Green City Landscape Design and Planning Based on GIS and Analytic Hierarchy Process Model. (16th May 2022) - Liu, Daochun; Tang, Mingming; Dong, Nan
Abstract : The spiritual connotation of contemporary cities has gradually become rich and colorful, and the concept of a green city has also been accepted by the public. People living in cities have higher and higher requirements for landscape, but traditional landscape design cannot achieve efficient and accurate planning. Therefore, the old design method, which cannot meet the complex and changeable urban needs and cannot keep up with the development and changes of the times, is doomed to be eliminated and replaced. This study introduces a GIS system and uses an AHP method to assist landscape design. Through rational resource utilization of topography, vegetation growth, population density, and other conditions, the city can be green and environmentally friendly, reduce energy consumption, and meet people's concept of healthy life. The experimental results show that: (1) through the GIS system, a multidecision model is constructed to choose a reasonable location for the garden. Based on the opinions and scores of all parties, the B place with a score as high as 8.97 was selected. (2) Using comprehensive landscape sensitivity to divide four levels of landscape land-use value and looking for the best landscape vision, it is divided into five scenic functional areas and 12 scenic spots. SPEI and NDVI were used to select cultivated plants and landscapes. (3) Route multifactor comprehensive model is designed to plan garden route. The coincidence degree between the model and the actual road condition is about 87.62%. Citizen satisfaction is between 7 and 9 points, and the comprehensive evaluation is high. (4) The evaluation system of landscape architecture, in which both B-level index and C-level index are higher than 7 points is constructed, which is in line with the expectations of the public and experts and convenient for feedback correction. The method based on this study is feasible and can be further optimized.