The national landslide database of Great Britain: development, evolution and applications

Landslide inventories are essential because they provide the basis for predictive landslide hazard and susceptibility assessments and because they allow for the manipulation and storage of temporal and spatial data. The National Landslide Database has been developed by the British Geological Survey (BGS). It is the most extensive source of information on landslides in Great Britain with over 15,000 records of landslide events each documented as fully as possible. This information is invaluable for planners and developers as it helps them investigate, avoid or mitigate areas of unstable ground in accordance with Government planning policy guidelines. Therefore, it is vital that the continual verification, collection and updating of landslide information is carried out as part of the Survey’s ‘National Capability’ work. This paper describes the evolution from a static database to one that is continually updated forming part of a suite of national digital hazard products. The history of the National Landslide Database and associated Geographical Information System (GIS) is discussed, together with its application and future development.     

Research and development of advanced technologies for landslide hazard analysis in Italy

The Research Centre on Prediction, Prevention and Control of Georisks, "Sapienza Università di Roma" (CERI) was selected as one of the World Centres of Excellence on Landslide Risk Reduction (WCoE) under the title of “Research and development of advanced technologies for landslide hazard analysis in Italy” in the period 2008–2011. The present paper provides background information on the activities carried out by the CERI, in the frame of the WCoE network. Pre-defined purposes have been achieved by the implementation of integrated monitoring systems and early warning systems for landslides movements. The application of GBInSAR technique for monitoring landslides interacting with infrastructures is discussed, and two case histories are presented. It is shown that this technique is particularly suitable for the displacements monitoring of landslides interacting with infrastructures, thanks to its peculiar features (i.e. areal mapping, operability under every weather and lighting conditions and continuous and completely remote monitoring). Hence, it can be considered a very important tool for the reduction of risks connected to the realisation and activity of large infrastructures.     

New Zealand’s National Landslide Database

A New Zealand Landslide Database has been developed to hold all of New Zealand’s landslide data and provide factual data for use in landslide hazard and risk assessment, including a probabilistic landslide hazard model for New Zealand, which is currently being developed by GNS Science. Design of a national Landslide Database for New Zealand required consideration of existing landslide data stored in a variety of digital formats and future data yet to be collected. Pre-existing landslide datasets were developed and populated with data reflecting the needs of the landslide or hazard project, and the database structures of the time. Bringing these data into a single database required a new structure capable of containing landslide information at a variety of scales and accuracy, with many different attributes. A unified data model was developed to enable the landslide database to be a repository for New Zealand landslides, irrespective of scale and method of capture. Along with landslide locations, the database may contain information on the timing of landslide events, the type of landslide, the triggering event, volume and area data, and impacts (consequences) for each landslide when this information is available. Information from contributing datasets include a variety of sources including aerial photograph interpretation, field reconnaissance and media accounts. There are currently 22,575 landslide records in the database that include point locations, polygons of landslide source and deposit areas, and linear landslide features. Access to all landslide data is provided with a web application accessible via the Internet. This web application has been developed in-house and is based on open-source software such as the underlying relational database (PostGIS) and the map generating Web Map Server (GeoServer). Future work is to develop automated data-upload routines and mobile applications to allow people to report landslides, adopting a consistent framework.     

Influence of Shannon’s entropy on landslide-causing parameters for vulnerability study and zonation—a case study in Sikkim, India

Landslide is a common hazard in the hilly regions, which causes heavy losses to life and properties every year. Since 1980, various researches and analyses have been carried out in the geographic information systems (GIS) environment to identify factors responsible for causing landslides. The important conditioning factors identified by the researchers are slope, geological, geomorphologic structures, and land use coupled with triggering factors like rainfall and a few of the anthropogenic activities. Almost all landslides vulnerability studies carried out so far used parameters of landslide events of the past as essential inputs and advanced methods like information value, regression analysis, fuzzy logic, etc. The present research is an attempt to investigate the landslide vulnerabilities in different slope areas with simple and realistic method of assignments of weights to the parameters based on experts?? opinion and generic logic, without using the parameters of past landslide events as inputs. The identified factors were assigned appropriate weights based on experts?? opinion and these weights were further balanced with respect to the Shannon??s entropy of their occurrences within the study area. The study area was finally classified into three zones namely least vulnerable zone, moderately vulnerable zone, and most vulnerable zone. When compared with the actual landslide history of the past, it was found that Shannon??s entropy applied zonation model matched to real landslide events with higher value of landslide density as compared to the model developed without Shannon??s entropy.     

