Comparing landslide inventory maps

Earth ArXiv preprint, 2022

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 build one susceptibility map for each type. The analysis-carried out by using a Bayeian 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 datases being assembled from twenty sub-inventories, each prepared by different administrations of the Italian regions. As a result, this spatial inhonomegenity may lead to a 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 suggested by the multiple intercepts results. 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.

2002

Evidence exists that the statistics of landslides triggered by extreme natural events exhibit a "universal" behaviour, where for increasing landslide area, the frequency of landslides increases to a maximum value and then decreases following a power law. This allows us to make quantitative comparisons of populations of triggered landslide events obtained from landslide inventory maps. We first discuss the characteristics and limitations of landslide inventory maps, followed by a presentation of three recent landslide event inventories in central and northern Italy. For each inventory, we calculate the probability and frequency densities of landslide areas, compare these results to each other and with the "universal" landslide distribution, and finally estimate each inventory's landslide event magnitude.

Earth-Science Reviews, 2022

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.

Event landslide inventory maps document the extent of populations of landslides caused by a single natural trigger, such as an earthquake, an intense rainfall event, or a rapid snowmelt event. Event inventory maps are important for landslide susceptibility and hazard modelling, and prove useful to manage residual risk after a landslide-triggering event. Standards for the preparation of event landslide inventory maps are lacking. Traditional methods are based on the visual interpretation of stereoscopic aerial photography, aided by field surveys. New and emerging techniques exploit remotely sensed data and semi-automatic algorithms. We describe the production and comparison of two independent event inventories prepared for the Pogliaschina catchment, Liguria, Northwest Italy. The two inventories show landslides triggered by an intense rainfall event on 25 October 2011, and were prepared through the visual interpretation of digital aerial photographs taken 3 days and 33 days after the event, and by processing a very-high-resolution image taken by the WorldView-2 satellite 4 days after the event. We compare the two inventories qualitatively and quantitatively using established and new metrics, and we discuss reasons for the differences between the two landslide maps. We expect that the results of our work can help in deciding on the most appropriate method to prepare reliable event inventory maps, and outline the advantages and the limitations of the different approaches. Published by Copernicus Publications on behalf of the European Geosciences Union. A. C. Mondini et al.: Comparison of event landslide inventories

Landslides, 2005

The purpose of the so-called IFFI project (Inventario dei Fenomeni Franosi in Italia -Inventory of Landslides in Italy) and of many other related activities carried out by the Centro Regionale per le Ricerche Territoriali e Geologiche of ARPA Piemonte (Agenzia Regionale per la Protezione Ambientale -Regional Agency for Environmental Protection), is to map all existing landslides in Piemonte (including both results of monitoring data and available historical data). ARPA carried out new systematic surveys using airphoto interpretation and created a specific alphanumeric GIS-based database to store and process all of the collected data.

Natural Hazards and Earth System Sciences, 2006

We present the results of the application of a recently proposed model to determine landslide hazard. The model predicts where landslides will occur, how frequently they will occur, and how large they will be in a given area. For the Collazzone area, in the central Italian Apennines, we prepared a multi-temporal inventory map through the interpretation of multiple sets of aerial photographs taken between 1941 and 1997 and field surveys conducted in the period between 1998 and 2004. We then partitioned the 79 square kilometres study area into 894 slope units, and obtained the probability of spatial occurrence of landslides by discriminant analysis of thematic variables, including morphology, lithology, structure and land use. For each slope unit, we computed the expected landslide recurrence by dividing the total number of landslide events inventoried in the terrain unit by the time span of the investigated period. Assuming landslide recurrence was constant, and adopting a Poisson probability model, we determined the exceedance probability of having one or more landslides in each slope unit, for different periods. We obtained the probability of landslide size, a proxy for landslide magnitude, by analysing the frequency-area statistics of landslides, obtained from the multi-temporal inventory map. Lastly, assuming independence, we determined landslide hazard for each slope unit as the joint probability of landslide size, of landslide temporal occurrence, and of landslide spatial occurrence.

Earth-Science Reviews, 2012

Landslides are present in all continents, and play an important role in the evolution of landscapes. They also represent a serious hazard in many areas of the world. Despite their importance, we estimate that landslide maps cover less than 1% of the slopes in the landmasses, and systematic information on the type, abundance, and distribution of landslides is lacking. Preparing landslide maps is important to document the extent of landslide phenomena in a region, to investigate the distribution, types, pattern, recurrence and statistics of slope failures, to determine landslide susceptibility, hazard, vulnerability and risk, and to study the evolution of landscapes dominated by mass-wasting processes. Conventional methods for the production of landslide maps rely chiefly on the visual interpretation of stereoscopic aerial photography, aided by field surveys. These methods are time consuming and resource intensive. New and emerging techniques based on satellite, airborne, and terrestrial remote sensing technologies, promise to facilitate the production of landslide maps, reducing the time and resources required for their compilation and systematic update. In this work, we first outline the principles for landslide mapping, and we review the conventional methods for the preparation of landslide maps, including geomorphological, event, seasonal, and multi-temporal inventories. Next, we examine recent and new technologies for landslide mapping, considering (i) the exploitation of very-high resolution digital elevation models to analyze surface morphology, (ii) the visual interpretation and semiautomatic analysis of different types of satellite images, including panchromatic, multispectral, and synthetic aperture radar images, and (iii) tools that facilitate landslide field mapping. Next, we discuss the advantages and the limitations of the new remote sensing data and technology for the production of geomorphological, event, seasonal, and multi-temporal inventory maps. We conclude by arguing that the new tools will help to improve the quality of landslide maps, with positive effects on all derivative products and analyses, including erosion studies and landscape modeling, susceptibility and hazard assessments, and risk evaluations.

Journal of Maps, 2013

Landslides are one of the most widespread natural hazards in many areas of Calabria region (Southern Italy), due to the combination of its peculiar geological, morphological, and climatic characteristics and very often to unsustainable land management. This study reports the reconnaissance and the characterization of landslides of northeastern Calabria (south Italy). The landslide inventory map was obtained by combining field surveys with the analysis of topographic maps and multi-temporal air photos, taken in the period ranging from 1954 to 2006. This analysis has provided the spatial and temporal evolution of mass movements. The integration and elaboration of the data obtained in a GIS environment provided the inventory map of landslides on a scale 1:50,000. Landslides are widespread in the study area and play an important role in the present-day landscape evolution. A total of 1003 landslides were recognized, occupying a surface of 230.4 km 2 , about 30.5% of the whole study area. The landslides were mapped on the basis of the movement type, as follows: slides, flows, falls and complex landslides. Slide and complex type massmovements are very common, and represent more than 87% of the landslides mapped. The pelitic lithologies show the highest density of landslides, mainly complex type. Multitemporal air photo interpretation and field surveys provided data for distinguishing the state of activity of the landslides; therefore, 29% of the landslides mapped has been assessed active while the remaining 71% has been considered inactive. This kind of map is an useful tool for land planning policy. As all the data are digitized and stored in GIS database, this will provide the basic input needed to generate the landslide susceptibility assessments besides evaluate the landslide hazard and risk.

Environmental and Engineering Geoscience, 2004

basis of the number and rank of the preparatory factors. The final landslide susceptibility classes, defined by a logical and easily replicable procedure, are considered to be useful in the decision-making procedures associated with territorial planning.

2013

Landslide inventories, their accuracy and the stored information are of major importance for landslide susceptibility modelling. Working on the scale of a province (Lower Austria with about 10,000 km²) challenges arise due to data availability and its spatial representation. Furthermore, previous studies on existing landslide inventories showed that only few inventories can be used for statistical susceptibility modelling. In this study two landslide inventories and their resulting susceptibility maps are compared: the Building Ground Register (BGR) of the Geological Survey of Lower Austria and an inventory that was mapped on the basis of a high resolution LiDAR DTM. This analysis was performed to estimate minimum requirements on landslide inventories to allow for deriving reliable susceptibility maps while minimizing mapping efforts. Therefore a consistent landslide inventory once from the BGR and once from the mapping was compiled. Furthermore, a logistic regression model was fitted with randomly selected points of each landslide inventory to compare the resulting maps and validation rates. The resulting landslide susceptibility maps show significant differences regarding their visual and statistical quality. We conclude that the application of randomly selected points in the main scarp of the mapped landslides gives satisfactory results.