Field Measurements of Soil Water Content at Shallow Depths for Landslide Monitoring

Monitoring changes in soil saturation is important for slope stability analyses. Soil moisture capacitive sensors have recently been developed; their response time is extremely fast, they require little maintenance, and they are relatively inexpensive. The use of low-cost sensors in landslide areas can allow the monitoring of large territories, but appropriate calibration is required. Installation in the field and the setting up of the monitoring network also require attention. In the ALCOTRA AD-VITAM project, the University of Genoa is involved in the development of a system, called LAMP, for the monitoring, analysis and forecasting of slides triggered by rainfalls. Multiple installations (along vertical alignments) of WaterScout sensors are placed in the nodes of the monitoring network. They provide real-time water content profiles in the shallow layers (typically in the upper meter) of a slope. With particular reference to these measurements, the present paper describes the reliability analysis of the instruments, the operations related to the sensor calibration and the installation phases for the monitoring networks. Finally, some of the data coming from a node, belonging to one of the five monitoring networks, are reported

Innovations in geomatics teaching during the COVID-19 emergency

The approach in the teaching process is changing, thanks to the increased awareness that a higher students\u2019 involvement leads to a better quality of their learning. The aim is to make the students more participative, avoiding a unidirectional lesson and encouraging their wish to keep updated on the course advancements. However, innovative teaching methodologies are not yet widespread, mainly in STEM (Science, Technology, Engineering, and Mathematics) disciplines. At the University of Genoa, the experimentation of innovative teaching techniques has been significant and worthy especially because it was planned before the COVID-19 emergency and applied in the scenario of forced remote teaching. Thanks to the introduc- tion of novel technological instruments, several techniques have been exploited to realize interactive lessons and promoting students\u2019 involvement. The present work discloses the employed techniques and frames them within the state of the art of innovative teaching, highlighting their contribution in the teaching activities related to the Geomatics field of knowledge. The acquired experiences in Geomatics dissemination and a critical analysis, including teachers\u2019 and students\u2019 perception about the tested innovative teaching/learning tools, are also reported. In general, the innovations introduced in teaching and learning processes during the COVID-19 sanitary emergency were warmly received by the entire community, including teachers, students, and teaching assistants

Deriving Coastal Shallow Bathymetry from Sentinel 2-, Aircraft- and UAV-Derived Orthophotos: A Case Study in Ligurian Marinas

Bathymetric surveys of shallow waters are increasingly necessary for navigational safety and environmental studies. In situ surveys with floating acoustic sensors allow the collection of high-accuracy bathymetric data. However, such surveys are often unfeasible in very shallow waters in addition to being expensive and requiring specific sectorial skills for the acquisition and processing of raw data. The increasing availability of optical images from Uncrewed Aerial Vehicles, aircrafts and satellites allows for bathymetric reconstruction from images thanks to the application of state-of-the-art algorithms. In this paper, we illustrate a bathymetric reconstruction procedure involving the classification of the seabed, the calibration of the algorithm for each class and the subsequent validation. We applied this procedure to high-resolution, UAV-derived orthophotos, aircraft orthophotos and Sentinel-2 Level-2A images of two marinas along the western Ligurian coastline in the Mediterranean Sea and validated the results with bathymetric data derived from echo-sounder surveys. Our findings showed that the aircraft-derived bathymetry is generally more accurate than the UAV-derived and Sentinel-2 bathymetry in all analyzed scenarios due to the smooth color of the aircraft orthophotos and their ability to reproduce the seafloor with a considerable level of detail

Application of machine learning techniques to derive sea water turbidity from Sentinel-2 imagery

Earth Observation (EO) from satellites has the potential to provide comprehensive, rapid and inexpensive information about water bodies, integrating in situ measurements. Traditional methods to retrieve optically active water quality parameters from satellite data are based on semiempirical models relying on few bands, which often revealed to be site and season specific. The use of machine learning (ML) for remotely sensed water quality estimation has spread in recent years thanks to the advances in algorithm development and computing power. These models allow to exploit the wealth of spectral information through more flexible relationships and are less affected by atmospheric and other background factors. The present study explores the use of Sentinel-2 MultiSpectral Instrument (MSI) Level-1C Top of Atmosphere spectral radiance to derive water turbidity, through application of machine learning techniques. A dataset of 222 combination of turbidity measurements, collected in the North Tyrrhenian Sea – Italy from 2015 to 2021, and values of the 13 spectral bands in the pixel corresponding to the sample location was used. Two regression techniques were tested and compared: a Stepwise Linear Regression (SLR) and a Polynomial Kernel Regression. The two models show accurate and similar performance (R2 = 0.736, RMSE = 2.03 NTU, MAE = 1.39 NTU for the SLR and R2 = 0.725, RMSE = 2.07 NTU, MAE = 1.40 NTU for the Kernel). A band importance analysis revealed the contribution of the different spectral bands and the main role of the red-edge range. The work shows that it is possible to reach a good accuracy in turbidity estimation from MSI TOA reflectance using ML models, fed by the whole spectrum of available bands, although the possible generation of errors related to atmospheric effect in turbidity estimates was not evaluated. Comparison between turbidity estimates obtained from the models with turbidity data from Copernicus CMEMS dataset named ‘Mediterranean Sea, Bio-Geo-Chemical, L3, daily observation’ produced consistent results. Finally, turbidity maps from satellite imagery were produced for the study area, showing the ability of the models to catch extreme events

