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Inland water bodies are essential sources of freshwater for several applications, making their level monitoring crucial for understanding the impact of climate change and human activities. The Global Ecosystem Dynamics Investigation (GEDI) [1,2] mission utilizes a spaceborne LiDAR altimeter to produce high-resolution measurements of Earth's canopy. GEDI has recently been added in Google Earth Engine (GEE), a cloud-based platform integrating different datasets and providing efficient analysis tools. This study aims to evaluate the accuracy of GEDI altimetric data over water bodies, comparing the levels recorded by GEDI with ground truth measurements. Furthermore, a robust procedure to remove outliers is proposed and implemented within GEE. Specifically, a number of lakes all over Italy were selected and analyzed for the period from April 2019 to June 2022. The proposed outlier detection methodology consists of two stages. The first employs two flags provided by GEDI metadata. The "quality_flag" and "degrade_flag" provide information about the footprint validity, based respectively on GEDI signal anomalies and the degradation state of pointing or positioning information. Secondly, the methodology employs a robust implementation of the standard 3σ-test, utilizing the Normalized Median Absolute Deviation (NMAD) to identify outliers: measurements not within the range of -/+3*NMAD from the median are removed. After the outlier removal, we obtain an average NMAD of 0.11m over the ten lakes for the considered period.[1] Dubayah et al., 2021. GEDI L3 gridded land surface metrics.[2] University of Maryland, 2022. GEDI ecosystem lidar. www.gedi.umd.edu., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Recently, there has been a growing interest in studying non-contact techniques for strain and displacement measurement. Within photogrammetry, Digital Image Correlation (DIC) has received particular attention thanks to the recent advances in the field of lowcost, high resolution digital cameras, computer power and memory storage. DIC is indeed an optical technique able to measure full field displacements and strain by comparing digital images of the surface of a material sample at different stages of deformation and thus can play a major role in structural monitoring applications. For all these reasons, a free and open source 2D DIC software, named py2DIC, was developed at the Geodesy and Geomatics Division of DICEA, University of Rome La Sapienza. Completely written in python, the software is based on the template matching method and computes the displacement and strain fields. The potentialities of Py2DIC were evaluated by processing the images captured during a tensile test performed in the Lab of Structural Engineering, where three different Glass Fiber Reinforced Polymer samples were subjected to a controlled tension by means of a universal testing machine. The results, compared with the values independently measured by several strain gauges fixed on the samples, demonstrate the possibility to successfully characterize the deformation mechanism of the investigated material. Py2DIC is indeed able to highlight displacements at few microns level, in reasonable agreement with the reference, both in terms of displacements (again, at few microns in the average) and Poisson’s module.

We address the problem of low amplitude oscillatory motion detection through different low-cost sensors: a LIS3LV02DQ MEMS accelerometer, a Microsoft Kinect v2 range camera, and a uBlox 6 GPS receiver. Several tests were performed using a one-direction vibrating table with different oscillation frequencies (in the range 1.5–3 Hz) and small challenging amplitudes (0.02 m and 0.03 m). A Mikrotron EoSens high-resolution camera was used to give reference data. A dedicated software tool was developed to retrieve Kinect v2 results. The capabilities of the VADASE algorithm were employed to process uBlox 6 GPS receiver observations. In the investigated time interval (in the order of tens of seconds) the results obtained indicate that displacements were detected with the resolution of fractions of millimeters with MEMS accelerometer and Kinect v2 and few millimeters with uBlox 6. MEMS accelerometer displays the lowest noise but a significant bias, whereas Kinect v2 and uBlox 6 appear more stable. The results suggest the possibility of sensor integration both for indoor (MEMS accelerometer + Kinect v2) and for outdoor (MEMS accelerometer + uBlox 6) applications and seem promising for structural monitoring applications.

On 19 October 2014 Matteo Miceli, a known italian oceanic sailor, left the Port of Riva di Traiano (Rome, IT) with the italian sailing boat ECO 40, for the Roma Ocean World Project. This ambitious challenge consists of a non-stop sailing alone around the World in energy and food self-sufficiency. ECO 40 is a Class 40 oceanic vessel (LOA of 12,0 m) that has been equipped with a data acquisition system for measuring both the met-ocean parameters recorded (apparent and real wind speed and wind direction, atmospheric pressure, current velocity, air temperature, sea temperature, etc.) and the kinematic characteristics of the boat itself (i.e., speed and course over ground). Furthermore, the boat has been equipped with three high precision GPS receivers, provided by Leica Geosystem, for measuring the motion of the boat and an inertial platform. Due to these high-precision instruments it is possible to fully measure and characterize the six degrees of freedom of the boat, and accordingly to use her as a sailing wave buoy. Within this paper we present the analysis of the met-ocean data measured by the boat during the storm occurred in the Gulf of Lion on the 21–22 October 2014 that ECO 40 has faced just few days after her departure. Furthermore, by analyzing the GPS signals by means of an innovative application of differential kinematic positioning technique, a detailed analysis of the boat heeling during the Gulf of Lion event has been carried out. The boat heeling measurements have been used to correct the measured wind data that have been compared with the hindcast time series.

Today range cameras are widespread low-cost sensors based on two different principles of operation: we can distinguish between Structured Light (SL) range cameras (Kinect v1, Structure Sensor, ...) and Time Of Flight (ToF) range cameras (Kinect v2, ...). Both the types are easy to use 3D scanners, able to reconstruct dense point clouds at high frame rate. However the depth maps obtained are often noisy and not enough accurate, therefore it is generally essential to improve their quality. Standard RGB cameras can be a valuable solution to solve such issue. The aim of this paper is therefore to evaluate the integration feasibility of these two different 3D modelling techniques, characterized by complementary features and based on standard low-cost sensors. For this purpose, a 3D model of a DUPLOTM bricks construction was reconstructed both with the Kinect v2 range camera and by processing one stereo pair acquired with a Canon Eos 1200D DSLR camera. The scale of the photgrammetric model was retrieved from the coordinates measured by Kinect v2. The preliminary results are encouraging and show that the foreseen integration could lead to an higher metric accuracy and a major level of completeness with respect to that obtained by using only separated techniques.