Using GIS in Landscape Visual Quality Assessment
journal contributionposted on 14.10.2016 by Wu, Yingxin, Bishop, Ian, Hossain, Hemayet, Sposito, Victor
Any type of content formally published in an academic journal, usually following a peer-review process.
Landscape Visual Quality (LVQ) assessment has become a core component of landscape architecture, landscape planning and spatial planning. Different approaches for assessing the scenic qualities of landscapes have been developed in the last few decades. Two contrasting paradigms, expert/design approach and community perception-based approach, have dominated methodology development. In the expert-design approach the landscape visual quality is defined by biological and physical (or biophysical) values, while the perception-based approach emphasises the human view (subjective) of the landscape. This paper outlines a methodology combining expert and perception approaches to assess the LVQ.The application of information technology to landscape analysis dates back to the early work in computer-based mapping. Much of the early work on what became Geographic Information Systems (GIS) and three-dimensional landscape modelling was carried out by landscape architects and landscape planners. In the past years, significant advances in computers and GIS have enabled analysis of vast amounts of spatial information, which is the foundation of the methodology described in this paper. The methodology is explained in detail through its application to assess the LVQ of the Mornington Peninsula Shire, Melbourne, in the State of Victoria, AUSTRALIA.There are six stages in the procedure: viewpoints selection; calculation of factor indices based on Visual Exposure Modelling; landscape preference rating; use of statistical methods (such as multiple regression model) to determine the key predictors of LVQ; application of the formula thus generated to assess the LVQ of viewpoints; and use of spatial interpolation to map LVQ across the study area.The results are discussed in the last section of this paper with reference to key methodological issues. Results show that the perceived LVQ increases with the area of water visible, the degree of wilderness and percentage of natural vegetation, and the presence of hills. On the other hand, it decreases with the presence of perceived negative human-made elements such as roads and buildings.