Annual change in the distribution and landscape health of mangrove ecosystems in China from 2016 to 2023 with Sentinel imagery
As a type of unique coastal wetland, mangroves are subjected to the compounded effects of human activities and climate change. Acquiring reliable and up-to-date information of mangroves is crucial to support their conservation and sustainable blue carbon development. The integration of remote sensin...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Global Ecology and Conservation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2351989424005596 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832590990007861248 |
|---|---|
| author | Yuchao Sun Mingzhen Ye Bin Ai Zhenlin Lai Jun Zhao Zhuokai Jian Xinyan Qi |
| author_facet | Yuchao Sun Mingzhen Ye Bin Ai Zhenlin Lai Jun Zhao Zhuokai Jian Xinyan Qi |
| author_sort | Yuchao Sun |
| collection | DOAJ |
| description | As a type of unique coastal wetland, mangroves are subjected to the compounded effects of human activities and climate change. Acquiring reliable and up-to-date information of mangroves is crucial to support their conservation and sustainable blue carbon development. The integration of remote sensing data and deep learning models enables precise identification of mangroves. This study evaluated the potential of various classical deep learning models for annual mangrove identification in China and analyzed their changes from 2016 to 2023, with a specific focus on the landscape health of mangrove ecosystems within 37 natural reserves and non-reserves. The research shows that compared to the combination model of PSPNet and DeepLabV3 with Resnet, the U-net+ResNet34 model gave the most optimal results in identifying mangroves. In addition, the F1-score of the model reached 0.843 when validated with the samples collected from the national mangrove dataset. Based on the mangrove distribution generated using this U–net+ResNet34, it is observed that the total mangrove area in China exhibited an upward trend during this period. Notably, the centroid of China’s mangroves shifted 26.23 km northeast from the western part of Guangdong. To evaluate the landscape health of mangrove reserves, various landscape pattern metrics were synthesized into the Landscape Health Composite Index through the application of the entropy weight method. Among the 37 natural reserves, 27 of them exhibited an upward trend in landscape health, with the most significant improvements observed in the Taishan Zhenhai Bay Mangrove Nature Reserve. This demonstrates that China has made considerable achievements in the mangrove conservation, encompassing both the quantity and quality of mangroves. However, future efforts should focus particularly on natural reserves where landscape health trends are declining, with an increase in human and material resources investment. This study may serve as a reference for efficiently assessing the health status of mangrove landscapes based on remote sensing data. It can enhance to understand the effectiveness of conservation efforts in mangrove reserves and further promote the scientific management of mangrove protected areas in China. |
| format | Article |
| id | doaj-art-cbf1530fd27842e991a9dc946678ba35 |
| institution | Kabale University |
| issn | 2351-9894 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Global Ecology and Conservation |
| spelling | doaj-art-cbf1530fd27842e991a9dc946678ba352025-01-23T05:26:53ZengElsevierGlobal Ecology and Conservation2351-98942025-01-0157e03355Annual change in the distribution and landscape health of mangrove ecosystems in China from 2016 to 2023 with Sentinel imageryYuchao Sun0Mingzhen Ye1Bin Ai2Zhenlin Lai3Jun Zhao4Zhuokai Jian5Xinyan Qi6School of Marine Sciences, Sun Yat-sen University, Zhuhai 519082, China; South China Sea Development Research Institute, Ministry of Natural Resources, China; Science and Technology Development Foundation of South China Sea Bureau, Ministry of Natural Resources, Guangzhou 510300, ChinaSchool of Marine Sciences, Sun Yat-sen University, Zhuhai 519082, ChinaSchool of Marine Sciences, Sun Yat-sen University, Zhuhai 519082, China; Correspondence to: School of Marine Sciences, Sun Yat-sen University, Zhuhai, Guangdong 519082, ChinaSchool of Marine Sciences, Sun Yat-sen University, Zhuhai 519082, ChinaSchool of Marine Sciences, Sun Yat-sen University, Zhuhai 519082, ChinaSchool of Marine Sciences, Sun Yat-sen University, Zhuhai 519082, ChinaCOSCO SHIPPING（Guangzhou）Co., Ltd, ChinaAs a type of unique coastal wetland, mangroves are subjected to the