Continuous monitoring of landslides is of basic importance for understanding their behavior, defining their 3D geometry, and providing a basis for early warning purposes. While a number of instrumentations can be used for tracking surface displacement, only automatic or fixed multi-module inclinometers can be used for continuous monitoring of displacement at depth, providing valuable information for landslide geometry reconstruction. Since these instruments are very expensive, thus rarely used, a low-cost and multi-module fixed inclinometer for continuous landslide monitoring has been developed. In this paper, the electronics of the system, including sensor characteristics and optimization, controlling software, and structure are presented. For system development, a single module prototype was first developed and tested in the field to ensure sufficient measuring performance. Subsequently, the multi-module system was designed, assembled, and tested in controlled conditions. Test results indicate the good performance of the system with a displacement measuring accuracy of 0.37% of the length of the inclinometer chain. The linearity test indicates the high linearity of the measures, especially in the range ±20°, which is the typical operating range of such kinds of instrumentations. The thermal efficiency test indicates the high efficiency of the system in preventing measuring errors caused by thermal drifting.

Low-cost MEMS accelerometers have the potential to be used in a number of tilt-based monitoring applications but have the disadvantage of being very sensitive to temperature variation (thermal drift). In this paper, we analyze the thermal behavior of a low-cost sensor in the range −10 to +45 °C in order to provide a simple compensation strategy to mitigate this problem. For sensor analysis, we have developed a miniaturized thermal chamber, which was mounted on a tilting device to account for tilt angle variation. The obtained raw data were used to construct low degree polynomial equations that by relating the measurement error induced by thermal drift (i.e., acceleration residuals) to temperature and inclination (of each specific axis), can be used for thermal compensation. To validate our compensation strategy, we performed a field monitoring test and evaluated the compensation performance by calculating RMS errors before and after correction. After compensation, the RMS errors calculated for both the X and Y axes decreased by 96%, indicating the potential of using a simple set of equations to solve common drawbacks that currently make low-cost MEMS sensors unsuitable for tilt-based monitoring applications.

Floods cause great losses in terms of human life and damages to settlements. Since the exposure is a proxy of the risk, it is essential to track flood evolution. The increasing availability of Synthetic Aperture Radar (SAR) imagery extends flood tracking capabilities because of its all-water and day/night acquisition. In this paper, in order to contribute to a better evaluation of the potential of Sentinel-1 SAR imagery to track floods, we analyzed a multi-pulse flood caused by a typhoon in the Camarines Sur Province of Philippines between the end of 2018 and the beginning of 2019. Multiple simple classification methods were used to track the spatial and temporal evolution of the flooded area. Our analysis indicates that Valley Emphasis based manual threshold identification, Otsu methodology, and K-Means Clustering have the potential to be used for tracking large and long-lasting floods, providing similar results. Because of its simplicity, the K-Means Clustering algorithm has the potential to be used in fully automated operational flood monitoring, also because of its good performance in terms of computation time.

In October 2015, two intense rainfall events hit the central and southern regions of Italy and triggered a combination of different and widespread effects, including floods, landslides, and soil erosion. These outcomes devastated about 68 municipalities of the Benevento province (Campania region), killed two people, and caused millions of euros worth of damage to structures, infrastructures, and agriculture. The town of Benevento was one of the sectors most affected by overflow. Extensive areas characterized by flyschoid outcrops experienced widespread occurrences of soil erosion and landslides, and destructive, high-velocity debris flows (about 50) afflicted areas that had experienced heavy rainfall of higher intensity (total rainfall of 415.6 mm). In this study, the characteristics of these rainfall events and related geomorphological processes were determined by (i) analyzing the available rainfall data to identify the spatial pattern, distribution, and statistical characteristics of the two storms and (ii) mapping the storm effects, such as flooded areas, landslide types, and soil erosion. These effects were then related to the spatial distribution of the storms and the local geological and geomorphologic settings that drove their initiation and development.

In this work, a low-cost, open-source and replicable system prototype for thermal analysis of low-cost Micro Electro-Mechanical Systems (MEMS) Inertial Measurement Unit (IMU) sensors in tilt measurement perspective is presented and tested. The system is formed of a 3D printed frame, a thermal cell consisting in a Peltier element mounted over a heat sink, and a control and power system. The frame is designed to allow the independent biaxial tilting of the thermal cell through two servomotors. The control board is formed by an Arduino^® and a self-made board including a power drive for controlling the thermal unit and servomotors. We tested the chamber analyzing the behavior of multiple MEMS IMU onboard accelerometers suitable for measuring tilt. Our results underline the variability of the thermal behavior of the sensors, also for different sensor boards of the same model, and consequently the need for the adoption of a thermal compensation strategy based on thermal analysis results. These data suggesting the need for the analysis of the thermal behavior of MEMS-based sensors, indicate the potential of our system in making low-cost sensors suitable in medium-to-high precision monitoring applications.