Reliably Mapping Low-intensity Forest Disturbance Using Satellite Radar Data

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.