Using a collection of imagery from June - 2018 taken by the Worldview-3 satellite, land cover was classified for the island of Moorea, French Polynesia. A deep learning pixel classification model was trained for each of four separate dates of image collection when clouds were sparse over the island. The model was trained at the native resolution of the imagery (<2m pixels). Training data included the multispectral WV-3 bands in addition to derived bands that index vegetation productivity (NDVI), vegetation texture (NDVI IDM), and water cover (NDWI). A consensus land cover map was generated from model predictions across the four sets of imagery.
This material uses data collected by the U.S. National Science Foundation's (NSF) Moorea Coral Reef Long Term Ecological Research (MCR LTER) site under Grant No. OCE 2224354 (and earlier awards). Additional financial support to the MCR LTER site was provided through a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2024).