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1.
This research paper assesses the vulnerability of landslide for the Bodi-Bodimettu Ghat section, Theni district, Tamil Nadu, India, using remotely sensed data and geographic information system (GIS). Landslide database was generated using IRS-1C satellite LISS III data and aerial photographs accompanied by field investigations using differential global positioning system to generate a landslide inventory map. Topographical, spatial, and field data were processed to construct the spatial thematic layers using image processing and GIS environment. Twelve landslide-inducing factors were used for landslide vulnerability analysis: elevation, slope, aspect, plan curvature, profile curvature, proximity to road, drainage and lineament, land use/land cover, geology, geomorphology, and run-off. The first five factors were derived from digital elevation model, and other thematic layers were prepared from spatial database. Frequency ratio of each factor was computed using the above thematic factors with past landslide locations. Landslide vulnerability map was produced using raster analysis. The landslide vulnerability map was classified into five zones: very low, low, moderate, high, and very high. The model is validated using the relative landslide density index (R-index method). The consistency of R-index indicates good performance of the vulnerability map. 相似文献
2.
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. 相似文献
3.
A spatial decision support system for extracting the core factors and thresholds for landslide susceptibility map 总被引：7，自引：0，他引：7
Spatial decision support system (SDSS) is an interactive, computer-based system designed to support a user in achieving a higher effectiveness of decision-making while solving a semi-structured spatial data. Satellite Remote Sensing and Digital Elevation Modeling are providing a systematic, rational framework for advancing scientific knowledge of our SDSS of geophysical phenomena that, often lead to observe the natural hazards or resources. Taking the advantage of these, more specifically, our study focused on using these to collect and measure the landslide data on a vast area located at Shei Pa National Park, Miao Li, Taiwan. Our source data includes (1) Digital Elevation Modeling is also used to investigate the landform, and (2) remote sensing image data are also employed to analyze the vegetation conditions. In addition, the process of generating landslide susceptibility maps involved on how to effectively extract the site-condition dominant attributes and thresholds for displaying the landslide occurrence accurately. Thus, the information from landslide must be categorized and thoroughly evaluated by an Advanced Data Mining Technique — Entropy-based classification method to construct the landslide knowledge rules. The knowledge scope with regards to core factors and thresholds are solved. Then, the susceptibility hazard maps are drawn and verifications are made. On the other hand, the conventional statistical method of Logistic Regression is used for comparison. 相似文献
4.
Landslide zonation studies emphasize on preparation of landslide hazard zonation maps considering major instability factors contributing to occurrence of landslides. This paper deals with geographic information system-based landslide hazard zonation in mid Himalayas of Himachal Pradesh from Mandi to Kullu by considering nine relevant instability factors to develop the hazard zonation map. Analytical hierarchy process was applied to assign relative weightages over all ranges of instability factors of the slopes in study area. To generate landslide hazard zonation map, layers in geographic information system were created corresponding to each instability factor. An inventory of existing major landslides in the study area was prepared and combined with the landslide hazard zonation map for validation purpose. The validation of the model was made using area under curve technique and reveals good agreement between the produced hazard map and previous landslide inventory with prediction accuracy of 79.08%. The landslide hazard zonation map was classified by natural break classifier into very low hazard, low hazard, moderate hazard, high hazard and very high landslide hazard classes in geographic information system depending upon the frequency of occurrence of landslides in each class. The resultant hazard zonation map shows that 14.30% of the area lies in very high hazard zone followed by 15.97% in high hazard zone. The proposed model provides the best-fit classification using hierarchical approach for the causative factors of landslides having complex structure. The developed hazard zonation map is useful for landslide preparedness, land-use planning, and social-economic and sustainable development of the region. 相似文献
5.
The national landslide database of Great Britain: development, evolution and applications 总被引：4，自引：0，他引：4
C. Foster C. V. L. Pennington M. G. Culshaw K. Lawrie 《Environmental Earth Sciences》2012,66(3):941-953
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. 相似文献
6.
Ali Bagherzadeh Mohammad Reza Mansouri Daneshvar 《Arabian Journal of Geosciences》2013,6(9):3377-3388
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. 相似文献
7.
