{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T20:01:36Z","timestamp":1786651296844,"version":"build-2736575974"},"reference-count":56,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T00:00:00Z","timestamp":1673827200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Monitoring 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 &gt;0.98, further confirmed by an independent LiDAR validation showing that 95% of tree cover pixels had a height &gt;5 m while 98% of non-tree cover pixels had a height &lt;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)\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/rs15020521","type":"journal-article","created":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T04:31:32Z","timestamp":1673843492000},"page":"521","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Mapping Tropical Forest Cover and Deforestation with Planet NICFI Satellite Images and Deep Learning in Mato Grosso State (Brazil) from 2015 to 2021"],"prefix":"10.3390","volume":"15","author":[{"given":"Fabien H.","family":"Wagner","sequence":"first","affiliation":[{"name":"Institute of the Environment and Sustainability, University of California, Los Angeles, CA 90095, USA"},{"name":"NASA-Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91105, USA"},{"name":"CTREES, Pasadena, CA 91105, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ricardo","family":"Dalagnol","sequence":"additional","affiliation":[{"name":"Institute of the Environment and Sustainability, University of California, Los Angeles, CA 90095, USA"},{"name":"NASA-Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91105, USA"},{"name":"CTREES, Pasadena, CA 91105, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1052-5551","authenticated-orcid":false,"given":"Celso H. L.","family":"Silva-Junior","sequence":"additional","affiliation":[{"name":"Institute of the Environment and Sustainability, University of California, Los Angeles, CA 90095, USA"},{"name":"NASA-Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91105, USA"},{"name":"CTREES, Pasadena, CA 91105, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Griffin","family":"Carter","sequence":"additional","affiliation":[{"name":"Institute of the Environment and Sustainability, University of California, Los Angeles, CA 90095, USA"},{"name":"CTREES, Pasadena, CA 91105, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1112-7424","authenticated-orcid":false,"given":"Alison L.","family":"Ritz","sequence":"additional","affiliation":[{"name":"CTREES, Pasadena, CA 91105, USA"},{"name":"Interdisciplinary Graduate Education Program in Remote Sensing, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mayumi C. M.","family":"Hirye","sequence":"additional","affiliation":[{"name":"Quap\u00e1 Lab, Faculty of Architecture and Urbanism, University of S\u00e3o Paulo\u2014USP, S\u00e3o Paulo 05508-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4221-1039","authenticated-orcid":false,"given":"Jean P. H. B.","family":"Ometto","sequence":"additional","affiliation":[{"name":"Earth System Sciences Center, National Institute for Space Research\u2014INPE, Sao Jos\u00e9 dos Campos, S\u00e3o Paulo 12227-010, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sassan","family":"Saatchi","sequence":"additional","affiliation":[{"name":"Institute of the Environment and Sustainability, University of California, Los Angeles, CA 90095, USA"},{"name":"NASA-Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91105, USA"},{"name":"CTREES, Pasadena, CA 91105, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1038\/s41558-020-00976-6","article-title":"Global maps of twenty-first century forest carbon fluxes","volume":"11","author":"Harris","year":"2021","journal-title":"Nat. Clim. 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