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.     

Use of satellite remote sensing data in the mapping of global landslide susceptibility

Satellite remote sensing data has significant potential use in analysis of natural hazards such as landslides. Relying on the recent advances in satellite remote sensing and geographic information system (GIS) techniques, this paper aims to map landslide susceptibility over most of the globe using a GIS-based weighted linear combination method. First, six relevant landslide-controlling factors are derived from geospatial remote sensing data and coded into a GIS system. Next, continuous susceptibility values from low to high are assigned to each of the six factors. Second, a continuous scale of a global landslide susceptibility index is derived using GIS weighted linear combination based on each factor’s relative significance to the process of landslide occurrence (e.g., slope is the most important factor, soil types and soil texture are also primary-level parameters, while elevation, land cover types, and drainage density are secondary in importance). Finally, the continuous index map is further classified into six susceptibility categories. Results show the hot spots of landslide-prone regions include the Pacific Rim, the Himalayas and South Asia, Rocky Mountains, Appalachian Mountains, Alps, and parts of the Middle East and Africa. India, China, Nepal, Japan, the USA, and Peru are shown to have landslide-prone areas. This first-cut global landslide susceptibility map forms a starting point to provide a global view of landslide risks and may be used in conjunction with satellite-based precipitation information to potentially detect areas with significant landslide potential due to heavy rainfall.     

Analysis with C- and X-band satellite SAR data of the Portalet landslide area

This paper describes the use of the Stable Point Network technique, a Persistent Scatterer Interferometry SAR technique, for the analysis of the Portalet landslide area (Central Pyrenees, Spain). For this purpose, different SAR datasets acquired by ERS-1, ERS-2, ENVISAT and TerraSAR-X satellites have been analysed. The use of different SAR images acquired by satellite radar sensors operating at different microwave lengths has allowed for a comparative assessment and illustration of the advantages and disadvantages of these satellites for landslide detection and monitoring. In the introduction, differential interferometry and the study area are briefly described. Then the specifics of the SPN processing and the results of the different datasets are described and compared. In Analysis of the results: the Portalet landslide area, the Portalet landslide area is introduced and the radar displacement measurements are analysed with available geo-information data. Additionally, X-band measurements are compared with those gathered by a ground-based SAR for a previous project. Finally, the most relevant conclusions of this work are discussed.     

Exploring a landslide inventory created by automated web data mining: the case of Italy

Landslides - Nowadays, several systems to set up landslide inventories exist although they rarely rely on automated or real-time updates. Mass media can provide reliable info about natural hazard...     

Systematic GIS-based landslide inventory as the first step for effective landslide-hazard management

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 the 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 geological information systems (GIS)-based database to store and process all the collected data. In order to obtain proper landslide-hazard zoning, it is necessary to provide a landslide inventory and to define the relationship between landslides and geological setting. A landslide inventory represents a fundamental base of knowledge, is a very basic tool for land planning, and strongly helps the local authorities in their decision making.     

Characteristics,mechanisms, and post-disaster lessons of the delayed semi-diagenetic landslide in Hanyuan,Sichuan, China

Landslides following rainfall occurrence are a widespread phenomenon. The neglect of this phenomenon leads to serious loss of life when disasters occur. At 03:45 (GMT?+?8) on August 21, 2020, a semi-diagenetic landslide occurred in Zhonghai Village, Hanyuan County, China, which occurred 42 h after earlier rainfall. Nine people privately returned to their homes after evacuation of the dangerous area. In this disaster, eight people were lost and one injured. This study explores the failure characteristics, inducement, and mechanisms of the landslide via field investigations, resident interviews, multi-temporal images, field drilling, and geotechnical tests. Hydrological numerical calculations were also performed to uncover the seepage and transfer processes of the groundwater in the slope. Finally, problems in the current community early warning system were analyzed and corresponding suggestions put forward. The results show that the maximum sliding depth of the landslide was 27.5 m, the total area was 80,000 m², and the sliding volume was about 58,0000 m³, making it a medium-sized deep landslide. In addition to the vertical seepage of rainfall in the landslide area, the downward movement of rainfall in the back and upper catchment areas along the silt sand strata also affected the stability of the landslide. More needs to be done to make the population aware of this lag phenomenon to achieve scientific disaster reduction. This study not only provides a case study of a lagging semi-diagenetic landslide, but also provides insight into hydrological boundary determination and landslide early warning system construction.

