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A Geospatial Analysis of Bark Beetle-Induced Wildfire Risk Zones in the Okanogan Wenatchee National Forest

Received: 28 December 2016     Accepted: 21 January 2017     Published: 2 March 2017
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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.

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

Keywords

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

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    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

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    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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  • @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}
    }
    

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    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
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    N1  - https://doi.org/10.11648/j.aff.20170601.15
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    T2  - Agriculture, Forestry and Fisheries
    JF  - Agriculture, Forestry and Fisheries
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    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
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Author Information
  • Department of Earth Science, Emporia State University, Emporia, USA

  • Department of Geography, San Jose State University, Fremont, USA

  • Department of Geography, University of California, Los Angeles, Los Angeles, USA

  • Department of Environmental Studies, University of California, Santa Barbara, USA

  • Department Computer Science, Stanford University, Stanford, CA, USA

  • Pacific Wildland Fire Sciences Laboratory, USDA Forest Service, Seattle, USA

  • National Aeronautic Space Administration, NASA Ames Research Center, Mountain View, USA

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