National wetland mapping using remote-sensing-derived environmental variables, archive field data, and artificial intelligence.

ABSTRACT: While wetland ecosystem services are widely recognized, the lack of fine-scale national inventories prevents successful implementation of conservation policies. Wetlands are difficult to map due to their complex fine-grained spatial pattern and fuzzy boundaries. However, the increasing amount of open high-spatial-resolution remote sensing data and accurately georeferenced field data archives, as well as progress in artificial intelligence (AI), provide opportunities for fine-scale national wetland mapping. The objective of this study was to map wetlands over mainland France (ca. 550,000 km²) by applying AI to environmental variables derived from remote sensing and archive field data. A random forest model was calibrated using spatial cross-validation according to the precision-r

SUBMITTER: Rapinel S 

PROVIDER: S-EPMC9929292 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature