Sub-Annual Scale LandTrendr: Sub-Annual Scale Deforestation Detection Algorithm Using Multi-Source Time Series Data

Abstract

In cloudy and rainy regions, frequent cloud cover limits clear data obtained using a single optical sensor, posing a substantial challenge for detecting deforestation events on subannual scale. In this study, a sub-annual scale deforestation detection algorithm, namely, the sub-annual scale LandTrendr (SSLT) change detection algorithm, was developed using synergies from multiple data sources. First, a combined time series was constructed by combining Landsat and Sentinel-2 data. Second, a sliding window was applied to spatially normalize the normalized burn ratioand eliminate the effects of forest phenological changes and sensor differences. Finally, an integrated time series was created to fit the SSLT trajectory, and the root mean square error (RMSE) of the fitted trajectory was calculated to determine the segmentation threshold. Pixels with a magnitude of change greater than the RMSE for three consecutive times were marked as deforestation pixels. Application of the algorithm to a subtropical forest with low density of clear observations resulted in spatial and temporal accuracies of 88% and 92.8%, respectively. Conclusively, this method provides accurate and timely identification of deforestation events on a sub-annual scale.