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.
| Published in | Agriculture, Forestry and Fisheries (Volume 6, Issue 1) |
| DOI | 10.11648/j.aff.20170601.15 |
| Page(s) | 34-44 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
MODIS, Bark Beetle Infestation, GIS, Wildfire, Spectral Indices
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APA Style
Marco Allain, Andrew Nguyen, Evan Johnson, Emily Williams, Stephanie Tsai, et al. (2017). A Geospatial Analysis of Bark Beetle-Induced Wildfire Risk Zones in the Okanogan Wenatchee National Forest. Agriculture, Forestry and Fisheries, 6(1), 34-44. https://doi.org/10.11648/j.aff.20170601.15
ACS Style
Marco Allain; Andrew Nguyen; Evan Johnson; Emily Williams; Stephanie Tsai, et al. A Geospatial Analysis of Bark Beetle-Induced Wildfire Risk Zones in the Okanogan Wenatchee National Forest. Agric. For. Fish. 2017, 6(1), 34-44. doi: 10.11648/j.aff.20170601.15
AMA Style
Marco Allain, Andrew Nguyen, Evan Johnson, Emily Williams, Stephanie Tsai, et al. A Geospatial Analysis of Bark Beetle-Induced Wildfire Risk Zones in the Okanogan Wenatchee National Forest. Agric For Fish. 2017;6(1):34-44. doi: 10.11648/j.aff.20170601.15
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Download@article{10.11648/j.aff.20170601.15,
author = {Marco Allain and Andrew Nguyen and Evan Johnson and Emily Williams and Stephanie Tsai and Susan Prichard and J. W. Skiles},
title = {A Geospatial Analysis of Bark Beetle-Induced Wildfire Risk Zones in the Okanogan Wenatchee National Forest},
journal = {Agriculture, Forestry and Fisheries},
volume = {6},
number = {1},
pages = {34-44},
doi = {10.11648/j.aff.20170601.15},
url = {https://doi.org/10.11648/j.aff.20170601.15},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.aff.20170601.15},
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.},
year = {2017}
}
TY - JOUR T1 - A Geospatial Analysis of Bark Beetle-Induced Wildfire Risk Zones in the Okanogan Wenatchee National Forest AU - Marco Allain AU - Andrew Nguyen AU - Evan Johnson AU - Emily Williams AU - Stephanie Tsai AU - Susan Prichard AU - J. W. Skiles Y1 - 2017/03/02 PY - 2017 N1 - https://doi.org/10.11648/j.aff.20170601.15 DO - 10.11648/j.aff.20170601.15 T2 - Agriculture, Forestry and Fisheries JF - Agriculture, Forestry and Fisheries JO - Agriculture, Forestry and Fisheries SP - 34 EP - 44 PB - Science Publishing Group SN - 2328-5648 UR - https://doi.org/10.11648/j.aff.20170601.15 AB - 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. VL - 6 IS - 1 ER -
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