Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W8, 53-58, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W8-53-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
13 Nov 2017
UTILIZATION OF LARGE SCALE SURFACE MODELS FOR DETAILED VISIBILITY ANALYSES
J. Caha1 and M. Kačmařík2 1Department of Regional Development, Mendel University in Brno, Zemědělská 1, 613 00 Brno, Czech Republic
2Institute of Geoinformatics, VŠB-Technical University of Ostrava, 17. listopadu 15, 708 33 Ostrava-Poruba, Czech Republic
Keywords: analysis of visibility, unmanned air vehicle, 3d point cloud, extended viewshed, viewshed Abstract. This article demonstrates utilization of large scale surface models with small spatial resolution and high accuracy, acquired from Unmanned Aerial Vehicle scanning, for visibility analyses. The importance of large scale data for visibility analyses on the local scale, where the detail of the surface model is the most defining factor, is described. The focus is not only the classic Boolean visibility, that is usually determined within GIS, but also on so called extended viewsheds that aims to provide more information about visibility. The case study with examples of visibility analyses was performed on river Opava, near the Ostrava city (Czech Republic). The multiple Boolean viewshed analysis and global horizon viewshed were calculated to determine most prominent features and visibility barriers of the surface. Besides that, the extended viewshed showing angle difference above the local horizon, which describes angular height of the target area above the barrier, is shown. The case study proved that large scale models are appropriate data source for visibility analyses on local level. The discussion summarizes possible future applications and further development directions of visibility analyses.
Conference paper (PDF, 5519 KB)


Citation: Caha, J. and Kačmařík, M.: UTILIZATION OF LARGE SCALE SURFACE MODELS FOR DETAILED VISIBILITY ANALYSES, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W8, 53-58, https://doi.org/10.5194/isprs-archives-XLII-2-W8-53-2017, 2017.

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