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Geomorphodiversity refers to the variety of landforms and morphological processes characterizing the landscape. The definition of an index to quantify geomorphodiversity is a relevant step for multiple fields of Earth sciences, since it is widely accepted that the variability of the geosphere deeply influences the diversity of the biosphere. Such an index should describe the number and type of landforms and geomorphological processes. We propose a quantitative land surface diversity index valid for Italy, considering multiple input quantities to describe geological constraints and geomorphological processes. Critical issues were the selection of moving window size for focal statistics operations, to calculate local diversity of slope, lithology, drainage density and terrain forms in individual raster maps. We compared the index with traditional geomorphological maps, in selected locations, in which information was available. Results show that a minimal set of heterogeneous data is a satisfactory approach to investigate the landscape diversity. Relating processing parameters and terrain spatial characteristics to the dataset resolution is a good choice to assess a reproducible land surface diversity index. Inclusion of drainage density allows the improvement of results in flat areas, in which other factors show trivial results. We argue that the index is relevant for land use management, assessment of ecodiversity, and it may help describing the interaction between abiotic and biotic compartments. [ABSTRACT FROM AUTHOR]

Lithological maps contain information about the different lithotypes cropping out in an area. At variance with geological maps, portraying geological formations, lithological maps may differ as a function of their purpose. Here, we describe the preparation of a lithological map of Italy at the 1:100000 scale, obtained from classification of a comprehensive digital database and aimed at describing geomechanical properties. We first obtained the full database, containing about 300 000 georeferenced polygons, from the Italian Geological Survey. We grouped polygons according to a lithological classification by expert analysis of the 5456 original unique descriptions of polygons, following compositional and geomechanical criteria. The procedure resulted in a lithological map with a legend including 19 classes, and it is linked to a database allowing ready interpretation of the classes in geomechanical properties and is amenable to further improvement. The map is mainly intended for statistical and physically based modelling of slope stability assessment and geomorphological and geohydrological modelling. Other possible applications include geoenvironmental studies, evaluation of river chemical composition, and estimation of raw material resources. The dataset is publicly available at 10.1594/PANGAEA.935673 (Bucci et al., 2021). [ABSTRACT FROM AUTHOR]

The dependence of rainfall on elevation has frequently been documented in the scientific literature and may be relevant in Italy, due to the high degree of geographical and morphological heterogeneity of the country. However, a detailed analysis of the spatial variability of short-duration annual maximum rainfall depths and their connection to the landforms does not exist. Using a new, comprehensive and position-corrected rainfall extreme dataset (I2 -RED, the Improved Italian-Rainfall Extreme Dataset), we present a systematic study of the relationship between geomorphological forms and the average annual maxima (index rainfall) across the whole of Italy. We first investigated the dependence of sub-daily rainfall depths on elevation and other landscape indices through univariate and multivariate linear regressions. The results of the national-scale regression analysis did not confirm the assumption of elevation being the sole driver of the variability of the index rainfall. The inclusion of longitude, latitude, distance from the coastline, morphological obstructions and mean annual rainfall contributes to the explanation of a larger percentage of the variance, even though this was in different ways for different durations (1 to 24 h). After analyzing the spatial variability of the regression residuals, we repeated the analysis on geomorphological subdivisions of Italy. Comparing the results of the best multivariate regression models with univariate regressions applied to small areas, deriving from morphological subdivisions, we found that "local" rainfall–topography relationships outperformed the country-wide multiple regressions, offered a uniform error spatial distribution and allowed the effect of morphology on rainfall extremes to be better reproduced. [ABSTRACT FROM AUTHOR]

Lithological maps contain information about the different lithotypes cropping out in an area. At variance with geological maps, portraying geologic formations, lithological maps may differ as a function of their purpose. Here, we describe the preparation of a lithological map of Italy at a scale of 1:100,000, obtained from classification of a comprehensive digital database and aimed at describing geo-mechanical properties. We first obtained the full database, containing about 300,000 geo-referenced polygons, from the Italian geological survey. We grouped polygons according to a lithological classification by expert analysis of the original 5,456 unique descriptions of polygons, following compositional and geo-mechanical criteria. The procedure resulted in a lithological map with a legend including 19 classes, and it is linked to a database allowing ready interpretation of the classes in geo-mechanical properties, and amenable to further improvement. The map is mainly intended for statistical and physically based modelling of slope stability assessment, geo-morphological and geo-hydrological modelling. Other possible applications include geo-environmental studies, evaluation of river chemical composition, estimation of raw material resources. [ABSTRACT FROM AUTHOR]

