Large landslides and deep-seated gravitational slope deformations in the Czech Flysch Carpathians: New LiDAR-based inventory

Pánek, Tomáš; Břežný, Michal; Kapustová, Veronika; Lenart, Jan; Chalupa, V.

doi:10.1016/j.geomorph.2019.106852

Cited by 48 publications

(30 citation statements)

References 81 publications

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“…The fault data were derived from a 1:200,000 Chinese active tectonic map [55].Using GIS software, all of the influencing factor maps were transformed into a raster format with a grid cell of 12.5 × 12.5 m. These influencing factor maps are shown in Figure 8. In this study, the probability density was used to describe the spatial distribution of landslides in each influencing factor in the study area, which is expressed as [56,57].…”

Section: Data and Methods For Further Analysismentioning

confidence: 99%

Inventory, Distribution and Geometric Characteristics of Landslides in Baoshan City, Yunnan Province, China

Shao

et al. 2020

Sustainability

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Inventorying landslides in mountainous areas is of great importance for prevention of geologic hazards. This study aimed to establish a detailed landslide inventory of Baoshan City, Yunnan Province, China, based on a large set of high-resolution satellite images from Google Earth. The landslides of this region were divided into two groups, i.e., recent landslides and old landslides. The spatial distribution and geometric characteristics of the two kinds of landslides were analyzed, respectively. Results show that 2427 landslides are present in the study area, including 2144 recent landslides and 283 old landslides with a total area of 7.2 km2 and 97.6 km2, respectively. The recent landslides occurred primarily at steep slopes with higher elevation, while old landslides took place at gentle terrains. For the slope position, most landslides, whether old or recent, cluster near ridges. The lower boundary of the recent landslides is far away from the valley, while the accumulation area of the old landslide is closer to the valley. The H/L (height to length) ratios are basically the same for all landslides, ranging from 0.2 to 0.5. Old landslides have larger mobility, as their travel distances are longer than recent landslides at the same height. The results would be helpful for further understanding the development and spatial distribution of the landslides in Southwest China, and also provide essential support for the subsequent landslide susceptibility mapping and geologic hazard assessment in this area.

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Section: Data and Methods For Further Analysismentioning

confidence: 99%

Inventory, Distribution and Geometric Characteristics of Landslides in Baoshan City, Yunnan Province, China

Shao

et al. 2020

Sustainability

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“…For example, for inventory and landslide detection it is more suitable to use Very High-Resolution (VHR, 0-5 m) and High-resolution (5-20 m) optical data (Table 1) with the possibility of Pan-sharpening (Nichol and Wong 2005b) or to improve historical and recent satellite images with the use of super-resolution algorithms (Lanaras et al 2018) and VHR orthoimages. But, for an exhaustive inventory, it is sometimes necessary to combine sources of images because of the incomplete spatial coverage (Shafique et al 2016), multitemporal images (Fan et al 2018) and historical inventories (Catani et al 2005;Ardizzone et al 2007;Arabameri et al 2019;Pánek et al 2019). The interest of satellite images time series is to detect examples of past landslides which may be remodelled by anthropogenic action or progressively hidden by vegetation.…”

Section: Tablementioning

confidence: 99%

“…Interpretation of aerial photos or satellite images are approaches commonly used to identify past and recent mass movements (Chigira et al 2004;Catani et al 2005;Ardizzone et al 2007;Galli et al 2008;Yang and Chen 2010;Song et al 2012;Chen et al 2014;Xu et al 2014;Zhang et al 2014;Ciampalini et al 2015;Fressard et al 2016;Fan et al 2017Fan et al , 2018Roulland et al 2019;Bui et al 2019;Görüm 2019;Lewkowicz and Way 2019;Pánek et al 2019;Pham et al 2019;Wang et al 2019;Du et al 2020). Based on morphological features of the landscape and visible 'anomalies', visual interpretation of images can be faster than the ground survey approach to identify mass movement.…”

Section: Data Interpretationmentioning

confidence: 99%

Remote Sensing for Assessing Landslides and Associated Hazards

et al. 2020

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Multi-platform remote sensing using space-, airborne and ground-based sensors has become essential tools for landslide assessment and disaster-risk prevention. Over the last 30 years, the multiplicity of Earth Observation satellites mission ensures uninterrupted optical and radar imagery archives. With the popularization of Unmanned Aerial Vehicles, free optical and radar imagery with high revisiting time, ground and aerial possibilities to perform high-resolution 3D point clouds and derived digital elevation models, it can make it difficult to choose the appropriate method for risk assessment. The aim of this paper is to review the mainstream remote-sensing methods commonly employed for landslide assessment, as well as processing. The purpose is to understand how remote-sensing techniques can be useful for landslide hazard detection and monitoring taking into consideration several constraints such as field location or costs of surveys. First we focus on the suitability of terrestrial, aerial and spaceborne systems that have been widely used for landslide assessment to underline their benefits and drawbacks for data acquisition, processing and interpretation. Several examples of application are presented such as Interferometry Synthetic Aperture Radar (InSAR), lasergrammetry, Terrestrial Optical Photogrammetry. Some of these techniques are unsuitable for slow moving landslides, others limited to large areas and others to local investigations. It can be complicated to select the most appropriate system. Today, the key for understanding landslides is the complementarity of methods and the automation of the data processing. All the mentioned approaches can be coupled (from field monitoring to satellite images analysis) to improve risk management, and the real challenge is to improve automatic solution for landslide recognition and monitoring for the implementation of near real-time emergency systems.

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“…(Němčok, 1982; (Figure 1 (a)). The largest number and density of mass movements are however associated with moderate-relief dominated landscape of the Outer Western Carpathians (Flysch Carpathians) (Figure 1(a)), which are known as one of the most landslide-prone regions in Europe (Margielewski, 2006a;Mrozek et al, 2014;Pánek et al, 2019). Landforms in the Outer Western Carpathians are strongly influenced by geological structure, dominated by thrusted-and-folded sequences of turbiditic rocks (flysch) (Margielewski, 2006a(Margielewski, , 2006b).…”

Section: Introductionmentioning

confidence: 99%

Geomorphology of the high-elevated flysch range – Mt. Babia Góra Massif (Western Carpathians)

Kłapyta

2020

Journal of Maps

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A detailed geomorphological map of the Mt. Babia Góra Massif (1725 m a.s.l.), at a scale 1:10,000 is presented. A slope, glacial, periglacial and fluvial features were mapped on the base of the coupled field studies and LiDAR DEM analyses. This study underlines the complexity of rock slope failures (RSFs), in shaping the morphology of compact and isolated upland, exhibiting one of the highest incidences of RSFs yet recorded in Europe (29% for a whole massif, and 45% for the north face). A total of 212 RSFs were mapped of which 18 failures are large landslides (>0.25 km 2), among them is one of the largest known (2.6 km 2 and 150 × 106 m 3) in the Polish Flysch Carpathians. The mapping and Schmidt-hammer results shed light on the problem of glacial relief of the massif, documenting the remnants of glacial deposits beyond the tongues of landslides in the Szumiąca Woda valley.

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Large landslides and deep-seated gravitational slope deformations in the Czech Flysch Carpathians: New LiDAR-based inventory

Cited by 48 publications

References 81 publications

Inventory, Distribution and Geometric Characteristics of Landslides in Baoshan City, Yunnan Province, China

Inventory, Distribution and Geometric Characteristics of Landslides in Baoshan City, Yunnan Province, China

Remote Sensing for Assessing Landslides and Associated Hazards

Geomorphology of the high-elevated flysch range – Mt. Babia Góra Massif (Western Carpathians)

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