Fully-covered bathymetry of clear tufa lakes using UAV-acquired overlapping images and neural networks
文献类型：期刊论文
| 作者 | He, Jinchen2,3; Lin, Jiayuan2,3; Liao, Xiaohan1 |
| 刊名 | JOURNAL OF HYDROLOGY
 |
| 出版日期 | 2022-12-01 |
| 卷号 | 615页码:11 |
| ISSN号 | 0022-1694 |
| 关键词 | Tufa lake Unmanned aerial vehicle (UAV) Bathymetry Optical image Neural network Digital bathymetric map (DBM) |
| DOI | 10.1016/j.jhydrol.2022.128666 |
| 通讯作者 | Lin, Jiayuan(joeylin@swu.edu.cn) |
| 英文摘要 | Accurate and updated bathymetric data is of great significance for the management and protection of alpine tufa lakes. In recent years, unmanned aerial vehicle (UAV)-borne optical remote sensing has become a cost-effective technique for obtaining water depth of small and clear waters like tufa lakes. UAV-based bathymetry can be categorized into photogrammetric approach and spectrally derived approach. Photogrammetric bathymetry is contactless but invalid in water areas with uniform texture, while spectral-based bathymetry requires a large amount of in-situ depth measurements. In this paper, we combined the strengths of the two bathymetric methods to retrieve the depth of clear tufa lakes using neural networks. The surface elevation and orthoimage were first produced from UAV-acquired overlapping images, and then water color-depth tie points were sampled in the orthoimage and refraction-corrected bathymetric map. Next, the shallow and deep neural networks were separately used to train the regression models for predicting water depth. Lastly, the combined bathymetric methods were compared with the single ones in terms of effective spatial coverage and bathymetry accuracy. The results indicated that the combined methods were superior to single bathymetric methods in fully-covered bathymetry of clear tufa lakes. The shallow neural network-based model achieved the highest accuracy, with the coefficient of determination (R2) of 0.91 and the Root Mean Square Error (RMSE) of 1.12 m, whereas the deep neural network-based model increased the details of water depth distribution. |
| WOS关键词 | STRUCTURE-FROM-MOTION ; REMOTE-SENSING TECHNIQUES ; SHALLOW STREAM BATHYMETRY ; WATER DEPTH ; PHOTOGRAMMETRY ; RESERVE ; SICHUAN |
| 资助项目 | National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Key Research and Development Program of the Sichuan Province ; [32071678] ; [XDA19050501] ; [2022YFQ0035] |
| WOS研究方向 | Engineering ; Geology ; Water Resources |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:000896647400003 |
| 资助机构 | National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Key Research and Development Program of the Sichuan Province |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/187928]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Lin, Jiayuan |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Southwest Univ, Chongqing Engn Res Ctr Remote Sensing Big Data App, Sch Geog Sci, Chongqing 400715, Peoples R China 3.Southwest Univ, Sch Geog Sci, Chongqing Jinfo Mt Karst Ecosyst Natl Observat & R, Chongqing 400715, Peoples R China |
| 推荐引用方式 GB/T 7714 | He, Jinchen,Lin, Jiayuan,Liao, Xiaohan. Fully-covered bathymetry of clear tufa lakes using UAV-acquired overlapping images and neural networks[J]. JOURNAL OF HYDROLOGY,2022,615:11. |
| APA | He, Jinchen,Lin, Jiayuan,&Liao, Xiaohan.(2022).Fully-covered bathymetry of clear tufa lakes using UAV-acquired overlapping images and neural networks.JOURNAL OF HYDROLOGY,615,11. |
| MLA | He, Jinchen,et al."Fully-covered bathymetry of clear tufa lakes using UAV-acquired overlapping images and neural networks".JOURNAL OF HYDROLOGY 615(2022):11. |
入库方式： OAI收割
来源：地理科学与资源研究所
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