Coastal Wetlands Classification and Carbon Storage Estimation: A Case Study of the Region Surrounding the South China Sea
Coastal wetlands are typical carbon sinks and play a crucial role in achieving global carbon neutrality goals. The region surrounding the South China Sea (SCS) contains abundant coastal wetland resources and strong carbon sequestration capabilities, which can be effectively assessed by the well-know...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10918765/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850202999105781760 |
|---|---|
| author | Guanglin Lai Zhi He Chengle Zhou Youwei Wang |
| author_facet | Guanglin Lai Zhi He Chengle Zhou Youwei Wang |
| author_sort | Guanglin Lai |
| collection | DOAJ |
| description | Coastal wetlands are typical carbon sinks and play a crucial role in achieving global carbon neutrality goals. The region surrounding the South China Sea (SCS) contains abundant coastal wetland resources and strong carbon sequestration capabilities, which can be effectively assessed by the well-known integrated valuation of ecosystem services and tradeoffs (InVEST) model. InVEST requires accurate spatial distribution information of wetlands as input data, which can be obtained by coastal wetlands classification methods. Among all classification methods, deep learning (DL) is the state-of-the-art. However, designing a DL method that is both easily trainable and suitable for large-scale coastal wetland classification remains a challenging issue. This article proposes a novel DL method and a new carbon correction strategy for large-scale coastal wetland classification and carbon storage assessment. First, the remote sensing (RS) data from the study area is acquired and preprocessed by the Google Earth Engine. Second, the Otsu algorithm and decision tree are used to extract the maximum wetland extent. Third, a multidirectional squeeze attention network (MDSAN) is proposed for large-scale coastal wetland classification. Finally, a new strategy is designed to correct measured carbon pool data using meteorological data. Experiments show that the proposed wetland classification method achieves an overall accuracy and Kappa coefficient of 0.9500 and 0.9411, respectively, demonstrating the effectiveness of MDSAN. Furthermore, the estimated carbon storage in the mangroves, tidal-flats, and swamps surrounding the SCS is approximately 1.2112<inline-formula><tex-math notation="LaTeX">$\boldsymbol{\times }10\boldsymbol{^{9}}$</tex-math></inline-formula> t, 6.9138<inline-formula><tex-math notation="LaTeX">$\boldsymbol{\times }10\boldsymbol{^{7}}$</tex-math></inline-formula> t, and 1.6980<inline-formula><tex-math notation="LaTeX">$\boldsymbol{\times }10\boldsymbol{^{8}}$</tex-math></inline-formula> t, respectively, revealing the carbon distribution pattern in the region. |
| format | Article |
| id | doaj-art-bfe25e884fa24b2ba55f68611d66b445 |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-bfe25e884fa24b2ba55f68611d66b4452025-08-20T02:11:37ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01189464948210.1109/JSTARS.2025.354948010918765Coastal Wetlands Classification and Carbon Storage Estimation: A Case Study of the Region Surrounding the South China SeaGuanglin Lai0Zhi He1https://orcid.org/0000-0001-9568-7076Chengle Zhou2https://orcid.org/0000-0003-3107-5446Youwei Wang3School of Geography and Planning, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Guangzhou, ChinaSchool of Geography and Planning, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Guangzhou, ChinaSchool of Geography and Planning, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Guangzhou, ChinaSchool of Geography and Planning, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Guangzhou, ChinaCoastal wetlands are typical carbon sinks and play a crucial role in achieving global carbon neutrality goals. The region surrounding the South China Sea (SCS) contains abundant coastal wetland resources and strong carbon sequestration capabilities, which can be effectively assessed by the well-known integrated valuation of ecosystem services and tradeoffs (InVEST) model. InVEST requires accurate spatial distribution information of wetlands as input data, which can be obtained by coastal wetlands classification methods. Among all classification methods, deep learning (DL) is the state-of-the-art. However, designing a DL method that is both easily trainable and suitable for large-scale coastal wetland classification remains a challenging issue. This article proposes a novel DL method and a new carbon correction strategy for large-scale coastal wetland classification and carbon storage assessment. First, the remote sensing (RS) data from the study area is acquired and preprocessed by the Google Earth Engine. Second, the Otsu algorithm and decision tree are used to extract the maximum wetland extent. Third, a multidirectional squeeze attention network (MDSAN) is proposed for large-scale coastal wetland classification. Finally, a new strategy is designed to correct measured carbon pool data using meteorological data. Experiments show that the proposed wetland classification method achieves an overall accuracy and Kappa coefficient of 0.9500 and 0.9411, respectively, demonstrating the effectiveness of MDSAN. Furthermore, the estimated carbon storage in the mangroves, tidal-flats, and swamps surrounding the SCS is approximately 1.2112<inline-formula><tex-math notation="LaTeX">$\boldsymbol{\times }10\boldsymbol{^{9}}$</tex-math></inline-formula> t, 6.9138<inline-formula><tex-math notation="LaTeX">$\boldsymbol{\times }10\boldsymbol{^{7}}$</tex-math></inline-formula> t, and 1.6980<inline-formula><tex-math notation="LaTeX">$\boldsymbol{\times }10\boldsymbol{^{8}}$</tex-math></inline-formula> t, respectively, revealing the carbon distribution pattern in the region.https://ieeexplore.ieee.org/document/10918765/Carbon storagecostal wetland classificationintegrated valuation of ecosystem services and tradeoffs (InVEST)lightweight network |
| spellingShingle | Guanglin Lai Zhi He Chengle Zhou Youwei Wang Coastal Wetlands Classification and Carbon Storage Estimation: A Case Study of the Region Surrounding the South China Sea IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Carbon storage costal wetland classification integrated valuation of ecosystem services and tradeoffs (InVEST) lightweight network |
| title | Coastal Wetlands Classification and Carbon Storage Estimation: A Case Study of the Region Surrounding the South China Sea |
| title_full | Coastal Wetlands Classification and Carbon Storage Estimation: A Case Study of the Region Surrounding the South China Sea |
| title_fullStr | Coastal Wetlands Classification and Carbon Storage Estimation: A Case Study of the Region Surrounding the South China Sea |
| title_full_unstemmed | Coastal Wetlands Classification and Carbon Storage Estimation: A Case Study of the Region Surrounding the South China Sea |
| title_short | Coastal Wetlands Classification and Carbon Storage Estimation: A Case Study of the Region Surrounding the South China Sea |
| title_sort | coastal wetlands classification and carbon storage estimation a case study of the region surrounding the south china sea |
| topic | Carbon storage costal wetland classification integrated valuation of ecosystem services and tradeoffs (InVEST) lightweight network |
| url | https://ieeexplore.ieee.org/document/10918765/ |
| work_keys_str_mv | AT guanglinlai coastalwetlandsclassificationandcarbonstorageestimationacasestudyoftheregionsurroundingthesouthchinasea AT zhihe coastalwetlandsclassificationandcarbonstorageestimationacasestudyoftheregionsurroundingthesouthchinasea AT chenglezhou coastalwetlandsclassificationandcarbonstorageestimationacasestudyoftheregionsurroundingthesouthchinasea AT youweiwang coastalwetlandsclassificationandcarbonstorageestimationacasestudyoftheregionsurroundingthesouthchinasea |