Flow-type landslides in pyroclastic soils on flysch bedrock in southern Italy: the Bosco de’ Preti case study

In the last 20 years, major efforts have been made to investigate shallow flow-type landslides. Such phenomena are usually rainfall-induced and in the geological context of Campania (Southern Italy) occur in pyroclastic soils resting on steep slopes mainly constituted by carbonate or volcanic bedrock and by flysch deposits. They are generally complex landslides with an early soil slide and a subsequent flow evolution. In this paper, a database of flowslides occurring in recent years within the flysch deposits of Avellino (Campanian Apennines) is first discussed and then the case study of Bosco de’ Preti landslide on March 4, 2005, is described. The geological and geotechnical characteristics of the soils involved are described and the monitoring of the groundwater heads collected over 1 year from June 2005 to June 2006 is also shown. The last part of the paper illustrates the results of numerical modelling of the landslide triggering to gain insights into such phenomena. Slope stability analyses are preceded by hydrological modelling of the slope based on the monitoring data. Numerical analysis demonstrated that the rainfall during the 2 months preceding the event was able to fully saturate the pyroclastic cover and to establish positive pore water pressure at the depth of the surface of rupture, a soil condition never witnessed in carbonatic contexts. Hence, a combination of antecedent (predisposing factors) and single rainfall events (triggering factors) led to slope failure, as usually happens in pyroclastic soils in carbonatic and volcanic contexts. Finally, analysis of the historical landslides together with detailed investigation of the Bosco de’ Preti case study permitted comparison between flow-type landslides in pyroclastic soils on carbonatic/volcanic bedrock and those on flysch.     

Slope units-based flow susceptibility model: using validation tests to select controlling factors

A susceptibility map for an area, which is representative in terms of both geologic setting and slope instability phenomena of large sectors of the Sicilian Apennines, was produced using slope units and a multiparametric univariate model. The study area, extending for approximately 90 km², was partitioned into 774 slope units, whose expected landslide occurrence was estimated by averaging seven susceptibility values, determined for the selected controlling factors: lithology, mean slope gradient, stream power index at the foot, mean topographic wetness index and profile curvature, slope unit length, and altitude range. Each of the recognized 490 landslides was represented by its centroid point. On the basis of conditional analysis, the susceptibility function here adopted is the density of landslides, computed for each class. Univariate susceptibility models were prepared for each of the controlling factors, and their predictive performance was estimated by prediction rate curves and effectiveness ratio applied to the susceptibility classes. This procedure allowed us to discriminate between effective and non-effective factors, so that only the former was subsequently combined in a multiparametric model, which was used to produce the final susceptibility map. The validation of this map latter enabled us to verify the reliability and predictive performance of the model. Slope unit altitude range and length, lithology and, subordinately, stream power index at the foot of the slope unit demonstrated to be the main controlling factors of landslides, while mean slope gradient, profile curvature, and topographic wetness index gave unsatisfactory results.     

Statistical modelling of Europe-wide landslide susceptibility using limited landslide inventory data

In many regions, the absence of a landslide inventory hampers the production of susceptibility or hazard maps. Therefore, a method combining a procedure for sampling of landslide-affected and landslide-free grid cells from a limited landslide inventory and logistic regression modelling was tested for susceptibility mapping of slide- and flow-type landslides on a European scale. Landslide inventories were available for Norway, Campania (Italy), and the Barcelonnette Basin (France), and from each inventory, a random subsample was extracted. In addition, a landslide dataset was produced from the analysis of Google Earth images in combination with the extraction of landslide locations reported in scientific publications. Attention was paid to have a representative distribution of landslides over Europe. In total, the landslide-affected sample contained 1,340 landslides. Then a procedure to select landslide-free grid cells was designed taking account of the incompleteness of the landslide inventory and the high proportion of flat areas in Europe. Using stepwise logistic regression, a model including slope gradient, standard deviation of slope gradient, lithology, soil, and land cover type was calibrated. The classified susceptibility map produced from the model was then validated by visual comparison with national landslide inventory or susceptibility maps available from literature. A quantitative validation was only possible for Norway, Spain, and two regions in Italy. The first results are promising and suggest that, with regard to preparedness for and response to landslide disasters, the method can be used for urgently required landslide susceptibility mapping in regions where currently only sparse landslide inventory data are available.     

