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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

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 >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.

Details

Title
Mapping Tropical Forest Cover and Deforestation with Planet NICFI Satellite Images and Deep Learning in Mato Grosso State (Brazil) from 2015 to 2021
Author
Wagner, Fabien H 1 ; Dalagnol, Ricardo 1 ; Silva-Junior, Celso H L 1   VIAFID ORCID Logo  ; Griffin, Carter 2 ; Ritz, Alison L 3 ; Hirye, Mayumi C M 4 ; Jean P H B Ometto 5   VIAFID ORCID Logo  ; Saatchi, Sassan 1 

 Institute of the Environment and Sustainability, University of California, Los Angeles, CA 90095, USA; NASA-Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91105, USA; CTREES, Pasadena, CA 91105, USA 
 Institute of the Environment and Sustainability, University of California, Los Angeles, CA 90095, USA; CTREES, Pasadena, CA 91105, USA 
 CTREES, Pasadena, CA 91105, USA; Interdisciplinary Graduate Education Program in Remote Sensing, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA 
 Quapá Lab, Faculty of Architecture and Urbanism, University of São Paulo—USP, São Paulo 05508-900, Brazil 
 Earth System Sciences Center, National Institute for Space Research—INPE, Sao José dos Campos, São Paulo 12227-010, Brazil 
First page
521
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
20724292
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2767301641
Copyright
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.