Identifying landslide boundaries and morphological features using traditional methods is labor-intensive, costly, and often limited—especially in areas altered by human activity or covered with dense vegetation. In such cases, modern remote sensing methods are considered a good alternative; however, their accuracy and reliability also depend on several factors. This study aims to identify landslide boundaries and morphological features using modern remote sensing techniques and to compare and validate the derived parameters with those obtained through traditional field methods. In this study, the remote sensing technology employed is a high-resolution digital elevation model (HRDEM) generated by a LiDAR sensor mounted on an unmanned aerial vehicle (UAV). Based on this dataset, various terrain parameters were analyzed, including slope, aspect, contour, curvature, hillshade, the topographic ruggedness index (TRI), the topographic position index (TPI), and the topographic wetness index (TWI). Individual analysis, composite analysis, and principal component analysis (PCA) of these parameters enabled the identification of the landslide boundaries and key morphological elements. This study was conducted on a landslide-prone slope in the Surami area of Georgia, which is characterized by extensive anthropogenic impact. The accuracy of the LiDAR-derived results was confirmed through field validation. This study demonstrates the effectiveness of UAV-LiDAR technology in areas affected by anthropogenic activity.