滑坡灾害空间区划及GIS应用研究

滑坡灾害空间区划研究是当前国内外滑坡领域的重要研究方向之一。虽然滑坡灾害的发生具有随机性的特点 ,但其发生的区域性和重复性特点则是区域滑坡分布与发生的总体规律。从减灾与土地规划的角度 ,开展滑坡灾害空间区划研究具有十分重要的理论和实际意义。文中重点探讨了滑坡灾害空间区划的理论体系、灾害风险评估的基本术语定义及GIS制图的基本原理 ,采用MAPGIS软件为平台及其二次开发的滑坡灾害信息分析系统 ,在中国滑坡重灾害的汉江流域开展了灾害危险性空间区划应用研究。     

Measuring displacements of the Thompson River valley landslides,south of Ashcroft,BC, Canada,using satellite InSAR

The Thompson River valley, south of Ashcroft in British Columbia, Canada, has experienced several landslides since the mid-1800s. The national railways that run along the valley cross a number of these landslides. All the landslides occur in glacial deposits, typically sliding on weak clay layers. Some have failed rapidly to very rapidly and are currently inactive or showing deformation rates from a few millimeters to centimeters per year. An evaluation of satellite InSAR (Interferometric Synthetic Aperture Radar) using RADARSAT-2 images between September 2013 and November 2015 provides new insight into landslide displacements in the Thompson River valley. This information enhances the ongoing hazard management of unstable terrain. This paper presents the comparison of the InSAR measurements with other instrumentation (GPS and ShapeAccelArrays? -SAA) installed at one moving landslide and then addresses the extent and magnitude of the slope movements observed. InSAR was found to provide similar displacement values to those measured otherwise. The stable location showed displacements of +/?1 mm with an average near zero during the whole monitoring period. Six areas of slope movement were identified within the study area, all within or adjacent to the footprints of past landslides. The average line of sight (LOS) displacement rates range between 11 and 39 mm/year. Most of the landslides exhibited seasonal variations in velocity that corresponds to changes in river elevation in the valley.     

Mapping of landslide hazard zonation using GIS at Golestan watershed, northeast of Iran

Landslide hazard zonation is essential for planning future developmental activities. At the present study, after the preparation of a landslide inventory of the study area, nine factors as well as sub-data layers of factor class weights were tested for an integrated analysis of landslide hazard in the region. The produced factor maps were weighted with the analytic hierarchy process method and then classified into four classes—negligible, low, moderate, and high. The final produced map for landslide hazard zonation in Golestan watershed revealed that: (1) about 53.85 % of the basin is prone to moderate and high threats of landslides. (2) Landslide events at the Golestan watershed were strongly correlated to the slope angle of the basin. It was observed that the active landslide zones, including moderate to high landslide hazard classes, have a high correlation to slope classes over 30° (R ²?=?0.769). (3) The regions most susceptible to landslide hazard are those located south and southwest of the watershed, which included rock topples, falls, and debris landslides.     

秦岭中部太白县地质灾害发育特征及危险性评估

以陕西省太白县为例,分析了秦岭中部山区地质灾害形成的地质环境条件,重点指出在植被茂密山区,异常强降雨及农耕、建房、修路和矿山开采4种人类工程活动对地质灾害的关键诱发作用。对崩塌、滑坡、泥石流和不稳定斜坡4类典型地质灾害进行了亚类细分和发育特征分析,并总结指出了地质灾害区域宏观分布特征。筛选了9种关键的地质灾害影响和诱发因素,基于将集中调查区指示的地质灾害发育规律,外推应用于全区地质灾害评估的思路,利用信息量模型对太白县全区进行了地质灾害危险性定量评估,结果显示高危险区主要集中分布在县域北部人口聚居的盆地区,以及南部河流与公路沿线地段。定量检验显示,危险性评估结果与地质灾害的实际分布十分吻合,表明基于信息量模型的地质灾害危险性评估方法能够很好地适用于秦岭腹地山区。     

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.     