FOSS tools and applications for education in geospatial sciences

While the theory and implementation of geographic information systems (GIS) have a history of more than 50 years, the development of dedicated educational tools and applications in this field is more recent. This paper presents a free and open source software (FOSS) approach for education in the geospatial disciplines, which has been used over the last 20 years at two Italian universities. The motivations behind the choice of FOSS are discussed with respect to software availability and development, as well as educational material licensing. Following this philosophy, a wide range of educational tools have been developed, covering topics from numerical cartography and GIS principles to the specifics regarding different systems for the management and analysis of spatial data. Various courses have been implemented for diverse recipients, ranging from professional training workshops to PhD courses. Feedback from the students of those courses provides an invaluable assessment of the effectiveness of the approach, supplying at the same time directions for further improvement. Finally, lessons learned after 20 years are discussed, highlighting how the management of educational materials can be difficult even with a very open approach to licensing. Overall, the use of free and open source software for geospatial (FOSS4G) science provides a clear advantage over other approaches, not only simplifying software and data management, but also ensuring that all of the information related to system design and implementation is available

The rapid spread of SARS-COV-2 Omicron variant in Italy reflected early through wastewater surveillance

The SARS-CoV-2 Omicron variant emerged in South Africa in November 2021, and has later been identified worldwide, raising serious concerns. A real-time RT-PCR assay was designed for the rapid screening of the Omicron variant, targeting characteristic mutations of the spike gene. The assay was used to test 737 sewage samples collected throughout Italy (19/21 Regions) between 11 November and 25 December 2021, with the aim of assessing the spread of the Omicron variant in the country. Positive samples were also tested with a real-time RT-PCR developed by the European Commission, Joint Research Centre (JRC), and through nested RT-PCR followed by Sanger sequencing. Overall, 115 samples tested positive for Omicron SARS-CoV-2 variant. The first occurrence was detected on 7 December, in Veneto, North Italy. Later on, the variant spread extremely fast in three weeks, with prevalence of positive wastewater samples rising from 1.0% (1/104 samples) in the week 5–11 December, to 17.5% (25/143 samples) in the week 12–18, to 65.9% (89/135 samples) in the week 19–25, in line with the increase in cases of infection with the Omicron variant observed during December in Italy. Similarly, the number of Regions/Autonomous Provinces in which the variant was detected increased fromone in the first week, to 11 in the second, and to 17 in the last one. The presence of the Omicron variant was confirmed by the JRC real-time RT-PCR in 79.1% (91/115) of the positive samples, and by Sanger sequencing in 66% (64/97) of PCR amplicons

The rapid spread of SARS-COV-2 Omicron variant in Italy reflected early through wastewater surveillance

The SARS-CoV-2 Omicron variant emerged in South Africa in November 2021, and has later been identified worldwide, raising serious concerns. A real-time RT-PCR assay was designed for the rapid screening of the Omicron variant, targeting characteristic mutations of the spike gene. The assay was used to test 737 sewage samples collected throughout Italy (19/21 Regions) between 11 November and 25 December 2021, with the aim of assessing the spread of the Omicron variant in the country. Positive samples were also tested with a real-time RT-PCR developed by the European Commission, Joint Research Centre (JRC), and through nested RT-PCR followed by Sanger sequencing. Overall, 115 samples tested positive for Omicron SARS-CoV-2 variant. The first occurrence was detected on 7 December, in Veneto, North Italy. Later on, the variant spread extremely fast in three weeks, with prevalence of positive wastewater samples rising from 1.0% (1/104 samples) in the week 5–11 December, to 17.5% (25/143 samples) in the week 12–18, to 65.9% (89/135 samples) in the week 19–25, in line with the increase in cases of infection with the Omicron variant observed during December in Italy. Similarly, the number of Regions/Autonomous Provinces in which the variant was detected increased from one in the first week, to 11 in the second, and to 17 in the last one. The presence of the Omicron variant was confirmed by the JRC real-time RT-PCR in 79.1% (91/115) of the positive samples, and by Sanger sequencing in 66% (64/97) of PCR amplicons. In conclusion, we designed an RT-qPCR assay capable to detect the Omicron variant, which can be successfully used for the purpose of wastewater-based epidemiology. We also described the history of the introduction and diffusion of the Omicron variant in the Italian population and territory, confirming the effectiveness of sewage monitoring as a powerful surveillance tool