Generating viewsheds based on the Digital Surface Model (DSM) and point cloud

Visual analysis has applications in diverse fields, including urban planning and environmental management. This study explores viewshed generation using two distinct datasets: Digital Surface Model (DSM) and LiDAR (Light Detection and Ranging) point cloud data. We assess the differences in viewsheds derived from these sources, evaluating their respective strengths and weaknesses. The DSM accurately captures terrain features and elevation changes, offering a comprehensive view of the land surface. Conversely, LiDAR point cloud data delivers detailed three-dimensional information, enabling precise mapping of terrain features and object detection. Our comparative analysis based on six selected locations with varied topographical arrangements considers factors such as visual acuity and computational efficiency. Additionally, we discuss the application of DSM and LiDAR point cloud data in view analysis, emphasizing their value in line-of-sight assessments and field operations. The results indicate greater precision of viewsheds created based on LiDAR point clouds. The analysis reveals that the greater precision in comparing differences between DSM and point LiDAR data ranges from 1.42% to 5.94%, while the results subtraction falls between 1.05% and 3.89% for the conditions analyzed, indicating a high degree of accuracy in the method. However, this process demands significant computational resources. It is best applied in limited areas, particularly in urban environments where such data is crucial for supporting research decisions.