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.

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%.

The International Service for the Geoid (ISG, https://www.isgeoid.polimi.it/, last access: 31 March 2021) provides free access to a dedicated and comprehensive repository of geoid models through its website. In the archive, both the latest releases of the most important and well-known geoid models, as well as less recent or less known ones, are freely available, giving to the users a wide range of possible applications to perform analyses on the evolution of the geoid computation research field. The ISG is an official service of the International Association of Geodesy (IAG), under the umbrella of the International Gravity Field Service (IGFS). Its main tasks are collecting, analysing, and redistributing local, regional, and continental geoid models and providing technical support to people involved in geoid-related topics for both educational and research purposes. In the framework of its activities, the ISG performs research taking advantage of its archive and organizes seminars and specific training courses on geoid determination, supporting students and researchers in geodesy as well as distributing training material on the use of the most common algorithms for geoid estimation. This paper aims at describing the data and services, including the newly implemented DOI Service for geoid models (https://dataservices.gfz-potsdam.de/portal/?fq=subject:isg, last access: 31 March 2021), and showing the added value of the ISG archive of geoid models for the scientific community and technicians, like engineers and surveyors (https://www.isgeoid.polimi.it/Geoid/reg_list.html, last access: 31 March 2021).

Dams are one of the most important engineering works of the current human society, and it is crucial to monitor and obtain analytical data to log their conditions, predict their behavior and, eventually, receive early warnings for planning interventions and maintenance activities. In this context, GNSS-based point displacement monitoring is nowadays a consolidated technique that is able to provide daily millimeter level accuracy, even with less sophisticated and less expensive single-frequency equipment. If properly designed, daily records of such monitoring systems produce time series that, when long enough, allow for an accurate reconstruction of the geometrical deformation of the structure, thus guiding semi-automatic early warning systems. This paper focuses on the procedure for the GNSS time series processing with a statistical approach. In particular, real-world times series collected from a dam monitoring test case are processed as an example of data filtering. A remove–restore technique based on a collocation approach is applied here. Basically, it consists of an initial deterministic modeling by polynomials and periodical components through least squares adjustment and Fourier transform, respectively, followed by a stochastic modeling based on empirical covariance estimation and a collocation approach. Filtered time series are interpreted by autoregressive models based on environmental factors such as air or water temperature and reservoir water level. Spatial analysis is finally performed by computing correlations between displacements of the monitored points, as well as by visualizing the overall structure deformation in time. Results positively validate the proposed data processing workflow, providing useful hints for the implementation of automatic early warning systems in the framework of structural monitoring based on continuous displacement measurements.

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.