A generalizable framework for urban wetland training samples generation and migration: A case study of global Ramsar Wetland Cities
Accurate urban wetland mapping requires reliable training samples, yet the cost reduction and efficiency enhancement of sample production in complex urban backgrounds remains challenging. This study introduces an automated framework Fusion Knowledge Rules and Spectral Matching (FKRSM) for training s...
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| Language: | English |
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Elsevier
2025-09-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X25010106 |
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| author | Zhe Yang Weiguo Jiang Xiaogan Yin Ziyan Ling Xiaoya Wang Miaolong Lin Shuhui Lai Xiao Li Qiaozhen Guo Zhijie Xiao Ze Zhang Qiuling Li Peiyu Yang Shihui Huang Xiang Long Keyi Yang Kaifeng Peng Yongbiao Yu Xuan Liu Yaheng Sheng Xiaorui Ren Xiangdong Yang Haicheng Tian |
| author_facet | Zhe Yang Weiguo Jiang Xiaogan Yin Ziyan Ling Xiaoya Wang Miaolong Lin Shuhui Lai Xiao Li Qiaozhen Guo Zhijie Xiao Ze Zhang Qiuling Li Peiyu Yang Shihui Huang Xiang Long Keyi Yang Kaifeng Peng Yongbiao Yu Xuan Liu Yaheng Sheng Xiaorui Ren Xiangdong Yang Haicheng Tian |
| author_sort | Zhe Yang |
| collection | DOAJ |
| description | Accurate urban wetland mapping requires reliable training samples, yet the cost reduction and efficiency enhancement of sample production in complex urban backgrounds remains challenging. This study introduces an automated framework Fusion Knowledge Rules and Spectral Matching (FKRSM) for training sample generation and migration. Implemented on Google Earth Engine (GEE), FKRSM applies dense Sentinel-2 time series and multi-source classification products to extract vegetation-hydrology dynamics, inundation frequency patterns, and geometric attributes of urban wetlands. A hybrid strategy combining index-threshold reclassification with morphological purification is used to delineate class-specific sample generation zone and to generate corresponding samples. A dual-constraint spectral matching method based on Spectral Angle Distance (SAD) and Euclidean Distance (ED) was developed to reduce the manual effort required for determining unchanged sample thresholds and to enable dynamic migration of urban wetland training samples. Tested across 43 Ramsar Wetland Cities (RWCs), FKRSM achieved automatic generation of 726,482 samples in the 2022 reference year with a sampling accuracy of 97.72 %. Migrated samples across 2016, 2018, 2020, and 2024 maintained an average accuracy of 92.53 %. Compared with recent research, FKRSM achieved 216.85 % of the performance (production time and classification accuracy) of prior methods in China’s first batch of 6 RWCs. FKRSM, with its demonstrated spatiotemporal generalizability, offers a scalable solution for ongoing fine-scale urban wetland mapping and further supports both six-year RWC re-accredited and new city accreditations. |
| format | Article |
| id | doaj-art-bf53c8f46bec4f17bf8fec3e202ce57a |
| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-bf53c8f46bec4f17bf8fec3e202ce57a2025-08-22T04:55:51ZengElsevierEcological Indicators1470-160X2025-09-0117811407810.1016/j.ecolind.2025.114078A generalizable framework for urban wetland training samples generation and migration: A case study of global Ramsar Wetland CitiesZhe Yang0Weiguo Jiang1Xiaogan Yin2Ziyan Ling3Xiaoya Wang4Miaolong Lin5Shuhui Lai6Xiao Li7Qiaozhen Guo8Zhijie Xiao9Ze Zhang10Qiuling Li11Peiyu Yang12Shihui Huang13Xiang Long14Keyi Yang15Kaifeng Peng16Yongbiao Yu17Xuan Liu18Yaheng Sheng19Xiaorui Ren20Xiangdong Yang21Haicheng Tian22State Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Corresponding author.State Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaSchool of Information and Control Engineering. Qingdao University of Technology, Qingdao 266520, ChinaCollege of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaState Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaSchool of Geology and Geomatics, Tianjin Chengjian University, Tianjin 300384, ChinaState Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaCollege of Geographical Science and Planning, Nanning Normal University, Nanning 530100, ChinaState Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; School of Information