Integrating satellite-based forest disturbance alerts improves detection timeliness and confidence

Abstract
Satellite-based near-real-time forest disturbance alerting systems have been widely used to support law enforcement actions against illegal and unsustainable human activities in tropical forests. The availability of multiple optical and radar-based forest disturbance alerts, each with varying detection capabilities depending mainly on the satellite sensor used, poses a challenge for users in selecting the most suitable system for their monitoring needs and workflow. Integrating multiple alerts holds the potential to address the limitations of individual systems. We integrated radar-based RAdar for Detecting Deforestation (RADD) (Sentinel-1), and optical-based Global Land Analysis and Discovery Sentinel-2 (GLAD-S2) and GLAD-Landsat alerts using two confidence rulesets at ten 1° sites across the Amazon Basin. Alert integration resulted in faster detection of new disturbances by days to months, and also shortened the delay to increased confidence. An increased detection rate to an average of 97% when combining alerts highlights the complementary capabilities of the optical and cloud-penetrating radar sensors in detecting largely varying drivers and environmental conditions, such as fires, selective logging, and cloudy circumstances. The most improvement was observed when integrating RADD and GLAD-S2, capitalizing on the high temporal observation density and spatially detailed 10 m Sentinel-1 and 2 data. We introduced the highest confidence class as an addition to the low and high confidence classes of the individual systems, and showed that this displayed no false detection. Considering spatial neighborhood during alert integration enhanced the overall labeled alert confidence level, as nearby alerts mutually reinforced their confidence, but it also led to an increased rate of false detections. We discuss implications of this study for the integration of multiple alert systems. We demonstrate that alert integration is an important data preparation step to make use of multiple alerts more user-friendly, providing stakeholders with reliable and consistent information on new forest disturbances in a timely manner. Google Earth Engine code to integrate various alert datesets is made openly available.