| 摘 要： |
Investigating coastal wetland plant communities is of great significance for wetland monitoring due to the important functions of coastal wetlands, such as maintaining biodiversity and mitigating global climate change. Current studies on wetland plants mostly rely on optical data, with few utilizing synthetic aperture radar (SAR) data. Moreover, these studies often analysed single temporal SAR data, which limited the exploration of the valuable information present in time-series SAR data. Therefore, in this paper, we proposed a technique for mapping coastal wetland plant types based on time-series SAR coherence and intensity data to fully utilize the information from these data. We utilized Sentinel-1 Single Look Complex (SLC) images covering the Yancheng coastal wetland for the entire year of 2021 to investigate the effectiveness of using dual-polarization interferometric coherence and intensity-derived information from time-series Sentinel-1 data as features for classification. Plant classification was conducted using support vector machine (SVM) and random forest (RF) methods. Our results demonstrated that integrating time-series dual-polarization coherence and intensity-derived information resulted the best classification accuracy, with an overall accuracy (OA) of 89.79% and a Kappa coefficient of 0.858. This highlights the effectiveness of combining coherence and intensity data from time-series Sentinel-1 for monitoring plant cover in coastal wetlands. |
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