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Article

Continuous Detection of Forest Loss in Vietnam, Laos, and Cambodia Using Sentinel-1 Data

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GlobEO, 31400 Toulouse, France
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CNRS/CNES/IRD/INRAE/UPS, CESBIO, Université de Toulouse, 31400 Toulouse, France
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Centre National d’Etudes Spatiales, 31400 Toulouse, France
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World Wildlife Fund France, 93310 Le Pré-Saint-Gervais, France
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LaSTIG, IGN, University of Gustave Eiffel, 77420 Champs-sur-Marne, France
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Cerema Sud-Ouest, 31400 Toulouse, France
*
Author to whom correspondence should be addressed.
Academic Editors: Janne Heiskanen and Luke Wallace
Remote Sens. 2021, 13(23), 4877; https://doi.org/10.3390/rs13234877
Received: 3 October 2021 / Revised: 5 November 2021 / Accepted: 20 November 2021 / Published: 1 December 2021
(This article belongs to the Section Forest Remote Sensing)
In this study, we demonstrate the ability of a new operational system to detect forest loss at a large scale accurately and in a timely manner. We produced forest loss maps every week over Vietnam, Cambodia, and Laos (>750,000 km2 in total) using Sentinel-1 data. To do so, we used the forest loss detection method based on shadow detection. The main advantage of this method is the ability to avoid false alarms, which is relevant in Southeast Asia where the areas of forest disturbance may be very small and scattered and detection is used for alert purposes. The estimated user accuracy of the forest loss map was 0.95 for forest disturbances and 0.99 for intact forest, and the estimated producer’s accuracy was 0.90 for forest disturbances and 0.99 for intact forest, with a minimum mapping unit of 0.1 ha. This represents an important step forward compared to the values achieved by previous studies. We also found that approximately half of forest disturbances in Cambodia from 2018 to 2020 occurred in protected areas, which emphasizes the lack of efficiency in the protection and conservation of natural resources in protected areas. On an annual basis, the forest loss areas detected using our method are found to be similar to the estimations from Global Forest Watch. These results highlight the fact that this method provides not only quick alerts but also reliable detections that can be used to calculate weekly, monthly, or annual forest loss statistics at a national scale. View Full-Text
Keywords: forest loss detection; Sentinel-1; tropical forest; Southeast Asia; protected areas forest loss detection; Sentinel-1; tropical forest; Southeast Asia; protected areas
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MDPI and ACS Style

Mermoz, S.; Bouvet, A.; Koleck, T.; Ballère, M.; Le Toan, T. Continuous Detection of Forest Loss in Vietnam, Laos, and Cambodia Using Sentinel-1 Data. Remote Sens. 2021, 13, 4877. https://doi.org/10.3390/rs13234877

AMA Style

Mermoz S, Bouvet A, Koleck T, Ballère M, Le Toan T. Continuous Detection of Forest Loss in Vietnam, Laos, and Cambodia Using Sentinel-1 Data. Remote Sensing. 2021; 13(23):4877. https://doi.org/10.3390/rs13234877

Chicago/Turabian Style

Mermoz, Stéphane, Alexandre Bouvet, Thierry Koleck, Marie Ballère, and Thuy Le Toan. 2021. "Continuous Detection of Forest Loss in Vietnam, Laos, and Cambodia Using Sentinel-1 Data" Remote Sensing 13, no. 23: 4877. https://doi.org/10.3390/rs13234877

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