earticle

영어

Soil surface displacement occurs often following timber harvesting. This displacement is caused by canopy removal from the forest area which makes bare soil to be exposed directly to the rainfall. Thus, the displacement may differ in due to precipitation intensity, and this phenomenon results in the displacement prediction to be uncertain. In this study, we investigated on 1) calculating displacement on timber harvested area in research forest by photogrammetry method, and 2) assessing erosion risk of the area by using three different supervised machine learning classification models. Two time-series of point cloud data (PCD) were acquired by photogrammetry method from June 10, 2022 and July 9, 2022, respectively. These two PCD sets were registered by ground control point (GCP). For displacement estimation, DEM of difference (DoD) was calculated by subtracting DEMs from registered PCD sets. A total of nine terrain variables were generated for analyzing the spatial relationship between displacements and topographic features. Erosion risk was analyzed by three different classification algorithms: random forest (RF), extra gradient boost (XGB), logistic regression (LG). Best performed model was RF with overall accuracy (OA) of 72.9% followed by XGB model (72%), and LG model (71.7%). The results of this research indicate that machine learning based classification algorithms have better performances on erosion risk analysis than statistical algorithm through this research.