartographie des sols B10 - Géographie U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques cartographie analyse d'image télédétection bassin versant couverture végétale couverture du sol paysage classification modèle imagerie imagerie par satellite image spot photographie aérienne http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_36762 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_8334 http://aims.fao.org/aos/agrovoc/c_25409 http://aims.fao.org/aos/agrovoc/c_37897 http://aims.fao.org/aos/agrovoc/c_4185 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_4881 http://aims.fao.org/aos/agrovoc/c_36760 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_16343 http://aims.fao.org/aos/agrovoc/c_8634 http://aims.fao.org/aos/agrovoc/c_4188 http://aims.fao.org/aos/agrovoc/c_3081 Tormos, Thierry Kosuth, Pascal Durrieu, Sylvie Dupuy, Stéphane Villeneuve, B. Wasson, Jean-Gabriel Object-based image analysis for operational fine-scale regional mapping of land cover within river corridors from multispectral imagery and thematic data |
description |
Accurate mapping of land-cover diversity within riparian areas at a regional scale is a major challenge for better understanding the influence of riparian landscapes and related natural and anthropogenic pressures on river ecological status. As the structure (composition and spatial organization) of riparian area land cover (RALC) is generally not accessible using moderate-scale satellite imagery, finer spatial resolution imagery and specific mapping techniques are needed. For this purpose, we developed a classification procedure based on a specific multiscale object-based image analysis (OBIA) scheme dedicated to producing fine-scale and reliable RALC maps in different geographical contexts (relief, climate and geology). This OBIA scheme combines information from very high spatial resolution multispectral imagery (satellite or airborne) and available spatial thematic data using fuzzy expert knowledge classification rules. It was tested over the Hérault River watershed (southern France), which presents contrasting landscapes and a total stream length of 1150 km, using the combination of SPOT (Système Probatoire d'Observation de la Terre) 5 XS imagery (10 m pixels), aerial photography (0.5 m pixels) and several national spatial thematic data. A RALC map was produced (22 classes) with an overall accuracy of 89% and a kappa index of 83%, according to a targeted land-cover pressures typology (six categories of pressures). The results of this experimentation demonstrate that the application of OBIA to multisource spatial data provides an efficient approach for the mapping and monitoring of RALC that can be implemented operationally at a regional or national scale. We further analysed the influence of map resolution on the quantification of riparian spatial indicators to highlight the importance of such data for studying the influence of landscapes on river ecological status at the riparian scale. |
format |
article |
topic_facet |
P31 - Levés et cartographie des sols B10 - Géographie U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques cartographie analyse d'image télédétection bassin versant couverture végétale couverture du sol paysage classification modèle imagerie imagerie par satellite image spot photographie aérienne http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_36762 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_8334 http://aims.fao.org/aos/agrovoc/c_25409 http://aims.fao.org/aos/agrovoc/c_37897 http://aims.fao.org/aos/agrovoc/c_4185 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_4881 http://aims.fao.org/aos/agrovoc/c_36760 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_16343 http://aims.fao.org/aos/agrovoc/c_8634 http://aims.fao.org/aos/agrovoc/c_4188 http://aims.fao.org/aos/agrovoc/c_3081 |
author |
Tormos, Thierry Kosuth, Pascal Durrieu, Sylvie Dupuy, Stéphane Villeneuve, B. Wasson, Jean-Gabriel |
author_facet |
Tormos, Thierry Kosuth, Pascal Durrieu, Sylvie Dupuy, Stéphane Villeneuve, B. Wasson, Jean-Gabriel |
author_sort |
Tormos, Thierry |
title |
Object-based image analysis for operational fine-scale regional mapping of land cover within river corridors from multispectral imagery and thematic data |
title_short |
Object-based image analysis for operational fine-scale regional mapping of land cover within river corridors from multispectral imagery and thematic data |
title_full |
Object-based image analysis for operational fine-scale regional mapping of land cover within river corridors from multispectral imagery and thematic data |
title_fullStr |
Object-based image analysis for operational fine-scale regional mapping of land cover within river corridors from multispectral imagery and thematic data |
title_full_unstemmed |
Object-based image analysis for operational fine-scale regional mapping of land cover within river corridors from multispectral imagery and thematic data |
title_sort |
object-based image analysis for operational fine-scale regional mapping of land cover within river corridors from multispectral imagery and thematic data |
