Fine monitoring method for mangrove wetland ecosystem based on sentinel data fusion and RF optimization
The study introduces the Sentinel Data Fusion and RF Optimization-Based Monitoring Model for Mangrove Wetlands (SDF-RF). This novel model integrates advanced satellite data processing (Sentinel series) with machine learning (Random Forest algorithm) to achieve fine-grained image segmentation and 3D...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Systems and Soft Computing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941925001954 |
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| _version_ | 1849229212899082240 |
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| author | Shuwen Wang Taisi Chen Zimin Li Haitao Sang |
| author_facet | Shuwen Wang Taisi Chen Zimin Li Haitao Sang |
| author_sort | Shuwen Wang |
| collection | DOAJ |
| description | The study introduces the Sentinel Data Fusion and RF Optimization-Based Monitoring Model for Mangrove Wetlands (SDF-RF). This novel model integrates advanced satellite data processing (Sentinel series) with machine learning (Random Forest algorithm) to achieve fine-grained image segmentation and 3D feature classification for mangrove ecosystems. By weighting the segmented features and organizing them into a 3D framework, the model demonstrated exceptional performance, with a misclassification rate below 0.001 and overall classification accuracy exceeding 98% on benchmark datasets. In the field verification of Zhanjiang Mangrove Reserve, SDF-RF outperformed other models. The accuracy rate in biomass prediction and environmental data classification was as high as 98%, highlighting its potential to completely change mangrove monitoring through precise land feature identification and ecological protection applications. This innovation bridges remote sensing and AI, offering a robust solution for mangrove ecosystem protection. |
| format | Article |
| id | doaj-art-0e801c36b9434be080c48e7d05996385 |
| institution | Kabale University |
| issn | 2772-9419 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj-art-0e801c36b9434be080c48e7d059963852025-08-22T04:58:58ZengElsevierSystems and Soft Computing2772-94192025-12-01720037610.1016/j.sasc.2025.200376Fine monitoring method for mangrove wetland ecosystem based on sentinel data fusion and RF optimizationShuwen Wang0Taisi Chen1Zimin Li2Haitao Sang3School of Electronic and Electrical Engineering, Lingnan Normal University, Zhanjiang, 524048, China; Mangrove Institute, Lingnan Normal University, Zhanjiang, 524048, ChinaSchool of Electronic and Electrical Engineering, Lingnan Normal University, Zhanjiang, 524048, China; Mangrove Institute, Lingnan Normal University, Zhanjiang, 524048, ChinaSchool of Electronic and Electrical Engineering, Lingnan Normal University, Zhanjiang, 524048, China; Mangrove Institute, Lingnan Normal University, Zhanjiang, 524048, ChinaCorresponding author.; School of Electronic and Electrical Engineering, Lingnan Normal University, Zhanjiang, 524048, China; Mangrove Institute, Lingnan Normal University, Zhanjiang, 524048, ChinaThe study introduces the Sentinel Data Fusion and RF Optimization-Based Monitoring Model for Mangrove Wetlands (SDF-RF). This novel model integrates advanced satellite data processing (Sentinel series) with machine learning (Random Forest algorithm) to achieve fine-grained image segmentation and 3D feature classification for mangrove ecosystems. By weighting the segmented features and organizing them into a 3D framework, the model demonstrated exceptional performance, with a misclassification rate below 0.001 and overall classification accuracy exceeding 98% on benchmark datasets. In the field verification of Zhanjiang Mangrove Reserve, SDF-RF outperformed other models. The accuracy rate in biomass prediction and environmental data classification was as high as 98%, highlighting its potential to completely change mangrove monitoring through precise land feature identification and ecological protection applications. This innovation bridges remote sensing and AI, offering a robust solution for mangrove ecosystem protection.http://www.sciencedirect.com/science/article/pii/S2772941925001954Mangrove forestMonitorSatellite dataDivisionGeographical features |
| spellingShingle | Shuwen Wang Taisi Chen Zimin Li Haitao Sang Fine monitoring method for mangrove wetland ecosystem based on sentinel data fusion and RF optimization Systems and Soft Computing Mangrove forest Monitor Satellite data Division Geographical features |
| title | Fine monitoring method for mangrove wetland ecosystem based on sentinel data fusion and RF optimization |
| title_full | Fine monitoring method for mangrove wetland ecosystem based on sentinel data fusion and RF optimization |
| title_fullStr | Fine monitoring method for mangrove wetland ecosystem based on sentinel data fusion and RF optimization |
| title_full_unstemmed | Fine monitoring method for mangrove wetland ecosystem based on sentinel data fusion and RF optimization |
| title_short | Fine monitoring method for mangrove wetland ecosystem based on sentinel data fusion and RF optimization |
| title_sort | fine monitoring method for mangrove wetland ecosystem based on sentinel data fusion and rf optimization |
| topic | Mangrove forest Monitor Satellite data Division Geographical features |
| url | http://www.sciencedirect.com/science/article/pii/S2772941925001954 |
| work_keys_str_mv | AT shuwenwang finemonitoringmethodformangrovewetlandecosystembasedonsentineldatafusionandrfoptimization AT taisichen finemonitoringmethodformangrovewetlandecosystembasedonsentineldatafusionandrfoptimization AT ziminli finemonitoringmethodformangrovewetlandecosystembasedonsentineldatafusionandrfoptimization AT haitaosang finemonitoringmethodformangrovewetlandecosystembasedonsentineldatafusionandrfoptimization |