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Landslides represent a major problem in human activities, especially in the proximity of cities or infrastructure. In this paper, we present the peculiar case of Pietracamela, Italy. Pietracamela is a small village located in the Central Apennines, a few kilometers north of the Gran Sasso Mountain. The peculiarity of the case study is the fact that the village is simultaneously affected by two different types of slope instabilities. The southwest sector, representing the historical part of the village, has been affected by large rockfalls generated from the "Capo le Vene" cliff located in the south of the village. The northeastern sector of the village represents the most recent urbanized area and is involved in a deep-seated landslide that, in the last decades, has damaged buildings and infrastructure. In this context, we have used two different types of remote sensing techniques to study the two phenomena. The rockfall area has been surveyed through the use of an Unmanned Aerial Vehicle (UAV) that allowed the definition of main joint sets and the volume of blocks associated with the most recent (2011) rockfall event. Three main joint sets have been highlighted, which are responsible for the failure of the "Capo le Vene" cliff. The volume of blocks that failed during the last rock failure in 2011 ranged from a few to 1500 m3. The deep-seated landslide has been studied by analyzing borehole data and 20 years of InSAR data from ERS1/2, ENVISAT, COSMO-SkyMed, and SENTINEL-1. It has been highlighted by InSAR analysis that the northeast sector of the village shows a perfectly linear displacement trend that generates movements up to about 230 mm (about 1 cm/year). [ABSTRACT FROM AUTHOR]

Monitoring slow-moving landslides is a crucial task for socioeconomic risk prevention and/or mitigation. Persistent scatterer interferometric synthetic aperture radar (PS-InSAR) is an advanced remote sensing method for monitoring ground deformation. In this research, PS-InSAR time series derived from COSMO-SkyMed (descending orbit) and Sentinel-1 (ascending orbit) are analyzed for a region in Central Apennines in Italy. The sequential turning point detection method (STPD) is implemented to detect the trend turning dates and their directions in the PS-InSAR time series within areas of interest susceptible to landslides. The monthly maps of significant turning points and their directions for years 2018, 2019, 2020, and 2021 are produced and classified for four Italian administrative regions, namely, Marche, Umbria, Abruzzo, and Lazio. Monthly global precipitation measurement (GPM) images at 0. 1 ∘ × 0. 1 ∘ spatial resolution and four local precipitation time series are also analyzed by STPD to investigate when the precipitation rate has changed and how they might have reactivated slow-moving landslides. Generally, a strong correlation ( r ≥ 0.7 ) is observed between GPM (satellite-based) and local precipitation (station-based) with similar STPD results. Marche and Abruzzo (the coastal regions) have an insignificant precipitation rate while Umbria and Lazio have a significant increase in precipitation from 2017 to 2023. The coastal regions also exhibit relatively lower precipitation amounts. The results indicate a strong correlation between the trend turning dates of the accumulated precipitation and displacement time series, especially for Lazio during summer and fall 2020, where relatively more significant precipitation rate of change is observed. The findings of this study may guide stakeholders and responsible authorities for risk management and mitigating damage to infrastructures. [ABSTRACT FROM AUTHOR]

Persistent Scatterers Interferometric Synthetic Aperture Radar (PS-InSAR) is an advanced satellite remote sensing technique which allows an effective monitoring of ground movement. In this work, PS-InSAR time series as well as precipitation and temperature time series in a region in Catania, Italy are utilized during 2018–2022, and their possible interconnections with land subsidence/uplift due to groundwater level change are investigated. First, the potential jumps in the displacement time series are removed, and then the Sequential Turning Point Detection (STPD) is applied to estimate the times when the velocity of the displacement time series changes. The results show a significant correlation between the frequency of turning points in displacement time series and precipitation trend change, particularly during the winter season. Furthermore, the Least-Squares Cross Wavelet Analysis (LSCWA) is applied to estimate the coherency and phase delay between the displacement and weather cycles in the time-frequency domain. The annual cycles of displacement and temperature show more coherency than the ones of displacement and precipitation across the study region. The results presented herein are important for infrastructure and water management planning. [ABSTRACT FROM AUTHOR]