Abstract:Over the past 30 years mountain pine beetle (MPB) outbreaks have become widespread throughout the western US and Canada. MPB attacks leave acres of dead trees that may predispose forest landscapes to large fires. With the use of field work and geospatial technology, these outbreaks can be better mapped and assessed to evaluate forest health. This study is designed to map and classify bark beetle infestation in Washington's Wenatchee National Forest. Field work on seventeen randomly selected sites was conducted using the point-centered quarter method. Recent MPB outbreak areas were classified using National Agriculture Imagery Program (NAIP) imagery. A link between MPB attack and forest fires was then quantified using MODIS fire data. Lastly, a predictive infestation model was constructed using the following geophysical parameters: disturbance indices, Landsat TM5 classification of groundcover as well as vegetation stress using hyperspectral data. Selected imagery from the Hyperion sensor was used to run a minimum distance supervised classification in ENVI, in attempt to detect the early "green stage" of infestation. This study detected MPB spread and assessed the fire risk related to infestation.
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