Time-series approach for mapping mountain pine beetle infestation extent and severity in the US Central Rocky Mountains

摘要

Severe mountain pine beetle (MPB) epidemics can degrade ecosystem services and socioeconomic assets. Mapping outbreak progression provides tools to mitigate damages and analyze MPB attack processes. Current time-series methods for mapping disturbance focus on extent rather than severity. Infestation severity, defined by within-pixel percentage, is more robust for answering a variety of ecologic questions. We develop a time-series regression approach to map infestation severity from 2005 to 2015 in the U.S. Central Rocky Mountains. Covariates include spectral data from all available dates of Landsat imagery, topographic data, and US Forest Service aerial detection survey (ADS) polygons. We collect model reference data by interpreting National Agricultural Imagery Program images. Validation against a randomly selected subset of the data results in no statistical difference between predicted and observed severity. The mean absolute deviation is 7.7% with a root-mean-square error of 9.9%. Average (maximum) severity increased from 9.4% (49.7%) in 2005 to 17.6% (58.8%) in 2015. Our raster maps identify widespread, lower severity infestation absent from the ADS. Our maps can improve mitigation efforts by allowing managers to: address low-severity infestations before they intensify, monitor intensifying infestations within previously identified outbreak extents, and combine infestation severity with other forest metrics. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE).