Suchergebnisse

Abstract. Historical rockfall catalogues are important data sources for the investigation of the temporal occurrence of rockfalls, which is crucial
information for rockfall hazard and risk assessments. However, such catalogues are rare and often incomplete. Here, we selected and analysed
seven catalogues of historical rockfalls in Austria, Italy, and the USA to
highlight existing relationships between data collection and mapping methods and representativeness of the resulting rockfall records. Heuristic and simple statistically based frequency analysis methods are applied to describe and compare the different historical rockfall catalogues. Our
results show that the mapping strategy may affect the frequency of the assessed rockfall occurrence and the completeness and representativeness of the related time series of historical rockfalls. We conclude by presenting the advantages and limitations of the application of different frequency-based methods for analysing rockfall catalogues and providing recommendations for rockfall mapping. We furthermore present non-parametric statistical methods for dealing with typically small rockfall datasets, which are particularly suited for the characterization of basic rockfall catalogues. Such recommendations should help in the definition of standards for collecting and using temporal rockfall data in hazard and risk assessments.

Abstract. 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.

Nougat is a confectionery foodstuff made of whipped egg white, sugar, honey, and nuts. In a traditional Italian nougat recipe, the mixture is flavoured with vanilla extract, and packed with toasted almonds. Traditional nougat is banned from the diets of diabetics due to high amount of sugar. In the present work, we aimed to develop innovative sugar-free nougat, and compare its textural and sensory properties with those of commercial nougat. Finally, we tested the residual hyperglycaemic effect in non-obese diabetic (NOD) mice, a model of type 1 diabetes. We developed a sugar-free nougat recipe by mixing xanthan gum, erythritol, and inulin with egg white and almonds using conventional industrial instruments. Technical analysis indicated that the structure, in terms of shear force, was comparable with that of traditional chewy nougat. Sensory analysis indicated that flavour and sweetness were preserved, whereas cohesiveness and fracturability changed significantly. Interestingly, the innovative food composition positively influenced two other texture parameters; solubility and adhesiveness. In vivo experiments showed that the number of mice in the group that was fed with experimental nougat that did not develop diabetes was significantly higher than that in the group fed with commercial nougat (66.6% vs. 33.0%; p < 0.05), and not different from that in the group fed without nougat (72.7%; p = 0.37). In conclusion, we produced, on a pilot scale, innovative sugar-free nougat with improved texture and similar sensory properties, in comparison with the traditional product. In vivo, the experimental nougat did not increase the diabetes incidence significantly.

Abstract. Over the last 2 decades, the topic of earthquake-triggered landslides (EQTLs) has shown increasing relevance in the scientific community. This interest is confirmed by the numerous articles published in international, peer-reviewed journals. In this work we present a database containing a selection of articles published on this topic from 1984 to 2021. The articles were selected through a systematic search on the Clarivate™ Web of Science™ Core Collection online platform and were catalogued into a web-based GIS (web-GIS), which was specifically designed to show different types of information. After a general analysis of the database, for each article the following aspects were identified: the bibliometric information (e.g. author(s), title, publication year), the relevant topic and sub-topic category (or categories), and the earthquake(s) addressed. The analysis allowed us to infer general information and statistics on EQTLs (e.g. relevant methodological approaches over time and in relation to the scale of investigation, most studied events), which can be useful to obtain a spatial distribution of the articles and a general overview of the topic.

Abstract. Landslides leave discernible signs on the land surface, most of which can be
captured in remote sensing images. Trained geomorphologists analyse remote
sensing images and map landslides through heuristic interpretation of
photographic and morphological characteristics. Despite a wide use of remote
sensing images for landslide mapping, no attempt to evaluate how the image
characteristics influence landslide identification and mapping exists. This
paper presents an experiment to determine the effects of optical image
characteristics, such as spatial resolution, spectral content and image type
(monoscopic or stereoscopic), on landslide mapping. We considered eight maps
of the same landslide in central Italy: (i) six maps obtained through expert
heuristic visual interpretation of remote sensing images, (ii) one map
through a reconnaissance field survey, and (iii) one map obtained through a
real-time kinematic (RTK) differential global positioning system (dGPS)
survey, which served as a benchmark. The eight maps were compared pairwise
and to a benchmark. The mismatch between each map pair was quantified by
the error index, E. Results show that the map closest to the benchmark
delineation of the landslide was obtained using the higher resolution image,
where the landslide signature was primarily photographical (in the landslide
source and transport area). Conversely, where the landslide signature was
mainly morphological (in the landslide deposit) the best mapping result was
obtained using the stereoscopic images. Albeit conducted on a single
landslide, the experiment results are general, and provide useful
information to decide on the optimal imagery for the production of event,
seasonal and multi-temporal landslide inventory maps.

