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

ABSTRACT: 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

SUBMITTER: Vallet A 

PROVIDER: S-EPMC11344052 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature