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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. • We present a novel, multisource method to monitor deforestation in near real-time. • Uses a simple approach for data harmonization based on time series models. • Demonstrated for two regions in northern Myanmar with contrasting forest types. • Tradeoffs between detection accuracy and latency are quantified. [ABSTRACT FROM AUTHOR]

As a World Cultural Heritage site with sacred landscape featuring an exceptional range of Buddhist art and architecture, much attention has been focused on the sustainable development of Bagan (Myanmar). Particularly, the monitoring of landscape surface subsidence and monument instability is of great importance to the protection and development planning of the Bagan heritage site. In this study, we applied high resolution TerraSAR-X imagery acquired from 2019 to 2020 for deformation monitoring based on the small baseline subset (SBAS) and persistent scatterer synthetic aperture radar interferometry (PSInSAR) approaches. We identified several hotspots and pagodas with displacement anomalies linked to land cover change and previous earthquakes. The cross comparison between SBAS and PSInSAR and the precision of height estimates derived by PSInSAR indicated a millimetric precision of the derived deformation products. The combination of the two multi-temporal SAR interferometry approaches satisfies the two-scale monitoring requirements from landscapes to monuments, particularly for large-scale World Heritage sites. The non-contact monitoring method has potential when traditional methods using field accessibility and surveillance are constrained. [ABSTRACT FROM AUTHOR]