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.

In recent years, the development of low-cost GNSS sensors allowed monitoring in a continuous way movement related to natural processes like landslides with increasing accuracy and limited efforts. In this work, we present the first results of an experimental low-cost GNSS continuous monitoring applied to an unstable slope affecting the Madonna del Sasso Sanctuary (NW Italy). The courtyard of Sanctuary is built on two unstable blocks delimited by a high cliff. Previous studies and non-continuous monitoring showed that blocks suffer a seasonal cycle of thermal expansion and a long-term trend to downslope a few millimeters (2/3) per year. The presence of a continuous monitoring solution could be an essential help to better understand the kinematics of unstable slope. Continuous monitoring could help to forecast a possible paroxysm phase that could end with a failure of the unstable area. The first year of experimental measurements shows a millimetric accuracy of low-cost GNSS, and the long-term trend is in agreement with other monitoring data. We also propose a methodological approach that considers the use of semi-automatized procedures for the identification of anomalous trends and a risk communication strategy. Pro and cons of the proposed methodology are also discussed.

Observation and monitoring of landslides and infrastructure is a very important basis for land planning, human activities, and safety. Geomatic techniques for deformation monitoring have usually involved GNSS and total station measurements or, more generally, expensive geodetic instruments, but other techniques, such as SAR (Synthetic Aperture Radar), can be efficiently applied. Using low-cost sensors could be an interesting alternative solution if the accuracy requirements can be satisfied. This paper shows the results obtained for tilt measurements using MEMS accelerometers, which were combined with mass-market GNSS sensors for monitoring five sites located on landslides. The use of a MEMS-like inclinometer requires an important calibration process to remove bias and improve the solution’s accuracy. In this paper, we explain the MEMS calibration procedure employed, with a simple and cheap solution. The results indicate that with a simple calibration, it is possible to improve measurement accuracy by one order of magnitude, reaching an angular accuracy of a few hundredths of a degree, verified by an independent technique.

In this last years, the differential GNSS positioning has had an intensive expansion, especially due to the development and realization of GNSS CORSs (Continuous Operating Reference Stations) networks. One of the main goals of these networks is to give the possibility to the users to extend the differential positioning (whether in real time or post-processing) up to 25-50 km, allowing a positioning useful for applications such as surveying, monitoring and precise navigation. It is possible to find in bibliography some studies that shown the performances of this type of positioning, with their limitations and peculiarities, also in function of the network size. The Network Real Time Kinematic (NRTK) positioning is a very common practice not only in academia but also in the professional world. Since its appearance, over 10 years ago, a growing number of people use this type of positioning not only for topographic applications, but also for the control of vehicles fleets, precision agriculture, land monitoring, etc. This chapter wants try to focus the attention on the methods and characteristics of NRTK positioning and to summarize principles and peculiarities of this type of GNSS positioning: the goal is to show how networks for NRTK positioning work and also to show the differential corrections that are available today, with their performances in order to obtain acceptable results, always considering the accuracy required by the purposes described above. So, first of all the GNSS network positioning will be discussed, starting from the network biases estimation to the rover positioning with particular attention to the differential GNSS corrections such as the Master Auxiliary Concept (MAC), Virtual Reference Station (VRS) and Flächen Korrektur Parameter (FKP). Particular attention will be also devoted both to the transmission of the differential corrections and their problems, such as the GSM coverage, and to the analysis of the RTCM protocol. A theoretical description of the network calculation and biases interpolation will be made and brief results for each correction will be shown, in order to give a practical exampl

Recently, Unmanned Aerial Vehicles (UAV) have opened up unparalleled opportunities for alpine glacier monitoring, as they allow for reconstructing extensive and high-resolution 3D models. In order to evaluate annual ice flow velocities and volume variations, six yearly measurements were carried out between 2015 and 2020 on the debris-covered Belvedere Glacier (Anzasca Valley, Italian Alps) with low-cost fixed-wing UAVs and quadcopters. Every year, ground control points and check points were measured with GNSS. Images acquired from UAV were processed with Structure-from-Motion and Multi-View Stereo algorithms to build photogrammetric models, orthophotos and digital surface models, with decimetric accuracy. Annual glacier velocities were derived by combining manually-tracked features on orthophotos with GNSS measurements. Velocities ranging between 17 m y⁻¹ and 22 m y⁻¹ were found in the central part of the glacier, whereas values between 2 m y⁻¹ and 7 m y⁻¹ were found in the accumulation area and at the glacier terminus. Between 2 × 10⁶ m³ and 3.5 × 10⁶ m³ of ice volume were lost every year. A pair of intra-year measurements (October 2017–July 2018) highlighted that winter and spring volume reduction was ∼1/4 of the average annual ice loss. The Belvedere monitoring activity proved that decimetric-accurate glacier models can be derived with low-cost UAVs and photogrammetry, limiting in-situ operations. Moreover, UAVs require minimal data acquisition costs and allow for great surveying flexibility, compared to traditional techniques. Information about annual flow velocities and ice volume variations of the Belvedere Glacier may have great value for further understanding glacier dynamics, compute mass balances, or it might be used as input for glacier flow modelling.

The use of a Remotely Piloted Aircraft System (RPAS) for the characterization and monitoring of landslides has been widely improved in the last decade. In particular, the use of this system is particularly effective for the study of areas prone to geohazards. Zones affected by landslides, such as rock slides and debris flows, are often quite critical in terms of accessibility due to unstable blocs that can strongly limit the direct access to the studied area. In this paper, we present the case study of Ponte Formazza in NW Italian Alps. In June 2019, a massive and complex debris flow re-mobilized about 300,000 m³ of a rockslide deposit that occurred in 2009. In this particular environment, we tested traditional, direct and mixed photogrammetric approaches using various configurations of Ground Control Points (GCPs) of the photogrammetric block and by calculating the relative errors. The minimum configuration of GCPs was established to reduce in situ measurements without degrading the accuracy of the cartographic products. The images of three RPAS campaigns (2017, 2018 and 2019), processed with a Structure from Motion (SfM) technique, allowed us to obtain very high-resolution orthophoto and digital surface models (DSMs) before and after the 2019 event. A few GCPs, geolocated with a Global Navigation Satellite System (GNSS), improved the orthophoto and DSM quality (Root Mean Squared Error RMSE 5 cm) even in the areas far from the drone deployment. The availability of high-resolution models has been fundamental for the identification of the volume changes. Furthermore, the 3D view supported and completed the geomorphological mapping of affected areas, particularly in the areas where the field survey is dangerous. The use of ancillary meteorological data and Sentinel-2 satellite images allows for a better definition of the kinematics and the predisposal and triggering factors of the 2019 debris flow.