当前位置：
X-MOL 学术
›
Urban Forestry Urban Green.
›
论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Assessing City-Scale Green Roof Development Potential Using Unmanned Aerial Vehicle (UAV) Imagery
Urban Forestry & Urban Greening ( IF 6.4 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.ufug.2020.126954 Huamei Shao , Peihao Song , Bo Mu , Guohang Tian , Qian Chen , Ruizhen He , Gunwoo Kim
Urban Forestry & Urban Greening ( IF 6.4 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.ufug.2020.126954 Huamei Shao , Peihao Song , Bo Mu , Guohang Tian , Qian Chen , Ruizhen He , Gunwoo Kim
Abstract While green roofs have been deemed promising in mitigating environmental issues caused by rapid urban development, city-scale green roof studies have faced various obstacles, especially difficulties in obtaining accurate data for analysis. This study developed a new, cost-effective approach to assessing green roof development potential by using ultra-high-resolution (UHR) (0.09 m) Unmanned Aerial Vehicle (UAV) imagery in a case study site (Central Luohe with an area of 158 km2) in China. Specifically, the data was processed, interpreted, and classified to create highly accurate land-use and building roof spatial resources databases. A decision-making flowchart was developed for preliminary determination of a building stock’s suitability for green roof implementation and the preferred type based on the five influencing factors and building roof classification. Subsequently, a two-stage strategy for large-scale green roof development was proposed. The approach demonstrated in this research greatly improves the accuracy of city-scale studies on roof spatial resources and enables better planning and development of urban green spaces at the local level.
更新日期：2021-01-01
京公网安备 11010802027423号