Landslide dam formation susceptibility analysis based on geomorphic features

Global climate change has increased the frequency of abnormally high rainfall; such high rainfall events in recent years have occurred in the mountainous areas of Taiwan. This study identifies historical earthquake- and typhoon-induced landslide dam formations in Taiwan along with the geomorphic characteristics of the landslides. Two separate groups of landslides are examined which are classified as those that were dammed by river water and those that were not. Our methodology applies spatial analysis using geographic information system (GIS) and models the geomorphic features with 20?×?20 m digital terrain mapping. The Spot 6 satellite images after Typhoon Morakot were used for an interpretation of the landslide areas. The multivariate statistical analysis is also used to find which major factors contribute to the formation of a landslide dam. The objective is to identify the possible locations of landslide dams by the geomorphic features of landslide-prone slopes. The selected nine geomorphic features include landslide area, slope, aspect, length, width, elevation change, runout distance, average landslide elevation, and river width. Our four geomorphic indexes include stream power, form factor, topographic wetness, and elevation–relief ratio. The features of the 28 river-damming landslides and of the 59 non-damming landslides are used for multivariate statistical analysis by Fisher discriminant analysis and logistic regression analysis. The principal component analysis screened out eleven major geomorphic features for landslide area, slope, aspect, elevation change, length, width, runout distance, average elevation, form factor, river width, stream power, and topography wetness. Results show that the correctness by Fisher discriminant analysis was 68.0 % and was 70.8 % by logistic regression analysis. This study suggests that using logistic regression analysis as the assessment model for identifying the potential location of a landslide dam is beneficial. Landslide threshold equations applying the geomorphic features of slope angle, angle of landslide elevation change, and river width (H _L/W _R) to identify the potential formation of natural dams are proposed for analysis. Disaster prevention and mitigation measures are enhanced when the locations of potential landslide dams are identified; further, in order to benefit such measures, dam volume estimates responsible for breaches are key.     

Landslide susceptibility mapping using rough sets and back-propagation neural networks in the Three Gorges, China

In the Three Gorges of China, there are frequent landslides, and the potential risk of landslides is tremendous. An efficient and accurate method of generating landslide susceptibility maps is very important to mitigate the loss of lives and properties caused by these landslides. This paper presents landslide susceptibility mapping on the Zigui-Badong of the Three Gorges, using rough sets and back-propagation neural networks (BPNNs). Landslide locations were obtained from a landslide inventory map, supported by field surveys. Twenty-two landslide-related factors were extracted from the 1:10,000-scale topographic maps, 1:50,000-scale geological maps, Landsat ETM + satellite images with a spatial resolution of 28.5 m, and HJ-A satellite images with a spatial resolution of 30 m. Twelve key environmental factors were selected as independent variables using the rough set and correlation coefficient analysis, including elevation, slope, profile curvature, catchment aspect, catchment height, distance from drainage, engineering rock group, distance from faults, slope structure, land cover, topographic wetness index, and normalized difference vegetation index. The initial, three-layered, and four-layered BPNN were trained and then used to map landslide susceptibility, respectively. To evaluate the models, the susceptibility maps were validated by comparing with the existing landslide locations according to the area under the curve. The four-layered BPNN outperforms the other two models with the best accuracy of 91.53 %. Approximately 91.37 % of landslides were classified as high and very high landslide-prone areas. The validation results show sufficient agreement between the obtained susceptibility maps and the existing landslide locations.     

Distribution of landslides in southwest New Zealand

This study examines the size distribution of a regional medium-scale inventory of 778 landslides in the mountainous southwest of New Zealand. The spatial density of mapped landslides per unit area can be expressed as a negative power–law function of Landslide area A_L spanning three orders of magnitude (10^–2–10¹ km²). Although observed in other studies on landslide inventories, this relationship is surprising, given the lack of absolute ages, and thus uncertainty about the temporal observation window encompassed by the data. Large slope failures (arbitrarily defined here as having a total affected area A_L>1 km²) constitute 83% of the total affected landslide area A_LT. This dominance by area affects slope morphology, where large-scale landsliding reduces slope angles below the regional modal value of hillslopes, _mod39°. More numerous smaller and shallower failures tend to be superimposed on the pre-existing relief. Empirical scaling relationships show that large landslides involve >10⁶ m³ of material. The volumes V_L of individual preserved and presumably prehistoric (i.e. pre-1840) landslide deposits equate to 10⁰–10² years of total sediment production from shallow landsliding in the respective catchments, and up to 10³ years of contemporary regional sediment yield from the mountain ranges. Their presence in an erosional landscape indicates the geomorphic importance of landslides as temporary local sediment storage.     