     

A landslide inventory system as a base for automated process and risk analyses

Landslide research requires consistent and widespread data. Many countries within the European Union have national landslide inventories to fulfill these demands for their respective research. However, those inventories were usually not intended to provide the technical basis for automated process and risk analyses during their design phase. The ongoing development of Germany’s national landslide database offers the opportunity to do this differently. This paper introduces a landslide inventory system called WISL suitable for data handling as well as for novel automated process and risk analyses on a national scale. WISL is designated to form the technical infrastructure for a German national database. Its core consists of an open source relational database management system (PostgreSQL), standardized input and registration methods as well as integrated analyses modules, which avoid large data movement and allow for rapid risk analyses. We present proof-of-concept results of endangered infrastructure related to automated risk mappings based on topography and proximity of active landslides. The use of open source software and the application of a standardized input and data acquisition system for experts, coupled with custom analysis modules, constitutes a step toward automated risk maps by a mere ‘button-press’. Future developments for the inventory lie in the field of refining and inventing analysis modules and collecting data, for which WISL provides a firm technical base.     

Relationships between natural terrain landslide magnitudes and triggering rainfall based on a large landslide inventory in Hong Kong

Rain-induced landslides are recognized as one of the most catastrophic hazards on hilly terrains. To develop strategies for landslide risk assessment and management, it is necessary to estimate not only the rainfall threshold for the initiation of landslides, but also the likely magnitudes of landslides triggered by a storm of a given intensity. In this study, the frequency distributions of both open hillside landslides and channelized debris flows in Hong Kong are established on the basis of the Enhanced Natural Terrain Landslide Inventory (ENTLI) with 19,763 records in Hong Kong up to 2013. The landslide magnitudes are measured in terms of the number, scar area, volume, or density of landslides. The mean values of the scar areas and volumes are 55.2 m² and 102.0 m³, respectively, for the open hillside landslides and 91.3 m² and 166.5 m³, respectively, for the channelized debris flows. Empirical correlations between the numbers, scar areas, and volumes of hillside landslides or channelized debris flows and the maximum rolling rainfall intensities of different periods have been derived. The maximum rolling 4- to 24-h rainfall amounts provide better predictions compared with those with the maximum rolling 1-h rainfall. Maximum rolling rainfall intensity-duration thresholds identifying the likely rainfall conditions that yield natural terrain landslides or debris flows of different magnitudes are also proposed. The initiation rainfall thresholds are identified as 75, 90, 100, 120, 150, 180, and 200 mm for the maximum rolling 1-, 2-, 4-, 6-, 8-, 12-, and 24-h rainfall, respectively.     

Flood and landslide applications of near real-time satellite rainfall products

Floods and associated landslides account for the largest number of natural disasters and affect more people than any other type of natural disaster. With the availability of satellite rainfall analyses at fine time and space resolution, it has also become possible to mitigate such hazards on a near-global basis. In this article, a framework to detect floods and landslides related to heavy rain events in near-real-time is proposed. Key components of the framework are: a fine resolution precipitation acquisition system; a comprehensive land surface database; a hydrological modeling component; and landslide and debris flow model components. A key precipitation input dataset for the integrated applications is the NASA TRMM-based multi-satellite precipitation estimates. This dataset provides near real-time precipitation at a spatial-temporal resolution of 3 h and 0.25° × 0.25°. In combination with global land surface datasets it is now possible to expand regional hazard modeling components into a global identification/monitoring system for flood/landslide disaster preparedness and mitigation.     

The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: geomorphological features and landslide distribution

In this paper, the updating of the landslide inventory of Tuscany region is presented. To achieve this goal, satellite SAR data processed with persistent scatter interferometry (PSI) technique have been used. The updating leads to a consistent reduction of unclassified landslides and to an increasing of active landslides. After the updating, we explored the characteristics of the new inventory, analysing landslide distribution and geomorphological features. Several maps have been elaborated, as sliding index or landslide density map; we also propose a density-area map to highlight areas with different landslide densities and sizes. A frequency-area analysis has been performed, highlighting a classical negative power-law distribution. We also explored landslide frequency for lithology, soil use and several morphological attributes (elevation, slope gradient, slope curvature), considering both all landslides and classified landslide types (flows, falls and slides).     

