GIS-based landscape scenicness estimation using machine learning for visual impact assessment of wind projects deployment in Europe

Wind energy is an important renewable energy source for the energy transition, but the implementation of wind projects faces many challenges. One of the major concerns is the landscape impact resulting from the high visibility of wind turbines. To determine a suitable location for a future wind farm, the landscape impact of the proposed project needs to be assessed based on the landscape quality and visibility to nearby populations. This study presents the first step of the landscape impact assessment, determining the landscape scenic quality using a Geographic Information System (GIS) approach. Firstly, various landscape metrics, including naturalness, human impact, remoteness, ruggedness, diversity, and abundance of land covers, are calculated for Great Britain, Switzerland, and Germany using open data sources. In contrast to previous studies, the calculation of the landscape metrics considers the entire viewshed (in other words, all surrounding points in the line of sight) instead of taking a single value at the specified location. Secondly, the existing crowd-sourced landscape scenic value dataset for Great Britain (ScenicOrNot) is used to identify the relationships between the perceived landscape aesthetic value and the above-mentioned landscape metrics using machine learning algorithms. Subsequently, the found relationships/patterns are validated using the available scenicness data for Germany, which comes from an empirically-based modelling approach. Finally, the validated method is propagated to the case of Switzerland to show the first estimation of landscape scenic value in high spatial resolution. The results are expected to reveal the major drivers of human perception of landscape aesthetic value and to provide a scalable method for quantitative estimation of landscape scenic value in Europe using open data. This would provide further insight for estimating the landscape impact of wind turbines beyond pure visibility analysis and expedite the deployment of wind power in Europe.