Landslides types controlled by tectonics-induced evolution of valley slopes (Northern Apennines, Italy)

Carlini, Mirko; Chelli, Alessandro; Francese, Roberto; Giacomelli, Serena; Giorgi, M.; Quagliarini, Andrea; Carpena, Andrea; Tellini, Claudio

doi:10.1007/s10346-017-0871-2

Cited by 21 publications

(6 citation statements)

References 27 publications

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“…The occurrence of flow-prone rocks, such as varicolored clays, and the tectonic arrangement of the area represents the main predisposing factor of landslide development, as observed in other geological settings [74][75][76][77][78][79][80][81].…”

Section: Discussionmentioning

confidence: 88%

Morphological Changes Detection of a Large Earthflow Using Archived Images, LiDAR-Derived DTM, and UAV-Based Remote Sensing

Conforti

Mercuri²,

Borrelli

2020

Remote Sensing

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In mountainous landscapes, where strongly deformed pelitic sediments outcrop, earthflows can dominate denudation processes and landscape evolution. This paper investigated geological and geomorphological features and space-time evolution over a 65-year time span (1954–2019) of a large earthflow, representative of wide sectors of the Apennine chain of southern Italy. The landslide, with a maximum length of 1.85 × 103 m, affects an area of 4.21 × 105 m2 and exhibits two source zones: a narrow and elongated transport zone and a lobate accumulation zone. Spatial and temporal morphological changes of the earthflow were assessed, comparing multi-source and multi-temporal data (aerial photographs, Google Earth satellite images, Light Detection and Ranging (LiDAR) and Unmanned Aerial Vehicles (UAV) system data). Geomorphic changes, quantified using Digital Terrain Models (DTMs) of differences, highlighted an extensive lowering of the topographic surface in the source area and a significant uplift at the landslide toe. Moreover, the multi-temporal analysis showed a high increase of landslide surface (more than 66%) during the last 65 years. The volumetric analyses showed that different sectors of the earthflow were active at different times, with different rates of topographic change. Overall, the used approach highlighted the great potentiality of the integration of multi-source and multi-temporal data for the diachronic reconstruction of morphological landslide evolution.

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Section: Discussionmentioning

confidence: 88%

Morphological Changes Detection of a Large Earthflow Using Archived Images, LiDAR-Derived DTM, and UAV-Based Remote Sensing

Conforti

Mercuri²,

Borrelli

2020

Remote Sensing

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“…As a complex process of material transport and energy transfer on the earth's surface, the formation and distribution of landslides are determined by the effects of climate, hydrology, geology, landforms, human activity, and other factors [46]. To a large extent, the formation and distribution of landslides is related to specific local factors; for example, in orogenic belts, slope, lithology, and structure are important factors [47,48], while…”

Section: Selection Of Factors Influencing Landslidesmentioning

confidence: 99%

AI-Based Susceptibility Analysis of Shallow Landslides Induced by Heavy Rainfall in Tianshui, China

Zhao

Meng

et al. 2021

Remote Sensing

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Groups of landslides induced by heavy rainfall are widely distributed on a global basis and they usually result in major losses of human life and economic damage. However, compared with landslides induced by earthquakes, inventories of landslides induced by heavy rainfall are much less common. In this study we used high-precision remote sensing images before and after continuous heavy rainfall in southern Tianshui, China, from 20 June to 25 July 2013, to produce an inventory of 14,397 shallow landslides. Based on the results of landslide inventory, we utilized machine learning and the geographic information system (GIS) to map landslide susceptibility in this area and evaluated the relative weight of various factors affecting landslide development. First, 18 variables related to geomorphic conditions, slope material, geological conditions, and human activities were selected through collinearity analysis; second, 21 selected machine learning models were trained and optimized in the Python environment to evaluate the susceptibility of landslides. The results showed that the ExtraTrees model was the most effective for landslide susceptibility assessment, with an accuracy of 0.91. This predictive ability means that our landslide susceptibility results can be used in the implementation of landslide prevention and mitigation measures in the region. Analysis of the importance of the factors showed that the contribution of slope aspect (SA) was significantly higher than that of the other factors, followed by planar curvature (PLC), distance to river (DR), distance to fault (DTF), normalized difference vehicle index (NDVI), distance to road (DTR), and other factors. We conclude that factors related to geomorphic conditions are principally responsible for controlling landslide susceptibility in the study area.

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“…Morphometric analysis, air photo interpretation, and airborne Lidar have been used to determine the geomorphic imprints of large landslides on hillslopes in such regions (Korup, 2006;Haneberg et al, 2009;Guzzetti et al, 2012;Pánek and Klimeš, 2016). In an orogenic belt, the formation, evolution, geomorphic characteristics and spatial distribution of large landslides are closely related to regional tectonic activity and local structure (Keefer, 1984;Pedrazzini et al, 2016;Borrelli and Gullà, 2017;Carlini et al, 2017;Cui et al, 2018). Previous research results have revealed that the control of landslides by tectonics is complex across time and space.…”

Section: Introductionmentioning

confidence: 99%

Distribution and characteristics of large landslides in a fault zone: A case study of the NE Qinghai-Tibet Plateau

Meng

Qing

et al. 2021

Geomorphology

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Landslides types controlled by tectonics-induced evolution of valley slopes (Northern Apennines, Italy)

Cited by 21 publications

References 27 publications

Morphological Changes Detection of a Large Earthflow Using Archived Images, LiDAR-Derived DTM, and UAV-Based Remote Sensing

Morphological Changes Detection of a Large Earthflow Using Archived Images, LiDAR-Derived DTM, and UAV-Based Remote Sensing

AI-Based Susceptibility Analysis of Shallow Landslides Induced by Heavy Rainfall in Tianshui, China

Distribution and characteristics of large landslides in a fault zone: A case study of the NE Qinghai-Tibet Plateau

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