Landscape classification with self-organizing map using user participation and environmental data: the case of the Seoul Metropolitan Area
This study aimed to develop a method for assessing landscapes using environmental data and user-generated data, which are commonly employed in landscape research. It focused on the Seoul metropolitan area in South Korea, devising evaluation indicators for five key concepts: naturalness, diversity, i...
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| Published in | Landscape and ecological engineering Vol. 20; no. 3; pp. 455 - 471 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Tokyo
Springer Japan
01.07.2024
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1860-1871 1860-188X |
| DOI | 10.1007/s11355-024-00607-8 |
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| Abstract | This study aimed to develop a method for assessing landscapes using environmental data and user-generated data, which are commonly employed in landscape research. It focused on the Seoul metropolitan area in South Korea, devising evaluation indicators for five key concepts: naturalness, diversity, imageability, historicity, and disturbance. These indicators were used to assess the landscapes based on each index. We employed a self-organizing map, an artificial neural network technique, to categorize the landscape units and developed eight evaluation indicators for the five key concepts, organizing the study area’s landscapes into six distinct landscape units. This study identified landscape unit types with increased vulnerability to visual blight or heightened public awareness by considering both user characteristics and environmental attributes in the metropolitan area landscapes. Finally, we discussed future tasks for appropriate landscape management based on each landscape area’s characteristics to maintain and enhance landscape quality. |
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| AbstractList | This study aimed to develop a method for assessing landscapes using environmental data and user-generated data, which are commonly employed in landscape research. It focused on the Seoul metropolitan area in South Korea, devising evaluation indicators for five key concepts: naturalness, diversity, imageability, historicity, and disturbance. These indicators were used to assess the landscapes based on each index. We employed a self-organizing map, an artificial neural network technique, to categorize the landscape units and developed eight evaluation indicators for the five key concepts, organizing the study area’s landscapes into six distinct landscape units. This study identified landscape unit types with increased vulnerability to visual blight or heightened public awareness by considering both user characteristics and environmental attributes in the metropolitan area landscapes. Finally, we discussed future tasks for appropriate landscape management based on each landscape area’s characteristics to maintain and enhance landscape quality. |
| Author | Kim, Jeeyoung Lee, Sunghee Son, Yonghoon Kang, DongJin Kim, Doeun Lee, Jukyung |
| Author_xml | – sequence: 1 givenname: Yonghoon orcidid: 0000-0003-1416-7488 surname: Son fullname: Son, Yonghoon email: sonyh@snu.ac.kr organization: Department of Environmental Design, Graduate School of Environment Studies, Seoul National University – sequence: 2 givenname: DongJin surname: Kang fullname: Kang, DongJin organization: Environmental Planning Institute, Seoul National University – sequence: 3 givenname: Jeeyoung surname: Kim fullname: Kim, Jeeyoung organization: Environmental Planning Institute, Seoul National University – sequence: 4 givenname: Sunghee surname: Lee fullname: Lee, Sunghee organization: Environmental Planning Institute, Seoul National University – sequence: 5 givenname: Jukyung surname: Lee fullname: Lee, Jukyung organization: Interdisciplinary Program in Landscape Architecture, Seoul National University – sequence: 6 givenname: Doeun surname: Kim fullname: Kim, Doeun organization: Interdisciplinary Program in Landscape Architecture, Seoul National University |
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| Keywords | Landscape quality Landscape assessing indicators Seoul metropolitan area Self-organizing map User-generated data |
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| SubjectTerms | Artificial neural networks Biomedical and Life Sciences Blight Civil Engineering Environmental Management Evaluation Indicators Landscape Ecology landscape management Landscape/Regional and Urban Planning landscapes Life Sciences Metropolitan areas Nature Conservation Neural networks Original Paper Plant Ecology Public awareness Self organizing maps South Korea Visual perception Visual tasks |
| Title | Landscape classification with self-organizing map using user participation and environmental data: the case of the Seoul Metropolitan Area |
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