Mapping Forest Disturbances across the Southwestern Amazon: Tradeoffs between Open-Source, Landsat-Based Algorithms.

Local and cross-continental road building, increased economic teleconnections, growing agricultural demands, logging and mining practices, and general development processes are putting pressure on even the least densely populated regions of the Amazon, where local, regional, and global demand for food, fuel and fiber are resulting in observable biophysical effects. It is essential, then, that stakeholders can both map and understand the effects of these forest disturbances on ecosystem services. Multiple remote sensing algorithms focused on detecting vegetation changes have been developed: the challenge now lies in understanding which algorithm best suits the user´s study area and research objective. Using Google Earth Engine, we compared the performance of three algorithms –Continuous Degradation Detection (CODED), Landsat-based detection of trends in disturbance and recovery (LandTrendr), and Multi-variate Time-series Disturbance Detection (MTDD)– to detect and characterize forest disturbances in the Southwestern Amazon (Ucayali, Peru and Acre, Brazil) during the 2000–2020 period. In general, the results of all of the algorithms agreed with the reference data: overall accuracies were 94% (± 0.6% LandTrendr), 95% (±0.6% MTDD), and 96% (± 0.6% CODED). Although the map products exhibit similar spatial patterns, they often differ on the specific disturbance extent. CODED works well in capturing disturbances associated with roads, MTDD excels best at capturing entire disturbance patches, and LandTrendr excels both in terms of user friendliness and range of output options. Through three case study regions, we highlight land-cover change dynamics that have occurred in this remote, transboundary region over the last two decades. We also describe the strengths and weaknesses of each algorithm and demonstrate that it would be incorrect to assume that any one algorithm is the most accurate. Our work, then, improves the capacity of the community to understand how well each algorithm is suited best to map various forest disturbances to promote sustainable decision making