Optimizing Viewpoint Selection for Route-Based Experiences: Assessing the Role of Viewpoints on Viewshed Accuracy

Garet Openshaw and Brent Chamberlain ()
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Garet Openshaw: Landscape Architecture and Environmental Planning, College of Agriculture and Applied Sciences, Utah State University, Logan, UT 84322, USA
Brent Chamberlain: Landscape Architecture and Environmental Planning, College of Agriculture and Applied Sciences, Utah State University, Logan, UT 84322, USA

Land, 2022, vol. 11, issue 8, 1-17

Abstract: A visual analysis is useful to assess potential impacts to our surroundings. There has been tremendous progress toward the optimization, accuracy, and techniques of these analyses. Viewshed analyses are a common type of visual analysis. The purpose of this study was to identify the optimal trade-off between the number of viewpoints needed to generate an accurate viewshed for a given route. In this study, we focused on identifying how a viewshed differs based on the sampling distance (interval) of viewpoints, topography, and distance of analysis. We employed the Geospatial Route Analysis and Visual Impact Assessment (GRAVIA) tool, a type of advanced viewshed that uses visual-magnitude measures. GRAVIA was applied across three different topographical environments (flat, hilly, and mountainous). We generated a one-mile-long segment for each environment and systematically discretized the route by varying the sampling-distance intervals from 1 m to 100 m. We also compared how the calculated results differed by distance from the route. The results showed a linear decrease in the correlation, though this was sensitive to the distance. When all distances were combined, a 30 m and 50 m sampling distance correlated to 0.9 and 0.7, respectively. However, when the results compared calculations beyond 300 m away from the route, the correlation values exceeded 97% for all the viewpoint-sampling distances. This suggests that for route-based analyses using visual magnitude, reducing the sampling rate can produce equivalent results with far less processing time while maintaining model precision.

Keywords: viewshed; visual magnitude; viewpoint selection; accuracy; distance; key observation points (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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