Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2020WR028795 |
Improving Remotely Sensed River Bathymetry by Image‐Averaging | |
C. J. Legleiter; P. J. Kinzel | |
2021-02-09 | |
发表期刊 | Water Resources Research
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出版年 | 2021 |
英文摘要 | Basic data on river bathymetry is critical for numerous applications in river research and management and is increasingly obtained via remote sensing, but the noisy, pixelated appearance of image‐derived depth maps can compromise subsequent analyses. We hypothesized that this noise originates from reflectance from an irregular water surface and introduced a framework for mitigating these effects by Inferring Bathymetry from Averaged River Images (IBARI). This workflow produces time‐averaged images from video frames stabilized to account for platform motion and/or computes a spatial average from an ensemble simulated by randomly shifting images relative to themselves. We used field observations of water depth and helicopter‐based videos from a clear‐flowing river to assess the potential of this approach to improve depth retrieval. Our results indicated that depths inferred from averaged images were more accurate and precise than those inferred from single frames; observed vs. predicted regression R2 increased from 0.80 to 0.88. In addition, IBARI significantly enhanced the texture of image‐derived depth maps, leading to smoother, more coherent representations of channel morphology. Depth retrieval improved with image sequence duration, but the number of images was more important than the length of time encompassed; shorter acquisitions at higher frame rates would economize data collection. We also demonstrated the potential to scale up the IBARI workflow by producing a mosaic of bathymetric maps derived from averaged images acquired at several hovering waypoints distributed along a 2.36 km reach. This approach is well‐suited to data collected from helicopters and small unmanned aircraft systems (sUAS). This article is protected by copyright. All rights reserved. |
领域 | 资源环境 |
URL | 查看原文 |
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/313877 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | C. J. Legleiter,P. J. Kinzel. Improving Remotely Sensed River Bathymetry by Image‐Averaging[J]. Water Resources Research,2021. |
APA | C. J. Legleiter,&P. J. Kinzel.(2021).Improving Remotely Sensed River Bathymetry by Image‐Averaging.Water Resources Research. |
MLA | C. J. Legleiter,et al."Improving Remotely Sensed River Bathymetry by Image‐Averaging".Water Resources Research (2021). |
条目包含的文件 | 条目无相关文件。 |
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