A distributed hydrological–geotechnical model using satellite-derived rainfall estimates for shallow landslide prediction system at a catchment scale

This paper describes the potential applicability of a hydrological–geotechnical modeling system using satellite-based rainfall estimates for a shallow landslide prediction system. The physically based distributed model has been developed by integrating a grid-based distributed kinematic wave rainfall-runoff model with an infinite slope stability approach. The model was forced by the satellite-based near real-time half-hourly CMORPH global rainfall product prepared by NOAA-CPC. The method combines the following two model outputs necessary for identifying where and when shallow landslides may potentially occur in the catchment: (1) the time-invariant spatial distribution of areas susceptible to slope instability map, for which the river catchment is divided into stability classes according to the critical relative soil saturation; this output is designed to portray the effect of quasi-static land surface variables and soil strength properties on slope instability and (2) a produced map linked with spatiotemporally varying hydrologic properties to provide a time-varying estimate of susceptibility to slope movement in response to rainfall. The proposed hydrological model predicts the dynamic of soil saturation in each grid element. The stored water in each grid element is then used for updating the relative soil saturation and analyzing the slope stability. A grid of slope is defined to be unstable when the relative soil saturation becomes higher than the critical level and is the basis for issuing a shallow landslide warning. The method was applied to past landslides in the upper Citarum River catchment (2,310 km²), Indonesia; the resulting time-invariant landslide susceptibility map shows good agreement with the spatial patterns of documented historical landslides (1985–2008). Application of the model to two recent shallow landslides shows that the model can successfully predict the effect of rainfall movement and intensity on the spatiotemporal dynamic of hydrological variables that trigger shallow landslides. Several hours before the landslides, the model predicted unstable conditions in some grids over and near the grids at which the actual shallow landslides occurred. Overall, the results demonstrate the potential applicability of the modeling system for shallow landslide disaster predictions and warnings.     

Numerical Modeling of the Seasonal Dynamic Characteristics of the Koxkar Glacier,in West Tianshan,China

Landslides commonly occurs in hilly areas and causes an enormous loss iof life and property every year. National highway-1D (NH-1D) is the only road link between the two districts (Kargil and Leh) of Ladakh region that connects these districts with Kashmir valley. The landslide failure record of the recent past along this sector of the highway is not available. The present study documents landslide susceptible zones and records occurrence of 60 landslides during the last 4 years showing an increasing trend in the occurrence of landslides over these years in this sector. The landslide susceptibility zonation map has been prepared based on the numerical rating of ten major factors viz. slope morphometry, lithology, structure, relative relief, land cover, landuse, rainfall, hydrological conditions, landslide incidences and Slope Erosion, categorised the area in different zones of instability based on the intensity of susceptibility. The landslide susceptibility map of the area encompassing 73.03 km² is divided into 150 facets. Out of the total of 150 facets, 85 facets fall in low susceptibility zone covering 43.56 km2 which constitute about 59.65% of the total area under investigation with a record of 5 landslides; 40 facets fall in the moderate susceptibility zone covering 16.94km² which constitutes about 23.19% of the study area with a record of 20 landslides; and 25 facets fall in the high susceptibility zone covering 12.53 km² which constitute about 17.15% of the study area with a record of 35 landslides. Most of the facets which fall in HSZ are attributed to slope modification for road widening.     

