Personalising the viewshed: Visibility analysis from the human perspective

Abstract Viewshed analysis remains one of the most popular GIS tools for assessing visibility, despite the recognition of several limitations when quantifying visibility from a human perspective. The visual significance of terrain is heavily influenced by the vertical dimension (i.e. slope, aspect and elevation) and distance from the observer, neither of which are adjusted for in standard viewshed analyses. Based on these limitations, this study aimed to develop a methodology which extends the standard viewshed to represent visible landscape as more realistically perceived by a human, called the ‘Vertical Visibility Index’ (VVI). This method was intended to overcome the primary limitations of the standard viewshed by calculating the vertical degrees of visibility between the eye-level of a human and the top and bottom point of each visible cell in a viewshed. Next, the validity of the VVI was assessed using two comparison methods: 1) the known proportion of vegetation visible as assessed through imagery for 10 locations; and 2) standard viewshed analysis for 50 viewpoints in an urban setting. While positive, significant correlations were observed between the VVI values and both comparators, the correlation was strongest between the VVI values and the image verified, known values (r = 0.863, p = 0.001). The validation results indicate that the VVI is a valid method which can be used as an improvement on standard viewshed analyses for the accurate representation of landscape visibility from a human perspective.

[1]  Steven M. Manson,et al.  Heights and locations of artificial structures in viewshed calculation : How close is close enough? , 2007 .

[2]  Ian D. Bishop,et al.  Visual assessment of off-shore wind turbines: The influence of distance, contrast, movement and social variables , 2007 .

[3]  C. KUMSAP,et al.  The technique of distance decayed visibility for forest landscape visualization , 2005, Int. J. Geogr. Inf. Sci..

[4]  B. Murgante,et al.  Visual Impact Assessment in Urban Planning , 2009 .

[5]  Jason E. VanHorn,et al.  Urban 3D GIS Modeling of Terrorism Sniper Hazards , 2010 .

[6]  Jayson J. Murgoitio,et al.  Improved visibility calculations with tree trunk obstruction modeling from aerial LiDAR , 2013, Int. J. Geogr. Inf. Sci..

[7]  Mark Gillings,et al.  Visual perception and GIS: developing enriched approaches to the study of archaeological visibility , 2000 .

[8]  Simon Kingham,et al.  Incorporating vegetation into visual exposure modelling in urban environments , 2011, Int. J. Geogr. Inf. Sci..

[9]  A. Turner,et al.  From Isovists to Visibility Graphs: A Methodology for the Analysis of Architectural Space , 2001 .

[10]  Marcos Llobera,et al.  Modeling visibility through vegetation , 2007, Int. J. Geogr. Inf. Sci..

[11]  Yoko NISHIMURA,et al.  Google Earth , 2008, Encyclopedia of GIS.

[12]  Wayne D. Iverson,et al.  VIEWIT: computation of seen areas, slope, and aspect for land-use planning , 1975 .

[13]  E. L. Amidon,et al.  Delineating landscape view areas...a computer approach , 1968 .

[14]  Tadahiko Higuchi THE VISUAL AND SPATIAL STRUCTURE OF LANDSCAPES , 1985, Landscape Journal.

[15]  Lin Yang,et al.  An integrative hierarchical stepwise sampling strategy for spatial sampling and its application in digital soil mapping , 2011, Int. J. Geogr. Inf. Sci..

[16]  Martin Tomko,et al.  Identification of Practically Visible Spatial Objects in Natural Environments , 2009, AGILE Conf..

[17]  Juan M. Domingo-Santos,et al.  The visual exposure in forest and rural landscapes: An algorithm and a GIS tool , 2011 .