Slope units represent surface slopes by means of polygons delimited by drainage and divide lines obtained on a digital topography. Objective slope unit delineation for a given digital elevation model is still an open issue and, often, a limitation that may dictate the use of a more traditional pixel-based approach for spatial analysis. Availability of slope unit maps facilitates many kinds of studies and allows scholars to focus on specific scientific issues rather than on preparing sound mapping units from scratch for their research. Here, we present a slope unit map of a large portion of the Himalayas. The map is prepared following a widely tested, parameter-free optimization algorithm. The area encompassed by the map is relevant to studies of the well-known 2015 Gorkha earthquake and monsoons, which makes it relevant to a vast portion of the scientific community working in natural hazards including, but not limited to, landslide scientists and practitioners. The map contains 112,674 polygons with average area of 0.38 km² and is published in vector form. The map is accompanied by a selection of data including morphometric and thematic quantities. In addition to describing the rationale behind the delineation of the polygonal map and selected data, we describe an application devoted to unsupervised terrain classification. We applied a k-means clustering procedure with two strategies: one at (coarser) basin scale and one at (finer) slope unit scale. We show similarities and differences between the two classification strategies, highlighting the role of the slope unit subdivision in the two cases. [ABSTRACT FROM AUTHOR]

The dependence of rainfall on elevation has frequently been documented in the scientific literature and may be relevant in Italy, due to the high degree of geographical and morphological heterogeneity of the country. However, a detailed analysis of the spatial variability of short-duration rainfall extremes and their connection to the landforms does not exist. Using a new, comprehensive and position-corrected rainfall extreme dataset (I²-RED), we present a systematic study of the relationship between geomorphological forms and the average of rainfall extremes (index rainfall) across the whole of Italy. We first investigated the dependence of sub-daily rainfall depths on elevation and other landscape indices through univariate and multivariate linear regressions. After analyzing the results, we repeated the analysis on geomorphological subdivisions of Italy. The results of the national-scale regression analysis did not confirm the assumption of elevation being the sole driver of the variability of rainfall extremes. The longitude, latitude, distance from the coastline, morphological obstructions and mean annual rainfall resulted to be significantly related to the index rainfall, and to play different roles for different durations (1- to 24-hours). However, when comparing the results of the best multivariate regression models with univariate regressions for morphological subdivisions, we found that "local" rainfall-topography relationships within the geomorphological subdivisions outperformed the country-wide multiple regressions and offered a reasonable representation of the effect of morphology on rainfall extremes. [ABSTRACT FROM AUTHOR]

Thus, if geomorphometry is the science of quantitative land surface analysis, a conference in Perugia would represent a nice opportunity. Other contributions, even if not specifically commercial, highlighted the use of algorithms borrowed from computer graphics for terrain segmentation (Feciskanin & Minár, this issue), and adaptation of general-purpose software for marine geomorphometry (Florinsky, Filippov, & Govorov, 2020). In 2019, we received the good news that the city of Perugia had been selected as the site for the International Geomorphometry 2020 Conference and that the Research Institute for Geo-Hydrological Protection of the Italian National Research Council (CNR IRPI) and the Department of Physics and Geology of the University of Perugia would organize the conference. [Extracted from the article]

Starting on 24th August 2016, Central Italy was struck by a six-month earthquake sequence that caused 303 victims and extensive major damages to urban areas and infrastructures, in some cases entire villages needed complete rebuilding. In this paper we present a map that portrays the overall susceptibility to multiple landslide types and the exposure to landslides of the rural-urban areas of the Castelsantangelo sul Nera Municipality, a typical village of the central Italian Apennine. The map is based on a procedure that ingests geomorphological data and models and groups the individual landslide susceptibility maps in a joint susceptibility and exposure map based on expert-defined criteria. The procedure has been applied to built-up and to undeveloped areas to highlight their exposure and was used as a tool for planning post-seismic reconstruction. We advise that such maps are used also as basic tool for ordinary urban planning. [ABSTRACT FROM AUTHOR]