compounded effects of human activities and climate change. Acquiring reliable and up-to-date information of mangroves is crucial to support their conservation and sustainable blue carbon development. The integration of remote sensing data and deep learning models enables precise identification of mangroves. This study evaluated the potential of various classical deep learning models for annual mangrove identification in China and analyzed their changes from 2016 to 2023, with a specific focus on the landscape health of mangrove ecosystems within 37 natural reserves and non-reserves. The research shows that compared to the combination model of PSPNet and DeepLabV3 with Resnet, the U-net+ResNet34 model gave the most optimal results in identifying mangroves. In addition, the F1-score of the model reached 0.843 when validated with the samples collected from the national mangrove dataset. Based on the mangrove distribution generated using this U–net+ResNet34, it is observed that the total mangrove area in China exhibited an upward trend during this period. Notably, the centroid of China’s mangroves shifted 26.23 km northeast from the western part of Guangdong. To evaluate the landscape health of mangrove reserves, various landscape pattern metrics were synthesized into the Landscape Health Composite Index through the application of the entropy weight method. Among the 37 natural reserves, 27 of them exhibited an upward trend in landscape health, with the most significant improvements observed in the Taishan Zhenhai Bay Mangrove Nature Reserve. This demonstrates that China has made considerable achievements in the mangrove conservation, encompassing both the quantity and quality of mangroves. However, future efforts should focus particularly on natural reserves where landscape health trends are declining, with an increase in human and material resources investment. This study may serve as a reference for efficiently assessing the health status of mangrove landscapes based on remote sensing data. It can enhance to understand the effectiveness of conservation efforts in mangrove reserves and further promote the scientific management of mangrove protected areas in China.http://www.sciencedirect.com/science/article/pii/S2351989424005596Annual changeLandscape healthMangrove ecosystemSentinel imageryU-net+ResNet34 |
| spellingShingle | Yuchao Sun Mingzhen Ye Bin Ai Zhenlin Lai Jun Zhao Zhuokai Jian Xinyan Qi Annual change in the distribution and landscape health of mangrove ecosystems in China from 2016 to 2023 with Sentinel imagery Global Ecology and Conservation Annual change Landscape health Mangrove ecosystem Sentinel imagery U-net+ResNet34 |
| title | Annual change in the distribution and landscape health of mangrove ecosystems in China from 2016 to 2023 with Sentinel imagery |
| title_full | Annual change in the distribution and landscape health of mangrove ecosystems in China from 2016 to 2023 with Sentinel imagery |
| title_fullStr | Annual change in the distribution and landscape health of mangrove ecosystems in China from 2016 to 2023 with Sentinel imagery |
| title_full_unstemmed | Annual change in the distribution and landscape health of mangrove ecosystems in China from 2016 to 2023 with Sentinel imagery |
| title_short | Annual change in the distribution and landscape health of mangrove ecosystems in China from 2016 to 2023 with Sentinel imagery |
| title_sort | annual change in the distribution and landscape health of mangrove ecosystems in china from 2016 to 2023 with sentinel imagery |
| topic | Annual change Landscape health Mangrove ecosystem Sentinel imagery U-net+ResNet34 |
| url | http://www.sciencedirect.com/science/article/pii/S2351989424005596 |
| work_keys_str_mv | AT yuchaosun annualchangeinthedistributionandlandscapehealthofmangroveecosystemsinchinafrom2016to2023withsentinelimagery AT mingzhenye annualchangeinthedistributionandlandscapehealthofmangroveecosystemsinchinafrom2016to2023withsentinelimagery AT binai annualchangeinthedistributionandlandscapehealthofmangroveecosystemsinchinafrom2016to2023withsentinelimagery AT zhenlinlai annualchangeinthedistributionandlandscapehealthofmangroveecosystemsinchinafrom2016to2023withsentinelimagery AT junzhao annualchangeinthedistributionandlandscapehealthofmangroveecosystemsinchinafrom2016to2023withsentinelimagery AT zhuokaijian annualchangeinthedistributionandlandscapehealthofmangroveecosystemsinchinafrom2016to2023withsentinelimagery AT xinyanqi annualchangeinthedistributionandlandscapehealthofmangroveecosystemsinchinafrom2016to2023withsentinelimagery |