Wagner, Fabien H.Dalagnol, RicardoSilva-Junior, Celso H. L.Carter, GriffinRitz, Alison L.Hirye, Mayumi C. M.Ometto, Jean P. H. B.Saatchi, Sassan2023-01-202023-01-202023-01-16Wagner, F.H.; Dalagnol, R.; Silva-Junior, C.H.L.; Carter, G.; Ritz, A.L.; Hirye, M.C.M.; Ometto, J.P.H.B.; Saatchi, S. Mapping Tropical Forest Cover and Deforestation with Planet NICFI Satellite Images and Deep Learning in Mato Grosso State (Brazil) from 2015 to 2021. Remote Sens. 2023, 15, 521.http://hdl.handle.net/10919/113316Monitoring changes in tree cover for assessment of deforestation is a premise for policies to reduce carbon emission in the tropics. Here, a U-net deep learning model was used to map monthly tropical tree cover in the Brazilian state of Mato Grosso between 2015 and 2021 using 5 m spatial resolution Planet NICFI satellite images. The accuracy of the tree cover model was extremely high, with an F1-score >0.98, further confirmed by an independent LiDAR validation showing that 95% of tree cover pixels had a height >5 m while 98% of non-tree cover pixels had a height <5 m. The biannual map of deforestation was then built from the monthly tree cover map. The deforestation map showed relatively consistent agreement with the official deforestation map from Brazil (67.2%) but deviated significantly from Global Forest Change (GFC)’s year of forest loss, showing that our product is closest to the product made by visual interpretation. Finally, we estimated that 14.8% of Mato Grosso’s total area had undergone clear-cut logging between 2015 and 2021, and that deforestation was increasing, with December 2021, the last date, being the highest. High-resolution imagery from Planet NICFI in conjunction with deep learning techniques can significantly improve the mapping of deforestation extent in tropical regions.application/pdfenCreative Commons Attribution 4.0 Internationaltropical forestssemantic segmentationU-netTensorFlow 2land-cover and land-useArticle - Refereed2023-01-20https://doi.org/10.3390/rs15020521