Comparing Landsat time series algorithms for detecting differing levels of tree mortality

Remote sensing methodologies are widely used for detecting and mapping tree mortality across large spatial extents. High-resolution imagery and lidar data can generate tree mortality and damage maps at the spatial resolution of tree crowns; however, these high-resolution products are usually of limited spatial and temporal extent. Hence, methodologies based on time series analysis of moderate-resolution satellite imagery, which provides global coverage at sub-monthly or finer time scales, are commonly used for detecting and mapping forest damage. The downside of the latter approach is the reduced ability to detect low severity tree mortality, i.e. tree mortality or damage that covers a small percentage of a 30-m pixel, as is common for biotic forest disturbances. Here we evaluate how well four Landsat time series algorithms— the Landsat-based detection of trends in disturbance and recovery (LandTrendr), continuous change detection and classification (CCDC), Meddens, and Landscape Change Monitoring System (LCMS) algorithms —detect various levels of tree damage. Tree-level damage maps, derived from field observations and high-resolution remote sensing imagery, were created across six study extents where biotic disturbance agents (mountain pine beetles, spruce beetles, Western spruce budworms, Armillaria root disease) had caused prevalent tree mortality and damage of varying severity (0%–100% dead or damaged tree cover within a 30 m pixel). We evaluated the four Landsat time series algorithms across these six study areas and found that the accuracy of Landsat time series algorithms decreased as the percentage of tree damage cover decreased in 30-m pixels, with accuracies of <50% when tree damage cover in a 30-m pixel was <25%. In general, we found that the LCMS and Meddens algorithms performed best, with the LCMS algorithm having the advantage of being nationally available and not needing to be trained locally. Our results indicate that Landsat time series analysis at broader scales can be used to capture higher-severity damage as well as identify areas in need of additional investigation with higher-resolution remotely sensed data or field observations.