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<p>Erosion is frequently cited as the most significant environmental impact from trails and can require costly design and management considerations. To address this issue, this study utilized sUAS-based remote sensing and topographic derivatives to analyze and predict locations susceptible to water-based trail erosion. A trail totaling 4 km 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 summary statistics from hydrological 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 sUASbased 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 novel approaches for novel approaches to sustainable trail design and monitoring.</p>

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Utilizing sUAS-based Remote Sensing for Sustainable Outdoor Recreational Trail Design and Monitoring

  • 期刊论文

  • 发表日期：

    2025年9月12日

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  • Isaac Kinder,
  • Michael P. Strager,
  • Shawn T. Grushecky,
  • Paul J. Kinder

摘要

Erosion is frequently cited as the most significant environmental impact from trails and can require costly design and management considerations. To address this issue, this study utilized sUAS-based remote sensing and topographic derivatives to analyze and predict locations susceptible to water-based trail erosion. A trail totaling 4 km 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 summary statistics from hydrological 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 sUASbased 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 novel approaches for novel approaches to sustainable trail design and monitoring.