In this work, we evaluated the influence of root structure on shallow landslide distribution. Root density measurements were acquired in the field and the corresponding root cohesion was estimated. Data were acquired from 150 hillslope deposit trenches dug in areas either devoid or affected by shallow landslides within the Garfagnana Valley (northern Tuscany, Italy). Results highlighted a correlation between the root reinforcement and the location of measurement sites. Namely, lower root density was detected within shallow landslides, with respect to neighboring areas. Root area ratio (RAR) data allowed us to estimate root cohesion by the application of the revised version of the Wu and Waldron Model. Then, we propose a new method for the assimilation of the lateral root reinforcement into the infinite slope model and the limit equilibrium approach by introducing the equivalent root cohesion parameter. The results fall within the range of root cohesion values adopted in most of the physically based shallow landslide susceptibility models known in the literature (mean values ranging between ca. 2 and 3 kPa). Moreover, the results are in line with the scientific literature that has demonstrated the link between root mechanical properties, spatial variability of root reinforcement, and shallow landslide locations.

In the present work, Raman, Fourier Transform Infrared (FTIR) and elemental Laser-Induced Breakdown Spectroscopy (LIBS) spectroscopic techniques were used for the assessment of the influence of plant root composition towards shallow landslide occurrence. For this purpose, analyses were directly carried out on root samples collected from chestnut forests of the Garfagnana basin (northern Apennines, Italy) in different areas devoid and affected by shallow landslides due to frequent heavy rain events. Results have highlighted a correlation between the biochemical constituents of wooden roots and the sampling areas. In particular, different content of lignin/cellulose, as well as minerals nutrients, have been detected in roots collected where shallow landslides occurred, with respect to more stable areas. The results achieved are in line with the scientific literature which has demonstrated the link between the chemical composition of roots with their mechanical properties and, in particular, tensile strength and cohesion. Finally, portable spectroscopic instrumentations were employed without the need for either any sample preparation for Raman and LIBS spectroscopy or minimal preparation for FTIR spectroscopy. This novel and fast approach has allowed achieving information on the content of the major constituents of the root cell, such as cellulose and lignin, as well as their mineral nutrients. This approach could be reasonably included among the vegetation protection actions towards instability, as well as for the evaluation of shallow landslide susceptibility, combining geological, vegetational and biochemical parameters with sustainability.

Variability in lithology and weathering degree affects physical and mechanical properties of rocks. In this study, we investigated the relationships between weathering degree and engineering geological properties of trachydacitic volcanic rocks from Monte Amiata (central Italy) by coupling field and laboratory analyses. We collected in situ Schmidt hammer tests in the field. We evaluated weathering quantifying the percentage of secondary minerals through thermal analysis in the laboratory. We also determined dry density (${\rho }_d$), specific gravity of solids (Gs), porosity (n) and two-dimensional (2D) porosity as resulted from scanning electron microscopy investigations. The results of our study indicate a negative linear correlation between Schmidt hammer rebound values and secondary mineral percentage. This correlation provides a tool to quantitatively estimate the deterioration of rock uniaxial compressive strength (UCS) as weathering increases. Moreover, thermal analysis turned out to be a quantitative and reproducible method to evaluate weathering degree of magmatic rocks.

Shallow landslides are among the most frequent and impactful geomorphological phenomena in areas affected by intense rainfall and complex lithological settings. In this study we present and analyse a new multi-temporal shallow landslide inventory for the Apuan Alps area (Northern Tuscany, Italy). The study area, covering 625 km², is characterized by high landslide susceptibility due to both occurrence of intense rainfall events and complex morphology and structural geology conditions. A visual interpretation of high spatial resolution orthophotos was performed to recognize and map both landslide features and examples of stable areas. The aerial images used for landslide mapping cover a period of 67 years, from 1954 to 2021. The acquisition was not evenly distributed over time, with intervals between successive images varying from 2 to 24 years and averaging approximately 6 years. Nevertheless, the last two decades (2003-2021) saw a more consistent acquisition rate, with aerial images captured every 3 years. The dataset, made up of 1433 positive landslide entities and 100 stable areas, was validated through field surveys, achieving an overall accuracy of 91%. During field validation, further information were acquired, such as movement type, material involved and scarp height. The overall inventory underwent spatial, temporal and statistical analysis. Spatial analysis revealed two high-density clusters (>25 landslides/km²), primarily associated with extreme rainfall events occurred in 1996, 2010, and 2012. Temporal analysis highlighted a significant increase of normalized annual landslide frequency during the recent decades; also the relationships with the increase of intense rainfall events was explored. Magnitude–frequency distribution analysis exhibited a negative power-law relationship for medium and larger landslides, with a rollover at areas around 100 m². The shape and the parameters of the magnitude-frequency relationship well fit to other functions published in the literature. In a general perspective, the new inventory shows high frequency of “small” landslides, which instead are almost lacking within published landslide inventories for the study area (e.g. IFFI, Inventario Fenomeni Franosi Italiani). The intersection between shallow landslides and bedrock lithological units allowed us to recognize highest landslide density for silt and clay-rich lithologies, such as flysch and metarenites, while carbonate units is characterized by higher stability. Analysis of morphometric variables revealed that south- and southeast-facing concave hillslopes with gradients between 30° and 50° are particularly susceptible to landslides. These results align with previous research highlighting the role of slope aspect, steepness, and contributing area in landslide initiation. Field validation provided further insights into the dynamics and geometry of shallow landslides. Avalanches were the most common type (50%), followed by slides (30%), flows (15%), and falls (5%). The variability for scarp height and sliding surface location highlights the involvement of both slope deposits and bedrock, providing relevant clues to help both understanding failure mechanisms and improving approaches for susceptibility models. This research highlights the added value of integrating remote sensing – based data extraction, spatial analyses, field validation and statistical methods to enhance the understanding of shallow landslide processes. Moreover, the inventory represents a new robust, high-quality dataset suitable for landslide susceptibility and hazard zoning.

