ArticleEnvironmental monitoring and assessment2021
An ecological restoration assessment integrating multi-criteria decision analysis with landscape sensitivity analysis for a hydroelectric power plant project: the Tokat-Niksar case.
Article in Environmental monitoring and assessment, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Due to Turkey's reliance on imported electricity, it has been using hydroelectric power plants (HPPs) to cover some of its electrical energy needs since 2011. However, HPP projects, which do not take landscape sensitivity into account, cause the ecological integrity of the basins and the ecosystem structure to deteriorate. This study presents the Tokat-Niksar HPP project field example in Turkey's Central Black Sea region with its steep slopes, rich forest and creek vegetation, protected endemic plants and wildlife, cultural heritage dating back to 3000 BC agricultural activities, and long-established local culture. The study, in fact, aims to develop a landscape restoration plan that considers the sensitivity of landscapes in the HPP project area. For this purpose, a landscape sensitivity analysis consisting of water, habitat, visibility, erosion functions, and multi-criteria decision analysis (MCDA) determining priority of functions conducted with experts were performed. According to the results, the water function was found to have the highest, and the visibility the lowest priority in the ranking. Consequently, 1st, 2nd, 3rd, and 4th-degree sensitive areas with very high, high, and moderate landscape sensitivity were determined by weighted sum function by considering the priority of each landscape function, and accordingly, a five-stage landscape restoration model was developed including soil protection, areas to be protected, erosion control, vegetation screening, and creek reclamation. This study presents a framework for sustainable landscape restoration solutions in the town by combining landscape sensitivity analysis via MCDA.
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