An InSAR and depth-integrated coupled model for potential landslide hazard assessment

Assessing the hazard of potential landslides is crucial for developing mitigation strategies for landslide disasters. However, accurate assessment of landslide hazard is limited by the lack of landslide inventory maps and difficulty in determining landslide run-out distance. To address these issues, this study developed a novel method combining the InSAR technique with a depth-integrated model. Within this new framework, potential landslides are identified through InSAR and their potential impact areas are subsequently estimated using the depth-integrated model. To evaluate its capability, the proposed method was applied to a landslide event that occurred on November 3, 2018 in Baige village, Tibet, China. The simulated results show that the area with a probability of more than 50% to be affected by landslides matched the real trimlines of the landslide and that the accuracy of the proposed method reached 85.65%. Furthermore, the main deposit characteristics, such as the location of maximum deposit thickness and the main deposit area, could be captured by the proposed method. Potential landslides in the Baige region were also identified and evaluated. The results indicate that in the event of landslides, the collapsed mass has a high probability to block the Jinsha River. It is therefore necessary to implement field monitoring and prepare hazard mitigation strategies in advance. This study provides new insights for regional-scale landslide hazard management and further contributes to the implementation of landslide risk assessment and reduction activities.

     

降雨型滑坡时间概率的逻辑回归拟合及连续概率滑坡危险性建模

提高降雨型滑坡危险性预警精度和空间辨识度具有重要意义.以江西宁都县1980—2001年156个降雨型滑坡为例,首先基于传统的EE-D（early effective rainfall-rainfall duration）阈值法计算不同降雨诱发滑坡的时间概率级别;然后以各级别临界降雨阈值曲线对应的时间概率为因变量,并以对应的前期有效降雨量（early effective rainfall,EE）和降雨历时（D）为自变量,采用逻辑回归拟合出上述因变量与自变量之间的非线性关系,得到降雨诱发滑坡的连续概率值;之后对比C5.0决策树和多层感知器的滑坡易发性预测性能;最后利用降雨诱发滑坡的连续概率值与易发性图相耦合以实现连续概率滑坡危险性预警.结果显示:（1）宁都降雨型滑坡连续概率值的逻辑回归方程为1/P=1+e4.062+0.747 4×D-0.079 44×EE,其拟合优度为0.983;（2）2002—2003年的20处用于连续概率阈值测试的降雨型滑坡大都落在连续概率值大于0.7的区域,只有4处落在小于0.7的区域;（3）C5.0决策树预测滑坡易发性的精度显著高于多层感知器;（4）近5年的4次降雨型滑坡的连续概率危险性值都在0.8以上,且高和极高预警区的面积较传统滑坡危险性分区更小.可见连续概率滑坡危险性预警法相较于传统危险性分区法具有更高的预警精度和空间辨识度,且通过叠加滑坡易发性图及其临界降雨阈值可开展实时滑坡危险性预警制图.      

Landslide Susceptibility Mapping Using Fuzzy Logic System and Its Influences on Mainlines in Lashgarak Region,Tehran, Iran

Landslide susceptibility mapping is among the useful tools applied in disaster management and planning development activities in mountainous areas. The susceptibility maps prepared in this research provide valuable information for landslide hazard management in Lashgarak region of Tehran. This study was conducted to, first, prepare landslide susceptibility maps for Lashgarak region and evaluate landslide effect on mainlines and, second, to analyze the main factors affecting landslide hazard increase in the study area in order to propose efficient strategies for landslide hazard mitigation. A GIS-based multi-criteria decision analysis model (fuzzy logic) is used in the present work for scientific evaluation of landslide susceptible areas in Lashgarak region. To this end, ArcGIS, PCIGeomatica, and IDIRISI software packages were used. Eight information layers were selected for information analysis: ground strength class, slope angle, terrain roughness, normalized difference moisture index, normalized difference vegetation index, distance from fault, distance from the river, and distance from the road. Next, eight different scenarios were created to determine landslide susceptibility of the study area using different operators (intersection (AND), union (OR), algebraic sum (SUM), multiplication (PRODUCT), and different fuzzy gamma values) of fuzzy overlay approach. After that, the performance of various fuzzy operators in landslide susceptibility mapping was empirically compared. The results revealed the excellent consistency of landslide susceptibility map prepared using the fuzzy union (OR) operator with landslide distribution map in the study area. Eventually, the accuracy of landslide susceptibility map prepared using the fuzzy union (OR) operator was evaluated using the frequency ratio diagram. The results showed that frequency values of the landslides gradually increase from “low susceptibility” to high “susceptibility” as 88.34% of the landslides are categorized into two “high” and “very high” susceptibility classes, implying the satisfactory consistency between the landslide susceptibility map prepared using fuzzy union (OR) operator and landslide distribution map.     

