An open source GIS tool to quantify the visual impact of wind turbines and photovoltaic panels
Introduction
Over the 21st century, global demand for energy is expected to double, arguably requiring growth in renewable energy production such as solar (photovoltaic panel) and wind turbines to reasonably meet demands (Lewis and Nocera, 2006). Although there are clear benefits to these renewable technologies, uptake does not match potential of renewable energy production for a variety of reasons (Painuly, 2001). At a local scale, one such barrier is the aesthetic impact of renewable energy facilities on the landscape (Wüstenhagen et al., 2007). Hence, there is a clear need to carefully locate wind farms and photovoltaic panels to minimise their visual impact and increase social acceptance.
At present, there is not a unilaterally agreed, standardized method to quantify the visual impact of photovoltaic fields and wind farms. Landscape quality evaluations may rely upon local guidelines (Hurtado et al., 2003, Regione Autonoma Della Sardegna, 2008), good practice manuals (Landscape Institute, 2002, Scottish Natural Heritage et al., 2006, Vissering et al., 2011), survey-based or index methods (Ladenburg, 2009, Tsoutsos et al., 2009), and/or colour and light based methods (e.g., blending with the landscape) (Bishop and Miller, 2007, Chiabrando et al., 2011, Shang and Bishop, 2000).
Typically, the visual impact of a range of environmental phenomena is assessed through viewshed analysis in a GIS. In this method, a digital elevation model is used to determine which parts of the landscape are visible or not visible from a particular vantage point (Longely et al., 2010). For instance, studies have been carried out on the visibility of Nuraghes (De Montis and Caschilli, 2012), native buildings from the Isle of Sardinia in Italy, on the visibility of electric transmission towers (Turnbull and Gourlay, 1987), and on the maximisation of the scenic viewpoints along a touristic road (Chamberlain and Meitner, 2013). Manchado et al. (2013) recently reviewed computer programmes available to perform visibility analysis for a variety of purposes.
Visibility analysis techniques have been applied to evaluate solar panel and wind turbine visibility (e.g. Moeller, 2006 and the references therein). We build upon this work by taking into account how the perceived size and shape of an object become distorted depending on the viewing point. An object's shape distortion as perceived by a human eye can affect the quantification of the area affected by visual impact on landscape perception, as we demonstrate.
This method is based on the concepts of (i) visibility analysis (Manchado et al., 2013) and visual magnitude (Chamberlain and Meitner, 2013), (ii) human eye perception and its field of view (Costella, 1992, Spector, 1990) and (iii) descriptive geometry (De Rubertis, 1979).
Quantitative analysis of visual impact is performed by (i) computing the field of view of an observer at a specific distance, (ii) evaluating the object shape distortion perceived by a human eye, and (iii) analysing the mutual relation between object, observer and earth morphology. The tool is developed as an add-on module for GRASS GIS, an Open Source GIS software (Neteler and Mitasova, 2008). As the code is completely available, users can freely read, verify, redistribute and modify the code, meaning that the tool is flexible and that the reproducibility of results is guaranteed (Ince et al., 2012).
Section snippets
Material and methods
The tool developed is named “r.wind.sun”. It is coded in the Python programming language (Van Rossum and Drake, 2001) as an add-on module to GRASS GIS, an Open Source GIS software (Neteler and Mitasova, 2008). The tool builds upon the existing GRASS GIS tool “r.viewshed” (Toma et al., 2012) which is based on the concept of line of sight (LOS), the straight line between the observer and object (e.g., Molina-Ruiz et al., 2011).
In the r.wind.sun tool, visual impact is quantified by the proportion
Case studies
We have tested the model using both synthetic (section Synthetic case study) and real data (section Real world application: Cima Mutali). The first application with synthetic data aims to explore the distortion effect in the quantification of visibility. The second experiment then demonstrates the tool function in a real setting.
Discussion and conclusions
To date, there is no precise set of rules to quantitatively (geometrically) estimate the visual impact of wind and photovoltaic farms. Perhaps because of this, often more prominence is given to other factors such as social or agricultural impacts (Cerroni and Venzi, 2009, Rogge et al., 2008). The tool we have developed here offers a more objective method to quantify this impact numerically, allowing direct comparison between sites and scenarios, providing a useful tool for landscape planners.
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