Landslides triggered by slipping-fault-generated earthquake on a plateau: an example of the 14 April 2010, Ms 7.1, Yushu, China earthquake

On 14 April 2010 at 07:49 (Beijing time), a catastrophic earthquake with Ms 7.1 struck Yushu County, Qinghai Province, China. A total of 2,036 landslides were interpreted from aerial photographs and satellite images, verified by selected field checking. These landslides cover about a total area of 1.194 km². The characteristics and failure mechanisms of these landslides are presented in this paper. The spatial distribution of the landslides is evidently strongly controlled by the locations of the main co-seismic surface fault ruptures. The landslides commonly occurred close together. Most of the landslides are small; there were only 275 individual landslide (13.5 % of the total number) surface areas larger than 1,000 m². The landslides are of various types. They are mainly shallow, disrupted landslides, but also include rock falls, deep-seated landslides, liquefaction-induced landslides, and compound landslides. Four types of factors are identified as contributing to failure along with the strong ground shaking: natural excavation of the toes of slopes, which mean erosion of the base of the slope, surface water infiltration into slopes, co-seismic fault slipping at landslide sites, and delayed occurrence of landslides due to snow melt or rainfall infiltration at sites where slopes were weakened by the co-seismic ground shaking. To analyze the spatial distribution of the landslides, the landslide area percentage (LAP) and landslide number density (LND) were compared with peak ground acceleration (PGA), distance from co-seismic main surface fault ruptures, elevation, slope gradient, slope aspect, and lithology. The results show landslide occurrence is strongly controlled by proximity to the main surface fault ruptures, with most landslides occurring within 2.5 km of such ruptures. There is no evident correlation between landslide occurrences and PGA. Both LAP and LND have strongly positive correlations with slope gradient, and additionally, sites at elevations between 3,800 and 4,000 m are relatively susceptible to landslide occurrence; as are slopes with northeast, east, and southeast slope aspects. Q₄ al-pl, N, and T₃ kn ¹ have more concentrated landslide activity than others. This paper provides a detailed inventory map of landslides triggered by the 2010 Yushu earthquake for future seismic landslide hazard analysis and also provides a study case of characteristics, failure mechanisms, and spatial distribution of landslides triggered by slipping-fault generated earthquake on a plateau.     

Geometrical characteristics of earthquake-induced landslides and correlations with control factors: a case study of the 2013 Minxian,Gansu, China, <Emphasis Type="Italic">Mw</Emphasis> 5.9 event

Geometric parameters are useful for characterizing earthquake-triggered landslides. This paper presents a detailed statistical analysis on this issue using the landslide inventory of the 2013, Minxian, China Mw 5.9 earthquake. Based on GIS software and a 5-m resolution DEM, geometric parameters of 635 coseismic landslides (with areas larger than 500 m²) were obtained, including height, length, width, reach angle (arc tangent of the height-length ratio), and aspect ratio (length-width ratio). The fitting relationship of height and length from these data is H = 0.6164L + 0.4589, with an average reach angle of 31.65°. The landslide aspect ratios concentrate in the range of 1.4～2.6, with an average of 2.11. According to the plane geometric shapes and aspect ratios, the landslides are classified into four categories: transverse landslide (LA1, L/W ≤ 0.8), isometric landslide (LA2, 0.8 < L/W ≤ 1.2), longitudinal landslide (LA3, 1.2 < L/W ≤ 3), and elongated landslide (LA4, L/W > 3). Statistics of these four types of landslides versus ten classified control factors (elevation, slope angle, slope aspect, curvature, slope position, distance to drainages, lithology, seismic intensity, peak ground acceleration, and distance to seismogenic fault) are used to examine their possible correlations and the landslide-prone areas, which would be helpful to the landslide disaster mitigation in the affected area.     

河北省顺平县地质灾害发育特征及易发性评价

河北省顺平县位于太行山东麓,主要发育有崩塌、滑坡、泥石流、地裂缝等四种类型地质灾害。基于该县地质灾害发育特征,采用GIS技术和信息量数学评价模型,选取坡度、起伏度、坡向、工程地质岩组、归一化植被指数（NDVI）、与河流的距离6个评价因子,对顺平县地质灾害进行易发性评价。评价结果显示:高易发区面积约为125 km2,占全县总面积的17.5%,分散分布于西北部的中低山和丘陵地区,密集发育崩塌灾害,少量发育滑坡、泥石流灾害;中易发区面积约为200 km2,占全县总面积的28.0%,成片分布于西北部的中低山和丘陵地区,少量发育崩塌、滑坡、泥石流灾害;低易发区面积约为389 km2,占全县总面积的54.5%,主要分布于东南部的平原地区及西北部丘陵地区内的宽阔沟谷,沿古河道发育地裂缝灾害,个别地区发育崩塌、滑坡灾害。      