Permanent Scatterers for landslide investigations: outcomes from the ESA-SLAM project

Within the SLAM project (Service for Landslide Monitoring), launched in 2003 by the European Space Agency (ESA) the Permanent Scatterers (PS) technique, a multi-image interferometric approach, coupled with the interpretation of aerial-photos and optical satellite images, was carried out for landslide investigations. The PS analysis was applied at a regional scale as support for landslide inventory mapping and at local scale for the monitoring of single well-known slope movements. For the integration of the PS measurements within a landslide inventory the Arno river basin (Italy) was chosen as test site for the presence of a high number of mass movements (to date about 300 areas at high landslide risk and more than 27,000 individual landslides mapped by the institutional authorities). About 350 SAR images have been interferometrically processed by means of the PS technique, with the detection of about 600,000 PS. The use of optical images contributed spatial meaning to the point-wise information provided by the PS, making it easier to identify terrain features related to slope instability and the landslide boundaries. Here we describe the employed methodology and its impact in the updating of a preexisting landslide inventory. 6.8% of the total number of landslides were characterized by ground displacement measurements from the PS: 6.1% of already mapped landslides and 0.8% of new unstable areas detected through the PS analysis. Moreover, most of the PS are located in urban areas, showing that the proposed methodology is suitable for landslide mapping in areas with a quite high density of urbanization, but that over vegetated areas it still suffers from the limitations induced by the current space-borne SAR missions (e.g. temporal de-correlation). On the other hand, the use of InSAR for the monitoring of single slow landslides threatening built-up areas has provided satisfactory results, allowing the measurement of superficial deformations with high accuracy on the landslide sectors characterized by a good radar reflectivity and coherence.     

Permanent Scatterers for landslide investigations: outcomes from the ESA-SLAM project

Within the SLAM project (Service for Landslide Monitoring), launched in 2003 by the European Space Agency (ESA) the Permanent Scatterers (PS) technique, a multi-image interferometric approach, coupled with the interpretation of aerial-photos and optical satellite images, was carried out for landslide investigations. The PS analysis was applied at a regional scale as support for landslide inventory mapping and at local scale for the monitoring of single well-known slope movements. For the integration of the PS measurements within a landslide inventory the Arno river basin (Italy) was chosen as test site for the presence of a high number of mass movements (to date about 300 areas at high landslide risk and more than 27,000 individual landslides mapped by the institutional authorities). About 350 SAR images have been interferometrically processed by means of the PS technique, with the detection of about 600,000 PS. The use of optical images contributed spatial meaning to the point-wise information provided by the PS, making it easier to identify terrain features related to slope instability and the landslide boundaries. Here we describe the employed methodology and its impact in the updating of a preexisting landslide inventory. 6.8% of the total number of landslides were characterized by ground displacement measurements from the PS: 6.1% of already mapped landslides and 0.8% of new unstable areas detected through the PS analysis. Moreover, most of the PS are located in urban areas, showing that the proposed methodology is suitable for landslide mapping in areas with a quite high density of urbanization, but that over vegetated areas it still suffers from the limitations induced by the current space-borne SAR missions (e.g. temporal de-correlation). On the other hand, the use of InSAR for the monitoring of single slow landslides threatening built-up areas has provided satisfactory results, allowing the measurement of superficial deformations with high accuracy on the landslide sectors characterized by a good radar reflectivity and coherence.     

Assessing landslide exposure in areas with limited landslide information

Landslide risk assessment is often a difficult task due to the lack of temporal data on landslides and triggering events (frequency), run-out distance, landslide magnitude and vulnerability. The probability of occurrence of landslides is often very difficult to predict, as well as the expected magnitude of events, due to the limited data availability on past landslide activity. In this paper, a qualitative procedure for assessing the exposure of elements at risk is presented for an area of the Apulia region (Italy) where no temporal information on landslide occurrence is available. Given these limitations in data availability, it was not possible to produce a reliable landslide hazard map and, consequently, a risk map. The qualitative analysis was carried out using the spatial multi-criteria evaluation method in a global information system. A landslide susceptibility composite index map and four asset index maps (physical, social, economic and environmental) were generated separately through a hierarchical procedure of standardising and weighting. The four asset index maps were combined in order to obtain a qualitative weighted assets map, which, combined with the landslide susceptibility composite index map, has provided the final qualitative landslide exposure map. The resulting map represents the spatial distribution of the exposure level in the study area; this information could be used in a preliminary stage of regional planning. In order to demonstrate how such an exposure map could be used in a basic risk assessment, a quantification of the economic losses at municipal level was carried out, and the temporal probability of landslides was estimated, on the basis of the expert knowledge. Although the proposed methodology for the exposure assessment did not consider the landslide run-out and vulnerability quantification, the results obtained allow to rank the municipalities in terms of increasing exposure and risk level and, consequently, to identify the priorities for designing appropriate landslide risk mitigation plans.     