The island of Gran Canaria (Canary Islands, Spain) is characterized by a large variability of volcanic rocks reflecting its volcanic evolution. The geological map provided by Geological Survey of Spain at 1:25.000 scale shows more than 109 different lithologies and it is too complex for environmental and engineering purposes. This work presents a simplified geotechnical map with a small number of classes grouping up units with similar geotechnical behaviours. The lithologies were grouped using about 350 rock samples, collected in the seven major islands of the Archipelago. The geotechnical map was used to model rockfall hazard in the entire island of Gran Canaria, where rockfalls are an important threat. The rockfall map was validated with 128 rockfall events along the GC-200 road, located in the NW sector of Gran Canaria. About 96% of the events occurred along sections of the road where the number of expected trajectories is high or moderate. [ABSTRACT FROM AUTHOR]

A landslide event is characterized by the distribution of landslides caused by a single triggering event. The severity of earthquake-induced landslide events can be quantified by the landslide-event magnitude, a metric derived from the frequency-size distribution of landslide inventories. However, reliable landslide inventories are not available for all earthquakes, because the preparation of a suitable inventory requires data, time, and expertise. Prediction of landslide-event magnitude immediately following an earthquake provides an estimate of the total landslide area and volume based on empirical relations. It allows to make an assessment of the severity of a landslide event in near-real time and to estimate the frequency-size distribution curve of the landslides. In this study, we used 23 earthquake-induced landslide inventories and propose a method to predict landslide-event magnitude. We selected five predictors, both morphometric and seismogenic, which are globally and readily available. We used the predictors within a stepwise linear regression and validated using the leave-one-out technique. We show that our approach successfully predicts landslide-event magnitude values and provides results along with their statistical significance and confidence levels. However, to test the validity of the approach globally, it should be calibrated using a larger and more representative dataset. A global, near real-time assessments regarding landslide-event magnitude scale can then be achieved by retrieving the readily available ShakeMaps, along with topographic and thematic information, and applying the calibrated model. The results may provide valuable information regarding landscape evolution processes, landslide hazard assessments, and contribute to the rapid emergency response after earthquakes in mountainous terrain. [ABSTRACT FROM AUTHOR]

The use of remotely piloted aircraft systems (RPASs) in geosciences is often aimed at the acquisition of an image sequence to produce digital models and orthophotographs of the topographic surface. The technology can be applied for rockfall hazard and risk assessment. To study rockfalls, an approach consists in the application of numerical models for the computation of rockfall trajectories. Data required for such simulations include digital terrain models, location of the instability source areas, and the mechanical properties of the terrain. In this article, we present an analysis of the earthquake-triggered rockfall that occurred along the SP18 in Villanova di Accumoli (Lazio, central Italy) during the seismic sequence that started on 24 August 2016. A survey with a multicopter was carried out to obtain a surface model of the terrain and identify and characterize the source areas and other instable blocks in areas not accessible in the field. The investigated area extends for 6500 m 2 and was covered by 161 photographs that were used to obtain an orthophoto with a ground resolution of 2.5 cm and a digital surface model with a ground resolution of 20 cm × 20 cm, which was processed and fused with GNSS real-time kinematic data. To obtain a map of potential rockfall trajectories, we run the numerical model STONE, using as origin of the boulders both source areas mapped in the field and pixels with a slope angle above a selected threshold. Results showed that only the part of the road SP18 already affected by the rockfall was exposed to further rockfall impacts. In particular, it was observed that 29.2 % (i.e. 12 123) of the 41 500 simulated trajectories may potentially reach or cross this tract of the road. Based on these data, limited protection measures were suggested. The combined use of RPAS data, fused with ground GPS points, an accurate geomorphological survey, and terrain static and dynamic parameters from the literature allows fast, low-cost, and replicable rockfall numerical modelling useful for emergency response and adoption of proper protection measures. [ABSTRACT FROM AUTHOR]

