A Novel Workflow for Seasonal Wetland Identification Using Bi-Weekly Multiple Remote Sensing Data
Accurate wetland mapping is essential for their protection and management; however, it is difficult to accurately identify seasonal wetlands because of irregular rainfall and the potential lack of water inundation. In this study, we propose a novel method to generate reliable seasonal wetland maps w...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/4/1037 |
| _version_ | 1797476740448124928 |
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| author | Liwei Xing Zhenguo Niu Cuicui Jiao Jing Zhang Shuqing Han Guodong Cheng Jianzhai Wu |
| author_facet | Liwei Xing Zhenguo Niu Cuicui Jiao Jing Zhang Shuqing Han Guodong Cheng Jianzhai Wu |
| author_sort | Liwei Xing |
| collection | DOAJ |
| description | Accurate wetland mapping is essential for their protection and management; however, it is difficult to accurately identify seasonal wetlands because of irregular rainfall and the potential lack of water inundation. In this study, we propose a novel method to generate reliable seasonal wetland maps with a spatial resolution of 20 m using a seasonal-rule-based method in the Zhalong and Momoge National Nature Reserves. This study used Sentinel-1 and Sentinel-2 data, along with a bi-weekly composition method to generate a 15-day image time series. The random forest algorithm was used to classify the images into vegetation, waterbodies, bare land, and wet bare land during each time period. Several rules were incorporated based on the intra-annual changes in the seasonal wetlands and annual wetland maps of the study regions were generated. Validation processes showed that the overall accuracy and kappa coefficient were above 89.8% and 0.87, respectively. The seasonal-rule-based method was able to identify seasonal marshes, flooded wetlands, and artificial wetlands (e.g., paddy fields). Zonal analysis indicated that seasonal wetland types, including flooded wetlands and seasonal marshes, accounted for over 50% of the total wetland area in both Zhalong and Momoge National Nature Reserves; and permanent wetlands, including permanent water and permanent marsh, only accounted for 11% and 12% in the two reserves, respectively. This study proposes a new method to generate reliable annual wetland maps that include seasonal wetlands, providing an accurate dataset for interannual change analyses and wetland protection decision-making. |
| first_indexed | 2024-03-09T21:07:59Z |
| format | Article |
| id | doaj.art-3be430a3399a41fba151f4fb7195c60d |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T21:07:59Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3be430a3399a41fba151f4fb7195c60d2023-11-23T21:56:00ZengMDPI AGRemote Sensing2072-42922022-02-01144103710.3390/rs14041037A Novel Workflow for Seasonal Wetland Identification Using Bi-Weekly Multiple Remote Sensing DataLiwei Xing0Zhenguo Niu1Cuicui Jiao2Jing Zhang3Shuqing Han4Guodong Cheng5Jianzhai Wu6Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs/Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaCollege of Economics, Sichuan University of Science & Engineering, Zigong 643000, ChinaKey Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs/Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaKey Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs/Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaKey Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs/Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaKey Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs/Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaAccurate wetland mapping is essential for their protection and management; however, it is difficult to accurately identify seasonal wetlands because of irregular rainfall and the potential lack of water inundation. In this study, we propose a novel method to generate reliable seasonal wetland maps with a spatial resolution of 20 m using a seasonal-rule-based method in the Zhalong and Momoge National Nature Reserves. This study used Sentinel-1 and Sentinel-2 data, along with a bi-weekly composition method to generate a 15-day image time series. The random forest algorithm was used to classify the images into vegetation, waterbodies, bare land, and wet bare land during each time period. Several rules were incorporated based on the intra-annual changes in the seasonal wetlands and annual wetland maps of the study regions were generated. Validation processes showed that the overall accuracy and kappa coefficient were above 89.8% and 0.87, respectively. The seasonal-rule-based method was able to identify seasonal marshes, flooded wetlands, and artificial wetlands (e.g., paddy fields). Zonal analysis indicated that seasonal wetland types, including flooded wetlands and seasonal marshes, accounted for over 50% of the total wetland area in both Zhalong and Momoge National Nature Reserves; and permanent wetlands, including permanent water and permanent marsh, only accounted for 11% and 12% in the two reserves, respectively. This study proposes a new method to generate reliable annual wetland maps that include seasonal wetlands, providing an accurate dataset for interannual change analyses and wetland protection decision-making.https://www.mdpi.com/2072-4292/14/4/1037seasonal wetlandirregular seasonal dynamicZhalong and Momogemultiple remote sensing data |
| spellingShingle | Liwei Xing Zhenguo Niu Cuicui Jiao Jing Zhang Shuqing Han Guodong Cheng Jianzhai Wu A Novel Workflow for Seasonal Wetland Identification Using Bi-Weekly Multiple Remote Sensing Data Remote Sensing seasonal wetland irregular seasonal dynamic Zhalong and Momoge multiple remote sensing data |
| title | A Novel Workflow for Seasonal Wetland Identification Using Bi-Weekly Multiple Remote Sensing Data |
| title_full | A Novel Workflow for Seasonal Wetland Identification Using Bi-Weekly Multiple Remote Sensing Data |
| title_fullStr | A Novel Workflow for Seasonal Wetland Identification Using Bi-Weekly Multiple Remote Sensing Data |
| title_full_unstemmed | A Novel Workflow for Seasonal Wetland Identification Using Bi-Weekly Multiple Remote Sensing Data |
| title_short | A Novel Workflow for Seasonal Wetland Identification Using Bi-Weekly Multiple Remote Sensing Data |
| title_sort | novel workflow for seasonal wetland identification using bi weekly multiple remote sensing data |
| topic | seasonal wetland irregular seasonal dynamic Zhalong and Momoge multiple remote sensing data |
| url | https://www.mdpi.com/2072-4292/14/4/1037 |
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