Long-term wetland mapping at 10 m resolution using super-resolution and hierarchical classification – a case study in Jianghan Plain, China
Wetland is a critical and complex ecosystem but highly susceptible to human activities. It has experienced significant change along with the increase of human intervention since decades ago. Currently, remote sensing-based mapping is believed an effective method for monitoring wetland changes. Howev...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2498605 |
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| author | Yifei Han Hong Chi Jinliang Huang Xiaoming Shi Juan Qiu Qihui Shao Yifan Li Chen Cheng Feng Ling |
| author_facet | Yifei Han Hong Chi Jinliang Huang Xiaoming Shi Juan Qiu Qihui Shao Yifan Li Chen Cheng Feng Ling |
| author_sort | Yifei Han |
| collection | DOAJ |
| description | Wetland is a critical and complex ecosystem but highly susceptible to human activities. It has experienced significant change along with the increase of human intervention since decades ago. Currently, remote sensing-based mapping is believed an effective method for monitoring wetland changes. However, long-term wetland mapping is difficult due to poor historical data quality and wetland complexity, resulting in the triangular contradiction of long-term, high-resolution, and comprehensive classification systems. To address these challenges, we first modified the single-image-super-resolution method Residual Channel Attention Network (RCAN) and combined it with the famous spatiotemporal fusion model STARFM to enhance the Landsat images to 10 m resolution, which enabled more wetland objects to be identified. Then we extracted water bodies using UNet and classified them by object-based shape indices. Next, we applied recursive feature elimination and random forest classifier to map vegetated wetlands. Our overall classification accuracy reached 85.65%. We observed that the wetland in Jianghan Plain expanded significantly since 2000, with ponds showing the most notable growth rate (>114.04%). Aquatic crops make up a large portion of the wetland area, remaining stable with slow growth since 2000, while lake and surrounding herbaceous wetland restoration has shown notable progress in recent years. |
| format | Article |
| id | doaj-art-3e4eded1bf8f4f0e88a4252fcd841287 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-3e4eded1bf8f4f0e88a4252fcd8412872025-08-25T11:28:29ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2498605Long-term wetland mapping at 10 m resolution using super-resolution and hierarchical classification – a case study in Jianghan Plain, ChinaYifei Han0Hong Chi1Jinliang Huang2Xiaoming Shi3Juan Qiu4Qihui Shao5Yifan Li6Chen Cheng7Feng Ling8Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, People’s Republic of ChinaKey Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, People’s Republic of ChinaKey Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, People’s Republic of ChinaHubei Institute of Photogrammetry and Remote Sensing, Wuhan, People’s Republic of ChinaKey Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, People’s Republic of ChinaSchool of Architecture, Hubei Engineering University, Xiaogan, People’s Republic of ChinaKey Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, People’s Republic of ChinaKey Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, People’s Republic of ChinaKey Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, People’s Republic of ChinaWetland is a critical and complex ecosystem but highly susceptible to human activities. It has experienced significant change along with the increase of human intervention since decades ago. Currently, remote sensing-based mapping is believed an effective method for monitoring wetland changes. However, long-term wetland mapping is difficult due to poor historical data quality and wetland complexity, resulting in the triangular contradiction of long-term, high-resolution, and comprehensive classification systems. To address these challenges, we first modified the single-image-super-resolution method Residual Channel Attention Network (RCAN) and combined it with the famous spatiotemporal fusion model STARFM to enhance the Landsat images to 10 m resolution, which enabled more wetland objects to be identified. Then we extracted water bodies using UNet and classified them by object-based shape indices. Next, we applied recursive feature elimination and random forest classifier to map vegetated wetlands. Our overall classification accuracy reached 85.65%. We observed that the wetland in Jianghan Plain expanded significantly since 2000, with ponds showing the most notable growth rate (>114.04%). Aquatic crops make up a large portion of the wetland area, remaining stable with slow growth since 2000, while lake and surrounding herbaceous wetland restoration has shown notable progress in recent years.https://www.tandfonline.com/doi/10.1080/17538947.2025.2498605Super-resolutionLandsat imagerySentinel-2 imageryWetland mappingJianghan Plain |
| spellingShingle | Yifei Han Hong Chi Jinliang Huang Xiaoming Shi Juan Qiu Qihui Shao Yifan Li Chen Cheng Feng Ling Long-term wetland mapping at 10 m resolution using super-resolution and hierarchical classification – a case study in Jianghan Plain, China International Journal of Digital Earth Super-resolution Landsat imagery Sentinel-2 imagery Wetland mapping Jianghan Plain |
| title | Long-term wetland mapping at 10 m resolution using super-resolution and hierarchical classification – a case study in Jianghan Plain, China |
| title_full | Long-term wetland mapping at 10 m resolution using super-resolution and hierarchical classification – a case study in Jianghan Plain, China |
| title_fullStr | Long-term wetland mapping at 10 m resolution using super-resolution and hierarchical classification – a case study in Jianghan Plain, China |
| title_full_unstemmed | Long-term wetland mapping at 10 m resolution using super-resolution and hierarchical classification – a case study in Jianghan Plain, China |
| title_short | Long-term wetland mapping at 10 m resolution using super-resolution and hierarchical classification – a case study in Jianghan Plain, China |
| title_sort | long term wetland mapping at 10 m resolution using super resolution and hierarchical classification a case study in jianghan plain china |
| topic | Super-resolution Landsat imagery Sentinel-2 imagery Wetland mapping Jianghan Plain |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2498605 |
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