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METHODS article

Front. Remote Sens.
Sec. Terrestrial Carbon Cycle
Volume 5 - 2024 | doi: 10.3389/frsen.2024.1416550
This article is part of the Research Topic One Forest Vision Initiative (OFVi) for Monitoring Tropical Forests: The Remote Sensing Pilar View all articles

A near-real-time tropical deforestation monitoring algorithm based on the CuSum change detection method

Provisionally accepted
Bertrand Ygorra Bertrand Ygorra 1,2*Frédéric Frappart Frédéric Frappart 1Jean-Pierre Wigneron Jean-Pierre Wigneron 1Thibault Catry Thibault Catry 3,4,5,6Benjamin Pillot Benjamin Pillot 3,4,5,6Antoine Pfefer Antoine Pfefer 3,4,5,6Jonas Courtalon Jonas Courtalon 1,7Serge Riazanoff Serge Riazanoff 2
  • 1 INRAE ​​Nouvelle-Aquitaine Bordeaux, Bordeaux, Aquitaine, France
  • 2 Visioterra, Champs-Sur-Marne, France
  • 3 IRD UMR228 Espace pour le développement (ESPACE-DEV), Montpellier, Languedoc-Roussillon, France
  • 4 Université de Montpellier, Montpellier, Languedoc-Roussillon, France
  • 5 Université de la Réunion, Saint-Denis, France
  • 6 Université des Antilles et de la Guyane, Pointe-à-Pitre, France
  • 7 Université de Bordeaux, Bordeaux, Aquitaine, France

The final, formatted version of the article will be published soon.

    Tropical forests are currently under pressure from increasing threats. These threats are mostly related to human activities. Earth observations (EO) are increasingly used for monitoring forest cover, especially synthetic aperture radar (SAR), that is less affected than optical sensors by atmospheric conditions. Since the launch of the Sentinel-1 satellites, numerous methods for forest disturbance monitoring have been developed, including near real-time (NRT) operational algorithms as systems providing early warnings on deforestation. These systems include Radar for Detecting Deforestation (RADD), Global Land Analysis and Discovery (GLAD), Real Time Deforestation Detection System (DETER), and Jica-Jaxa Forest Early Warning System (JJ-FAST). These algorithms provide online disturbance maps and are applied at continental / global scales with a Minimum Mapping Unit (MMU) ranging from 0.1 ha to 6.25 ha. For local operators, these algorithms are hard to customize to meet users' specific needs. Recently, the Cumulative sum change detection (CuSum) method has been developed for the monitoring of forest disturbances from long time series of Sentinel-1 images. Here, we present the development of a NRT version of CuSum with a MMU of 0.03 ha. The values of the different parameters of this NRT CuSum algorithm were determined to optimize the detection of changes using the F1-score. In the best configuration, 68% precision, 72% recall, 93% accuracy and 0.71 F1-score were obtained.

    Keywords: SAR remote sensing, time-series analysis, Tropical forest monitoring, deforestation, Near-real-time monitoring

    Received: 12 Apr 2024; Accepted: 03 Jul 2024.

    Copyright: © 2024 Ygorra, Frappart, Wigneron, Catry, Pillot, Pfefer, Courtalon and Riazanoff. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Bertrand Ygorra, INRAE ​​Nouvelle-Aquitaine Bordeaux, Bordeaux, Aquitaine, France

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