Engineering, China University of Geoscience, Beijing 100083, ChinaCollege of Geographical Science and Planning, Nanning Normal University, Nanning 530100, ChinaThe Institute for Advanced Study of Coastal Ecology, Ludong University, Shandong 264025, ChinaCollege of Land Science and Technology, China Agricultural University, Beijing 100083, ChinaFaculty of Geography, Tianjin Normal University, Tianjin 300387, ChinaCollege of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaCollege of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Geology and Geomatics, Tianjin Chengjian University, Tianjin 300384, ChinaSchool of Geology and Geomatics, Tianjin Chengjian University, Tianjin 300384, ChinaSchool of Geology and Geomatics, Tianjin Chengjian University, Tianjin 300384, ChinaSchool of Geology and Geomatics, Tianjin Chengjian University, Tianjin 300384, ChinaAccurate urban wetland mapping requires reliable training samples, yet the cost reduction and efficiency enhancement of sample production in complex urban backgrounds remains challenging. This study introduces an automated framework Fusion Knowledge Rules and Spectral Matching (FKRSM) for training sample generation and migration. Implemented on Google Earth Engine (GEE), FKRSM applies dense Sentinel-2 time series and multi-source classification products to extract vegetation-hydrology dynamics, inundation frequency patterns, and geometric attributes of urban wetlands. A hybrid strategy combining index-threshold reclassification with morphological purification is used to delineate class-specific sample generation zone and to generate corresponding samples. A dual-constraint spectral matching method based on Spectral Angle Distance (SAD) and Euclidean Distance (ED) was developed to reduce the manual effort required for determining unchanged sample thresholds and to enable dynamic migration of urban wetland training samples. Tested across 43 Ramsar Wetland Cities (RWCs), FKRSM achieved automatic generation of 726,482 samples in the 2022 reference year with a sampling accuracy of 97.72 %. Migrated samples across 2016, 2018, 2020, and 2024 maintained an average accuracy of 92.53 %. Compared with recent research, FKRSM achieved 216.85 % of the performance (production time and classification accuracy) of prior methods in China’s first batch of 6 RWCs. FKRSM, with its demonstrated spatiotemporal generalizability, offers a scalable solution for ongoing fine-scale urban wetland mapping and further supports both six-year RWC re-accredited and new city accreditations.http://www.sciencedirect.com/science/article/pii/S1470160X25010106Urban wetland sampleKnowledge rulesSpectral matchingGoogle Earth EngineRamsar Wetland City |
| spellingShingle | Zhe Yang Weiguo Jiang Xiaogan Yin Ziyan Ling Xiaoya Wang Miaolong Lin Shuhui Lai Xiao Li Qiaozhen Guo Zhijie Xiao Ze Zhang Qiuling Li Peiyu Yang Shihui Huang Xiang Long Keyi Yang Kaifeng Peng Yongbiao Yu Xuan Liu Yaheng Sheng Xiaorui Ren Xiangdong Yang Haicheng Tian A generalizable framework for urban wetland training samples generation and migration: A case study of global Ramsar Wetland Cities Ecological Indicators Urban wetland sample Knowledge rules Spectral matching Google Earth Engine Ramsar Wetland City |
| title | A generalizable framework for urban wetland training samples generation and migration: A case study of global Ramsar Wetland Cities |
| title_full | A generalizable framework for urban wetland training samples generation and migration: A case study of global Ramsar Wetland Cities |
| title_fullStr | A generalizable framework for urban wetland training samples generation and migration: A case study of global Ramsar Wetland Cities |
| title_full_unstemmed | A generalizable framework for urban wetland training samples generation and migration: A case study of global Ramsar Wetland Cities |
| title_short | A generalizable framework for urban wetland training samples generation and migration: A case study of global Ramsar Wetland Cities |
| title_sort | generalizable framework for urban wetland training samples generation and migration a case study of global ramsar wetland cities |
| topic | Urban wetland sample Knowledge rules Spectral matching Google Earth Engine Ramsar Wetland City |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X25010106 |
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