Abstract. The increasing availability of free-access satellite data represents a relevant opportunity for the analysis and assessment of natural hazards. The systematic acquisition of spaceborne imagery allows for monitoring areas prone to geohydrological disasters, providing relevant information for risk evaluation and management. In cases of major landslide events, for example, spaceborne radar data can provide an effective solution for the
detection of slope failures, even in cases with persistent cloud cover. The
information about the extension and location of the landslide-affected areas may support decision-making processes during emergency responses. In this paper, we present an automatic procedure based on Sentinel-1
Synthetic Aperture Radar (SAR) images, aimed at facilitating the detection of
landslides over wide areas. Specifically, the procedure evaluates changes of radar backscattered signals associated with land cover modifications that may be also caused by mass movements. After a one-time calibration of some
parameters, the processing chain is able to automatically execute the
download and preprocessing of images, the detection of SAR amplitude
changes, and the identification of areas potentially affected by landslides, which are then displayed in a georeferenced map. This map should help decision makers and emergency managers to organize field investigations. The process of automatization is implemented with specific scripts running on a GNU/Linux operating system and exploiting modules of open-source software. We tested the processing chain, in back analysis, on an area of about 3000 km2 in central Papua New Guinea that was struck by a severe seismic sequence in February–March 2018. In the area, we simulated a periodic survey of about 7 months, from 12 November 2017 to 6 June 2018, downloading 36 Sentinel-1 images and performing 17 change detection analyses automatically. The procedure resulted in statistical and graphical evidence of widespread land cover changes that occurred just after the most severe seismic events. Most of the detected changes can be interpreted as mass movements triggered by the seismic shaking.

Abstract. The inhabited zone of the Ugandan Rwenzori Mountains is affected by landslides, frequently causing loss of life, damage to infrastructure and loss of livelihood. This area of ca. 1230 km2 is characterized by contrasting geomorphologic, climatic and lithological patterns, resulting in different landslide types. In this study, the spatial pattern of landslide susceptibility is investigated based on an extensive field inventory constructed for five representative areas within the region (153 km2) and containing over 450 landslides. To achieve a reliable susceptibility assessment, the effects of (1) using different topographic data sources and spatial resolutions and (2) changing the scale of assessment by comparing local and regional susceptibility models on the susceptibility model performances are investigated using a pixel-based logistic regression approach. Topographic data are extracted from different digital elevation models (DEMs) based on radar interferometry (SRTM and TanDEM-X) and optical stereophotogrammetry (ASTER DEM). Susceptibility models using the radar-based DEMs tend to outperform the ones using the ASTER DEM. The model spatial resolution is varied between 10, 20, 30 and 90 m. The optimal resolution depends on the location of the investigated area within the region but the lowest model resolution (90 m) rarely yields the best model performances while the highest model resolution (10 m) never results in significant increases in performance compared to the 20 m resolution. Models built for the local case studies generally have similar or better performances than the regional model and better reflect site-specific controlling factors. At the regional level the effect of distinguishing landslide types between shallow and deep-seated landslides is investigated. The separation of landslide types allows us to improve model performances for the prediction of deep-seated landslides and to better understand factors influencing the occurrence of shallow landslides such as tangent curvature and total rainfall. Finally, the landslide susceptibility assessment is overlaid with a population density map in order to identify potential landslide risk hotspots, which could direct research and policy action towards reduced landslide risk in this under-researched, landslide-prone region.

Abstract. In karst environments, heavy rainfall is known to cause multiple geohydrological hazards, including inundations, flash floods, landslides and sinkholes. We studied a period of intense rainfall from 1 to 6 September 2014 in the Gargano Promontory, a karst area in Puglia, southern Italy. In the period, a sequence of torrential rainfall events caused severe damage and claimed two fatalities. The amount and accuracy of the geographical and temporal information varied for the different hazards. The temporal information was most accurate for the inundation caused by a major river, less accurate for flash floods caused by minor torrents and even less accurate for landslides. For sinkholes, only generic information on the period of occurrence of the failures was available. Our analysis revealed that in the promontory, rainfall-driven hazards occurred in response to extreme meteorological conditions and that the karst landscape responded to the torrential rainfall with a threshold behaviour. We exploited the rainfall and the landslide information to design the new ensemble–non-exceedance probability (E-NEP) algorithm for the quantitative evaluation of the possible occurrence of rainfall-induced landslides and of related geohydrological hazards. The ensemble of the metrics produced by the E-NEP algorithm provided better diagnostics than the single metrics often used for landslide forecasting, including rainfall duration, cumulated rainfall and rainfall intensity. We expect that the E-NEP algorithm will be useful for landslide early warning in karst areas and in other similar environments. We acknowledge that further tests are needed to evaluate the algorithm in different meteorological, geological and physiographical settings.