Assessment of forest landscape scenic beauty in Huisun forest area

• Main Authors: Jing-Yuan Liu, 劉景元
• Other Authors: Tian-Ming Yen
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/kew7m4

博士 === 國立中興大學 === 森林學系所 === 101 === The purpose of this research was to assess the beauty quality preference of landscape pattern, and to find the factors of influence for the forest scenic beauty quality in order to explain the relationship between the factors and the beauty quality preference. The study area was located in Huisun Forest Area. A total of 411 valid questionnaires were obtained from 450 questionnaires. This study found that there are seven items, which belongs to the demographic background, have remarkable effect with scenic beauty quality, including: gender, age, the level of enjoying Huisun Forest Area, happiness, understanding forest management, ticket price, and service satisfaction. Visitors’ happy experiences and scenic beauty quality are in direct proportion, and visitors’ realization of landscape level and scenic beauty quality are in direct proportion, too. The age, education, the level of joyful, happiness, understanding the management, knowing information, service satisfaction, and the elements of loving landscape, all of them have extraordinary impact with landscape cognition. There are nine items, which belong to in visitors’ mentality, have significant influence with joyful experiences, including: age, education, occupation, level of joyful, happiness, understanding the way to manage forest, motive, ticket price, and service satisfaction. Visitors’ landscape cognition and joyful experiences are in direct proportion. In the pictures of scenic beauty quality, the man-made forest and grassplot in the type of forest landscape have the highest scenic beauty estimation (SBE), which is 68.94; and in the landscape element, the highest SBE is water part, which is 63.02. In the landscape distance, man-made forest and grassplot get highest point since they have long distance. The fractal dimension of landscape index has connection with scenic beauty quality, and the relationship belongs to cubic equation model. When the fractal dimension is around 1.56, the scenic beauty quality is the top. Fractal dimension, landscape type, and landscape element are in inverse proportion. In the application of landscape index, diversity index by using Fray scale method is lower than Box counting method and Area-perimeter method; however, it shows higher in the evenness index and fractal dimension index. Using Box counting method and Area-perimeter method have not so much difference in diversity index and evenness index, but the value is the highest by Box counting method in fractal dimension.Scenic beauty quality predictable model proceeds multi-regression analysis throughout disposition, pleasure experiences, and landscape cognizance etc. to get the standard regression equation, which is Y=0.305X1+0.158X2+0.152X3+0.138X4 (Y=scenic beauty quality；X1= happy experience；X2= service satisfaction；X3= knowing of inside landscape；X4= knowing of outside landscape). This model could forecast the variation of scenic beauty quality is 24.9%. However, when we use fractal dimension to predict the standard regression equation of scenic beauty quality, that is Y=-44.7X+44.331X^3, (Y= scenic beauty quality；X= value of fractal dimension Box counting method), and this model could expect the variation of scenic beauty quality is 37.9%.