A route-based visibility analysis for landscape management

Abstract Viewsheds and similar visibility analyses are used for a wide range of applications. These analyses were not originally designed for applications which are human-centered, such as with landscape planning. In these cases, a viewshed should be able to link a visibility evaluation to aspects of vision and perception. Visual Magnitude provides a way to calculate the amount of visibility by quantifying the effect of slope, aspect and distance of an area relative to the observer. We propose a new way of adapting Visual Magnitude for route-based applications so that planners can better understand not just how much is visible, but how what is visible may relate to an individual's judgment and response. The approach is tested using a popular tourist highway route through the Clayoquot Sound UNESCO Biosphere Reserve located on Vancouver Island of British Columbia, Canada. The outputs are: a probability surface of the average visibility of the visual space on the landscape, the maximum visibility from along the route, and from where this occurs. The results are 2D GIS-based images that enable the planner to identify areas where emotional peak responses may occur, in order to maximize viewing opportunities of beautiful areas or mitigate potential negative responses from these locations. This research represents a shift toward the development of geographic information system tools that provide vital information important for making decisions with people as the focus.

[1]  Peter F. Fisher,et al.  An Exploration of Probable Viewsheds in Landscape Planning , 1995 .

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

[3]  H SCHLOSBERG,et al.  A note on depth perception, size constancy, and related topics. , 1950, Psychological review.

[4]  Michael J. Meitner,et al.  Automating the visual resource management and harvest design process , 2009 .

[5]  P. Fisher,et al.  Spatial Analysis of Visible Areas from the Bronze Age Cairns of Mull , 1997 .

[6]  D. Kahneman,et al.  Heuristics and Biases: The Psychology of Intuitive Judgment , 2002 .

[7]  PETER F. FISHER,et al.  Algorithm and Implementation Uncertainty in Viewshed Analysis , 1993, Int. J. Geogr. Inf. Sci..

[8]  Ian D. Bishop,et al.  Using Image Depth Variables as Predictors of Visual Quality , 2000 .

[9]  Denis J. Dean,et al.  An Investigation into the Causes of Errors and Inconsistencies in Predicted Viewsheds , 2007, Trans. GIS.

[10]  David Izraelevitz,et al.  A Fast Algorithm for Approximate Viewshed Computation , 2003 .

[11]  Denis J. Dean,et al.  AN ACCURACY ASSESSMENT OF VARIOUS GIS-BASED VIEWSHED DELINEATION TECHNIQUES , 2001 .

[12]  P. Burrough,et al.  Principles of geographical information systems , 1998 .

[13]  Peter F. Fisher,et al.  Extending the applicability of viewsheds in landscape planning , 1996 .

[14]  M. Germino,et al.  Estimating visual properties of Rocky Mountain landscapes using GIS , 2001 .

[15]  P. Fisher.  First experiments in viewshed uncertainty : the accuracy of the viewshed area , 1991 .

[16]  Adrienne Grêt-Regamey,et al.  Predicting the Scenic Beauty Value of Mapped Landscape Changes in a Mountainous Region through the Use of GIS , 2007 .

[17]  Kenneth Barton Fairhurst.  Geoptics landscape apparency : a dynamic visual resource indicator and tool for multi-functional landscape planning , 2010 .

[18]  Mark P. Kumler.  An Intensive Comparison of Triangulated Irregular Networks (TINs) and Digital Elevation Models (DEMs) , 1994 .

[19]  Leila De Floriani,et al.  Applications of Computational Geometry to Geographic Information Systems , 2000, Handbook of Computational Geometry.

[20]  David G. Kirkpatrick,et al.  Right-Triangulated Irregular Networks , 2001, Algorithmica.

[21]  Jugurta Lisboa Filho,et al.  An Efficient Algorithm to Compute the Viewshed on DEM Terrains Stored in the External Memory , 2007, GEOINFO.

[22]  Sanjay Rana.  Fast Approximation of Visibility Dominance Using Topographic Features as Targets and the Associated Uncertainty , 2003 .

[23]  Daniel Kahneman,et al.  Evaluation by Moments: Past and Future , 2002 .

[24]  Simon Kingham,et al.  Advancing visibility modelling algorithms for urban environments , 2010, Comput. Environ. Urban Syst..

[25]  Young-Hoon Kim,et al.  Exploring multiple viewshed analysis using terrain features and optimisation techniques , 2004, Comput. Geosci..

[26]  Ian D. Bishop,et al.  VISUAL THRESHOLDS FOR DETECTION, RECOGNITION AND VISUAL IMPACT IN LANDSCAPE SETTINGS , 2000 .

[27]  Ian D. Bishop,et al.  Assessment of Visual Qualities, Impacts, and Behaviours, in the Landscape, by Using Measures of Visibility , 2003 .

[28]  P. Slovic,et al.  The affect heuristic , 2007, European Journal of Operational Research.

[29]  J. Palmer.  Using spatial metrics to predict scenic perception in a changing landscape: Dennis, Massachusetts , 2004 .

[30]  Tao Pei,et al.  An Experiment Using a Circular Neighborhood to Calculate Slope Gradient from a DEM , 2007 .

[31]  David O'Sullivan,et al.  International Journal of Geographical Information Science , 2022 .

[32]  Denis J. Dean.  Improving the accuracy of forest viewsheds using triangulated networks and the visual permeability method , 1997 .

[33]  Tolga Kurt,et al.  Analysis of the urban/rural broadband divide in Canada: Using GIS in planning terrestrial wireless deployment , 2006, Gov. Inf. Q..

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

[35]  Jianjun Wang,et al.  A FAST SOLUTION TO LOCAL VIEWSHED COMPUTATION USING GRID-BASED DIGITAL ELEVATION MODELS , 1996 .

[36]  J. Corripio.  Snow surface albedo estimation using terrestrial photography , 2004 .

[37]  Greg Lindsey,et al.  Viewshed characteristics of urban pedestrian trails, Indianapolis, Indiana, USA , 2008 .

[38]  John P. Felleman,et al.  Foundations for visual project analysis , 1986 .

[39]  M. Meitner.  Scenic beauty of river views in the Grand Canyon : relating perceptual judgments to locations , 2004 .

[40]  Javier G. Corripio,et al.  Vectorial algebra algorithms for calculating terrain parameters from DEMs and solar radiation modelling in mountainous terrain , 2003, Int. J. Geogr. Inf. Sci..

[41]  Leila De Floriani,et al.  Algorithms for Visibility Computation on Terrains: A Survey , 2003 .

[42]  Massimo Giovannini,et al.  A Primer On The Physics Of The Cosmic Microwave Background , 2008 .

[43]  M. Llobera,et al.  Extending GIS-based visual analysis: the concept of visualscapes , 2003, Int. J. Geogr. Inf. Sci..

[44]  Carl Steinitz,et al.  Landscape Visibility Computation: Necessary, but Not Sufficient , 2003 .

[45]  Sölve Stenström.  Optics and the eye , 1964 .

[46]  Wayne D. Iverson.  And That's About the Size of It: Visual Magnitude as a Measurement of the Physical Landscape , 1985, Landscape Journal.

[47]  Bernd Möller,et al.  Changing wind-power landscapes: regional assessment of visual impact on land use and population in Northern Jutland, Denmark , 2006 .

[48]  P. F. Fischer,et al.  First experiments in viewshed uncertainty : simulating fuzzy viewsheds , 1992 .

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

[50]  David P. Lanter,et al.  Two algorithms for determining partial visibility and reducing data structure induced error in viewshed analysis , 1993 .