Turkish Journal of Forestry (Apr 2015)
A study of visual assessment of different vegetation types on Hatila Valley National Park (Artvin)
Abstract
In the study, we conducted a visual assessment dealing with subjective and objective assessments in order to ensure the sustainability of prominent feature of the natural landscape with its unique characteristics of the area such as Hatila Valley National Park and contribute to the protection of visual values of forest ecosystems hosting different vegetation types. 9 images which contains the forest, streams and rock vegetation taken from the vista points of forest road along the route of the ongoing between 400-3000 m altitude in Hatila Valley National Park (Artvin) were used in visual assessment of this study. In the first stage of visual quality and assessment, we applied a survey using semantic differentiation techniques which contains 15 adjective pairs to landscape architects and graduate students of department of landscape architecture, a total of 75 people. In the second stage, fractal dimension of images and relationship between the fractal dimension and visual quality scores are calculated. In the third stage, which parameters to be effective on visual quality were determined by regression analysis. Consequently, forest vegetation (V2) and stream vegetation (V4) get the highest value in terms of visual quality scores were determined. We found that the average fractal dimension score (Db = 1.691) of the images get higher value and relatively recent trend between the visual quality scores and fractal dimension values of images. The findings of the study revealed that interesting, pleasant, lively, inviting, novelty, exciting, relaxing and color parameters were the descriptive parameters for vegetation types. Based on the different vegetation types, to determine the value of visual resources of the natural landscape and incorporate in landscape planning, management and design processes will help to increase the ecological, economic and aesthetic values of these areas in the long term.
Keywords
