Tracking the Dynamics of Salt Marsh Including Mixed-Vegetation Zones Employing Sentinel-1 and Sentinel-2 Time-Series Images
Salt marshes, as one of the most productive ecosystems on earth, have experienced fragmentation, degradation, and losses due to the impacts of climate change and human overexploitation. Accurate monitoring of vegetation distribution and composition is crucial for salt marsh protection. However, achi...
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| author | Yujun Yi Kebing Chen Jiaxin Xu Qiyong Luo |
| author_facet | Yujun Yi Kebing Chen Jiaxin Xu Qiyong Luo |
| author_sort | Yujun Yi |
| collection | DOAJ |
| description | Salt marshes, as one of the most productive ecosystems on earth, have experienced fragmentation, degradation, and losses due to the impacts of climate change and human overexploitation. Accurate monitoring of vegetation distribution and composition is crucial for salt marsh protection. However, achieving accurate mapping has posed a challenge. Leveraging the high spatiotemporal resolution of the Sentinel series data, this study developed a method for high-accuracy mapping based on monthly changes across the vegetation life cycle, utilizing the random forest algorithm. This method was applied to identify <i>Phragmites australis</i>, <i>Suaeda salsa</i>, <i>Spartina alterniflora</i>, and the mixed-vegetation zones of <i>Tamarix chinensis</i> in the Yellow River Delta, and to analyze the key features of the model. The results indicate that: (1) integrating Sentinel-1 and Sentinel-2 satellite data achieved superior mapping accuracy (OA = 90.7%) compared to using either satellite individually; (2) the inclusion of SAR data significantly enhanced the classification accuracy within the mixed-vegetation zone, with “SAR<sub>divi</sub>” in July emerging as the pivotal distinguishing feature; and (3) the overall extent of salt marsh vegetation in the Yellow River Delta remained relatively stable from 2018 to 2022, with the largest area recorded in 2020 (265.69 km<sup>2</sup>). These results demonstrate the robustness of integrating Sentinel-1 and Sentinel-2 features for mapping salt marsh, particularly in complex mixed-vegetation zones. Such insights offer valuable guidance for the conservation and management of salt marsh ecosystems. |
| format | Article |
| id | doaj-art-84f89349dd0442cab071636546c5e8b4 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-84f89349dd0442cab071636546c5e8b42025-01-10T13:20:06ZengMDPI AGRemote Sensing2072-42922024-12-011715610.3390/rs17010056Tracking the Dynamics of Salt Marsh Including Mixed-Vegetation Zones Employing Sentinel-1 and Sentinel-2 Time-Series ImagesYujun Yi0Kebing Chen1Jiaxin Xu2Qiyong Luo3State Key Laboratory of Water Environment Simulation, Beijing Normal University, Beijing 100875, ChinaSchool of Environment, Beijing Normal University, Beijing 100875, ChinaSchool of Environment, Beijing Normal University, Beijing 100875, ChinaSchool of Environment, Beijing Normal University, Beijing 100875, ChinaSalt marshes, as one of the most productive ecosystems on earth, have experienced fragmentation, degradation, and losses due to the impacts of climate change and human overexploitation. Accurate monitoring of vegetation distribution and composition is crucial for salt marsh protection. However, achieving accurate mapping has posed a challenge. Leveraging the high spatiotemporal resolution of the Sentinel series data, this study developed a method for high-accuracy mapping based on monthly changes across the vegetation life cycle, utilizing the random forest algorithm. This method was applied to identify <i>Phragmites australis</i>, <i>Suaeda salsa</i>, <i>Spartina alterniflora</i>, and the mixed-vegetation zones of <i>Tamarix chinensis</i> in the Yellow River Delta, and to analyze the key features of the model. The results indicate that: (1) integrating Sentinel-1 and Sentinel-2 satellite data achieved superior mapping accuracy (OA = 90.7%) compared to using either satellite individually; (2) the inclusion of SAR data significantly enhanced the classification accuracy within the mixed-vegetation zone, with “SAR<sub>divi</sub>” in July emerging as the pivotal distinguishing feature; and (3) the overall extent of salt marsh vegetation in the Yellow River Delta remained relatively stable from 2018 to 2022, with the largest area recorded in 2020 (265.69 km<sup>2</sup>). These results demonstrate the robustness of integrating Sentinel-1 and Sentinel-2 features for mapping salt marsh, particularly in complex mixed-vegetation zones. Such insights offer valuable guidance for the conservation and management of salt marsh ecosystems.https://www.mdpi.com/2072-4292/17/1/56salt marshmixed-vegetation zonesremote sensingrandom forestYellow River DeltaSentinel satellite |
| spellingShingle | Yujun Yi Kebing Chen Jiaxin Xu Qiyong Luo Tracking the Dynamics of Salt Marsh Including Mixed-Vegetation Zones Employing Sentinel-1 and Sentinel-2 Time-Series Images Remote Sensing salt marsh mixed-vegetation zones remote sensing random forest Yellow River Delta Sentinel satellite |
| title | Tracking the Dynamics of Salt Marsh Including Mixed-Vegetation Zones Employing Sentinel-1 and Sentinel-2 Time-Series Images |
| title_full | Tracking the Dynamics of Salt Marsh Including Mixed-Vegetation Zones Employing Sentinel-1 and Sentinel-2 Time-Series Images |
| title_fullStr | Tracking the Dynamics of Salt Marsh Including Mixed-Vegetation Zones Employing Sentinel-1 and Sentinel-2 Time-Series Images |
| title_full_unstemmed | Tracking the Dynamics of Salt Marsh Including Mixed-Vegetation Zones Employing Sentinel-1 and Sentinel-2 Time-Series Images |
| title_short | Tracking the Dynamics of Salt Marsh Including Mixed-Vegetation Zones Employing Sentinel-1 and Sentinel-2 Time-Series Images |
| title_sort | tracking the dynamics of salt marsh including mixed vegetation zones employing sentinel 1 and sentinel 2 time series images |
| topic | salt marsh mixed-vegetation zones remote sensing random forest Yellow River Delta Sentinel satellite |
| url | https://www.mdpi.com/2072-4292/17/1/56 |
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