Landslide vulnerability and risk assessment for multi-hazard scenarios using airborne laser scanning data (LiDAR) 总被引：1，自引：1，他引：0
Landslide hazard, vulnerability, and risk-zoning maps are considered in the decision-making process that involves land use/land cover (LULC) planning in disaster-prone areas. The accuracy of these analyses is directly related to the quality of spatial data needed and methods employed to obtain such data. In this study, we produced a landslide inventory map that depicts 164 landslide locations using high-resolution airborne laser scanning data. The landslide inventory data were randomly divided into a training dataset: 70 % for training the models and 30 % for validation. In the initial step, a susceptibility map was developed using logistic regression approach in which weights were assigned to every conditioning factor. A high-resolution airborne laser scanning data (LiDAR) was used to derive the landslide conditioning factors for the spatial prediction of landslide hazard areas. The resultant susceptibility was validated using the area under the curve method. The validation result showed 86.22 and 84.87 % success and prediction rates, respectively. In the second stage, a landslide hazard map was produced using precipitation data for 15 years. The precipitation maps were subsequently prepared and show two main categories (two temporal probabilities) for the study area (the average for any day in a year and abnormal intensity recorded in any day for 15 years) and three return periods (15-, 10-, and 5-year periods). Hazard assessment was performed for the entire study area. In the third step, an element at risk map was prepared using LULC, which was considered in the vulnerability assessment. A vulnerability map was derived according to the following criteria: cost, time required for reconstruction, relative risk of landslide, risk to population, and general effect to certain damage. These criteria were applied only on the LULC of the study area because of lack of data on the population and building footprint and types. Finally, risk maps were produced using the derived vulnerability and hazard information. Thereafter, a risk analysis was conducted. The LULC map was cross-matched with the results of the hazard maps for the return period, and the losses were aggregated for the LULC. Then, the losses were calculated for the three return periods. The map of the risk areas may assist planners in overall landslide hazard management. 相似文献
8.
Saibal Ghosh Cees J. van Westen Emmanuel John M. Carranza Victor G. Jetten 《Landslides》2012,9(3):371-384
Landslide risk assessment is based on spatially integrating landslide hazard with exposed elements-at-risk to determine their vulnerability and to express the expected direct and indirect losses. There are three components that are relevant for expressing landslide hazard: spatial, temporal, and magnitude probabilities. At a medium-scale analysis, this is often done by first deriving a landslide susceptibility map, and to determine the three types of probabilities on the basis of landslide inventories linked to particular triggering events. The determination of spatial, temporal, and magnitude probabilities depend mainly on the availability of sufficiently complete historical records of past landslides, which in general are rare in most countries (e.g., India, etc.). In this paper, we presented an approach to use available historical information on landslide inventories for landslide hazard and risk analysis on a medium scale (1:25,000) in a perennially typical data-scarce environment in Darjeeling Himalayas (India). We demonstrate how the incompleteness in the resulting landslide database influences the various components in the calculation of specific risk of elements-at-risk (e.g., buildings, population, roads, etc.). We incorporate the uncertainties involved in the risk estimation and illustrate the range of expected losses in the form of maximum and minimum loss curves. The study demonstrates that even in data-scarce environments, quantitative landslide risk assessment is a viable option, as long as the uncertainties involved are expressed. 相似文献
9.
Landslide hazard or susceptibility assessment is based on the selection of relevant factors which play a role on the slope instability, and it is assumed that landslides will occur at similar conditions to those in the past. The selected statistical method compares parametric maps with the landslide inventory map, and results are then extrapolated to the entire evaluated territory with a final product of landslide hazard or susceptibility map. Elements at risk are defined and analyzed in relation with landslide hazard, and their vulnerability is thus established. The landslide risk map presents risk scenarios and expected financial losses caused by landslides, and it utilizes prognoses and analyses arising from the landslide hazard map. However, especially the risk scenarios for future in a selected area have a significant importance, the literature generally consists of the landslide susceptibility assessment and papers which attempt to assess and construct the map of the landslide risk are not prevail. In the paper presented herein, landslide hazard and risk assessment using bivariate statistical analysis was applied in the landslide area between Hlohovec and Sered?? cities in the south-western Slovakia, and methodology for the risk assessment was explained in detail. 相似文献
10.
A procedure for landslide risk assessment is presented. The underlying hypothesis is that statistical relationships between
past landslide occurrences and conditioning variables can be used to develop landslide susceptibility, hazard and risk models.
The latter require also data on past damages. Landslides occurred during the last 50 years and subsequent damages were analysed.
