Version 1
: Received: 26 September 2023 / Approved: 26 September 2023 / Online: 27 September 2023 (06:01:45 CEST)
How to cite:
Kim, J.; Kim, I.; Choi, B. Development of Soil Erosion Susceptibility Model Using UAV Photogrammetry in a Timber Harvesting Area, South Korea. Preprints2023, 2023091803. https://doi.org/10.20944/preprints202309.1803.v1
Kim, J.; Kim, I.; Choi, B. Development of Soil Erosion Susceptibility Model Using UAV Photogrammetry in a Timber Harvesting Area, South Korea. Preprints 2023, 2023091803. https://doi.org/10.20944/preprints202309.1803.v1
Kim, J.; Kim, I.; Choi, B. Development of Soil Erosion Susceptibility Model Using UAV Photogrammetry in a Timber Harvesting Area, South Korea. Preprints2023, 2023091803. https://doi.org/10.20944/preprints202309.1803.v1
APA Style
Kim, J., Kim, I., & Choi, B. (2023). Development of Soil Erosion Susceptibility Model Using UAV Photogrammetry in a Timber Harvesting Area, South Korea. Preprints. https://doi.org/10.20944/preprints202309.1803.v1
Chicago/Turabian Style
Kim, J., Ikhyun Kim and Byoungkoo Choi. 2023 "Development of Soil Erosion Susceptibility Model Using UAV Photogrammetry in a Timber Harvesting Area, South Korea" Preprints. https://doi.org/10.20944/preprints202309.1803.v1
Abstract
Unmanned aerial vehicle (UAV) systems are widely used in many forest-related fields owing to their cost-intensive and precise surveying technology. This study classified erosion susceptibility (ES) in a timber harvesting area using machine learning (ML) and statistical approaches. In dataset generation for the training and testing process, the digital surface model (DSM) of difference (DoD) for July–June was utilized as a dependent variable, and six terrain maps of the DSM for June were used as independent variables. The ES threshold was set at 5 cm for the binary classification of ES pixels while processing using ML (e.g., random forest and extra gradient boost [XGB]) and statistical (e.g., logistic regression) algorithms for model development. The overall accuracy (OA), receiver operating characteristics, and area under the curve (AUC) were calculated for model accuracy and validation. Although the AUC of all models did not appear acceptable (AUC > 0.7), the XGB model showed the best performance regarding time duration, OA, and AUC by 2 h, 64%, and 0.63, respectively. Despite the low AUC and accuracy of the XGB model, the wheel tracks and edges of the operation road were determined to be susceptible areas in the ES map of the XGB.
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
