Forest insect outbreaks have caused and will continue to cause extensive tree mortality worldwide, affecting ecosystem services provided by forests. Remote sensing is an effective tool for detecting and mapping tree mortality caused by forest insect outbreaks. In this study, we map insect-caused tree mortality across three coniferous forests in the Western United States for the years 1984 to 2018. First, we mapped mortality at the tree level using field observations and high-resolution multispectral imagery collected in 2010, 2011, and 2018. Using these high-resolution maps of tree mortality as reference images, we then classified moderate-resolution Landsat imagery as disturbed or undisturbed and for disturbed pixels, predicted percent tree mortality with random forest (RF) models. The classification approach and RF models were then applied to time series of Landsat imagery generated with Google Earth Engine (GEE) to create annual maps of percent tree mortality. We separated disturbed from undisturbed forest with overall accuracies of 74% to 80%. Cross-validated RF models explained 61% to 68% of the variation in percent tree mortality within disturbed 30-m pixels. Landsat-derived maps of tree mortality were comparable to vector aerial survey data for a variety of insect agents, in terms of spatial patterns of mortality and annual estimates of total mortality area. However, low-level tree mortality was not always detected. We conclude that our methodology has the potential to generate reasonable estimates of annual tree mortality across large extents.
Mapping Multiple Insect Outbreaks across Large Regions Annually Using Landsat Time Series Data