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A CNN-Based Framework for Automatic Extraction of High-Resolution River Bankfull Width

文献类型：期刊论文

作者Li, Wenqi1,8; Zhang, Chendi1,2; Puhl, David5; Pan, Xiao6,7; Hassan, Marwan A.5; Bird, Stephen4; Yang, Kejun1; Zhao, Yang3
刊名REMOTE SENSING
出版日期2024-12-01
卷号16期号:23页码:19
关键词Convolutional Neural Network (CNN) channel width bankfull width automatic extraction boundary delineation
DOI10.3390/rs16234614
产权排序3
英文摘要River width is a crucial parameter that correlates and reflects the hydrological, geomorphological, and ecological characteristics of the channel. However, the width data with high spatial resolution is limited owing to the difficulties in extracting channel width under complex and variable riverine surroundings. To address this issue, we aimed to develop an automatic framework specifically for delineating river channels and measuring the bankfull widths at small spatial intervals along the channel. The DeepLabV3+ Convolutional Neural Network (CNN) model was employed to accurately delineate channel boundaries and a Voronoi Diagram approach was complemented as the river width algorithm (RWA) to calculate river bankfull widths. The CNN model was trained by images across four river types and performed well with all the evaluating metrics (mIoU, Accuracy, F1-score, and Recall) higher than 0.97, referring to the accuracy over 97% in prediction. The RWA outperformed other existing river width calculation methods by showing lower errors. The application of the framework in the Lillooet River, Canada, presented the capacity of this methodology to obtain detailed distributions of hydraulic and hydrological parameters, including flow resistance, flow energy, and sediment transport capacity, based on high-resolution channel widths. Our work highlights the significant potential of the newly developed framework in acquiring high-resolution channel width information and characterizing fluvial dynamics based on these widths along river channels, which contributes to facilitating cost-effective integrated river management.
WOS关键词BRITISH-COLUMBIA ; CHANNEL ; SEDIMENT ; IMAGES ; MORPHOLOGY ; NETWORK ; SLOPE ; AREA
资助项目Advanced Research Computing at the University of British Columbia ; National Key R & D Program of China[2023YFC3006700] ; National Natural Science Foundation of China[42471086] ; National Natural Science Foundation of China[52479058] ; international partnership program of the Chinese Academy of Sciences[177GJHZ2022064FN] ; Sichuan Science and Technology Program[2021YFH0028] ; China Scholarship Council[202006240220] ; NSERC Discovery ; Canada Foundation for Innovation
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001377623400001
出版者MDPI
资助机构Advanced Research Computing at the University of British Columbia ; National Key R & D Program of China ; National Natural Science Foundation of China ; international partnership program of the Chinese Academy of Sciences ; Sichuan Science and Technology Program ; China Scholarship Council ; NSERC Discovery ; Canada Foundation for Innovation
源URL[http://ir.igsnrr.ac.cn/handle/311030/211815]  
专题陆地表层格局与模拟院重点实验室_外文论文
通讯作者Hassan, Marwan A.
作者单位1.Sichuan Univ, State Key Lab Hydraul & Mt River Engn, Chengdu 610065, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China
3.Sichuan Zipingpu Dev Co Ltd, Chengdu 610091, Peoples R China
4.Fluvial Syst Res Inc, White Rock, BC V4B 0A7, Canada
5.Univ British Columbia, Dept Geog, Vancouver, BC V6T 1Z2, Canada
6.Univ British Columbia, Dept Civil Engn, Vancouver, BC V6T 1Z4, Canada
7.Hong Kong Univ Sci & Technol, Dept Civil & Environm Engn, Clear Water Bay, Hong Kong, Peoples R China
8.Changjiang River Sci Res Inst, Wuhan 430010, Peoples R China
推荐引用方式
GB/T 7714		 Li, Wenqi,Zhang, Chendi,Puhl, David,et al. A CNN-Based Framework for Automatic Extraction of High-Resolution River Bankfull Width[J]. REMOTE SENSING,2024,16(23):19.
APA Li, Wenqi.,Zhang, Chendi.,Puhl, David.,Pan, Xiao.,Hassan, Marwan A..,...&Zhao, Yang.(2024).A CNN-Based Framework for Automatic Extraction of High-Resolution River Bankfull Width.REMOTE SENSING,16(23),19.
MLA Li, Wenqi,et al."A CNN-Based Framework for Automatic Extraction of High-Resolution River Bankfull Width".REMOTE SENSING 16.23(2024):19.

入库方式： OAI收割

来源：地理科学与资源研究所

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