Promises and uncertainties in remotely sensed riverine hydro-environmental attributes: Field testing of novel approaches to unmanned aerial vehicle-borne lidar and imaging velocimetry
Promises and uncertainties in remotely sensed riverine hydro-environmental attributes: Field testing of novel approaches to unmanned aerial vehicle-borne lidar and imaging velocimetry
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遥感河流水文环境属性的前景和不确定性：无人机载激光雷达和成像测速新方法的现场测试
DOI：
10.1002/rra.4042
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发表时间：
2022
影响因子：
2.2
通讯作者：
Md Touhidul Islam;Keisuke Yoshida;Satoshi Nishiyama;Koichi Sakai;Shin Adachi;Shijun Pan
中科院分区：
文献类型：
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作者：
小柳颯輝;瓜生大地;熊倉俊郎;中井専人;安達聖;鈴木紘一;山崎正喜;斎藤隆幸;山倉祐也;Md Touhidul Islam;Keisuke Yoshida;Satoshi Nishiyama;Koichi Sakai;Shin Adachi;Shijun Pan
遥感技术的最新进展显著扩大了河流研究中的数据采集潜力，但由于不同现场条件的不确定性，这些技术的适用性必须得到验证和改进。这项研究是使用新设计的无人机（UAV）机载绿色激光雷达系统（GLS）和深度学习自动化时空图像测速（STIV）远程调查日本冈山县朝日河砾石床的水力和植被数量的第一个实验证据。GLS点云除了识别浅于2 m水域的河床变形外，还以块体细节模式表征淹没基础设施，从而识别位置位移和严重损坏部位。本文还提出了一种估算河流增量流量的非接触方法。与基准流量模型估算值相比，覆盖较浅、较深和部分淹没木本植被区的3个断面的遥感流量高估了1-11%，另一个断面的遥感流量低估了4%。STIV分析还显示了复杂的流动模式，深度平均模型的流动向量合理地证实了这一点。最终，深度平均流量模型估计验证了从GLS和STIV远程获得的水力参数，反之亦然。除了近似植被生长速率外，该研究还利用GLS属性准确地将河岸植被类型确定为草本（70%）、木本（86%）和竹林（65%）。最后，我们的研究结果为浅水清澈植被河流的管理和河流流量的遥感提供了见解，以验证水动力数值方法。
Recent advancements in remotely sensed techniques have markedly expanded data acquisition potential in riverine studies, but the techniques' applicability must be validated and improved because of uncertainties associated with diverse field conditions. This study is the first experimental evidence of using a newly designed unmanned aerial vehicle (UAV)‐borne green lidar system (GLS) and deep learning‐automated space–time image velocimetry (STIV) for remote investigation of hydraulic and vegetation quantities of the gravel‐bed Asahi River in Okayama Prefecture, Japan. In addition to identifying bed deformation in waters shallower than 2 m, the GLS point clouds characterized the submerged infrastructure with block detailing patterns, thereby identifying positional displacement and severely damaged parts. This paper also presents a noncontact method of estimating incremental river discharge. Compared to benchmarked flow model estimates, remotely sensed discharges for three transects covering shallower, deeper, and partially submerged woody vegetation areas were overestimated by 1–11%, with 4% underestimation for another cross‐section. The STIV analysis also showed complicated flow patterns that were reasonably confirmed by flow vectors from depth‐averaged modeling. Ultimately, depth‐averaged flow model estimates validated hydraulic parameters derived remotely from GLS and STIV, and vice versa. In addition to approximating vegetation growth rates, the study using GLS attributes accurately identified riparian vegetation types as herbaceous (70%), woody (86%), and bamboo groves (65%). Finally, our findings provide insight into the management of shallow clear‐flowing vegetated rivers and remote sensing of streamflow to validate hydrodynamic‐numerical methods.