Geomorphological field mapping is a conventional method used to prepare landslide inventories. The approach is typically hampered by the accessibility and visibility, during field campaigns for landslide mapping, of the different portions of the study area. Statistical significance of landslide susceptibility maps can be significantly reduced if the classification algorithm is trained in unsurveyed regions of the study area, for which landslide absence is typically assumed, while ignorance about landslide presence should actually be acknowledged.We compare different landslide susceptibility zonations obtained by training the classification model either in the entire study area or in the only portion of the area that was actually surveyed, which we name effective surveyed area. The latter was delineated by an automatic procedure specifically devised for the purpose, which uses information gathered during surveys, along with landslide locations. The method was tested in Gipuzkoa Province (Basque Country), north of the Iberian Peninsula, where digital thematic maps were available and a landslide survey was performed. We prepared the landslide susceptibility maps and the associated uncertainty within a logistic regression model, using both slope units and regular grid cells as the reference mapping unit. Results indicate that the use of effective surveyed area for landslide susceptibility zonation is a valid approach that minimises the limitations stemming from unsurveyed regions at landslide mapping time. Use of slope units as mapping units, instead of grid cells, mitigates the uncertainties introduced by training the automatic classifier within the entire study area. Our method pertains to data preparation and, as such, the relevance of our conclusions is not limited to the logistic regression but are valid for virtually all the existing multivariate landslide susceptibility models. [ABSTRACT FROM AUTHOR]

The relation between climate change and its potential effects on the stability of slopes remains an open issue. For rainfall induced landslides, the point consists in determining the effects of the projected changes in the duration and amounts of rainfall that can initiate slope failures. We investigated the relationship between fine-scale climate projections obtained by downscaling and the expected modifications in landslide occurrence in Central Italy. We used rainfall measurements taken by 56 rain gauges in the 9-year period 2003–2011, and the RainFARM technique to generate downscaled synthetic rainfall fields from regional climate model projections for the 14-year calibration period 2002–2015, and for the 40-year projection period 2010–2049. Using a specific algorithm, we extracted a number of rainfall events, i.e. rainfall periods separated by dry periods of no or negligible amount of rain, from the measured and the synthetic rainfall series. Then, we used the selected rainfall events to forcethe Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model TRIGRS v. 2.1. We analyzed the results in terms of variations (or lack of variations) in the rainfall thresholds for the possible initiation of landslides, in the probability distribution of landslide size (area), and in landslide hazard. Results showed that the downscaled rainfall fields obtained by RainFARM can be used to single out rainfall events, and to force the slope stability model. Results further showed that while the rainfall thresholds for landslide occurrence are expected to change in future scenarios, the probability distribution of landslide areas are not. We infer that landslide hazard in the study area is expected to change in response to the projected variations in the rainfall conditions. We expect our results to contribute to regional investigations of the expected impact of projected climate variations on slope stability conditions and on landslide hazards. [ABSTRACT FROM AUTHOR]

The use of Remotely Piloted Aircraft Systems (RPASs) in geosciences is often aimed at the acquisition of an image sequence to produce digital models and orthophotographs of the topographic surface. The technology can be applied for rockfall hazard and risk assessment. To study rockfalls, an approach consists in the application of numerical models for the computation of rockfall trajectories. Data required for such models include accurate digital terrain models, location of the instability source areas, and the mechanical properties of the terrain. In this article, we present an analysis of the earthquake-triggered rockfall that occurred along the SP18 in Villanova di Accumoli (Lazio, central Italy) during the 24 August 2016 seismic sequence. A survey with a multicopter was carried out to obtain an accurate surface model of the terrain, the identification and characterization of the source areas and of other instable blocks in areas not accessible in the field. The investigated area extends for 6,500 m² and was covered by 161 photographs that were used to obtain an orthophoto with a ground resolution of 2.5 cm, and a digital surface model with a ground resolution of 20 cm x 20 cm, which was processed and fused with GNSS RTK data. We run the numerical model STONE, using the source areas mapped in the field and adopting a slope threshold to get a map showing the rockfall potential trajectories. Results showed that only the part of the road SP18 hit by the rockfall was exposed to further rockfall impacts. In particular, it was observed that 16% (i.e. 5,108) of the 31,800 simulated trajectories reached or crossed this tract of the road. Based on these data, limited protection measures were suggested. The combined use of RPAS data, fused with ground GPS points, an accurate geomorphological survey, and terrain static and dynamic parameters from the literature, allows fast, low-cost and replicable numerical modelling for emergency response and adoption of proper protection measures. [ABSTRACT FROM AUTHOR]

