对示踪染料浓度的遥感,以支持河道中的分散研究

IF 4.6 Q2 ENVIRONMENTAL SCIENCES Journal of ecohydraulics Pub Date : 2019-07-03 DOI:10.1080/24705357.2019.1662339
C. Legleiter, R. McDonald, J. Nelson, P. Kinzel, R. Perroy, Donghae Baek, I. Seo
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引用次数: 12

摘要

通过提供可见光染料浓度的空间分布信息,我们评估了遥感技术在增强示踪实验方面的潜力。在实验设施和大型自然通道的测试中,我们测量了罗丹明WT浓度和光谱反射率。作为韩国河流实验中心的初步概念验证,使用小型无人机系统(sUAS)获取弯曲的室外水槽的高光谱图像。在库特奈河上,从船上收集了现场光谱,从有人驾驶飞机获得了高光谱图像和高分辨率航空照片。我们修改了一种最佳波段比分析算法,以确定波长组合,这些波长组合在光谱数量X和染料浓度C之间产生很强的相关性。水槽和现场测试在广泛的可见波长范围内得出X和C之间非常强的关系(R2从0.94到0.99)。在Kootenai上,我们发现从场光谱中得到的X与C关系可以应用于高光谱图像,并且从三波段图像中估计染料浓度几乎与从高光谱数据中估计染料浓度一样可靠。这些结果表明,遥感可以成为绘制染料图案的有力工具。这种能力将使我们能够对数值流动模型进行更严格的测试,从而促进我们对分散过程的理解。
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Remote sensing of tracer dye concentrations to support dispersion studies in river channels
Abstract We evaluated the potential of remote sensing to enhance tracer experiments by providing spatially distributed information on visible dye concentration. During tests in an experimental facility and a large natural channel, we measured Rhodamine WT concentration and spectral reflectance. As an initial proof-of-concept at the River Experiment Center in Korea, a small unmanned aircraft system (sUAS) was used to acquire hyperspectral images of a sinuous outdoor flume. On the Kootenai River, field spectra were collected from a boat and hyperspectral images and high resolution aerial photographs were obtained from manned aircraft. We modified an Optimal Band Ratio Analysis algorithm to identify wavelength combinations that yielded strong correlations between a spectrally based quantity X and dye concentration C. Both the flume and field tests yielded very strong (R2 from 0.94 to 0.99) relationships between X and C across a broad range of visible wavelengths. On the Kootenai, we found that X vs. C relations derived from field spectra could be applied to hyperspectral images and that dye concentrations could be estimated nearly as reliably from three-band images as from hyperspectral data. These results imply that remote sensing could become a powerful tool for mapping dye patterns. Such a capability would advance our understanding of dispersion processes by enabling more rigorous testing of numerical flow models.
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