ORIGINAL RESEARCH article
Sec. Forest Disturbance
This article is part of the Research Topic
Synthetic Aperture Radar (SAR) Remote Sensing Methods for Forest Parameters, Disturbance and Change Studies
Reliably Mapping Low-intensity Forest Disturbance Using Satellite Radar Data
- 1University of Edinburgh, United Kingdom
- 2University College London, United Kingdom
- 3Sylvera, United Kingdom
- 4Escuela Profesional de Biologia, Universidad Nacional de San Antonio Abad del Cusco, Peru
- 5Omar Bongo University, Gabon
- 6Agence Gabonaise d’Etudes et d’Observations Spatiale, Gabon
- 7Asociación para la Investigación y Desarrollo Integral, Peru
- 8Robotic Air Systems, Peru
- 9COTECMI, Peru
- 10NERC National Centre for Earth Observation (NCE), United Kingdom
In the last decades tropical forests have experienced increased fragmentation due to a global growing demand for agricultural and forest commodities. Satellite remote sensing offers a valuable tool for monitoring forest loss, thanks to the global coverage and the temporal consistency of the acquisitions. In tropical regions, C-band Synthetic Aperture Radar (SAR) data from the Sentinel-1 mission provides cloud-free and open imagery on a 6 or 12-day repeat cycle, offering the unique opportunity to monitor forest disturbances in a timely and continuous manner. Despite recent advances, mapping subtle forest losses, such as those due to small-scale and irregular selective logging, remains problematic. A Cumulative Sum (CuSum) approach has been recently proposed for forest monitoring applications, with preliminary studies showing promising results. Unfortunately, the lack of accurate in-situ measurements of tropical forest loss has prevented a full validation of this approach, especially in the case of low-intensity logging. In this study, we used high-quality field measurements from the tropical Forest Degradation Experiment (FODEX), combining UAV LiDAR, Terrestrial Laser Scanning (TLS) and field-inventoried data of forest structural change collected in two logging concessions in Gabon and Peru. The CuSum algorithm was applied to VV-polarised Sentinel-1 ground range detected (GRD) time series to monitor a range of canopy loss events. We developed a single change metric using the maximum of the CuSum distribution, retrieving location, time and magnitude of the disturbance. A comparison of the CuSum algorithm with the LiDAR reference map resulted in more than 65% success rate for disturbances as small as 0.01 ha in size and for canopy height losses as fine as 10 m. A correlation between the change metric and above ground biomass (AGB) change was found with R^2 = 0.95, and R^2 = 0.83 for canopy height loss. From the regression model we directly estimated local AGB loss maps for the year 2020, at 1 ha scale and in percentages of AGB loss. The results of this study confirm this approach as a simple and reproducible change detection method for quantifying fine-scale to high intensity forest disturbances, even in the case of multi-storied and high biomass forests.
Keywords: Forest degradation, deforestation, change detection, Sentinel-1, Earth Observation, Biomass mapping, Synthetic Aperture Radar, Radar, Logging
Received: 13 Aug 2022;
Accepted: 02 Sep 2022.
Copyright: © 2022 Aquino, Mitchard, McNicol, Carstairs, Burt, Puma Vilca, Obiang Ebanega, Modinga Dikongo, Dassi, Mayta, Tamayo, Grijalba, Miranda and Disney. 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) and the copyright owner(s) 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: Mx. Chiara Aquino, University of Edinburgh, Edinburgh, United Kingdom