Remotely sensed data acquired from an Uncrewed Aerial System (UAS) and field measurements of flow depth and velocity from the North Santiam River, Oregon, collected in July 2022

A reach of the North Santiam River, Oregon, was used as a case study in an ongoing effort to develop and test uncrewed aircraft system (UAS)-based salmon habitat mapping techniques using: (1) particle image velocimetry (PIV) for estimating surface flow velocities from remotely sensed data; and (2) two-dimensional (2D) flow modeling based on remotely sensed topography and bathymetry (topo-bathymetry). Direct measurements of flow velocity were obtained using an acoustic Doppler current profiler (ADCP) and used to assess the accuracy of the image-derived velocity estimates and modeled flow fields. Water depth was measured using a single beam echosounder and was used to calibrate and validate image-derived depth estimates and to test the accuracy of the flow model. The topography of dry land and water surface elevations were measured using UAS-based near-infrared (NIR) light detection and ranging (lidar) data. River bathymetry was mapped by applying a spectrally based depth retreival algorithm to multispectral image data. Video was acquired from a small UAS and used as input to a PIV algorithm.

The in situ velocity measurements were collected using a SonTek M9 RiverSurveyor ADCP deployed from a cataraft. The SonTek RiverSurveyor Live software package was used to set up the ADCP prior to data collection, control the instrument, view the data in real-time, and save the raw data in MATLAB *.mat data files. A total of four passes back and forth across the channel were completed at seven cross sections located within the field of view of the UAS-based videos. These files were then read into the USGS Velocity Mapping Toolbox (VMT) and further processed to combine the four passes into a single mean cross section and compute depth-averaged velocities (Parsons et al., 2013). The VMT output was summarized by creating a single csv file consisting of a header row with variable names and five columns: 1) East_meters: easting (x) spatial coordinate in meters; 2) North_meters: northing (y) spatial coordinate in meters; 3) velU_meters_per_second: east (u) component of the depth averaged velocity vector in meters per second; 4) velV_meters_per_second: north (v) component of the depth averaged velocity vector in meters per second; and 5) velMag_meters_per_second: velocity magnitude in meters per second. The spatial coordinates are in the UTM Zone 10 projection, WGS84 datum.

The depth measurements in this data release were obtained using a single beam echosounder and are provided in a comma-delimited (*.csv) text file with three columns: East_meters, North_meters, Depth_meters; the units of the spatial coordinates and the depths are meters. The spatial coordinates of the depth data are in the UTM Zone 10 projection, WGS84 datum.

NIR lidar data on the North Santiam River were acquired on July 25, 2022, to measure elevations on dry land and the water surface, and to support the development of a 2D flow model. These data were collected using a Qube240 lidar scanner. The Qube240 uses a YellowScan UltraSurveyor lidar scanner integrated with an Applanix 15 inertial navigation system (INS).  The data were acquired from a Quantum-Systems Trinity F90+ UAS platform and were used to produce an interpolated topographic raster Digital Elevation Model (DEM's) with a 1 m cell size in GeoTiff format. The map projection and datum for the lidar contained in this data release is UTM Zone 10 N and NAD83.

Multispectral imagery data on the North Santiam River were acquired on July 25, 2022, to map river bathymetry, which is a required input for flow modeling. These data were collected using a MicaSense RedEdge-MX camera, which is integrated with the Trinity F90+ UAS platform. The RedEdge-MX is a radiometrically-calibrated spectral imager with ten bands between 400 and 900 nm. The redEdge-MX multispectral imagery had pixel sizes of 0.085 m at a flying height of 120 m, and an orthoimage is provided in GeoTiff format.  The multispectral image was used to map water depth using the Optimal Band Ratio Analysis spectrally based depth retrieval algorithm (Legleiter and Harrison, 2019). The map projection and datum for the multispectral image contained in this data release is UTM Zone 10 N and NAD83.

Following completion of the depth mapping, we subtracted the image-based depth estimates from the lidar water surface to convert depths to bed elevations using the approach implemented in the ORByT software package (Legleiter, 2021). We fused the bathymetry data with the lidar elevations on dry land to make a continuous DEM, with a resolution of 1 m. The hybrid topographic DEM is provided in this data release as a csv file, file with three columns: East_meters, North_meters, Elevation_meters; the units of the spatial coordinates and the elevation are meters. The map projection and datum for the DEM contained in this data release is UTM Zone 10 N and NAD83.

