Date of Award

6-2025

Degree Name

MS in Agriculture - Animal Science

Department/Program

Animal Science

College

College of Agriculture, Food, and Environmental Sciences

Advisor

Marc Horney

Advisor Department

Animal Science

Advisor College

College of Agriculture, Food, and Environmental Sciences

Abstract

Accurate vegetation classification is critical for modeling wildfire behavior, particularly in fire-prone ecosystems where fuel type influences fire spread and intensity. This study evaluates the effectiveness of UAV-based aerial LiDAR, supplemented by multispectral imagery, for classifying grass, shrub, and tree vegetation types across a grass-dominated landscape. Using canopy height thresholds, vertical profile analysis, LiDAR predictions were compared against field-verified vegetation observations at 80 survey points. Results showed that LiDAR classified grass with high accuracy but was less reliable for detecting shrubs and trees, especially under canopy cover. Misclassifications were most common beneath tree canopies and in areas where low-stature shrubs were present, highlighting structural limitations in the current model. The study also explores the benefits of integrating multispectral data to improve vegetation differentiation, particularly where structural information alone is insufficient. Findings suggest that while LiDAR is effective for mapping dominant fuel types, future improvements—such as expanding shrub height thresholds and enhancing sub-canopy detection—are necessary for broader application in wildfire risk modeling.

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