DOI: 10.5061/dryad.dv41ns1ws
title: Data from: Disturbance detection in Landsat time series is influenced by tree mortality agent and severity, not by prior disturbance

description: 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. 
description: Briefly, this archive file includes R code, field data, and a
 range of spatial datasets used in Rodman et al. (2021; Remote Sensing of
 Environment). The included datasets are from a landscape in the San Juan
 Mountains, CO, USA that was heavily affected by spruce beetle
 (<em>Dendroctonus rufipennis</em>) and wildfire since the late
 1990s. For a description of subfolders and individual datasets, see
 "README.txt" and .docx files within each subfolder.
description: No constraints are placed on the use of these data. Descriptions
 of individual files and file structure are provided
 in "README.txt" and .docx files within each subfolder. For
 additional information, please contact the authors.