url |
http://agritrop.cirad.fr/563161/ http://agritrop.cirad.fr/563161/1/document_563161.pdf |
work_keys_str_mv |
AT tormosthierry objectbasedimageanalysisforoperationalfinescaleregionalmappingoflandcoverwithinrivercorridorsfrommultispectralimageryandthematicdata AT kosuthpascal objectbasedimageanalysisforoperationalfinescaleregionalmappingoflandcoverwithinrivercorridorsfrommultispectralimageryandthematicdata AT durrieusylvie objectbasedimageanalysisforoperationalfinescaleregionalmappingoflandcoverwithinrivercorridorsfrommultispectralimageryandthematicdata AT dupuystephane objectbasedimageanalysisforoperationalfinescaleregionalmappingoflandcoverwithinrivercorridorsfrommultispectralimageryandthematicdata AT villeneuveb objectbasedimageanalysisforoperationalfinescaleregionalmappingoflandcoverwithinrivercorridorsfrommultispectralimageryandthematicdata AT wassonjeangabriel objectbasedimageanalysisforoperationalfinescaleregionalmappingoflandcoverwithinrivercorridorsfrommultispectralimageryandthematicdata |
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1792498075339587584 |
spelling |
dig-cirad-fr-5631612024-01-28T20:05:48Z http://agritrop.cirad.fr/563161/ http://agritrop.cirad.fr/563161/ Object-based image analysis for operational fine-scale regional mapping of land cover within river corridors from multispectral imagery and thematic data. Tormos Thierry, Kosuth Pascal, Durrieu Sylvie, Dupuy Stéphane, Villeneuve B., Wasson Jean-Gabriel. 2012. International Journal of Remote Sensing, 33 (14) : 4603-4633.https://doi.org/10.1080/01431161.2011.637093 <https://doi.org/10.1080/01431161.2011.637093> Object-based image analysis for operational fine-scale regional mapping of land cover within river corridors from multispectral imagery and thematic data Tormos, Thierry Kosuth, Pascal Durrieu, Sylvie Dupuy, Stéphane Villeneuve, B. Wasson, Jean-Gabriel eng 2012 International Journal of Remote Sensing P31 - Levés et cartographie des sols B10 - Géographie U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques cartographie analyse d'image télédétection bassin versant couverture végétale couverture du sol paysage classification modèle imagerie imagerie par satellite image spot photographie aérienne http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_36762 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_8334 http://aims.fao.org/aos/agrovoc/c_25409 http://aims.fao.org/aos/agrovoc/c_37897 http://aims.fao.org/aos/agrovoc/c_4185 http://aims.fao.org/aos/agrovoc/c_1653 http://aims.fao.org/aos/agrovoc/c_4881 http://aims.fao.org/aos/agrovoc/c_36760 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_16343 http://aims.fao.org/aos/agrovoc/c_8634 Languedoc-Roussillon France http://aims.fao.org/aos/agrovoc/c_4188 http://aims.fao.org/aos/agrovoc/c_3081 Accurate mapping of land-cover diversity within riparian areas at a regional scale is a major challenge for better understanding the influence of riparian landscapes and related natural and anthropogenic pressures on river ecological status. As the structure (composition and spatial organization) of riparian area land cover (RALC) is generally not accessible using moderate-scale satellite imagery, finer spatial resolution imagery and specific mapping techniques are needed. For this purpose, we developed a classification procedure based on a specific multiscale object-based image analysis (OBIA) scheme dedicated to producing fine-scale and reliable RALC maps in different geographical contexts (relief, climate and geology). This OBIA scheme combines information from very high spatial resolution multispectral imagery (satellite or airborne) and available spatial thematic data using fuzzy expert knowledge classification rules. It was tested over the Hérault River watershed (southern France), which presents contrasting landscapes and a total stream length of 1150 km, using the combination of SPOT (Système Probatoire d'Observation de la Terre) 5 XS imagery (10 m pixels), aerial photography (0.5 m pixels) and several national spatial thematic data. A RALC map was produced (22 classes) with an overall accuracy of 89% and a kappa index of 83%, according to a targeted land-cover pressures typology (six categories of pressures). The results of this experimentation demonstrate that the application of OBIA to multisource spatial data provides an efficient approach for the mapping and monitoring of RALC that can be implemented operationally at a regional or national scale. We further analysed the influence of map resolution on the quantification of riparian spatial indicators to highlight the importance of such data for studying the influence of landscapes on river ecological status at the riparian scale. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/563161/1/document_563161.pdf application/pdf Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1080/01431161.2011.637093 10.1080/01431161.2011.637093 info:eu-repo/semantics/altIdentifier/doi/10.1080/01431161.2011.637093 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1080/01431161.2011.637093 |