Tracking disturbance-regrowth dynamics in tropical forests using structural change detection and Landsat time series

Increasing attention on tropical deforestation and forest degradation has necessitated more detailed knowledge of forest change dynamics in the tropics. With an increasing amount of satellite data being released to the public free of charge, understanding forest change dynamics in the tropics is gradually becoming a reality. Methods to track forest changes using dense satellite time series allow for description of forest changes at unprecedented spatial, temporal and thematic resolution. We developed a data-driven approach based on structural change monitoring methods to track disturbance-regrowth dynamics using dense Landsat Time Series (LTS) in a tropical forest landscape in Madre de Dios, southern Peru. Whereas most existing post-disturbance regrowth monitoring methods rely on annual or near-annual time series, our method uses all available Landsat data. Using our disturbance-regrowth method, we detected annual disturbance from 1999 to 2013 with a total area-weighted accuracy of 91 ± 2.3%. Accuracies of the regrowth results were strongly dependent on the timing of the original disturbance. We estimated a total area-weighted regrowth accuracy of 61 ± 3.9% for pixels where original disturbances were predicted earlier than 2006. While the user's accuracy of the regrowth class for these pixels was high (84 ± 8.1%), the producer's accuracy was low (56 ± 9.4%), with markedly lower producer's accuracies when later disturbances were also included. These accuracies indicate that a significant amount of regrowth identified in the reference data was not captured with our method. Most of these omission errors arose from disturbances late in the time series or a lack of sensitivity to long-term regrowth due to lower data densities near the end of the time series. Omission errors notwithstanding, our study represents the first demonstration of a purely data-driven algorithm designed to detect disturbances and post-disturbance regrowth together using all available LTS data. With this method, we propose a continuous disturbance-regrowth monitoring framework, where LTS data are continually monitored for disturbances, post-disturbance regrowth, repeat disturbances, and so on