三峡水库区何家湾滑坡监测及防治措施研究

在研究了三峡水库区何家湾滑坡体空间形态、自然地理及地层岩性的基础上,从气象、水文、库区蓄水及水位变动因素入手,分析了滑坡形成的原因。根据库区地质灾害监测预警工程设计,结合何家湾滑坡的结构和变形特征,确定具体监测方法。对该滑坡进行了大地形变监测、地下水位监测、滑体深部位移监测及宏观监测。其中,大地形变监测数据分析表明:何家湾滑坡的最大变形量已超过2cm,且一直呈现增大趋势;地下水位、滑体深部位移均未发现明显异常,宏观监测亦未发现明显的新的变形迹象。通过分析监测资料并考虑到未来三峡水库蓄水,认为何家湾滑坡目前处于潜在不稳定状态。滑坡体在饱水及水库蓄水后,将处于临界蠕滑或失稳状态。结合滑坡体实际情况对滑坡防治进行初步研究,提出了采用回填压脚支档为主、辅以排水的综合治理措施;并建议加强数据远程传输的研究与实践,以解决目前监测效率不高的问题。     

基于多期DEM和滑坡强度的滑坡风险评估

滑坡危险性定量评估是滑坡风险评估中的关键和难点,也是当前国际风险管理研究中的热点问题.以滑坡密集分布的黑方台南塬为研究区,以32处典型滑坡为研究对象,依据多期三维数字高程模型(DEM),提出了一种基于强度的滑坡危险性定量评估技术方法.根据多期三维地形信息的解译及野外调查,编制多期滑坡分布图,计算滑坡活动的频率.利用GIS技术,利用滑坡体积与速度的乘积计算滑坡强度.将滑坡危险性定义为滑坡频率和滑坡强度的乘积,同时调查和分析了黑方台地区各类承灾体的类型、价值及其在相应滑坡强度下的易损性,在此基础上开展了单体滑坡风险评估和黑方台南塬滑坡风险区划.     

GPS在三峡水库区云阳县滑坡监测中的应用

文章概述了在库区云阳段建立GPS滑坡监测网的观测实践,以黄泥巴蹬坎滑体为例,介绍了GPS相对定位技术在云阳县滑坡的应用现状和进展。在采用GPS监测、深部位移监测、宏观地质巡视的同时,还监测降雨量、地下水位、库水位及不合理的人类工程活动。通过多种监测方法分析,发现在雨季滑坡容易产生变形,非雨季变形缓慢。目前,雨季日变率均小于5mm,而在非雨季平均日变率小于1mm。认为该边坡处于缓慢变形阶段(警惕线)。此外,在巡视过程中,也发现了多条宽1~5mm、长度不等的裂缝和高度不等的局部错台。根据近2年所获得的观测成果分析,深部位移监测、GPS监测和宏观巡视的结果十分吻合,效果令人满意。文章还总结了在滑坡监测中消除GPS测量主要误差的对策。满足方便快捷的同时,在不同时段采集GPS监测数据,适当延长监测时段,每个监测点至少观测2h的要求。多方收集信息,尽量避开潮汐、太阳黑子等活动的时段进行GPS监测。     

Application and verification of a fractal approach to landslide susceptibility mapping

Landslide susceptibility mapping is essential for land-use activities and management decision making in hilly or mountainous regions. The existing approaches to landslide susceptibility zoning and mapping require many different types of data. In this study, we propose a fractal method to map landslide susceptibility using historical landslide inventories only. The spatial distribution of landslides is generally not uniform, but instead clustered at many different scales. In the method, we measure the degree of spatial clustering of existing landslides in a region using a box-counting method and apply the derived fractal clustering relation to produce a landslide susceptibility map by means of GIS-supported spatial analysis. The method is illustrated by two examples at different regional scales using the landslides inventory data from Zhejiang Province, China, where the landslides are mainly triggered by rainfall. In the illustrative examples, the landslides from the inventory are divided into two time periods: The landslides in the first period are used to produce a landslide susceptibility map, and those in the late period are taken as validation samples for examining the predictive capability of the landslide susceptibility maps. These examples demonstrate that the landslide susceptibility map created by the proposed technique is reliable.