The environmental impact of landslides on the population living on the eastern footslopes of the Aberdare ranges in Kenya: a case study of Maringa Village landslide

 Landslides are a recognized but poorly studied phenomena in the eastern footslopes of the Aberdare ranges in central Kenya. The Aberdare ranges traverse the Murang'a, Kiambu, Nyeri and Nyandarua districts of Kenya where fatal landslides have occurred in the past. A detailed study of a major landslide which occurred at Maringa village in Murang'a district on 30 April 1997, has been undertaken. The landslide buried three houses at the toe of a slope and killed all the 11 occupants in their sleep. It is shown that the geology and climate of the study area contribute to the prevalence of landslides not only in Murang'a but in all the andosol-covered areas throughout the eastern footslopes of Aberdare ranges. Rapid weathering of pyroclastic rocks in a warm and wet climate create a regolith which is generally weaker than the underlying better-cemented basalt and basaltic agglomerates. The landsliding occurred when the weathered pyroclastic regolith become saturated after a heavy rainstorm on high mechanically unstable slope which slid over the more stable basalts. Over-saturation of clay soils (andosol) which were also derived from weathering of pyroclastic rocks contributed to the slope failure. The results of the study show that besides fatalities and injuries, the landslide destroyed homes, plantations and roads. Received: 16 March 1998 · Accepted: 22 September 1998     

Spatial distribution analysis and susceptibility mapping of landslides triggered before and after Mw7.8 Gorkha earthquake along Upper Bhote Koshi,Nepal

The 2015 Mw7.8 Gorkha earthquake triggered thousands of landslides of various types scattered over a large area. In the current study, we utilized pre- and post-earthquake high-resolution satellite imagery to compile two landslide inventories before and after earthquake and prepared three landslide susceptibility maps within 404 km² area using frequency ratio (FR) model. From the study, we could map about 519 landslides including 178 pre-earthquake slides and 341 coseismic slides were identified. This study investigated the relationship between landslide occurrence and landslide causative factors, i.e., slope, aspect, altitude, plan curvature, lithology, land use, distance from streams, distance from road, distance from faults, and peak ground acceleration. The analysis showed that the majority of landslides both pre-earthquake and coseismic occurred at slope >30°, preferably in S, SE, and SW directions and within altitude ranging from 1000 to 1500 m and 1500 to 3500 m. Scatter plots between number of landslides per km^?2 (LN) and percentage of landslide area (LA) and causative factors indicate that slope is the most influencing factor followed by lithology and PGA for the landslide formation. Higher landslide susceptibility before earthquake is observed along the road and rivers, whereas landslides after earthquake are triggered at steeper slopes and at higher altitudes. Combined susceptibility map indicates the effect of topography, geology, and land cover in the triggering of landslides in the entire basin. The resultant landslide susceptibility maps are verified through AUC showing success rates of 78, 81, and 77%, respectively. These susceptibility maps are helpful for engineers and planners for future development work in the landslide prone area.     

Zoning for flowslide and debris flow in pyroclastic soils of Campania Region based on “infinite slope” analysis

Campania Region (Italy), one of the most densely populated areas in Europe, is probably the one with the highest risk of landslide. A large part of the region is covered by unsaturated cohesionless pyroclastic deposits subjected to rainfall-induced landslides. According to experience, these can display different features and magnitude. The most catastrophic landslides are liquefied debris flows which periodically occur on steep slopes, causing death and destruction in areas located downslope. Therefore, zoning of those areas which can be the source of liquefied debris flow is necessary. The paper reports some useful elements for zoning based on infinite slope analysis, accounting for the results of recent research on the mechanics of rainfall-induced landslides in pyroclastic soils.     

