Analysis of Viewshed Accuracy with Variable Resolution LIDAR Digital Surface Models and Photogrammetrically- Derived Digital Elevation Models

The analysis of visibility between two points on the earth’s terrain is a common use of GIS software. Most commercial GIS software packages include the ability to generate a viewshed, or a map of terrain surrounding a particular location that would be visible to an observer. Viewsheds are often generated using “bare-earth” Digital Elevation Models (DEMs) derived from the process of photogrammetry. More detailed models, known as Digital Surface Models (DSMs), are often generated using Light Detection and Ranging (LIDAR) which uses an airborne laser to scan the terrain. In addition to having greater accuracy than photogrammetric DEMs, LIDAR DSMs include surface features such as buildings and trees. This project used a visibility algorithm to predict visibility between observer and target locations using both photogrammetric DEMs and LIDAR DSMs of varying resolution. A field survey of the locations was conducted to determine the accuracy of the visibility predictions and to gauge the extent to which the presence of surface features in the DSMs affected the accuracy. The use of different resolution terrain models allowed for the analysis of the relationship between accuracy and optimal grid size. Additionally, a series of visibility predictions were made using Monte Carlo methods to add random error to the terrain elevation to estimate the probability of a target’s being visible. Finally, the LIDAR DSMs were used to determine the linear distance of terrain along the lines-of-sight between the observer and targets that were obscured by trees or bushes. A logistic regression was performed between that distance and the visibility of the target to determine the extent to which a greater amount of vegetation along the line-of-sight impacted the target’s visibility.