
This study employs Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) to evaluate wind farm siting in Garfield County, Oklahoma. A countywide analysis assessed visual impacts of existing wind farms, excluded unsuitable areas using diverse spatial datasets (e. g., wind speed, land use, infrastructure), and applied a grid method for turbine spacing. An MCDA suitability index, weighting wind speed and proximity to transmission lines, identified 1,394 potential turbine sites. Windhold, a proposed wind farm, was selected for its top suitability ranking, accommodating 63 turbines. Viewshed analysis for Windhold revealed negligible visual impact on Enid, the county’s largest city, though minor effects were noted for two nearby towns (combined population: 160). Energy output, estimated from real-world data of comparable turbines due to unavailable power curves, projects Windhold to generate 533,433 MWh annually, powering 55,000-60,000 households. Limitations include the absence of avian/wildlife data, community engagement, and site visits, suggesting avenues for future refinement. This GIS-MCDA framework offers a replicable, technically focused approach to wind farm planning, emphasising energy potential and grid connectivity while underscoring the need for broader ecological and social considerations.
Autor / Author: | Khanano, Emmanuel; Pietsch, Matthias; Lawrence, Bryce T. |
Institution / Institution: | Anhalt University of Applied Sciences, Sachsen-Anhalt/Germany; Anhalt University of Applied Sciences, Sachsen-Anhalt/Germany; TU Dortmund University, Dortmund/Germany |
Seitenzahl / Pages: | 15 |
Sprache / Language: | Englisch |
Veröffentlichung / Publication: | JoDLA – Journal of Digital Landscape Architecture, 10-2025 |
Tagung / Conference: | Digital Landscape Architecture 2025 – Collaboration |
Veranstaltungsort, -datum / Venue, Date: | Dessau Campus of Anhalt University, Germany 04-06-25 - 07-06-25 |
Schlüsselwörter (de): | |
Keywords (en): | Multiple-Criteria Decision Analysis, environmental footprint, spatial data analysis, renewable energy development |
Paper review type: | Full Paper Review |
DOI: | doi:10.14627/537754019 |
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