Landslide susceptibility models were obtained by means of Spatial Data Analysis techniques and independently validated. Scenarios
defined on the basis of past landslide frequency and magnitude were used to transform susceptibility into quantitative hazard
models. To assess vulnerability, a detailed inventory of exposed elements (infrastructures, buildings, land resources) was
carried out. Vulnerability values (0–1) were obtained by comparing damages experienced in the past by each type of element
with its actual value. Quantitative risk models, with a monetary meaning, were obtained for each element by integrating landslide
hazard and vulnerability models. Landslide risk models showing the expected losses for the next 50 years were thus obtained
for the different scenarios. Risk values obtained are not precise predictions of future losses but rather a means to identify
areas where damages are likely to be greater and require priority for mitigation actions. 相似文献
11.
滑坡灾害空间区划及GIS应用研究 总被引：76，自引：3，他引：76
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。 相似文献
12.
A review of the content, structure, accuracy, and completeness of the Catalogue of Landslide OCcurrences in the Emilia-Romagna Region (CLOCkER) is presented. CLOCkER is a historical database, designed and developed for all types of landslides in the hilly-mountain area of the Emilia-Romagna section of the Northern Italian Apennines. Historical data have been gathered through a collection of numerous sources, including technical reports, historical archives, scientific literature, and newspapers. The information obtained, which has been evaluated to assess its temporal precision and spatial accuracy, has been recorded in a catalogue consisting of a Database Management System (DBMS) linked to a geographical information system (GIS) interface. The catalogue presently includes 14,416 records of documented landslide occurrences, dating from Middle Ages up to the present. The catalogue is associated with a landslide inventory, continuously updated by the Geological Survey of the Emilia-Romagna Region, where information on the shape, typology, and state of activity of more than 80,000 landslides is included. Our assessment of catalogue quality reveals a satisfactory spatial accuracy and a level of completeness comparable with the theoretical target proposed in the literature for complete inventories. Outputs indicate that CLOCkER can be a reference example useful for other regional historical landslide catalogues. Such reference datasets are useful for a wide range of landslide assessment purposes and can provide practical assistance for stakeholders involved in both scientific and technical fields, forming the basis for landslide temporal trend reconstruction that is essential for landslide hazard evaluation at different spatial-temporal scales. CLOCkER is open access, freely available online. 相似文献
13.
滑坡空间预测数学模型的对比及其应用 总被引：1，自引：1，他引：1
滑坡灾害空间预测经历了从定性-半定量-定量、从确定性-非确定性-概率论的发展过程,其中预测模型的建立、预测方法的选取是滑坡空间预测的核心过程,关系到预测结果的最终确定.讨论了信息量模型、信息-物元模型、信息-神经网络模型的预测流程和关键技术问题,将这3种非确定性数学模型运用于万州安乐寺古滑坡区的滑坡危险性预测中,并对3种模型的预测结果进行了对比分析,指出3种预测数学模型的优劣及其应用中需注意的问题,对比研究表明3种模型均不失为滑坡空间预测中较为有效的数学模型. 相似文献
14.
Integrated Landslide Susceptibility Analysis and Hazard Assessment in the Principality of Andorra 总被引：9，自引：2，他引：9
Corominas Jordi Copons Ramon Vilaplana Joan Manuel Altimir Joan Amigó Jordi 《Natural Hazards》2003,30(3):421-435
In the paper we present the procedure for hazard assessment that has been used to prepare the landslide hazard map of the Principality of Andorra at 1:5,000 scale. The main phases of the hazard assessment are discussed. Susceptibility analysis has involved the location of the potential slope failures, and the estimation of both landslide volume and runout distance. In the susceptible areas, landslide magnitude and frequency has been determined in order to produce the Hazard Zoning Map. Data required for hazard assessment have been introduced into a GIS or derived directly from available Digital Terrain Models. Data handling and treatment with the GIS has allowed the performance of the landslide hazard assessment and mapping in a fast and reproducible way. 相似文献
15.
为了改变传统滑坡遥感技术方法效率低、调查精度难以提高的状况,经多年实践与探索,笔者于 1999 年提出了“数字滑坡”概念。该概念使传统的“地学滑坡”拓展为能以数字形式表达的,具有三维空间、“多维”时间信息的,由“多元”要素组成的“数字滑坡”。数字滑坡技术系统由滑坡解译基础技术、遥感识辨滑坡技术、滑坡数据库及滑坡模型4部分构成。多年来,数字滑坡技术已成功应用于我国大型水电站建设、山区交通线建设、区域开发环境治理以及抗震减灾等领域,也用于大规模个体滑坡调查研究,并取得了显著的经济和社会效益,有效服务于国家防灾减灾战略。该文主要以西藏帕里河及川东天台乡2个典型滑坡调查为例,阐述数字滑坡技术的创新应用。 相似文献
16.