Automatic subdivision of landscapes into terrain units remains a challenge. Slope-units are terrain units bounded by drainage and divide lines, but their use in hydrological and geomorphological studies is limited because of the lack of reliable software for their automatic delineation. We present the r.slopeunits software for the automatic delineation of slope-units, given a digital elevation model and a few input parameters. We further propose an approach for the selection of optimal parameters controlling the terrain subdivision for landslide susceptibility modelling. We tested the software and the optimization approach in Central Italy, where terrain, landslide, and geo-environmental information was available. The software was capable of capturing the variability of the landscape, and to partition the study area into slope-units suited for landslide susceptibility modelling and zonation. We expect r.slopeunits to be used in different physiographical settings for the production of reliable and reproducible landslide susceptibility zonations. [ABSTRACT FROM AUTHOR]

Landslide susceptibility corresponds to the probability of landslide occurrence across a given geographic space. This probability is usually estimated by using a binary classifier which is informed of landslide presence/absence data and associated landscape characteristics. Here, we consider the Italian national landslide inventory to prepare slope-unit based landslide susceptibility maps. These maps are prepared for the eight types of mass movements existing in the inventory, (Complex, Deep Seated Gravitational Slope Deformation, Diffused Fall, Fall, Rapid Flow, Shallow, Slow Flow, Translational) and we build one susceptibility map for each type. The analysis – carried out by using a Bayesian version of a Generalized Additive Model with a multiple intercept for each Italian region – revealed that the inventory may have been compiled with different levels of detail. This would be consistent with the dataset being assembled from twenty sub–inventories, each prepared by different administrations of the Italian regions. As a result, this spatial heterogeneity may lead to biased national–scale susceptibility maps. On the basis of these considerations, we further analyzed the national database to confirm or reject the varying quality hypothesis on the basis of the model equipped with multiple regional intercepts. For each landslide type, we then tried to build unbiased susceptibility models by removing regions with a poor landslide inventory from the calibration stage, and used them only as a prediction target of a simulation routine. We analyzed the resulting eight maps finding out a congruent dominant pattern in the Alpine and Apennine sectors. The whole procedure is implemented in R–INLA. This allowed to examine fixed (linear) and random (nonlinear) effects from an interpretative standpoint and produced a full prediction equipped with an estimated uncertainty. We propose this overall modeling pipeline for any landslide datasets where a significant mapping bias may influence the susceptibility pattern over space. [ABSTRACT FROM AUTHOR]

Abstract: Natural landslides exhibit scaling properties revealed by power law relationships. These relationships include the frequency of the size (e.g., area, volume) of the landslides, and the rainfall conditions responsible for slope failures in a region. Reasons for the scaling behavior of landslides are poorly known. We investigate the possibility of using the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability analysis code (TRIGRS), a consolidated, physically-based, numerical model that describes the stability/instability conditions of natural slopes forced by rainfall, to determine the frequency statistics of the area of the unstable slopes and the rainfall intensity (I)–duration (D) conditions that result in landslides in a region. We apply TRIGRS in a portion of the Upper Tiber River Basin, Central Italy. The spatially distributed model predicts the stability/instability conditions of individual grid cells, given the local terrain and rainfall conditions. We run TRIGRS using multiple, synthetic rainfall histories, and we compare the modeling results with empirical evidences of the area of landslides and of the rainfall conditions that have caused landslides in the study area. Our findings revealed that TRIGRS is capable of reproducing the frequency of the size of the patches of terrain predicted as unstable by the model, which match the frequency size statistics of landslides in the study area, and the mean rainfall D, I conditions that result in unstable slopes in the study area, which match rainfall I − D thresholds for possible landslide occurrence. Our results are a step towards understanding the mechanisms that give rise to landslide scaling properties. [Copyright &y& Elsevier]

