Classification and Monitoring of Salt Marsh Vegetation in the Yellow River Delta Based on Multi-Source Remote Sensing Data Fusion
Salt marsh vegetation in the Yellow River Delta, including <i>Phragmites australis</i> (<i>P. australis</i>), <i>Suaeda salsa</i> (<i>S. salsa</i>), and <i>Tamarix chinensis</i> (<i>T. chinensis</i>), is essential for the stabil...
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MDPI AG
2025-01-01
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| author | Ran Xu Yanguo Fan Bowen Fan Guangyue Feng Ruotong Li |
| author_facet | Ran Xu Yanguo Fan Bowen Fan Guangyue Feng Ruotong Li |
| author_sort | Ran Xu |
| collection | DOAJ |
| description | Salt marsh vegetation in the Yellow River Delta, including <i>Phragmites australis</i> (<i>P. australis</i>), <i>Suaeda salsa</i> (<i>S. salsa</i>), and <i>Tamarix chinensis</i> (<i>T. chinensis</i>), is essential for the stability of wetland ecosystems. In recent years, salt marsh vegetation has experienced severe degradation, which is primarily due to invasive species and human activities. Therefore, the accurate monitoring of the spatial distribution of these vegetation types is critical for the ecological protection and restoration of the Yellow River Delta. This study proposes a multi-source remote sensing data fusion method based on Sentinel-1 and Sentinel-2 imagery, integrating the temporal characteristics of optical and SAR (synthetic aperture radar) data for the classification mapping of salt marsh vegetation in the Yellow River Delta. Phenological and polarization features were extracted to capture vegetation characteristics. A random forest algorithm was then applied to evaluate the impact of different feature combinations on classification accuracy. Combining optical and SAR time-series data significantly enhanced classification accuracy, particularly in differentiating <i>P. australis</i>, <i>S. salsa</i>, and <i>T. chinensis</i>. The integration of phenological features, polarization ratio, and polarization difference achieved a classification accuracy of 93.51% with a Kappa coefficient of 0.917, outperforming the use of individual data sources. |
| format | Article |
| id | doaj-art-0ef87126511648218184074b6f797e14 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-0ef87126511648218184074b6f797e142025-01-24T13:49:14ZengMDPI AGSensors1424-82202025-01-0125252910.3390/s25020529Classification and Monitoring of Salt Marsh Vegetation in the Yellow River Delta Based on Multi-Source Remote Sensing Data FusionRan Xu0Yanguo Fan1Bowen Fan2Guangyue Feng3Ruotong Li4School of Oceanography and Spatial Information, China University of Petroleum East China—Qingdao Campus, Qingdao 266580, ChinaSchool of Oceanography and Spatial Information, China University of Petroleum East China—Qingdao Campus, Qingdao 266580, ChinaCollege of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, ChinaSchool of Oceanography and Spatial Information, China University of Petroleum East China—Qingdao Campus, Qingdao 266580, ChinaSchool of Oceanography and Spatial Information, China University of Petroleum East China—Qingdao Campus, Qingdao 266580, ChinaSalt marsh vegetation in the Yellow River Delta, including <i>Phragmites australis</i> (<i>P. australis</i>), <i>Suaeda salsa</i> (<i>S. salsa</i>), and <i>Tamarix chinensis</i> (<i>T. chinensis</i>), is essential for the stability of wetland ecosystems. In recent years, salt marsh vegetation has experienced severe degradation, which is primarily due to invasive species and human activities. Therefore, the accurate monitoring of the spatial distribution of these vegetation types is critical for the ecological protection and restoration of the Yellow River Delta. This study proposes a multi-source remote sensing data fusion method based on Sentinel-1 and Sentinel-2 imagery, integrating the temporal characteristics of optical and SAR (synthetic aperture radar) data for the classification mapping of salt marsh vegetation in the Yellow River Delta. Phenological and polarization features were extracted to capture vegetation characteristics. A random forest algorithm was then applied to evaluate the impact of different feature combinations on classification accuracy. Combining optical and SAR time-series data significantly enhanced classification accuracy, particularly in differentiating <i>P. australis</i>, <i>S. salsa</i>, and <i>T. chinensis</i>. The integration of phenological features, polarization ratio, and polarization difference achieved a classification accuracy of 93.51% with a Kappa coefficient of 0.917, outperforming the use of individual data sources.https://www.mdpi.com/1424-8220/25/2/529salt marsh vegetationYellow River Deltaremote sensingSAR polarizationvegetation classification |
| spellingShingle | Ran Xu Yanguo Fan Bowen Fan Guangyue Feng Ruotong Li Classification and Monitoring of Salt Marsh Vegetation in the Yellow River Delta Based on Multi-Source Remote Sensing Data Fusion Sensors salt marsh vegetation Yellow River Delta remote sensing SAR polarization vegetation classification |
| title | Classification and Monitoring of Salt Marsh Vegetation in the Yellow River Delta Based on Multi-Source Remote Sensing Data Fusion |
| title_full | Classification and Monitoring of Salt Marsh Vegetation in the Yellow River Delta Based on Multi-Source Remote Sensing Data Fusion |
| title_fullStr | Classification and Monitoring of Salt Marsh Vegetation in the Yellow River Delta Based on Multi-Source Remote Sensing Data Fusion |
| title_full_unstemmed | Classification and Monitoring of Salt Marsh Vegetation in the Yellow River Delta Based on Multi-Source Remote Sensing Data Fusion |
| title_short | Classification and Monitoring of Salt Marsh Vegetation in the Yellow River Delta Based on Multi-Source Remote Sensing Data Fusion |
| title_sort | classification and monitoring of salt marsh vegetation in the yellow river delta based on multi source remote sensing data fusion |
| topic | salt marsh vegetation Yellow River Delta remote sensing SAR polarization vegetation classification |
| url | https://www.mdpi.com/1424-8220/25/2/529 |
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