Forest disturbance alerts for the Congo Basin using Sentinel-1

Abstract

A humid tropical forest disturbance alert using Sentinel-1 radar data is presented for the Congo Basin. Radar satellite signals can penetrate through clouds, allowing Sentinel-1 to provide gap-free observations for the tropics consistently every 6–12 days at 10 m spatial scale. In the densely cloud covered Congo Basin, this represents a major advantage for the rapid detection of small-scale forest disturbances such as subsistence agriculture and selective logging. Alerts were detected with latest available Sentinel-1 images and results are presented from January 2019 to July 2020. We mapped 4 million disturbance events during this period, totalling 1.4 million ha with nearly 80% of events smaller than 0.5 ha. Monthly distribution of alert totals varied widely across the Congo Basin countries and can be linked to regional differences in wet and dry season cycles, with more forest disturbances in the dry season. Results indicated high user’s and producer’s accuracies and the rapid confirmation of alerts within a few weeks. Our disturbance alerts provide confident detection of events larger than or equal to 0.2 ha but do not include smaller events, which suggests that disturbance rates in the Congo Basin are even higher than presented in this study. The new alert product can help to better study the forest dynamics in the Congo Basin with improved spatial and temporal detail and near real-time detections, and highlights the value of dense Sentinel-1 time series data for large-area tropical forest monitoring. The research contributes to the Global Forest Watch initiative in providing timely and accurate information to support a wide range of stakeholders in sustainable forest management and law enforcement. The alerts are available via the https://www.globalforestwatch.org and http://radd-alert.wur.nl.

Intra-Annual Identification of Local Deforestation Hotspots in the Philippines Using Earth Observation Products

“…Such comparison was demonstrated in Congo where higher detection was reported using the radar-based system in comparison with GLAD and the GFC loss data [33] and in Peru when also considering forest edges to detect deforestation [37]. Though it was reported also that the early 2020 RADD alerts in Asia may be overestimated due to the forest baselining of the RADD product [33]. On the other hand, there can still be potential commission errors (false positives) for an alert product since they detect losses based on single-date images instead of image composites [21].…”

“…This limitation can also be the reason for the higher number of hotspots detected when using RADD (radar) compared to GLAD (optical). Such comparison was demonstrated in Congo where higher detection was reported using the radar-based system in comparison with GLAD and the GFC loss data [33] and in Peru when also considering forest edges to detect deforestation [37]. Though it was reported also that the early 2020 RADD alerts in Asia may be overestimated due to the forest baselining of the RADD product [33].…”

Exploring Archetypes of Tropical Fire-Related Forest Disturbances Based on Dense Optical and Radar Satellite Data and Active Fire Alerts

“…The monitoring continued when the flagged forest disturbance was not confirmed. The confirmation of flagged forest disturbances was orbit-specific for the radar time series [66].…”

“…Next, we removed outliers in the time series due to the remaining cloud and cloud shadow or atmospheric noise after the atmospheric correction using a pixel-wise approach following Hamunyela et al [65]. Additionally, an image normalization was applied to mitigate dry season and drought effects [38,66].…”

“…Prior to the ingestion in the GEE archive, several pre-processing steps were applied, including apply orbit file, GRD border noise removal, thermal noise removal, radiometric calibration, terrain correction and geocoding [67]. Additionally, we applied an angular-based radiometric slope correction [68], adaptive multitemporal speckle filtering [69], conversion from linear to dB scale, resampling to the 30 m Landsat grid and image normalization to mitigate dry season and drought effects [38,66].…”