Semester

Spring

Date of Graduation

2024

Document Type

Thesis

Degree Type

MS

College

Davis College of Agriculture, Natural Resources and Design

Department

Division of Resource Economics & Management

Committee Chair

Michael Strager

Committee Co-Chair

Shawn Grushecky

Committee Member

Walter Veselka

Abstract

This study utilizes sUAS-based remote sensing and hydrologic models to analyze and predict locations susceptible to water-based trail erosion. Erosion is frequently cited as the most significant environmental impact of trails and often requires costly design and management considerations. A professionally designed trail totaling 4 km in length was segmented based on presence or absence of water-based erosion for analyses and then flown with sUAS technology. Three Logistic regression (LR) models were generated utilizing several hydrologic terrain models of varying resolutions to determine the effects of spatial resolution on the models’ predictive accuracies. Receiver operator characteristics, kappa, and overall accuracy assessments all indicated better predictive accuracy for the highest resolution sUAS-based data. The LR model for the sUAS-based data identified areas with high total catchment area and low profile curvature values as having higher probability for erosion. This study offers guidelines for novel approaches to sustainable trail design and monitoring.

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