ASAP or AAAP? The Importance of Tradeoffs Between Detection Time and Accuracy for Multisource Deforestation Monitoring
Abstract
Detecting deforestation quickly and accurately has long been a focus of remote sensing, and with the large availability of satellite data, methods have continuously advanced. To lower temporal latency and increase accuracy, a growing number of studies have pursued multi-source approaches. For instance, in areas of persistent cloud cover, using synthetic aperture radar (SAR) may be the only source of observations. Typically, near real-time (NRT) monitoring approaches have used retrospective change detection methods to maximize an accuracy metric like the F1 score. Much less attention has been paid to potential parameter tradeoffs. Can faster detections be achieved with alternative inputs, and at what cost to accuracy? We developed a novel NRT approach that monitors Landsat-8, Sentinel-2, and Sentinel-1 SAR time series in order to calculate a daily probability of disturbance. After combining standardized residuals of sensor-specific models, we converted an exponentially-weighted moving average (EWMA) to a disturbance probability. We explored how altering the EWMA sensitivity affected detection accuracy (F1) and latency (days until detection) using training data manually identified from PlanetScope in northern Myanmar. For a moderate parameterization, the algorithm detected disturbances within a median of 1-2 observations (mean of 3.3-9.5 days), with an overall F1 score of greater than 0.90. We found two main trade-offs. The most sensitive inputs detected quickly (average of 3.3-9.5 days) compared to the conservative inputs (9.5-15.6 days) at the expense of accuracy, with overall F1 scores of above 0.91 and 0.95, respectively. Even though including S1 increased time series density, it did not result in lower latency or higher accuracy detections, primarily because of its lower signal-to-noise ratio. Once understood and accounted for, the tradeoffs can allow for applications in a variety of contexts. Plus, we anticipate that as more data becomes available (e.g. NISAR L-band SAR), the method will give faster detections. Overall, our novel, multi-source approach clearly advances NRT deforestation monitoring by providing a quick, simple, and effective way of combining multi-source satellite data. NB This poster is a slightly-updated version of the one used for the conference in Germany in September. To see this poster in full detail, please see the event for the NCSU Graduate Research Symposium.
