A multi-source change detection algorithm supporting user customization and near real-time deforestation detections

Abstract

The abundance of free and accessible satellite data has revolutionized our ability to study deforestation with remote sensing. Recent advances have enabled us to monitor deforestation in near real-time (NRT), and a number of operational NRT alert systems using both optical and synthetic aperture radar (SAR) data have been developed. Yet despite their success, there are three primary issues with current systems. First, alerts often require multiple observations to confirm deforestation, which does not help enforcement if deforestation events are short-lived. Second, the methods can be computationally intensive and often focus on presenting binary detection alerts. Third, results are often reported as static accuracies or temporal lags, with relatively little discussion about how either could be improved for different use-cases. We therefore propose a novel, multisource NRT monitoring algorithm that addresses these issues by quickly achieving high-accuracy detections, using simple data harmonization to combine data sources while yielding probabilities of deforestation, and can be customized to meet management goals where users can explicitly change the algorithm to prioritize latency or accuracy in their detections based on their chosen data inputs. To demonstrate the algorithm's potential, we used combined Landsat-8, Sentinel-2, and Sentinel-1 SAR data in northern Myanmar to monitor areas with contrasting forest types. When testing all possible parameterizations, we achieved comparable results to previous studies, with median false positive rates and false negative rates under 10% and 25%, respectively, and median F1 scores consistently above 75% across the sensor combinations. Median post-data acquisition detection times were faster than other studies and ranged from 2 to 8 days, though the majority were between 2 and 5 days. In attempting to optimize the algorithm for our demonstrated region, we discovered there was no optimal parameterization that simultaneously provided the best accuracy and detection. In addition, we found that including Sentinel-1 data did not improve latency or accuracy, most likely due to relatively high levels of noise in our monsoonal study system that experienced high seasonal precipitation variation. We finally chose an example parameterization to create daily landscape maps of deforestation at 10 m resolution in two regions within the study area (∼60,000 ha). We assert this new, customizable method with the quantification of accuracy and latency trade-offs represents a clear advance for NRT change detection and can improve current operational systems.