Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김건우 | - |
dc.date.accessioned | 2022-10-20T01:02:18Z | - |
dc.date.available | 2022-10-20T01:02:18Z | - |
dc.date.issued | 2021-01 | - |
dc.identifier.citation | URBAN FORESTRY & URBAN GREENING, v. 57, article no. 126954 | en_US |
dc.identifier.issn | 1618-8667; 1610-8167 | en_US |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S1618866720307718?via%3Dihub | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/175596 | - |
dc.description.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 km(2)) 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. | en_US |
dc.description.sponsorship | This research was partially supported by Henan Overseas Expertise Introduction Center for Discipline Innovation (GXJD006), National Natural Science Foundation of China (NSFC31470029), and Korea Ministry of Environment (MOE) (2020002780001) and Korea Environment Industry & Technology Institute (KEITI) through its Urban Ecological Health Promotion Technology Development Project. The authors would like to thank Tian Bai, Guifang Wang, Muqing Jin, Ni Xia and Qing Li for their assistance in classifying UAV imagery. | en_US |
dc.language.iso | en | en_US |
dc.publisher | ELSEVIER GMBH | en_US |
dc.subject | China; City-scale; Green roof; Green spaces; Roof spatial resources; Unmanned Aerial Vehicle (UAV) | en_US |
dc.title | Assessing city-scale green roof development potential using Unmanned Aerial Vehicle (UAV) imagery | en_US |
dc.type | Article | en_US |
dc.relation.volume | 57 | - |
dc.identifier.doi | 10.1016/j.ufug.2020.126954 | en_US |
dc.relation.page | 126954-126954 | - |
dc.relation.journal | URBAN FORESTRY & URBAN GREENING | - |
dc.contributor.googleauthor | Shao, Huamei | - |
dc.contributor.googleauthor | Song, Peihao | - |
dc.contributor.googleauthor | Mu, Bo | - |
dc.contributor.googleauthor | Tian, Guohang | - |
dc.contributor.googleauthor | Chen, Qian | - |
dc.contributor.googleauthor | He, Ruizhen | - |
dc.contributor.googleauthor | Kim, Gunwoo | - |
dc.relation.code | 2021042421 | - |
dc.sector.campus | S | - |
dc.sector.daehak | GRADUATE SCHOOL OF URBAN STUDIES[S] | - |
dc.sector.department | DEPARTMENT OF URBAN AND REGIONAL DEVELOPMENT | - |
dc.identifier.pid | gwkim1 | - |
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