The implications for visual simulation and analysis of temporal variation in the visibility of wind turbines

Abstract The visual impact of wind turbines is a central issue in their public acceptance. New wind farm proposals are commonly subject to visual simulation and visual impact assessment. Guidelines for both these processes are already used in a number of jurisdictions and there is widespread interest in making simulation and impact assessments as meaningful as possible. To a large degree the guidelines for both processes tend to be based on worst-case (full-frontal) conditions. There are two quite good reasons for this. Firstly, the worst-case sets a boundary for visual impact. Secondly, we seldom know enough about how visibility or visual impact changes over time and what ‘typical’ conditions are like – or how the range of conditions is distributed. This paper argues that if we can address this knowledge gap, then we may also be able to take a more nuanced approach to simulation and analysis. It should be possible, using widespread atmospheric visibility data, to determine the temporal distribution of visual impacts at a particular location rather computing a single estimate. It may also be valid to create simulations of both worst-case and more typical conditions. This paper explores the key variables affecting visual impacts – visual magnitude and color difference – and how they may be monitored and analyzed efficiently and effectively. A wind farm in southern Victoria, Australia is used as the case study. Recommendations are made on how the approaches could be used more widely.

[1]  Ian D. Bishop Testing perceived landscape colour difference using the Internet , 1997 .

[2]  Ian D. Bishop,et al.  Determination of Thresholds of Visual Impact: The Case of Wind Turbines , 2002 .

[3]  U. Wissen Hayek,et al.  Dissecting perceptions of wind energy projects: A laboratory experiment using high-quality audio-visual simulations to analyze experiential versus acceptability ratings and information effects , 2018 .

[4]  T. M. Klein,et al.  3D augmented reality for improving social acceptance and public participation in wind farms planning , 2016 .

[5]  Valentin Gomez-Jauregui,et al.  Visibility analysis and visibility software for the optimisation of wind farm design , 2013 .

[6]  Johannes Pohl,et al.  Acceptance and stress effects of aircraft obstruction markings of wind turbines , 2012 .

[7]  Christian Stock,et al.  Using collaborative virtual environments to plan wind energy installations , 2010 .

[8]  Andrew Lothian,et al.  Scenic Perceptions of the Visual Effects of Wind Farms on South Australian Landscapes , 2008 .

[9]  John Furze Stealth wind turbines , 2002 .

[10]  Wencheng Wu,et al.  The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations , 2005 .

[11]  Christophe Claramunt,et al.  Method to estimate the visual impact of an offshore wind farm , 2017 .

[12]  Ian D. Bishop Location based information to support understanding of landscape futures , 2015 .

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

[14]  Yu Song,et al.  Visibility trends in six megacities in China 1973–2007 , 2009 .

[15]  Theocharis Tsoutsos,et al.  Visual impact evaluation of a wind park in a Greek island , 2009 .

[16]  Jackson Cothren,et al.  Research Articles: Offshore Wind Turbine Visibility and Visual Impact Threshold Distances , 2013 .

[17]  Víctor-Andrés Cloquell-Ballester,et al.  Development and validation of a multicriteria indicator for the assessment of objective aesthetic impact of wind farms , 2009 .

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

[19]  A. Robertson The CIE 1976 Color-Difference Formulae , 1977 .

[20]  Fabio Gagliardi Cozman,et al.  Depth from scattering , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[21]  Massimiliano Masullo,et al.  Individual reactions to a multisensory immersive virtual environment: the impact of a wind farm on individuals , 2012, Cognitive Processing.