This paper illustrates the potential of Sentinel-1 for landslide detection, mapping and characterization with the aim of updating inventory maps and monitoring landslide activity. The study area is located in Molise, one of the smallest regions of Italy, where landslide processes are frequent. The results achieved by integrating Differential Synthetic Aperture Radar Interferometry (DInSAR) deformation maps and time series, and Geographical Information System (GIS) multilayer analysis (optical, geological, geomorphological, etc.) are shown. The adopted methodology is described followed by an analysis of future perspectives. Sixty-two landslides have been detected, thus allowing the updating of pre-existing landslide inventory maps. The results of our ongoing research show that Sentinel-1 might represent a significant improvement in terms of exploitation of SAR data for landslide mapping and monitoring due to both the shorter revisit time (up to 6 days in the close future) and the wavelength used, which determine an higher coherence compared to other SAR sensors.

The paper explores the potential of the satellite advanced differential synthetic aperture radar interferometry (A-DInSAR) technique for the identification of impending slope failure. The advantages and limitations of satellite InSAR in monitoring pre-failure landslide behaviour are addressed in five different case histories back-analysed using data acquired by different satellite missions: Montescaglioso landslide (2013, Italy), Scillato landslide (2015, Italy), Bingham Canyon Mine landslide (2013, UT, USA), Big Sur landslide (2017, CA, USA) and Xinmo landslide (2017, China). This paper aimed at providing a contribution to improve the knowledge within the subject area of landslide forecasting using monitoring data, in particular exploring the suitability of satellite InSAR for spatial and temporal prediction of large landslides. The study confirmed that satellite InSAR can be successful in the early detection of slopes prone to collapse; its limitations due to phase aliasing and low sampling frequency are also underlined. According to the results, we propose a novel landslide predictability classification discerning five different levels of predictability by satellite InSAR. Finally, the big step forward made for landslide forecasting applications since the beginning of the first SAR systems (ERS and Envisat) is shown, highlighting that future perspectives are encouraging thanks to the expected improvement of upcoming satellite missions that could highly increase the capability to monitor landslides’ pre-failure behaviour.

Ten small rock slides (with a volume ranging from 10¹ to 10³ m³) on a slope affected by working activities were detected, located, and timed using pictures collected by an automatic camera during 40 months of continuous monitoring with terrestrial SAR interferometry (TInSAR). These landslides were analyzed in detail by examining their pre-failure time series of displacement inferred from high-sampling frequency (approximately 5 min) TInSAR monitoring. In most of these cases, a typical creep behavior was observed with the displacement starting 370 to 12 h before the collapse. Additionally, an evident acceleration decrease of the displacement a few hours before the failure was observed in some rock/debris slides, thus suggesting the possibility of a mechanical feature of the slope that differs from the classical creep theory. The efficacy of the linear Fukuzono approach for the prediction of time of failure was tested by back-analyzing the ten landslides. Furthermore, a modified Fukuzono approach named average data Fukuzono (ADF) was implemented and applied to our dataset. Such an approach is able to improve forecasting effectiveness by reducing the error due to anomalies in the time series of displacement, like the acceleration decrease before failure. A prediction with a temporal accuracy of at least 2 h was obtained for all the analyzed rock/debris slides.

Land Surface Temperature (LST) is an important climate factor for understanding the relationship between the land surface and atmosphere. Furthermore, LST is linked to soil moisture and evapotranspiration, which can potentially alter the severity and regime of wildfires, landslide-triggering precipitation thresholds, and others. In this paper, the monthly daytime and nighttime LST products of Moderate Resolution Imaging Spectroradiometer (MODIS) are employed for the period 2000–2023 in order to find areas that have been cooling or warming in a region of great interest in Central Italy, due to its complex geological and geomorphological settings and its recent seismic sequences and landslide events. The annual MODIS land cover images for 2001–2022 are also utilized to investigate the interconnection between LST and land cover change. The results of the non-parametric Mann–Kendall trend test and its associated Sen’s slope reveal a significant nighttime warming trend in the region, particularly in July, linked to forest and woodland expansion. Grasslands toward the coastline with low elevation (less than 500 m a.s.l.) have experienced significant heat waves during the summer, with an LST of more than 35 °C. A significant negative correlation between the elevation and LST is observed for each calendar month. In particular, the daytime and nighttime LST have more than 80% correlation with elevation during winter and summer, respectively. In addition, nighttime warming and gradual drainage are noticed in Lake Campotosto. The results of this study could be useful for wildfire and landslide susceptibility analyses and hazard management.

In this work, the ability of advanced satellite interferometry to monitor pre-failure landslide behaviours and the potential application of this technique to Failure Forecasting Methods (FFMs) are analysed. Several limits affect the ability of the technique to monitor a landslide process, especially during the pre-failure phase (tertiary creep). In this study, two of the major limitations affecting the technique have been explored: (1) the low data sampling frequency and (2) the phase ambiguity constraints. We explored the time series of displacements for 56 monitored landslides inferred from the scientific literature and from different in situ and remote monitoring instruments (i.e., extensometers, inclinometers, distometers, Ground Base InSAR, and total station). Furthermore, four different forecasting techniques have been applied to the monitoring data of the selected landslides. To analyse the reliability of the FFMs based on the InSAR satellite data, the 56 time series have been sampled based on different satellite features, simulating the satellite revisit time and the phase ambiguity constraints. Our analysis shows that the satellite InSAR technique could be successful in monitoring the landslide’s tertiary creep phase and, in some cases, for forecasting the corresponding time of failure using FFMs. However, the low data sampling frequency of the present satellite systems do not capture the necessary detail for the application of FFMs in actual risk management problems or for early warning purposes.