Mapping Ecosystem Functional Groups in the Republic of Korea Based on the IUCN Global Ecosystem Typology
This study presents a national-scale mapping of Ecosystem Functional Groups (EFGs) in the Republic of Korea using the International Union for Conservation of Nature (IUCN) Global Ecosystem Typology (GET), a hierarchical classification system, integrated with spatial datasets, satellite imagery, and...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/10/1659 |
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| author | Kyungil Lee Haedam Baek Chul-Hyun Choi Sang-Hak Han Seonyoung Park |
| author_facet | Kyungil Lee Haedam Baek Chul-Hyun Choi Sang-Hak Han Seonyoung Park |
| author_sort | Kyungil Lee |
| collection | DOAJ |
| description | This study presents a national-scale mapping of Ecosystem Functional Groups (EFGs) in the Republic of Korea using the International Union for Conservation of Nature (IUCN) Global Ecosystem Typology (GET), a hierarchical classification system, integrated with spatial datasets, satellite imagery, and a random forest (RF) classifier. By incorporating locally relevant ecological data, the original typology was refined to resolve issues of overgeneralization and spatial overlap. The resulting map delineates 20 distinct ecosystem types, offering improved spatial accuracy and better alignment with the actual land extent. To evaluate the potential of EFG classification, the RF model was trained on seasonal satellite composites and environmental variables, achieving an overall accuracy of 80%. Elevation and temperature were found to be the most influential predictors, effectively distinguishing ecological patterns across diverse landscapes. This integrated approach supports consistent tracking of ecosystem changes and helps address the limitations of static or infrequently updated spatial datasets. The developed EFG map supports biodiversity conservation by providing a practical foundation for national spatial planning and contributing to the Red List of Ecosystems assessments, which is in line with the goals of the Global Biodiversity Framework. |
| format | Article |
| id | doaj-art-7d8d7191444a426283fb01b5d2bcad10 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-7d8d7191444a426283fb01b5d2bcad102025-08-20T03:12:12ZengMDPI AGRemote Sensing2072-42922025-05-011710165910.3390/rs17101659Mapping Ecosystem Functional Groups in the Republic of Korea Based on the IUCN Global Ecosystem TypologyKyungil Lee0Haedam Baek1Chul-Hyun Choi2Sang-Hak Han3Seonyoung Park4AI Semiconductor Research Center, Seoul National University of Science and Technology, Seoul 01811, Republic of KoreaDepartment of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul 01811, Republic of KoreaEcosystem Services Team, National Institute of Ecology, Seocheon 33657, Republic of KoreaClimate Change and Carbon Research Team, National Institute of Ecology, Seocheon 33657, Republic of KoreaDepartment of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul 01811, Republic of KoreaThis study presents a national-scale mapping of Ecosystem Functional Groups (EFGs) in the Republic of Korea using the International Union for Conservation of Nature (IUCN) Global Ecosystem Typology (GET), a hierarchical classification system, integrated with spatial datasets, satellite imagery, and a random forest (RF) classifier. By incorporating locally relevant ecological data, the original typology was refined to resolve issues of overgeneralization and spatial overlap. The resulting map delineates 20 distinct ecosystem types, offering improved spatial accuracy and better alignment with the actual land extent. To evaluate the potential of EFG classification, the RF model was trained on seasonal satellite composites and environmental variables, achieving an overall accuracy of 80%. Elevation and temperature were found to be the most influential predictors, effectively distinguishing ecological patterns across diverse landscapes. This integrated approach supports consistent tracking of ecosystem changes and helps address the limitations of static or infrequently updated spatial datasets. The developed EFG map supports biodiversity conservation by providing a practical foundation for national spatial planning and contributing to the Red List of Ecosystems assessments, which is in line with the goals of the Global Biodiversity Framework.https://www.mdpi.com/2072-4292/17/10/1659IUCN GETbiodiversityecosystem extentremote sensingrandom forest |
| spellingShingle | Kyungil Lee Haedam Baek Chul-Hyun Choi Sang-Hak Han Seonyoung Park Mapping Ecosystem Functional Groups in the Republic of Korea Based on the IUCN Global Ecosystem Typology Remote Sensing IUCN GET biodiversity ecosystem extent remote sensing random forest |
| title | Mapping Ecosystem Functional Groups in the Republic of Korea Based on the IUCN Global Ecosystem Typology |
| title_full | Mapping Ecosystem Functional Groups in the Republic of Korea Based on the IUCN Global Ecosystem Typology |
| title_fullStr | Mapping Ecosystem Functional Groups in the Republic of Korea Based on the IUCN Global Ecosystem Typology |
| title_full_unstemmed | Mapping Ecosystem Functional Groups in the Republic of Korea Based on the IUCN Global Ecosystem Typology |
| title_short | Mapping Ecosystem Functional Groups in the Republic of Korea Based on the IUCN Global Ecosystem Typology |
| title_sort | mapping ecosystem functional groups in the republic of korea based on the iucn global ecosystem typology |
| topic | IUCN GET biodiversity ecosystem extent remote sensing random forest |
| url | https://www.mdpi.com/2072-4292/17/10/1659 |
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