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WHDL - 00011935
Submitted to the Department of Mathematics and Computer Science in partial fulfillment of the requirements for the degree of Bachelor of Arts
Over the past decade, wildland fires have continued to increase in severity with wildfires burning an average of five to ten million acres in the United States a year. This elevated activity increases the costs of fighting them, with the 2017 season costing $2.9 billion in wildland-fire suppression. For the past three years, NNU’s Fire Monitoring and Assessment Platform (FireMAP) team has been using Small Unmanned Aircraft Systems (sUAS) to capture hyperspatial imagery to map post-fire effects. The purpose of this project was to add capabilities to the existing FireMAP analytic tools and refine and document the process used to gather hyperspatial imagery with sUAS. The analytic tools were improved by adding the ability to identify crown underburn, which is defined by an unburned crown being surrounded contiguously with burned surface vegetation. As tree canopy blocks the drones view from capturing the ground for classification, other methods need to be used to infer this information. The Denoise tool was used to detect pixels that are crown underburn and reclassify them as burned. This improvement allowed for more accurate classification of burned surface vegetation which is obstructed by unburned crown vegetation in forested environments.74 Resources
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