By analyzing recent microscopic many-body calculations of few-nucleon systems and complex nuclei performed by different groups in terms of realistic nucleon–nucleon (NN) interactions, it is shown that NN short-range correlations (SRCs) have a universal character, in that the correlation hole that they produce in nuclei appears to be almost A-independent and similar to the correlation hole in the deuteron. The correlation hole creates high-momentum components, missing in a mean-field (MF) description and exhibiting several scaling properties and a peculiar spin–isospin structure. In particular, the momentum distribution of a pair of nucleons in spin–isospin state (ST) = (10), depending upon the pair relative (krel) and center-of-mass (c.m.) (Kc.m.) momenta, as well as upon the angle Θ between them, exhibits a remarkable property: in the region krel≳2 fm-1 and Kc.m.≲1 fm-1, the relative and c.m. motions are decoupled and the two-nucleon momentum distribution factorizes into the deuteron momentum distribution and an A-dependent momentum distribution describing the c.m. motion of the pair in the medium. The impact of these and other properties of one- and two-nucleon momentum distributions on various nuclear phenomena, on ab initio calculations in terms of low-momentum interactions, as well as on ongoing experimental investigations of SRCs, are briefly commented. [ABSTRACT FROM AUTHOR]

Slope failures pose a substantial threat to mining activity due to their destructive potential and high probability of occurrence on steep slopes close to limit equilibrium conditions, which are often found both in open pits and in waste and tailing disposal facilities. The development of slope monitoring and modeling programs usually entails the exploitation of in situ and remote sensing data, together with the application of numerical modeling, and it plays an important role in the definition of prevention and mitigation measures aimed at minimizing the impact of slope failures in mining areas. In this paper, a new methodology is presented; one that combines satellite radar interferometry and 2D finite element modeling for slope stability analysis at a regional scale, and applied within slope unit polygons. Although the literature includes many studies applying radar interferometry and modeling for slope stability analysis, the addition of slope units as input data for radar interferometry and modeling purposes has, to our knowledge, not previously been reported. A former mining area in southeast Spain was studied, and the method proved useful for detecting and characterizing a large number of unstable slopes. Out of the 1959 slope units used for the spatial analysis of the radar interferometry data, 43 were unstable, with varying values of safety factor and landslide size. Out of the 43 active slope units, 21 exhibited line of sight velocities greater than the maximum error obtained through validation analysis (2.5 cm/year). Finally, this work discusses the possibility of using the results of the proposed approach to devise a proxy for landslide hazard. The proposed methodology can help to provide non-expert final users with intelligible, clear, and easily comparable information to analyze slope instabilities in different settings, and not limited to mining areas. [ABSTRACT FROM AUTHOR]

Potential effects on the stability of slopes due to climate change remains an open issue. Forrainfall induced landslides, the point consists in determining the effects of the projectedchanges in the duration and amounts of rainfall that can initiate slope failures. Weinvestigated the relationship between fine-scale climate projections obtained by downscalingand the expected modifications in landslide occurrence in Central Italy, using a deterministiclandslide model within rainfall events extracted from measured and downscaled precipitationdata. We used rainfall measurements taken by 56 rain gauges in the 9-year period 2003–2011,and the RainFARM technique to generate downscaled synthetic rainfall fields from regionalclimate model projections for the 14- year calibration period 2002–2015, and for the40-year projection period 2010–2049. Downscaled precipitation data for the futurescenario was obtained in RainFARM from WRF RPC4.5 model data. Using a specificalgorithm, we extracted a number of rainfall events, i.e. rainfall periods separated by dryperiods of no or negligible amount of rain, from the measured and the syntheticrainfall series. Out of the many thousands of extracted rainfall events, we selected asubset of 50 representative events from the measured series, and a subset of 50events from the synthetic series for each decadal time period, amounting to a totalof 250 events from the downscaled precipitation data. Then, we used each of theselected rainfall events to force the Transient Rainfall Infiltration and Grid-BasedRegional Slope-Stability Model TRIGRS v. 2.1. We analyzed the results in terms ofvariations in the rainfall thresholds for the possible initiation of landslides, in theprobability distribution of landslide size (area), and in landslide hazard. Analyzingresults in terms of rainfall thresholds and size distributions is a novel approachto investigate the effects of climate change on landslide hazard, which allows tomitigate the uncertainties introduced by the specific modeling chain adopted in thisstudy. Results showed that the downscaled rainfall fields obtained by RainFARM can be used tosingle out rainfall events, and to force the slope stability model. Results further showedthat while the rainfall thresholds for landslide occurrence are expected to changein future scenarios, the probability distribution of landslide areas is not. We inferthat landslide hazard in the study area is expected to change in response to theprojected variations in the rainfall conditions, within the scenario considered in thisstudy. We expect our results to contribute to regional investigations of the expectedimpact of projected climate variations on slope stability conditions and on landslidehazards. [ABSTRACT FROM AUTHOR]

