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Center for Spatial Information and Research

Regional Comprehensive Visual Sensitivity Assessment for Renewable Energy Facility Development


As interest in developing sustainable methods of energy production increases in the United States , rural regions with the land base to accommodate large-scale renewable energy projects, such as wind farms and solar arrays are in a position to benefit economically. At the same time, research indicates that amenities contribute significantly to economic growth and quality of life in many rural regions of the United States (Deller, Tsai, Marcouiller, & English, 2001). The visual impacts of proposed renewable energy project are a major reason many rural community members reject them (Wolsink, 2007). Rural jurisdictions are attempting to encourage these projects, while mitigating for visual impacts on the landscape. To reduce resistance and develop a diverse and sustainable economic base, it is important for jurisdictions to develop a comprehensive understanding of the qualities of visibility within the landscape. Knowledge of visually sensitive areas in a regional can aid decision-makers in creating comprehensive zoning plans for the location of potential renewable energy projects. The results can help site development responsibly and limit the impact to aesthetic amenities.

To aid rural jurisdictions in mitigating visual impacts of large-scale renewable energy development, RGIS-PNW developed a methodology for creating a visual sensitivity model of a regional landscape. The model is based on cumulative visibility analysis, which identifies the frequency with which locations in the landscape can be seen from other locations (Llobera, 2005; Wheatley, 1995). When a single viewshed is created using Geographic Information Systems, the terrain is given a Boolean classification of visible or not visible from the observer point. A cumulative visibility surface is created by overlaying multiple viewsheds calculated over the same terrain and summing the number of intersections across that terrain. The result is a model of cardinality, which identifies the number of times locations in the terrain are visible from the sampled observer point set (DeFloriani & Magill, 2003). This cumulative visibility surface reveals areas of low and high visibility within that terrain. A standard cumulative visibility surface is too simplistic for use in community planning because it does not account for the number of people that can potentially see the terrain from the observer point. The model developed by RGIS-PNW incorporates information about regional population to weight the importance of each viewshed based on population density at the observer point. The weighted visual sensitivity model identifies areas of low and high visibility to the population by summing the weights, based on population density at the observer point, of overlapping viewshed. In the final model, visually sensitive areas are locations that have high weighted values, which indicate that they can be seen by a large portion of the regional population.

The weighted cumulative visibility model is an objective measure of visibility within a landscape. The cultural importance of views and the scenic preferences of the population for different types of renewable energy development are not considered in the model. The results of this model are meant to be analyzed in conjunction with data pertaining to culturally important places in the landscape and the local inhabitants' scenic preferences. Modeling visual sensitivity is a useful for designating zoned locations for potential renewable energy projects. The results can help site development projects responsibly and limit their impact on scenic amenities.

In addition to creating a comprehensive visual sensitivity assessment methodology, this project also developed a repeatable process for creating a digital terrain model (DTM) which incorporates heights of buildings and trees. Standard USGS digital elevation models (DEMS) do not include structures or vegetation, which greatly reduces the accuracy of viewsheds that are calculated using them. Digital terrain models that accurately represent top surface elevations that include buildings and trees, rather than bare earth, are difficult for rural jurisdictions to obtain. Lidar is not widely available in rural regions and it is expensive to obtain. As part of this objective RGIS-PNW created a DTM that includes buildings and vegetation. The DTM was derived from 1m resolution NAIP orthorectified color aerial photographs and USGS 10m DEMs. ESRI ArcGIS and Visual Learning Systems' Feature Analyst were used to extract building and tree features from the NAIP color aerial photographs (Miller, Nelson, & Hess, 2009). The resulting extracted features were assigned average heights based on photograph and field analysis. The modeled elevations of these features were added to standard USGS 10m DEMs to create a top surface DTM.

To test the methodologies, RGIS-PNW conducted a case study in Kittitas County . Census population statistics for the county were used to calculate population densities and weight the viewsheds that were generated for the model. The cumulative weighted visibility model was created from a sample of 17,735 locations. Sample locations were selected from publicly accessible locations that have a high likelihood of public use. Sample locations were located on highways, local roads, and trails. The sampling scheme captured samples on paths and nodes (Lynch, 1960). Paths were defined as roads and trails. Nodes were defined as intersections of paths and trails. Hawth's Tools extension for ArcGIS was used to generate a systematic sample of observer points approximately 150 m apart on all roads and trails and at all intersections.

This research resulted in an online technical report describing: 1) a repeatable methodology to model the visual sensitivity of terrain in a regional landscape, 2) a methodology for creating a digital terrain model that incorporated buildings and trees from free data for input into the visual sensitivity modeling, 3) a DTM for Kittitas County that incorporates trees and buildings, and 4) a comprehensive weighted visual sensitivity model for Kittitas County and a separate each incorporated city

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