Influence of seismic acceleration on landslide susceptibility maps: a case study from NE Turkey (the Kelkit Valley)

Particularly in the last decade, landslide susceptibility and hazard maps have been used for urban planning and site selection of infrastructures. Most of the procedures for preparing of landslide susceptibility maps need high-quality landslide inventory map. Although the rainfall and seismic activities are accepted as triggering factor for landslides, designation of the triggering factor for each landslide in the inventory is almost impossible when well-documented records are unavailable. Therefore, during preparation of landslide susceptibility map, whole landslide records in the inventory map are used together without classifying based on the triggering factors. Although seismic activity is accepted as a triggering factor, possible effect of the use of seismic activity on production of landslide susceptibility map was investigated in this study, and the subject is open to discussion. For this purpose, a series of stability analyses based on circular failure and infinite slope model were performed considering different pseudostatic conditions. The results of analyses show that gentle slopes have higher susceptibility to failure than steeper ones, even if their stability conditions (susceptibilities) are similar for static condition. The seismic forces acting on failure surfaces may not be sufficiently taken into consideration in the conventionally prepared landslide susceptibility maps. Employing the general decreasing trend in stability condition based on slope face angle and the seismic acceleration, a new procedure was introduced for preparing of the landslide susceptibility map for a scenario earthquake. The prediction performance of occurring landslides increased after the procedure was applied to the conventionally prepared landslide susceptibility map. According to the threshold independent spatial performance analyses of the proposed methodology and the produced landslide susceptibility maps, the area under ROC curve values were calculated as 0.801, 0.933, and 0.947 for the maps prepared by considering conventional method and scenario earthquakes having M _w values of 5.5 and 7.5, respectively.     

Application of logistic regression model for slope instability prediction in Cuyahoga River Watershed,Ohio, USA

High incidences of slope movement are observed throughout Cuyahoga River watershed in northeast Ohio, USA. The major type of slope failure involves rotational movement in steep stream walls where erosion of the banks creates over-steepened slopes. The occurrence of landslides in the area depends on a complex interaction of natural as well as human induced factors, including: rock and soil strength, slope geometry, permeability, precipitation, presence of old landslides, proximity to streams and flood-prone areas, land use patterns, excavation of lower slopes and/or increasing the load on upper slopes, alteration of surface and subsurface drainage. These factors were used to evaluate the landslide-induced hazard in Cuyahoga River watershed using logistic regression analysis, and a landslide susceptibility map was produced in ArcGIS. The map classified land into four categories of landslide susceptibility: low, moderate, high, and very high. The susceptibility map was validated using known landslide locations within the watershed area. The landslide susceptibility map produced by the logistic regression model can be efficiently used to monitor potential landslide-related problems, and, in turn, can help to reduce hazards associated with landslides.     

Applying different scenarios for landslide spatial modeling using computational intelligence methods

Landslides every year impose extensive damages to human beings in various parts of the world; therefore, identifying prone areas to landslides for preventive measures is essential. The main purpose of this research is applying different scenarios for landslide susceptibility mapping by means of combination of bivariate statistical (frequency ratio) and computational intelligence methods (random forest and support vector machine) in landslide polygon and point formats. For this purpose, in the first step, a total of 294 landslide locations were determined from various sources such as aerial photographs, satellite images, and field surveys. Landslide inventory was randomly split into a testing dataset 70% (206 landslide locations) for training the different scenarios, and the remaining 30% (88 landslides locations) was used for validation purposes. To providing landslide susceptibility maps, 13 conditioning factors including altitude, slope angle, plan curvature, slope aspect, topographic wetness index, lithology, land use/land cover, distance from rivers, drainage density, distance from fault, distance from roads, convergence index, and annual rainfall are used. Tolerance and the variance inflation factor indices were used for considering multi-collinearity of conditioning factors. Results indicated that the smallest tolerance and highest variance inflation factor were 0.31 and 3.20, respectively. Subsequently, spatial relationship between classes of each landslide conditioning factor and landslides was obtained by frequency ratio (FR) model. Also, importance of the mentioned factors was obtained by random forest (RF) as a machine learning technique. The results showed that according to mean decrease accuracy, factors of altitude, aspect, drainage density, and distance from rivers had the greatest effect on the occurrence of landslide in the study area. Finally, the landslide susceptibility maps were produced by ten scenarios according to different ensembles. The receiver operating characteristics, including the area under the curve (AUC), were used to assess the accuracy of the models. Results of validation of scenarios showed that AUC was varying from 0.668 to 0.749. Also, FR and seed cell area index indicators show a high correlation between the susceptibility classes with the landslide pixels and field observations in all scenarios except scenarios 10_RF and 10_SVM. The results of this study can be used for landslides management and mitigation and development activities such as construction of settlements and infrastructure in the future.     

