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Classification of plants based on time-series SAR coherence and intensity data in Yancheng coastal wetland

文献类型：期刊论文

作者Bian, Shuaichen2,3,4; Xie, Chou2,3,4; Tian, Bangsen2; Guo, Yihong2; Zhu, Yu2; Yang, Ying3,5; Zhang, Ming1; Yang, Yanchen2,3; Ruan, Yimin2,3
刊名INTERNATIONAL JOURNAL OF REMOTE SENSING
出版日期2024-11-24
卷号N/A页码:23
关键词Wetland plant SAR Sentinel-1 time series coherence support vector machine
ISSN号0143-1161
DOI10.1080/01431161.2024.2423908
产权排序4
英文摘要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.
WOS关键词L-BAND ; INSAR COHERENCE ; RADAR DETECTION ; RANDOM FOREST ; COVER CHANGES ; C-BAND ; IMAGES ; BACKSCATTER ; CANOPY
资助项目National Key R&D Program of China[2022YFC3005601]
WOS研究方向Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001364527000001
出版者TAYLOR & FRANCIS LTD
资助机构National Key R&D Program of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/210543]  
专题中国科学院地理科学与资源研究所
通讯作者Xie, Chou
作者单位1.China Geol Survey, Changsha Nat Resources Comprehens Survey Centereso, Changsha, Hunan, Peoples R China
2.Chinese Acad Sci, Aerosp Informat Res Inst, 1 Beichen West Rd, Beijing 100094, Peoples R China
3.Univ Chinese Acad Sci, Coll Resources & Environm CRE, Beijing, Peoples R China
4.Deqing Acad Satellite Applicat, Lab Target Microwave Properties, Hangzhou, Zhejiang, Peoples R China
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Bian, Shuaichen,Xie, Chou,Tian, Bangsen,et al. Classification of plants based on time-series SAR coherence and intensity data in Yancheng coastal wetland[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2024,N/A:23.
APA Bian, Shuaichen.,Xie, Chou.,Tian, Bangsen.,Guo, Yihong.,Zhu, Yu.,...&Ruan, Yimin.(2024).Classification of plants based on time-series SAR coherence and intensity data in Yancheng coastal wetland.INTERNATIONAL JOURNAL OF REMOTE SENSING,N/A,23.
MLA Bian, Shuaichen,et al."Classification of plants based on time-series SAR coherence and intensity data in Yancheng coastal wetland".INTERNATIONAL JOURNAL OF REMOTE SENSING N/A(2024):23.

入库方式： OAI收割

来源：地理科学与资源研究所

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