We describe an automatic procedure for the classification of satellite imagery into landslideor no landslide categories, aimed at preparing inventory maps for a landslide event. Wedevised a two-steps procedure, which requires knowledge of the occurrence of a landslideevent, availability of a pre- and post- event pseudo-stereo pair and a digital elevation model.The first step consists in the evaluation of a discriminant function, applied to acombination of well-known change detection indices tuned on landslide spectralresponse. The second step is devoted to discriminant function classification, aimed atdistinguishing the only landslide class, through an improvement of the usual 'thresholding’method. The novel feature of the approach is represented by the use of slope units astopographic-aware subsets of the scene within which we apply a multiple thresholdingmethod to classify a landslide class membership tuned on the sole landslide spectralresponse. SUs are morphological terrain units, bounded by drainage and divide lines delineated insuch a way that terrain homogeneity is maximized within the units, and inhomogeneity ismaximized across neighboring units. We obtained SUs for our study area using ther.slopeunits specialized software. The software is adaptive, in that SUs are delineated withvarying sizes and shapes in different regions of the study area. SUs are particularly suited inthe present context, since they encompass areas with similar slope-facing direction(aspect), accounting for the fact that locations located in regions homogeneouslyfacing the same direction likely provide consistent spectral response in satelliteimagery. The proposed method was tested in an area of about 1000 m2 in Myanmar, wheretorrential rainfall triggered extensive landslides in 2015, which made the news due to theoccurrence of the massive Tonzang landslide and the large number of fatalities. Results of ourautomatic mapping were calibrated and validated against a landslide inventory map preparedthrough photo-interpretation by expert geomorphologists. The numerical results of thecomparison of the automatic, multi-threshold mapping procedure with the ground-truth of theinventory map prepared by visual interpretation reveal that the topographic-aware subdivisionof the territory allows for a better classification performance both than thresholding appliedglobally, or within a topographic-blind subdivision. This is particularly true in the validationarea, where the grid-based method shows little gain with respect to the global thresholdingmethod. The method is fully automatic after site-dependent operations, required only once, areperformed, and exhibits improved classification performance with limited trainingrequirements. We argue that the improved classification performance and limited trainingrequirements represent a step forward towards an automatic, real-time landslide mappingfrom satellite imagery. [ABSTRACT FROM AUTHOR]

• Rainfall thresholds for shallow landslides were defined in data-scarce basins. • Two physically-based methods were validated comparing with triggering rainfall events. • One method is devised at catchment scale and the other is for individual grid cells. • Both methods are potentially good tools for landslide early warning systems. Rainfall thresholds are intensity-duration relations that are supposedly able to distinguish precipitation events that may or may not trigger landslides. The most common method for defining rainfall thresholds relies on observed landslides and the corresponding values of rainfall intensity and duration that caused each failure. Alternative methods to define rainfall thresholds, using physically-based models, recently gained importance, as they may provide complementary information to other methods. Still, their applicability in most of the world's regions, including the mountainous basins of the Colombian Andes, has not been demonstrated or validated. In this study, we evaluated the applicability of the physically-based model TRIGRS to define rainfall intensity and duration thresholds in individual basins from the Colombian Andes. We obtained rainfall thresholds using two different methods and compared them with landslide-triggering rainfall events in two distinct basins, namely La Arenosa and La Liboriana. Furthermore, we used a (presumably incomplete) landslide database from Medellín to rebuild the rainfall events associated with individual landslides and compared them with the physically-based thresholds. The rainfall thresholds calculated in the three study areas and the applicability of the methods in data-scarce environments were assessed. Results showed that both methods for defining rainfall intensity and duration thresholds have merits and represent potential tools for improving or complementing landslide early warning systems, especially in data-scarce regions. [ABSTRACT FROM AUTHOR]