国内外地震滑坡研究：现状、问题与展望

地震滑坡一直是地学界关注的研究热点之一,特别是2005年10月8日克什米尔M_s 7.8地震、2008年5月12日汶川M_s 8.0地震、2010年玉树M_s 7.1地震、2014年云南鲁甸M_s 6.5地震、2015年4月25日尼泊尔M_s 8.1地震以及2017年8月8日九寨沟M_s 7.0地震等一系列强震活动,都不同程度地触发了大量地震滑坡,并伴生了许多次生灾害,从而促进地震滑坡研究进入了新的阶段。本文通过总结国内外地震滑坡研究现状,归纳了地震滑坡的特征和分布规律、地震动参数与地震滑坡关系、边坡地质条件与地震滑坡关系、地震滑坡的动力响应特征、地震滑坡预测以及地震滑坡危险性与风险区划等6方面的研究现状,在此基础上进一步提出了未来地震滑坡应关注的主要研究方向和重点。     

Topographical features of rainfall-triggered landslides in Mon State,Myanmar, August 2019: spatial distribution heterogeneity and uncommon large relative heights

Continuous 5-day (August 4–9, 2019) torrential rainfall in the monsoon season triggered more than 90 landslides on northwest-southeast extended mountain range of Mon State, Myanmar. In this study, remote sensing images, DEM, and limited fieldworks were used to create the landslide inventory. The topography features of these landslides are analyzed via ArcGIS. The largest one occurred on 9 August 2019 and caused 75 deaths and 27 buildings were damaged. This landslide occurred on gentle topography (slope angle, 23°) with long run-out, in which the angle of reach was relatively low (10°). The volume was 111,878 m³ was mainly composed of weathered granite and red soil and the sliding depth was approximately 7.5 m. Topographic characteristics including the relative slope height, angle of reach, and slope angle of source area of 35 landslides with areas?>?4000 m² were analyzed. The spatial distribution characteristics and topographic features of the 35 landslides below are distinguished: (1) the concentration of most of landslides on southwest-facing slopes showing the heterogeneous spatial distribution of landslide; (2) an uncommon landslide distribution in which more than half of landslide originates from upper slope; (3) the range of the angle of the source area (17°–38°) compatible with the internal friction angle of soils in tropical regions (17°–33°); and (4) the tangent of the angle of reach is generally smaller than 0.5 (angle of reach?<?27°) shows a relative high mobility and the relation between landslide mobility and the slope angle of the landslide source area is similar to the one of earthquake-triggered landslides, even though the triggering mechanism, landslide type, and landslide volume are dramatically different.

     

Regional mapping and characterisation of old landslides in hilly regions using LiDAR-based imagery in Southern Flanders

Analysis of LiDAR-derived imagery led to the discovery of more than 330 pre-Holocene to recent landslides in Southern Flanders (4850 km²). The morphology of three landslides, including the 266.5 ha deep-seated gravitational slope deformation in Alden Biesen, was investigated in more detail. The analysis of the morphological and topographical characteristics (width–length relation, frequency–area distribution and topographical threshold) of the landslides revealed important differences compared to the characteristics reported in other landslide studies, and helped understanding possible landslide triggering mechanisms. Especially the possibility of a seismic origin of the landslides was investigated. Finally, a heuristic model for region-wide landslide susceptibility mapping was successfully tested. The susceptibility model and map allow prediction of future landslide locations and contribute to better understanding the role of individual causal factors on landslide location and spatial density. The results suggest that landslides on low-gradient, soil-mantled hills are a more important contributor to landscape evolution of hilly areas than was hitherto thought. The morphology of all hilly regions of Flanders is clearly marked by landslide processes and higher landslide densities often coincide with the presence of quaternary active faults. This study further shows that high-resolution topographical data such as LiDAR significantly contributes to a better detection of old, previously unknown landslides.