The DEM contained in this data release was used as input to develop a two-dimensional (2D) hydrodynamic model using the Delft3D-Flexible Mesh (Delft3D-FM, 2023.02 release) model developed by Deltares (2024). We used a curvilinear grid with a cell size of 1 m and included a spiral flow parameter, which accounts for the effects of secondary flow induced by streamline curvature. We set the time step to ensure a Courant number less than 0.7, and specified a minimum depth for wetting/drying calculations of 0.05 m. We prescribed an upstream discharge of 25 m3/s and ran steady flow simulations. To account for turbulence in the model, we used a uniform eddy viscosity value of 0.15 m2/s. The flow resistance was defined using a uniform roughness height (ks) which was converted to spatially explicit Chezy C coefficients via the Colebrook-White equation. Additional Delft3D-FM model input values are provided as a supplemental (*.csv) text file.

Seven UAS-based videos were acquired from a DJI Matrice 210 quadcopter equipped with a Zenmuse X4S optical camera on July 25, 2022. The videos were acquired from a nominal flying height of 120 meters above ground level and are provided in their native form, with a frame rate of 30 Hertz and an *.mov file format. The videos were used to estimate surface flow velocities via PIV, as implemented in the TRiVIA software package (Legleiter and Kinzel, 2023).

References cited:
Deltares. 2024. Delft3D Flexible Mesh Suite User Manual. Delft, Netherlands: Deltares. Available from: https://www.deltares.nl/en/software/delft3d-flexible-mesh-suite/.

Legleiter, C. J., and Harrison, L. 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. https://doi.org/10.1029/2018WR023586

Legleiter, C. J. (2021). The optical river bathymetry toolkit. River Research and Applications, 37(4), 555–568. https://doi.org/10.1002/rra.3773

Legleiter, C. J., and Kinzel, P. J. (2023). The Toolbox for River Velocimetry using Images from Aircraft (TRiVIA). River Research and Applications, 39(8), 1457–1468. https://doi.org/10.1002/rra.4147

Parsons, D. R., Jackson, P. R., Czuba, J. A., Engel, F. L., Rhoads, B. L., Oberg, K. A., Best, J. L., Mueller, D. S., Johnson, K. K., and Riley, J. D. 2013. Velocity Mapping Toolbox, VMT: a processing and visualization suite for moving-vessel ADCP measurements. Earth Surface Processes and Landforms, 38(11), 1244–1260. https://doi.org/10.1002/esp.3367

美国俄勒冈州北桑蒂亚姆河的一段河段被选为案例研究区域，用于开展并验证基于无人机系统（uncrewed aircraft system, UAS）的鲑鱼栖息地测绘技术，具体采用两种方法：（1）粒子图像测速法（particle image velocimetry, PIV），通过遥感数据估算地表流速；（2）基于遥感地形与水深地形（topo-bathymetry）的二维（2D）水流模拟。研究采用声学多普勒流速剖面仪（acoustic Doppler current profiler, ADCP）获取流速直接测量数据，用于评估遥感图像反演流速估算结果与模拟流场的精度。研究采用单波束回声测深仪测量水深，用于校准与验证图像反演的水深估算结果，并检验水流模型的精度。研究基于搭载于无人机系统的近红外（near-infrared, NIR）激光雷达（light detection and ranging, lidar）数据，测量陆地与水面高程地形。河道水深地形通过将基于光谱的水深反演算法应用于多光谱图像数据进行绘制。研究从小型无人机系统获取视频数据，将其作为粒子图像测速法算法的输入数据。

本研究采用从充气筏布放的SonTek M9 RiverSurveyor型声学多普勒流速剖面仪获取原位流速测量数据。在数据采集前，使用SonTek RiverSurveyor Live软件套件配置声学多普勒流速剖面仪，控制仪器运行、实时查看数据，并将原始数据保存为MATLAB *.mat格式文件。研究在无人机系统视频视场内的7个河道断面处，共计完成4次往返过流测量。随后将这些数据导入美国地质调查局流速测绘工具箱（USGS Velocity Mapping Toolbox, VMT），进一步处理以将4次往返测量数据合并为单一生效断面，并计算断面平均流速（Parsons等，2013）。流速测绘工具箱输出结果被整理为单个逗号分隔值（comma-separated values, CSV）文件，该文件包含一行变量名表头与5列数据：1）East_meters：以米为单位的东向（x）空间坐标；2）North_meters：以米为单位的北向（y）空间坐标；3）velU_meters_per_second：以米每秒为单位的断面平均流速矢量东向（u）分量；4）velV_meters_per_second：以米每秒为单位的断面平均流速矢量北向（v）分量；5）velMag_meters_per_second：以米每秒为单位的流速幅值。空间坐标采用UTM 10N投影坐标系，WGS84椭球基准面。