Rainfall-induced landslide event of May 2010 in the eastern part of the Czech Republic

More than 150 landslides originated in the eastern part of the Czech Republic (region of the Flysch Outer Western Carpathians—hereinafter, OWC) due to soil saturation caused by antecedent precipitation and long lasting and intensive rainfalls on 16–18 May 2010 (>300 mm as measured by some stations). As a consequence, a multitude of small failures originated 88% of which was smaller than 10⁴ m². Most landslides are characterised as shallow (<10 m) or middle–deep (10–30 m) incipient (rather short travel) landslides, debris slides and soil slips spatially clustered to a geological domain underlain by rather weak thin-bedded flysch and unconsolidated Quaternary deposits. An exception to this is represented by a kilometre-long rockslide (∼2–3 mil m³) affecting tectonically weakened and weathered claystone/mudstone-dominated flysch on the southern slope of Mt. Girová (the Beskydy Mountains). The rockslide is one of the largest long runout landslides in the territory of the Czech Republic activated over the past few decades as it reaches the dimensions of the largest documented Holocene long runout landslides in the Czech part of the OWC. A majority of the May 2010 landslide events developed inside older (Holocene or historic) landslide terrains, which points to their spatial persistency and recurrent nature. In spite of the fact that the May 2010 landslide event was not as destructive as some previous landslide activisation in the OWC region (e.g. July 1997 event), it left many slope failures at the initial stage of their potential future reactivation.     

A new approach of combining aerial photography with satellite imagery for landslide detection

Subsequent rainfall after a strong earthquake can easily trigger landslides. Aerial photography is always available after a strong earthquake but not always available in a timely manner after a subsequent rainfall following the earthquake. Sometimes, only panchromatic imagery is available because of its relatively low cost and large cover capacity. To detect multi-temporal landslides induced by earthquake and its post long-term effect, in company of other factors such as subsequent rainfall, traditionally, it needs to carry out image classification multi-times to calculate the variance information. Therefore, the accuracy will be affected by accumulated errors from multi-classification, and the process is very time-consuming. In this paper, a new semi-automatic approach combing aerial photograph with satellite imagery was proposed for rapid mapping of multi-temporal landslides. The approach can enhance the change information of each landslide event in one detection process. In addition, slope units were introduced to separate the detected conjoint landslides. Chenjiaba area, which located in the highest seismic intensity zone of Wenchuan earthquake in Beichuan, China, and had a strong rainfall 4 months later, was selected as a case study to demonstrate the usefulness of this methodology. Accuracy assessment was carried out by comparing those extracted ones with a manually prepared landslide inventory map. Correctly detected were 90.1 and 94.2 % for earthquake-induced landslides and new landslides, respectively. Results show that this approach is capable of mapping different temporal landslides efficiently and quickly.     

Spatial and temporal analysis of a fatal landslide inventory in China from 1950 to 2016

Landslides result in severe casualties every year in China. However, there are few historical fatal landslide catalogs available to quantitatively assess the impact as well as the temporal and spatial patterns of landslides. The Fatal Landslide Event Inventory of China (FLEIC), which spans from 1950 to 2016, was compiled based on multiple data sources. The inventory contains 1911 non-seismically triggered landslides, which resulted in a total of 28,139 deaths in China during 1950–2016. The occurrence frequency of fatal landslides presented significantly different trends for different grades of events. Very large fatal landslide events (fatalities >?=?30) were on the rise during 1950–1999 and declined from 2000 to 2016. The decreasing trend after 2000 can be attributed to the increase in landslide mitigation investments. The small and medium-sized fatal landslide events (fatalities <?10) showed a significant increasing trend between 1950 and 2016, especially during the period of 2000–2016. This significant increasing trend is partly due to the improvement of the availability of landslide data online and may also be related to other factors including an increase in extreme precipitation events, the effects of land urbanization, and so on. This suggested that the inherent incompleteness of the landslide time series should be considered when analyzing. The fatal landslides mainly occurred between April and September (82.15%), which is consistent with the monthly precipitation variation in China. Spatially, most of the fatal landslides occurred in 14 provinces: five southwestern provinces (Yunnan, Sichuan, Guangxi, Guizhou, and Chongqing), five southeastern provinces (Hunan, Guangdong, Fujian, Jiangxi, and Zhejiang), Shaanxi and Shanxi, Hubei and Gansu. These 14 provinces account for 86% of the total fatal landslides and their associated fatalities. The spatial association between the fatal landslide density and possible influencing factors was assessed based on a geographical detector method. The results showed that the interacting factors between the precipitation and topography, soil, lithology, vegetation and population density are more closely related to the spatial distribution of fatal landslides than each individual factor.     