云南小江流域滑坡关键影响因子研究 总被引：5，自引：0，他引：5
确定诱发滑坡失稳的关键因素是滑坡研究的一个重要内容。采用不同影响因子图层进行危险性分区结果存在明显差异,这是由于第一因子对于滑坡变形失稳的贡献程度不同,即不同影响因子与滑坡的相关性不同。在进行滑坡灾害分析时,必须首先确定影响滑坡的关键因子以建立准确的统计分析模型。采用滑坡确定性系数的合并检验方法,在GIS中对云南小江流域进行了滑坡影响因子分析,并确定了影响滑坡的关键性因子。据此建立的多元统计分析预测模型经检验具有较高精度,要以为小江流域的灾害防治、规划建设提供科学依据。 相似文献
17.
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. 相似文献
18.
Abstract: Landslide research at the British Geological Survey (BGS) is carried out through a number of activities, including surveying, database development and real-time monitoring of landslides. Landslide mapping across the UK has been carried out since BGS started geological mapping in 1835. Today, BGS geologists use a combination of remote sensing and ground-based investigations to survey landslides. The development of waterproof tablet computers (BGS·SIGMAmobile), with inbuilt GPS and GIS for field data capture provides an accurate and rapid mapping methodology for field surveys. Regional and national mapping of landslides is carried out in conjunction with site-specific monitoring, using terrestrial LiDAR and differential GPS technologies, which BGS has successfully developed for this application. In addition to surface monitoring, BGS is currently developing geophysical ground-imaging systems for landslide monitoring, which provide real-time information on subsurface changes prior to failure events. BGS’s mapping and monitoring activities directly feed into the BGS National Landslide Database, the most extensive source of information on landslides in Great Britain. It currently holds over 14?000 records of landslide events. By combining BGS’s corporate datasets with expert knowledge, BGS has developed a landslide hazard assessment tool, GeoSure, which provides information on the relative landslide hazard susceptibility at national scale. 相似文献
19.
Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models 总被引：23，自引：5，他引：18
This paper summarizes findings of landslide hazard analysis on Penang Island, Malaysia, using frequency ratio, logistic regression, and artificial neural network models with the aid of GIS tools and remote sensing data. Landslide locations were identified and an inventory map was constructed by trained geomorphologists using photo-interpretation from archived aerial photographs supported by field surveys. A SPOT 5 satellite pan sharpened image acquired in January 2005 was used for land-cover classification supported by a topographic map. The above digitally processed images were subsequently combined in a GIS with ancillary data, for example topographical (slope, aspect, curvature, drainage), geological (litho types and lineaments), soil types, and normalized difference vegetation index (NDVI) data, and used to construct a spatial database using GIS and image processing. Three landslide hazard maps were constructed on the basis of landslide inventories and thematic layers, using frequency ratio, logistic regression, and artificial neural network models. Further, each thematic layer’s weight was determined by the back-propagation training method and landslide hazard indices were calculated using the trained back-propagation weights. The results of the analysis were verified and compared using the landslide location data and the accuracy observed was 86.41, 89.59, and 83.55% for frequency ratio, logistic regression, and artificial neural network models, respectively. On the basis of the higher percentages of landslide bodies predicted in very highly hazardous and highly hazardous zones, the results obtained by use of the logistic regression model were slightly more accurate than those from the other models used for landslide hazard analysis. The results from the neural network model suggest the effect of topographic slope is the highest and most important factor with weightage value (1.0), which is more than twice that of the other factors, followed by the NDVI (0.52), and then precipitation (0.42). Further, the results revealed that distance from lineament has the lowest weightage, with a value of 0. This shows that in the study area, fault lines and structural features do not contribute much to landslide triggering. 相似文献
20.
Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models 总被引：15，自引：10，他引：5
This paper presents landslide hazard analysis at Cameron area, Malaysia, using a geographic information system (GIS) and remote sensing data. Landslide locations were identified from interpretation of aerial photographs and field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence are topographic slope, topographic aspect, topographic curvature, and distance to rivers, all from the topographic database; lithology and distance to faults were taken from the geologic database; land cover from TM satellite image; the vegetation index value was taken from Landsat images; and precipitation distribution from meteorological data. Landslide hazard area was analyzed and mapped using the landslide occurrence factors by frequency ratio and bivariate logistic regression models. The results of the analysis were verified using the landslide location data and compared with the probabilistic models. The validation results showed that the frequency ratio model (accuracy is 89.25%) is better in prediction of landslide than bivariate logistic regression (accuracy is 85.73%) model. 相似文献