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Date Permissions Signed
Date of Award
Master of Science (MS)
Medler, Michael J.
Wallin, David O.
Smith, Douglas F.
One of the continuing challenges in wildland fire management is maintaining accurate vegetation and fuel data of an adequate resolution on an ever-changing landscape. The USGS’s LANDFIRE program produces national, mid-level resolution datasets of fuel, vegetation, and fire regime data useful in the modeling of wildland fire behavior. One of the most effective and least expensive ways for maintaining the accuracy of these layers is to incorporate area updates by detecting landscape changes. While many algorithms exist for detecting change and disturbances, these algorithms are often tuned for a particular landscape and require very precise training data or rely heavily on scene statistics. This research looks at a method for detecting wildland fire across a broad array of landscapes using a collection of computer-generated rules built from hundreds of thousands of points of training data. Verification of the results were assessed by visual comparison to a time series of high spatial resolution imagery through Google Earth and cross-referenced to fires from various historical databases. A majority of the fires detected in this assessment were in either a conifer or grassland landscape. The methods outlined in this thesis performed best in those two landscapes, detecting 73% to 78% of conifer fires correctly and 79% to 83% of grassland fires correctly.
Wildfires--Management--Computer programs, Wildfires--Prevention and control--Computer programs, Landscape changes--Computer programs
Western Washington University
Copying of this thesis in whole or in part is allowable only for scholarly purposes. It is understood, however, that any copying or publication of this thesis for commercial purposes, or for financial gain, shall not be allowed without the author's written permission.
Lesser, Jacob D. (Jacob Daniel), "Detecting Fires: A Nationally Consistent, Rule Based Approach" (2016). WWU Graduate School Collection. 532.