Lorenzo Rossi

Measuring the volume of flushed sediments in a reservoir using multi-temporal images acquired with UAS

Abstract We compute the volume of flushed sediments in a dam using photogrammetry-based multi-temporal surveys with an unmanned aerial system (UAS). Coping with sediments accumulation and erosion in reservoir is a living topic in modern hydraulics of dams, since the increase of sediment may reduce the reservoir capacity, endanger dam’s stability, and represent an economical loss. As a result, a number of remedies can be considered, such as flushing or mechanical removal. To evaluate the performance of these operations, measuring the volume of removed sediments and their spatial distribution is important. Here, we show that photogrammetry from UASs represents a suitable solution to reckon the volume of removed sediments. The case study is the Fusino dam (Lombardia region, Northern Italy). Two surveys were performed, before and after sediment removal. In both cases, the flight has been planned with an average flight height equal to 65 m, leading to a mean ground sample distance (GSD) equal to 0.013 m. The 22 ground control points (GCP) used to adjust the photogrammetric block were measured with both global navigation satellite system (GNSS) and a total station. Each survey produced a cloud of about 40 million of points. Moreover, the digital surface model (DSM) produced by each photogrammetric flight has been validated with sample points measured with a robotic total station. Results show high consistency between computed DSMs and validation dataset, with a mean height difference equal, respectively, to 0.003 and −0.004 m considering the two different surveys, with a standard deviation around 0.05 m in both the cases. The volume of sediments flushed was estimated to be about 26,000 m3, which represents about 2%–3% of the total reservoir capacity. We estimated also a 6% difference in terms of reservoir capacity between the present condition and the no-sediments condition.

Accuracy of Flight Altitude Measured with Low-Cost GNSS, Radar and Barometer Sensors: Implications for Airborne Radiometric Surveys

Flight height is a fundamental parameter for correcting the gamma signal produced by terrestrial radionuclides measured during airborne surveys. The frontiers of radiometric measurements with UAV require light and accurate altimeters flying at some 10 m from the ground. We equipped an aircraft with seven altimetric sensors (three low-cost GNSS receivers, one inertial measurement unit, one radar altimeter and two barometers) and analyzed ~3 h of data collected over the sea in the (35–2194) m altitude range. At low altitudes (H < 70 m) radar and barometric altimeters provide the best performances, while GNSS data are used only for barometer calibration as they are affected by a large noise due to the multipath from the sea. The ~1 m median standard deviation at 50 m altitude affects the estimation of the ground radioisotope abundances with an uncertainty less than 1.3%. The GNSS double-difference post-processing enhanced significantly the data quality for H > 80 m in terms of both altitude median standard deviation and agreement between the reconstructed and measured GPS antennas distances. Flying at 100 m the estimated uncertainty on the ground total activity due to the uncertainty on the flight height is of the order of 2%.

Gravity for Detecting Caves: Airborne and Terrestrial Simulations Based on a Comprehensive Karstic Cave Benchmark

Underground caves bear a natural hazard due to their possible evolution into a sink hole. Mapping of all existing caves could be useful for general civil usages as natural deposits or tourism and sports. Natural caves exist globally and are typical in karst areas. We investigate the resolution power of modern gravity campaigns to systematically detect all void caves of a minimum size in a given area. Both aerogravity and terrestrial acquisitions are considered. Positioning of the gravity station is fastest with GNSS methods the performance of which is investigated. The estimates are based on a benchmark cave of which the geometry is known precisely through a laser-scan survey. The cave is the Grotta Gigante cave in NE Italy in the classic karst. The gravity acquisition is discussed, where heights have been acquired with dual-frequency geodetic GNSS receivers and Total Station. Height acquisitions with non-geodetic low-cost receivers are shown to be useful, although the error on the gravity field is larger. The cave produces a signal of −1.5xa0×xa010−5xa0m/s2, with a clear elliptic geometry. We analyze feasibility of airborne gravity acquisitions for the purpose of systematically mapping void caves. It is found that observations from fixed wing aircraft cannot resolve the caves, but observations from slower and low-flying helicopters or drones do. In order to detect the presence of caves the size of the benchmark cave, systematic terrestrial acquisitions require a density of three stations on square 500 by 500xa0m2 tiles. The question has a large impact on civil and environmental purposes, since it will allow planning of urban development at a safe distance from subsurface caves. The survey shows that a systematic coverage of the karst would have the benefit to recover the position of all of the greater existing void caves.

