Generating high-resolution land use and land cover maps for the greater Mariño watershed in 2019 with machine learning

• DOI: 10.1038/s41597-024-03750-x
• Publication Date: 2024-08-23T11:03:37Z
• Authors: Améline Vallet; Stéphane Dupuy; Matthieu Verlynde; Raffaele Gaetano
• Keywords: Data Descriptor; 550; http://aims.fao.org/aos/agrovoc/c_24420; Ecosystem ecology; télédétection; Science; gestion des ressources naturelles; utilisation des terres; http://aims.fao.org/aos/agrovoc/c_35131; http://aims.fao.org/aos/agrovoc/c_36761; http://aims.fao.org/aos/agrovoc/c_9000100; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; http://aims.fao.org/aos/agrovoc/c_5725; évaluation des ressources; cartographie de l'occupation du sol; apprentissage machine; http://aims.fao.org/aos/agrovoc/c_1344; http://aims.fao.org/aos/agrovoc/c_37876; http://aims.fao.org/aos/agrovoc/c_6498; http://aims.fao.org/aos/agrovoc/c_8334; Geography; cartographie de l'utilisation des terres; [SDE.IE]Environmental Sciences/Environmental Engineering; Q; http://aims.fao.org/aos/agrovoc/c_49834; http://aims.fao.org/aos/agrovoc/c_9000094; http://aims.fao.org/aos/agrovoc/c_331583; bassin versant; http://aims.fao.org/aos/agrovoc/c_28019; système d'information géographique; Environmental sciences; E11 - Économie et politique foncières; imagerie par satellite; séquestration du carbone; 526; [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]; P31 - Levés et cartographie des sols; http://aims.fao.org/aos/agrovoc/c_9000115; surveillance de l'environnement; http://aims.fao.org/aos/agrovoc/c_4182; cartographie; impact sur l'environnement; U30 - Méthodes de recherche

Land Use and Land Cover (LULC) maps are important tools for environmental planning and social-ecological 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 multi-sensor 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.