Generating high-resolution land use and land cover maps for the greater Mariño watershed in 2019 with machine learning
2024
Vallet, Améline | Dupuy, Stéphane | Verlynde, Matthieu | Gaetano, Raffaele | Centre International de Recherche sur l'Environnement et le Développement (CIRED) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École des hautes études en sciences sociales (EHESS)-AgroParisTech-École nationale des ponts et chaussées (ENPC)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS) | Ecologie Systématique et Evolution (ESE) ; AgroParisTech-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS) | Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | MSH Paris-Saclay | CLAND
International audience
Afficher plus [+] Moins [-]anglais. Land Use and Land Cover (LULC) maps are important tools for environmental planning and socialecological modeling, as they provide critical information for evaluating risks, managing natural resources, and facilitating effective decision-making. This study aimed to generate a very high spatial resolution (0.5 m) and detailed (21 classes) LULC map for the greater Mariño watershed (Peru) in 2019, using the MORINGA processing chain. This new method for LULC mapping consisted in a supervised object-based LULC classification, using the random forest algorithm along with multisensor satellite imagery from which spectral and textural predictors were derived (a very high spatial resolution Pléiades image and a time serie of high spatial resolution Sentinel-2 images). The random forest classifier showed a very good performance and the LULC map was further improved through additional post-treatment steps that included cross-checking with external GIS data sources and manual correction using photointerpretation, resulting in a more accurate and reliable map. The final LULC provides new information for environmental management and monitoring in the greater Mariño watershed. With this study we contribute to the efforts to develop standardized and replicable methodologies for high-resolution and high-accuracy LULC mapping, which is crucial for informed decision-making and conservation strategies.
Afficher plus [+] Moins [-]Mots clés AGROVOC
Informations bibliographiques
Cette notice bibliographique a été fournie par Institut national de la recherche agronomique
Découvrez la collection de ce fournisseur de données dans AGRIS