本数据集发布的水深测量数据采用单波束回声测深仪获取，以逗号分隔（*.csv）文本文件形式提供，包含3列数据：East_meters、North_meters、Depth_meters；空间坐标与水深的单位均为米。水深数据的空间坐标采用UTM 10N投影坐标系，WGS84椭球基准面。

2022年7月25日获取北桑蒂亚姆河的近红外激光雷达数据，用于测量陆地与水面高程，并为二维水流模型的构建提供支撑。该数据采用Qube240激光雷达扫描仪采集，其搭载了集成于Applanix 15惯性导航系统（inertial navigation system, INS）的YellowScan UltraSurveyor激光雷达扫描仪。数据采集平台为Quantum-Systems Trinity F90+无人机系统，最终生成像元尺寸为1米的插值地形栅格数字高程模型（Digital Elevation Model, DEM），格式为GeoTiff。本数据集发布的激光雷达数据采用UTM 10N投影坐标系与NAD83椭球基准面。

2022年7月25日获取北桑蒂亚姆河的多光谱影像数据，用于绘制河道水深地形，该数据是水流模型构建的必要输入项。该数据采用集成于Trinity F90+无人机系统平台的MicaSense RedEdge-MX相机采集。RedEdge-MX是经过辐射定标的光谱成像仪，拥有400~900 nm波段范围内的10个光谱波段。在120米飞行高度下，RedEdge-MX多光谱影像的像元尺寸为0.085米，正射影像以GeoTiff格式提供。研究采用最优波段比分析光谱水深反演算法（Legleiter和Harrison，2019），基于多光谱影像绘制河道水深。本数据集发布的多光谱影像采用UTM 10N投影坐标系与NAD83椭球基准面。

水深测绘完成后，研究采用ORByT软件套件实现的方法（Legleiter，2021），通过从激光雷达水面高程中减去图像反演的水深估算值，将水深转换为河床高程。随后将水深地形数据与陆地激光雷达高程数据融合，生成分辨率为1米的连续数字高程模型。本数据集发布的混合地形数字高程模型以csv文件形式提供，包含3列数据：East_meters、North_meters、Elevation_meters；空间坐标与高程的单位均为米。该数字高程模型采用UTM 10N投影坐标系与NAD83椭球基准面。

本数据集发布的数字高程模型被用作输入数据，用于构建由Deltares（2024）开发的Delft3D柔性网格（Delft3D-Flexible Mesh, Delft3D-FM，2023.02版本）二维水动力模型。研究采用像元尺寸为1米的曲线网格，并添加螺旋流参数以考虑流线曲率引发的次生流效应。设置时间步长以确保库朗数小于0.7，并指定干湿计算的最小水深为0.05米。设定上游入流流量为25立方米每秒，开展恒定流模拟。为考虑模型中的湍流效应，采用统一的涡粘系数值0.15平方米每秒。水流阻力采用统一的粗糙高度（ks）定义，通过科尔布鲁克-怀特方程（Colebrook-White equation）转换为空间显式的谢才系数（Chezy C coefficients）。Delft3D-FM模型的其他输入参数以补充性（*.csv）文本文件形式提供。

2022年7月25日，从搭载Zenmuse X4S光学相机的DJI Matrice 210四旋翼无人机上获取7段无人机系统视频数据。视频采集的标称飞行高度为距地面120米，以原生格式提供，帧率为30赫兹，文件格式为*.mov。研究采用TRiVIA软件套件实现的粒子图像测速法算法（Legleiter和Kinzel，2023），基于视频数据估算地表流速。

参考文献：
Deltares. 2024. 《Delft3D柔性网格套件用户手册》. 荷兰代尔夫特：Deltares. 可获取于：https://www.deltares.nl/en/software/delft3d-flexible-mesh-suite/.
Legleiter, C. J. 与Harrison, L. R. (2019). 河道水深地形遥感：美国加利福尼亚州萨克拉门托河上游多传感器、平台与算法评估. 《水资源研究》，55(3)，2142–2169. https://doi.org/10.1029/2018WR023586
Legleiter, C. J. (2021). 光学河道水深工具箱. 《河道研究与应用》，37(4)，555–568. https://doi.org/10.1002/rra.3773
Legleiter, C. J. 与Kinzel, P. J. (2023). 机载图像河道测速工具箱（TRiVIA）. 《河道研究与应用》，39(8)，1457–1468. https://doi.org/10.1002/rra.4147
Parsons, D. R. 等, (2013). 流速测绘工具箱（VMT）：移动载体声学多普勒流速剖面仪测量数据处理与可视化套件. 《地表过程与地形》，38(11)，1244–1260. https://doi.org/10.1002/esp.3367