Integrating Geological Prior Information into the Inverse Gravimetric Problem: The Bayesian Approach

It is well known that the inverse gravimetric problem is generally ill-posed and therefore its solution requires some restrictive hypotheses and strong numerical regularization. However, if these initial assumptions are improperly used, the final results could be theoretically and physically admissible but far from the actual mass density distribution. In this work, a Bayesian approach to estimate the mass density distribution from gravity data coupled with a-priori geological information is presented. It requires to model the masses in voxels, each of them characterized by two random variables: one is a discrete label defining the type of material (or the geological unit), the other is a continuous variable defining the mass density (considered constant inside the single voxel). The a-priori geological information is translated in terms of this model, providing for each class of material the mean density and the corresponding variability and for each voxel the a-priori most probable label. Basically the method consists in a simulated annealing aided by a Gibbs sampler with the aim to find the MAP (maximum a posteriori) of the posterior probability distribution of labels and densities given the observations and the a-priori geological model. Some proximity constrains between labels of adjacent voxels are also introduced into the solution.

Evaluation of the Laser Response of Leica Nova MultiStation MS60 for 3D Modelling and Structural Monitoring

The use of Terrestrial Laser Scanner (TLS) is quite common for architectural surveys, however it requires to arrange special targets on the scanned object and to acquire several overlapping scans, which have to be aligned and edited externally. Recently, Leica released on the market a new kind of instrument, known as MultiStation (MS). It includes both the main characteristics of a TLS and of a Total Station (TS), meaning that no targets are required for the scan alignment, since the whole survey can be directly georeferenced. In this paper, some analyses about the use of this instrument for 3D modelling applications are discussed. First of all, the laser signal response is evaluated considering different materials, acquired using several combinations of distances and incidence angles. Then, the survey of the Casalbagliano Castle is presented and analyzed. All the performed tests show the great potentiality of the MS, allowing to reach accuracies of the order of few millimeters.

Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation

Performing two independent surveys in 2016 and 2017 over a flat sample plot (6700 m 2 ), we compare snow-depth measurements from Unmanned-Aerial-System (UAS) photogrammetry and from a new high-resolution laser-scanning device (MultiStation) with manual probing, the standard technique used by operational services around the world. While previous comparisons already used laser scanners, we tested for the first time a MultiStation, which has a different measurement principle and is thus capable of millimetric accuracy. Both remote-sensing techniques measured point clouds with centimetric resolution, while we manually collected a relatively dense amount of manual data (135 pt in 2016 and 115 pt in 2017). UAS photogrammetry and the MultiStation showed repeatable, centimetric agreement in measuring the spatial distribution of seasonal, dense snowpack under optimal illumination and topographic conditions (maximum RMSE of 0.036 m between point clouds on snow). A large fraction of this difference could be due to simultaneous snowmelt, as the RMSE between UAS photogrammetry and the MultiStation on bare soil is equal to 0.02 m. The RMSE between UAS data and manual probing is in the order of 0.20–0.30 m, but decreases to 0.06–0.17 m when areas of potential outliers like vegetation or river beds are excluded. Compact and portable remote-sensing devices like UASs or a MultiStation can thus be successfully deployed during operational manual snow courses to capture spatial snapshots of snow-depth distribution with a repeatable, vertical centimetric accuracy.

Joint Use of Image-Based and GNSS Techniques for Urban Navigation

The use of position-based devices is constantly increasing with a wide spectrum of applications, e.g. the continuous demand of mapping services based on user’s location. Depending on the specific application, a different level of accuracy could be requested, going from room level to few centimeters of error. The navigation problem is typically faced by using Global Navigation Satellite Systems (GNSS), but this technique cannot efficiently work in case of poor sky visibility, as happens in urban areas. An option could be the combination of image-based and GNSS solutions. Two different assisted photogrammetry techniques are here presented and discussed. First, an image-based navigation solution constrained by using ground control points (GCPs) extracted from urban maps is presented. It was tested considering a set of different scenarios, reaching accuracies of the order of 0.20 m. A second outdoor navigation solution has been realized by integrating the data acquired by a Microsoft Kinect device (RGB and depth images) and a GNSS receiver through a proper Kalman filter. Also in this case the achieved accuracy is of the order of 0.20 m.

Improving Low-Cost GNSS Navigation in Urban Areas by Integrating a Kinect Device

In the last decades, low-cost GNSS receivers have been widely used for navigation purposes. Some of them deliver also raw data, allowing for a more sophisticated processing, such as the double-difference approach, and therefore a more accurate positioning, typically at the decimeter level. However, these accuracies can be generally achieved only with a good sky visibility, that is a critical issue in urban areas even using low-cost receivers equipped with a high-sensitive antenna. In this respect, a significant contribution comes from the use of digital images or dense point clouds which provides an estimate of the sensor kinematic position. To maintain the low-cost target, the Kinect device, endowed with RGB and depth cameras, can be used. In this work, we have first processed the GNSS raw data from a u-blox receiver by using the free and open source goGPS software. Then, we have studied the integration of the Kinect device by a proper Kalman filter. An outdoor experiment has been arranged with the aim of testing the hardware and software system.