A fast serial computation approach for computing viewshed of a region by detecting key spots on the terrain. Issue 27 (13th December 2022)

• Record Type: Journal Article
• Authors: Salehi, Afshin; Halabian, Amirhossein; Ahmadi Beni, Farkhondeh; Salehi, Mohammadmahdi; Dadras, Bijan
• Is Part Of: Geocarto international. Volume 37:Issue 27(2023)
• Page Start: 17625
• Page End: 17641
• Publication Date: 2022-12-13
• Subjects: Viewshed -- viewshed of a region -- visibility algorithm -- terrain aspect -- GIS; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2022.2129841
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: Computing a viewshed for a space (a polygon), rather than a single location (a point), requires analysis of the maximum terrain extent observable from any location within the space. For a digital elevation model (DEM), it is the sum of viewshed analyses for all the pixels inside the confined space. As the raster spatial resolution increases, the process becomes computationally expensive. In this study, we propose a new serial computation approach, named Regional Viewshed by Aspect Peaks (RVAP), in which key pixels that define the cumulative visibility map on a digital elevation model (DEM) are found, making the process computationally affordable. We compare our proposed method next to 1) a 5-cell grid interval method 2) and an all-encompassing but slow complete approach where the viewshed is computed for all the pixels and then overlaid. Six regions-of-interests (ROIs) on two Shuttle radar (∼555 × 555 km dimensions, 90-meter resolution) DEMs are selected and categorized by their terrain ruggedness index (TRI) (i.e. slightly rugged, mildly rugged, highly rugged). The proposed approach is much faster, while its coverage is identical to the all-encompassing approach. Moreover, as the TRI increases, the accuracy of the proposed algorithm is not diminished, while its execution time decreases.