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Integrating Multispectral and SAR Satellite Data for Alpine Wetland Mapping and Spatio-Temporal Change Analysis in the Qinghai Lake Basin.

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Title: Integrating Multispectral and SAR Satellite Data for Alpine Wetland Mapping and Spatio-Temporal Change Analysis in the Qinghai Lake Basin.
Authors: Zhuang, Qianle1 (AUTHOR), Tang, Zeyu2 (AUTHOR), Li, Chenggang1,2 (AUTHOR), Fang, Meiting2 (AUTHOR), Ling, Xiaolu2 (AUTHOR) lingxl@cumt.edu.cn
Source: Remote Sensing. Apr2026, Vol. 18 Issue 8, p1173. 19p.
Subjects: Synthetic aperture radar, Multispectral imaging, Peatlands, Remote sensing, Feature selection, Watersheds, Wetlands, Spatio-temporal variation
Geographic Terms: Qinghai Lake (China)
Abstract: Highlights: What are the main findings? The incorporation of geometric and shape–textural features significantly improved the classification accuracy of alpine wetlands. Feature optimization based on the SEaTH method yielded the best performance (overall accuracy (OA) = 86.24%, Kappa = 0.79) and effectively reduced redundancy within the feature set. What are the implications of the main findings? The integration of SAR data with optimized feature selection, particularly shape and texture features, provides a robust and efficient framework for enhancing wetland mapping accuracy in the Qinghai Lake Basin, where alpine conditions, complex terrain, and heterogeneous land-cover patterns pose substantial challenges to conventional monitoring approaches. Improved delineation of marsh meadows and inland tidal flats offers reliable spatial evidence to support conservation and management efforts in the Qinghai Lake Basin, thereby contributing to ecological assessment and sustainable land-use planning for fragile wetland ecosystems on the Qinghai–Tibetan Plateau. Alpine wetlands in the Qinghai Lake Basin, located on the northeastern Qinghai–Tibetan Plateau, are ecologically important but highly vulnerable to climate change and anthropogenic disturbance. Traditional field-based surveys are labor-intensive and spatially constrained, underscoring the need for automated remote sensing approaches for large-scale wetland mapping. In this study, an object-based image analysis (OBIA) framework was developed by integrating Sentinel-2 optical imagery with Sentinel-1 synthetic aperture radar (SAR) data to classify two representative plateau wetland types: marsh meadows and inland tidal flats. Seven categories of features were evaluated, including spectral features, vegetation indices, water indices, red-edge features, topographic variables, radar backscatter, and geometric-textural metrics. The Separability and Thresholds (SEaTH) algorithm was employed for feature selection and optimization prior to classification using a Random Forest model. The results indicate that the incorporating geometric and textural features significantly improved classification performance, achieving an overall accuracy (OA) of 82.53% and a Kappa coefficient of 0.74. Moreover, the SEaTH-based feature optimization scheme yielded the best performance, with an OA of 86.24% and a Kappa coefficient of 0.79. Compared with the full feature set, this approach improved producer's accuracy by 3.96–6.11% and increased overall accuracy by 1.48%. The proposed framework provides an effective and computationally efficient approach for mapping ecologically fragile alpine wetlands and offers valuable support for wetland conservation in the Qinghai Lake Basin. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Integrating Multispectral and SAR Satellite Data for Alpine Wetland Mapping and Spatio-Temporal Change Analysis in the Qinghai Lake Basin.
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  Data: <searchLink fieldCode="DE" term="%22Synthetic+aperture+radar%22">Synthetic aperture radar</searchLink><br /><searchLink fieldCode="DE" term="%22Multispectral+imaging%22">Multispectral imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Peatlands%22">Peatlands</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Watersheds%22">Watersheds</searchLink><br /><searchLink fieldCode="DE" term="%22Wetlands%22">Wetlands</searchLink><br /><searchLink fieldCode="DE" term="%22Spatio-temporal+variation%22">Spatio-temporal variation</searchLink>
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  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Qinghai+Lake+%28China%29%22">Qinghai Lake (China)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? The incorporation of geometric and shape–textural features significantly improved the classification accuracy of alpine wetlands. Feature optimization based on the SEaTH method yielded the best performance (overall accuracy (OA) = 86.24%, Kappa = 0.79) and effectively reduced redundancy within the feature set. What are the implications of the main findings? The integration of SAR data with optimized feature selection, particularly shape and texture features, provides a robust and efficient framework for enhancing wetland mapping accuracy in the Qinghai Lake Basin, where alpine conditions, complex terrain, and heterogeneous land-cover patterns pose substantial challenges to conventional monitoring approaches. Improved delineation of marsh meadows and inland tidal flats offers reliable spatial evidence to support conservation and management efforts in the Qinghai Lake Basin, thereby contributing to ecological assessment and sustainable land-use planning for fragile wetland ecosystems on the Qinghai–Tibetan Plateau. Alpine wetlands in the Qinghai Lake Basin, located on the northeastern Qinghai–Tibetan Plateau, are ecologically important but highly vulnerable to climate change and anthropogenic disturbance. Traditional field-based surveys are labor-intensive and spatially constrained, underscoring the need for automated remote sensing approaches for large-scale wetland mapping. In this study, an object-based image analysis (OBIA) framework was developed by integrating Sentinel-2 optical imagery with Sentinel-1 synthetic aperture radar (SAR) data to classify two representative plateau wetland types: marsh meadows and inland tidal flats. Seven categories of features were evaluated, including spectral features, vegetation indices, water indices, red-edge features, topographic variables, radar backscatter, and geometric-textural metrics. The Separability and Thresholds (SEaTH) algorithm was employed for feature selection and optimization prior to classification using a Random Forest model. The results indicate that the incorporating geometric and textural features significantly improved classification performance, achieving an overall accuracy (OA) of 82.53% and a Kappa coefficient of 0.74. Moreover, the SEaTH-based feature optimization scheme yielded the best performance, with an OA of 86.24% and a Kappa coefficient of 0.79. Compared with the full feature set, this approach improved producer's accuracy by 3.96–6.11% and increased overall accuracy by 1.48%. The proposed framework provides an effective and computationally efficient approach for mapping ecologically fragile alpine wetlands and offers valuable support for wetland conservation in the Qinghai Lake Basin. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/rs18081173
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 19
        StartPage: 1173
    Subjects:
      – SubjectFull: Synthetic aperture radar
        Type: general
      – SubjectFull: Multispectral imaging
        Type: general
      – SubjectFull: Peatlands
        Type: general
      – SubjectFull: Remote sensing
        Type: general
      – SubjectFull: Feature selection
        Type: general
      – SubjectFull: Watersheds
        Type: general
      – SubjectFull: Wetlands
        Type: general
      – SubjectFull: Spatio-temporal variation
        Type: general
      – SubjectFull: Qinghai Lake (China)
        Type: general
    Titles:
      – TitleFull: Integrating Multispectral and SAR Satellite Data for Alpine Wetland Mapping and Spatio-Temporal Change Analysis in the Qinghai Lake Basin.
        Type: main
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          Name:
            NameFull: Zhuang, Qianle
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            NameFull: Tang, Zeyu
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            NameFull: Li, Chenggang
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            NameFull: Fang, Meiting
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            NameFull: Ling, Xiaolu
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              M: 04
              Text: Apr2026
              Type: published
              Y: 2026
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