Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2018WR023586 |
Remote Sensing of River Bathymetry: Evaluating a Range of Sensors, Platforms, and Algorithms on the Upper Sacramento River, California, USA | |
Legleiter, Carl J.1,2; Harrison, Lee R.3,4 | |
2019-03-01 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2019 |
卷号 | 55期号:3页码:2142-2169 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Remote sensing has become an increasingly viable tool for characterizing fluvial systems. In this study, we used field measurements with a 1.6-km reach of the upper Sacramento River, CA, to evaluate the potential of mapping water depths with a range of platforms, sensors, and depth retrieval methods. Field measurements of water column optical properties also were compared to similar data sets from other rivers to provide context for our results. We considered field spectra, a multispectral satellite image, hyperspectral data collected from conventional and unmanned aircraft, and a bathymetric LiDAR and applied a generalized version of Optimal Band Ratio Analysis and the K nearest neighbors regression machine learning algorithm. Linear, quadratic, exponential, power, and lowess Optimal Band Ratio Analysis models enabled flexible curve-fitting in calibrating spectrally based quantities to depth; an exponential formulation avoided artifacts associated with other model types. K nearest neighbors regression increased observed versus predicted (OP) R-2 values, particularly for the satellite image; we also found that preprocessing of satellite images was unnecessary and that a basic data product could be used for depth retrieval. Bathymetric LiDAR was highly accurate and precise in shallow water, but a lack of bottom returns from areas greater than 2 m deep resulted in large gaps in coverage. The maximum detectable depth imposes an important constraint on fluvial remote sensing and a hybrid approach combined with field surveys of deep areas might be a more realistic operational strategy for bathymetric mapping. Future work will focus on scaling up from short reaches to long river segments. |
英文关键词 | remote sensing of rivers bathymetric mapping hyperspectral unmanned aircraft system (UAS) bathymetric LiDAR satellite image data |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000464660000021 |
WOS关键词 | STRUCTURE-FROM-MOTION ; 2D HYDRODYNAMIC MODEL ; MULTISPECTRAL IMAGERY ; WATER DEPTH ; MORPHOLOGY ; HABITAT ; TOPOGRAPHY ; REGRESSION ; LANDSCAPES ; FRAMEWORK |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/181570 |
专题 | 资源环境科学 |
作者单位 | 1.US Geol Survey, Integrated Modeling & Predict Div, Golden, CO 80401 USA; 2.Univ Wyoming, Dept Geog, Laramie, WY 82071 USA; 3.NOAA, Southwest Fisheries Sci Ctr, Santa Cruz, CA USA; 4.Univ Calif Santa Barbara, Earth Res Inst, Santa Barbara, CA 93106 USA |
推荐引用方式 GB/T 7714 | Legleiter, Carl J.,Harrison, Lee R.. Remote Sensing of River Bathymetry: Evaluating a Range of Sensors, Platforms, and Algorithms on the Upper Sacramento River, California, USA[J]. WATER RESOURCES RESEARCH,2019,55(3):2142-2169. |
APA | Legleiter, Carl J.,&Harrison, Lee R..(2019).Remote Sensing of River Bathymetry: Evaluating a Range of Sensors, Platforms, and Algorithms on the Upper Sacramento River, California, USA.WATER RESOURCES RESEARCH,55(3),2142-2169. |
MLA | Legleiter, Carl J.,et al."Remote Sensing of River Bathymetry: Evaluating a Range of Sensors, Platforms, and Algorithms on the Upper Sacramento River, California, USA".WATER RESOURCES RESEARCH 55.3(2019):2142-2169. |
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