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WHDL - 00016169
Submitted to the Department of Mathematics and Computer Science in partial fulfillment of the requirements for the degree of Bachelor of Science
Various machine learning algorithms have been shown to be effective methods of mapping forest fire burn extent and tree mortality. The algorithms use drone imagery to classify pixels as burned or unburned. Recent efforts used a mask region-based convolutional neural network (MR-CNN) and support vector machine (SVM) to label pixels in a post-fire forest as being within the fire’s extent. These algorithms reclassified the pixels using the Unburned Tree Noise and Sub-Crown Burn Reclassifications. The objective was that these reclassifications would produce more accurate results than the previously computed Surface Burn Classification. The purpose of this project was to use analysis methods to determine statistical significance in the results, and decide whether the reclassifications gave significantly better results than the original classification. The primary tools used in the analysis were the one- and two-tailed paired Student’s t-tests. These tests were conducted on the sensitivity results given by the algorithms, because sensitivity was considered the metric of most importance due to precedence being placed on minimizing the false negative percentage. Results calculated from the t-tests demonstrated that the new reclassifications produced a statistically significant increase in sensitivity over relying solely on the Surface Burn Classification for burn extent mapping.
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