The contribution of satellite SAR-derived displacement measurements in landslide risk management practices

Natural Hazards - Landslides are common phenomena that occur worldwide and are a main cause of loss of life and damage to property. The hazards associated with landslides are a challenging concern...     

滑坡监测点多信息相似性度量方法研究

滑坡监测点在一段时间内对外界影响因素的响应可以看作滑坡在当前时刻的状态。滑坡监测点在相似的状态下受到相似的外界激励时会表现出相似的响应。据此,可以对相似的状态进行类比,从而将相似状态运用于滑坡的稳定性分析、数据修正和预测预报等。提出一种基于多信息的状态单元矩阵的相似性度量方法,运用该方法同时对滑坡的外界影响因素和运动状态进行相似性匹配,既保证了外界影响因素的相似性,也保证了滑坡当前运动状态的相似性。为验证该方法的有效性,运用三峡库区4个滑坡34个监测点的1 770个状态作为状态单元集,对白水河滑坡2015年1月至2016年6月的位移数据进行预测。预测结果显示,该方法优于现有的BP神经网络、支持向量回归等经典预测方法。     

Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China

The main purpose of this paper is to present the use of multi-resource remote sensing data, an incomplete landslide inventory, GIS technique and logistic regression model for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China. Landslide location polygons were delineated from visual interpretation of aerial photographs, satellite images in high resolutions, and verified by selecting field investigations. Eight factors, including slope angle, slope aspect, elevation, distance from drainages, distance from roads, distance from main faults, seismic intensity and lithology were selected as controlling factors for earthquake-triggered landslide susceptibility mapping. Qualitative susceptibility analyses were carried out using the map overlaying techniques in GIS platform. The validation result showed a success rate of 82.751 % between the susceptibility probability index map and the location of the initial landslide inventory. The predictive rate of 86.930 % was obtained by comparing the additional landslide polygons and the landslide susceptibility probability index map. Both the success rate and the predictive rate show sufficient agreement between the landslide susceptibility map and the existing landslide data, and good predictive power for spatial prediction of the earthquake-triggered landslides.     

A novel data mining technique of analysis and classification for landslide problems

Landslides during earthquakes have led to severe casualties and have resulted in damaged structures and facilities. The goal of the present study is to analyze the landslide problems in a remote area—Shei-Pa National Park in Taiwan. Spatial information techniques (Remote Sensing and Geographic Information System) with an innovative data mining technique, Discrete Rough Set (DRS) method, are incorporated to our study for analyzing landslides, their distribution, and classification. The present study provides how to find (1) the most representative data of landslide samples from the existing database, (2) the core attributes of the target categories: Normalized Difference Vegetation Index (NDVI) and Vegetation Index (VI), and (3) the thresholds (segment points) of each attribute on the target categories. A conventional approach, C4.5 Decision Tree Analysis, is used as a comparison. The methodology discussed in this study is of help to the analysis of landslide problems and thus facilitates the informed decision-making process.     

Survey and monitoring of landslide displacements by means of L-band satellite SAR interferometry

This paper illustrates the capabilities of L-band satellite SAR interferometry for the investigation of landslide displacements. SAR data acquired by the L-band JERS satellite over the Italian and Swiss Alps have been analyzed together with C-band ERS-1/2 SAR data and in situ information. The use of L-band SAR data with a wavelength larger than the usual C-band, generally considered for ground motion measurements, reduces some of the limitations of differential SAR interferometry, in particular, signal decorrelation induced by vegetation cover and rapid displacements. The sites of the Alta Val Badia region in South Tyrol (Italy), Ruinon in Lombardia (Italy), Saas Grund in Valais (Switzerland) and Campo Vallemaggia in Ticino (Switzerland), representing a comprehensive set of different mass wasting phenomena in various environments, are considered. The landslides in the Alta Val Badia region are good examples for presenting the improved performance of L-band in comparison to C-band for vegetated areas, in particular concerning open forest. The landslides of Ruinon, Saas Grund, and Campo Vallemaggia demonstrate the strength of L-band in observing moderately fast displacements in comparison to C-band. This work, performed with historical SAR data from a satellite which operated until 1998, demonstrates the capabilities of future planned L-band SAR missions, like ALOS and TerraSAR-L, for landslide studies.