Hydraulics and drones: observations of water level, bathymetry and water surface velocity from Unmanned Aerial Vehicles

Abstract

The planet faces several water-related threats, including water scarcity, floods, and pollution. Satellite and airborne sensing technology is rapidly evolving to improve the observation and prediction of surface water and thus prevent natural disasters. While technological developments require extensive research and funding, they are far less expensive and therefore more important than disaster restoration and remediation. Thus, our research question was “Can we retrieve hydraulic observations of inland surface water bodies, whenever and wherever it is required, with (i) high accuracy, (ii) high spatial resolution and (iii) at a reasonable cost?”. Unmanned Aerial Vehicles (UAVs) and their miniaturized components can solve this challenge. Indeed, they can monitor dangerous or difficult-to-reach areas delivering real time data. Furthermore, they ensure high accuracy and spatial resolution in monitoring surface water bodies, at a limited cost and with high flexibility. This PhD project investigates and demonstrates how UAVs can enrich the set of available hydraulic observations in inland water bodies, including: 1. Orthometric water level. 2. Water depth (bathymetry). 3. Surface water speed. The novelty of this research is to retrieve water level and bathymetry measurements from UAVs. The objective is to retrieve these observations with an accuracy of few cm, without any need for GCPs (Ground Control Points), and without any dependency on river morphology, water turbidity, and maximum water depth. Although UAV-borne measurements of surface water speed have already been documented in the literature, a novel approach was developed to avoid GCPs. This research is the first demonstration that orthometric water level can be measured from UAVs with a radar system and a GNSS (Global Navigation Satellite System) receiver. As in satellite altimetry, the GNSS receiver measures the altitude above mean sea level, while the radar measures the range to the water surface. The orthometric water level is then computed by subtracting the range measured by the radar from the GNSS-derived altitude. However, compared to satellites, UAVs have several advantages: high spatial resolution, repeatability of the flight missions and good tracking of the water bodies. Nevertheless, UAVs face several constraints: vibrations, limited size, weight, and electric power available for the sensors. In this thesis, we present the first studies on UAV altimetry. Studies were conducted to measure orthometric water level (height of water surface above sea mean level) in rivers, lakes, and in the worldwide unique cenotes and lagoons of the Yucatan peninsula. An accuracy of ca. 5-7 cm is achievable with our technology. This accuracy is higher than any other spaceborne radar or spaceborne LIDAR altimeter. Water depths were measured by UAV with a tethered sonar controlled by the UAV. Bathymetry can be estimated by subtracting water depth from water level. Our technology aims to combine the large spatial and temporal coverage capabilities of remote sensing techniques, with the accuracy of in-situ measurements. An accuracy of ca. 2.1% of the actual depth was achieved with our system, with a maximum depth capability potentially up to 80 m. Since remote sensing techniques (e.g LIDARs, through-water photogrammetry, spectral-depth signature of multispectral imagery) can survey water depths up to few meters only, our technology has a maximum depth capability and an applicability range superior to any other remote sensing technique. Compared to manned or unmanned vessels equipped with echo sounders, our UAV-borne technology can also survey non-navigable rivers and overpass obstacles (e.g. river structures). Computer vision, autopilot system and beyond visual line-of-sight (BVLOS) flights will ensure the possibility to retrieve hyper-spatial observations of water depth, without requiring the operator to access the area. Surface water speed can be measured with UAVs using image cross correlation techniques. UAV-borne water speed observations can overcome the practical difficulties of traditional methods. Indeed flow measurements are often intrusive (e.g. flow meters) or require deployment of vessels equipped with expensive acoustic Doppler current profilers (ADCPs). For these reasons, water speed observations have been traditionally challenging, especially in difficult-to-access environments. Conversely, UAV-borne observations open up the possibility of measuring water speed over extended regions at a low cost. The 2D water surface velocity field is computed by analysing the UAV-borne video frames using a technique called large scale PIV (Particle Image Velocimetry). PIV is well known in micro scale applications, but large scale PIV faces several challenges. For instance, it is not possible to use laser systems to better illuminate the water surface. Our preliminary studies show that UAVs can measure surface water speed of rivers. However, seeding of the water surface is required due to the lack of natural tracers (e.g. bubbles, debris, and foam) occurring in the Danish free-flowing rivers. Furthermore, video stabilization techniques are essential to remove the effects of drone vibrations. An innovative procedure was adopted to convert from image units (pixels) into metric units, by using the on-board radar observations. A study was conducted to evaluate the potential of UAV-borne water observations for calibrating a hydrological model. The hydrological model simulates Mølleåen river (Denmark) and its catchment. The model-derived estimates of groundwater-surface water (GW-SW) interaction were significantly improved after calibration against synthetic UAV-borne observations. After calibration against UAV-borne water level observations, the sharpness (width of the confidence interval) of GW-SW time series is improved by ca. 50%, RMSE (Root Mean Square Error) decreases by ca. 75%, and the direction of the GW-SW flux is better simulated.