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

Area of Science:

• Geospatial analysis
• Environmental science
• Urban planning

Background:

• Visual analysis is integral to urban planning and environmental management.
• Viewshed generation relies on accurate terrain and elevation data.
• Digital Surface Models (DSM) and LiDAR point cloud data are key datasets for geospatial analysis.

Purpose of the Study:

• To compare the precision of viewsheds generated from DSM versus LiDAR point cloud data.
• To evaluate the strengths and weaknesses of each data source for visual analysis.
• To assess the application of these datasets in line-of-sight assessments and field operations.

Main Methods:

• Comparative analysis of viewshed generation using DSM and LiDAR point cloud data.
• Evaluation across six diverse topographical locations.
• Consideration of visual acuity, computational efficiency, and data precision.

Main Results:

• Viewsheds derived from LiDAR point clouds demonstrate superior precision compared to DSM.
• Precision differences ranged from 1.42% to 5.94% (comparison) and 1.05% to 3.89% (subtraction).
• LiDAR-based viewshed generation requires significant computational resources, best suited for limited areas.

Conclusions:

• LiDAR point cloud data provides higher accuracy for viewshed generation and line-of-sight assessments.
• DSM offers a comprehensive view of land surface features and elevation changes.
• The high accuracy of LiDAR makes it invaluable for urban environments and critical research decisions, despite computational demands.