Disturbance detection in landsat time series is influenced by tree mortality agent and severity, not by prior disturbance

Abstract

Landsat time series (LTS) and associated change detection algorithms are useful for monitoring the effects of global change on Earth's ecosystems. Because LTS algorithms can be easily applied across broad areas, they are commonly used to map changes in forest structure due to wildfire, insect attack, and other important drivers of tree mortality. But factors such as initial forest density, tree mortality agent, and disturbance severity (i.e., percent tree mortality) influence patterns of surface reflectance and may influence the accuracy of LTS algorithms. And while LTS algorithms are widely used in areas with a history of multiple disturbance events during the Landsat record, the effectiveness of LTS algorithms in these conditions is not well understood. We compared products from the LTS algorithm LandTrendr (Landsat-based Detection of Trends in Disturbance and Recovery) with a unique field dataset from a landscape heavily influenced by both wildfire and spruce beetles (Dendroctonus rufipennis) since c. 2000. We also compared LandTrendr to other common methods of mapping fire- and spruce beetle-affected areas. We found that LandTrendr more accurately detected wildfire than spruce beetle-induced tree mortality, and both mortality agents were more easily detected when they occurred at high severity. Surprisingly, prior spruce beetle outbreaks did not influence the detectability of subsequent wildfire. Compared to alternative disturbance mapping approaches, LandTrendr predicted a c. 40% lower area affected by wildfire or spruce beetle outbreaks. Our findings indicate that disturbance type- and severity-specific differences in omission error may have broad implications for disturbance mapping efforts that utilize Landsat data. Gradual, low-severity disturbances (e.g., background tree mortality and non-stand replacing disturbance) are pervasive in forest ecosystems, yet they can be difficult to detect using automated LTS algorithms. Whenever possible, methods to account for these biases should be incorporated in LTS-based mapping efforts, including the use of multispectral ensembles and ancillary spatial data to refine predictions. However, our findings also indicate that LTS algorithms appear to be robust in areas with multiple disturbance events, which is important because these areas will increase as new acquisitions extend the length of the Landsat record.