Abstract

Coastal erosion poses a serious threat to ecosystems, infrastructure, and coastal livelihoods. Traditional monitoring using satellite or aerial imagery often struggles to distinguish between forest, beach, and sea regions, particularly in complex coastal environments. This study aims to enhance shoreline monitoring accuracy by introducing a LiDAR-based multiclass classification framework that effectively differentiates these terrain types. The proposed method integrates geometric and color-based features extracted from UAV-based LIDAR data collected in Khlung, Chanthaburi, Thailand. Six features were derived from segmented point cloud grids: elevation variability (v1), directional slope variability (v2), and the mean RGB values (Rmean, Gmean, Bmean, RGBmean). Four machine learning models, Random Forest, Decision Tree, Support Vector Machine, and Logistic Regression, were evaluated using cross-validation on 1500 samples. Results indicate that the Random Forest classifier achieved the highest accuracy of 99.78%, outperforming other models. The proposed approach effectively separates forest, beach, and sea regions, reducing misclassification errors and improving the reliability of coastal terrain classification. This study demonstrates the potential of combining LiDAR point cloud geometry and radiometric features for robust multiclass classification. The findings support more precise and automated coastal erosion analysis, offering a foundation for future integration with deep learning and hybrid image point cloud systems.