Shallow landslide susceptibility modelling at regional scale may be performed using both a physically based and statistical approach. For the same area, these two approaches can have inconsistent results, mainly because the two methods are conceptually different. Physically based models are based on the infinite slope model and consists on the computation cell by cell of a safety factor comparing between driving and resisting forces. The assumption that landslides occur in slopes that are characterized by predisposing factors similar to those in which landslides have occurred in the past, is the concept behind the statistical models. The aim of this work is to compare the two approach and investigate the differences between the two models. The study area is located in northern Tuscany, central Italy, in which an extensive field survey highlighted that about 60% of landslides involve bedrock. For this reason, we developed a physically based susceptibility analysis taking into account both the surficial layer (slope deposit, SD) and the underlying layer (BR), characterized by weathered and fractured bedrock. This model is compared to the statistically based one, which take into account topographic and geologic predisposing factor as well as bedrock geo-mechanical properties, such Geological Strength Index (GSI), Schmidt hammer rebound values (Rv) and Joint density (Jv). The accuracy of the models is evaluated using a multi-temporal landslide inventory, in which involving bedrock landslides are distinct from slope deposits landslides. Within this general framework results are discussed regarding the model’s predictive capacity and spatial agreement.

The knowledge of rock masses behaviour is an important information in various fields such as civil engineering, land use planning and hazard/risk zoning. Different rock mass classification methods, initially aimed at assisting underground excavations (Hoek, 2007), are widely used nowadays for preliminary design procedures (Bieniawski, 1989; Hoek, 2007), like the RMR (Bieniawski, 1976) and the Q (Barton et al., 1974) and their modifications. These methods incorporate geological, geomechanical and geometric parameters in order to obtain a quantitative estimation of the rock mass quality, but, on the other hand, their implementation is time-consuming. Despite the dominance of these two methods, further rock mass classifications systems have been proposed in the last decades and, among these, the Geological Strength Index (GSI) classification system is currently widely used as it allows to estimate the strength of rock mass through empirical semi-quantitative evaluation (Hoek, 1994; Cai et al., 2004), based on both rock mass structure and condition of the joints (Hoek et al., 1995). Estimating the GSI is straightforward and fast, but it comes at the cost of a certain degree of subjectivity. Moreover, the index does not adequately account for the lithology of the rock mass matrix. Hence, for the above reasons, these classification methods are not fully suitable to collect rock mass data over wide scale areas for engineering geological mapping. The Rock mass Quality Index (RQI, Disperati et al., 2016; Mammoliti et al., 2018) is a rock mass classification system developed for cartographic purposes and it is based on the systematic fieldwork measurement and processing of sets of the Schmidt hammer rebound values (R). Each representative rock mass outcrop is analysed by collecting ca. 20 R values at the 15-25 nodes of a regular grid conceived to investigate the typical features of the rock mass. This allows to perform statistical analyses and to calculate the RQI, a quantitative indicator of the global strength and quality of the rock mass. In the last decade, a dataset of ca 1100 outcrops sites spreading over a large area (ca. 12000 km2) were acquired in Tuscany (Italy), according to different lithology, weathering, jointing conditions. The dataset consists of both RQI measurements and GSI estimations for the main different lithological groups (flysch, limestones, marls, magmatic rocks and schists) of the Northern Apennines (Italy), as well as the laboratory determinations of the Slake Durability Index (Id2; Franklin & Chandra, 1974) obtained by testing representative outcrop rock samples. The large dataset has allowed to analyse the correlation among RQI, GSI and Id2 and to perform an in-depth critical analysis of the relationships among RQI, lithology, rock mass structure, as well as the suitability of the RQI as reference index for engineering geological mapping of near-surface rock mass quality.