基于GIS与ANN模型的地震滑坡易发性区划

基于遥感数据、地理信息系统(GIS)技术和人工神经网络(ANN)模型,开展地震滑坡易发性区划研究.2010年4月14日玉树地震后,基于航片与卫星影像目视解译,并辅以野外调查的方法,在地震区圈定了2036处地震诱发滑坡.选择高程、坡度、坡向、斜坡曲率、坡位、与水系距离、地层岩性、与断裂距离、与公路距离、归一化植被指数(NDVI)、与同震地表破裂距离、地震动峰值加速度(PGA)共12个因子作为地震滑坡易发性评价因子.这些因子均是应用GIS技术与遥感影像处理技术,基于地形数据、地质数据、遥感数据得到.训练样本中的滑动样本有两组,一组是滑坡区整个单滑坡体的质心位置,另一组是滑坡滑源区滑前的坡体高程最高的位置.应用这12个影响因子,分别采用这两组评价样本,基于ANN模型建立地震滑坡易发性索引图,基于GIS工具建立地震滑坡易发性分级图.分别应用训练样本中滑坡分布的点数据去检验各自的结果正确率,正确率分别为81.53％与81.29％,表明ANN模型是一种高效科学的地震滑坡易发性区划模型.     

基于GIS空间分析的山东青岛崂山区地质灾害易发程度区划

崂山区地质环境条件复杂,人类工程活动强烈,崩塌、滑坡、泥石流地质灾害频发,严重威胁居民安全,影响旅游业发展。本文针对以上3类地质灾害,分别选择8个评价因子进行打分,然后进行网格剖分,再采用综合指数法并利用Mapgis空间分析功能,得出崩塌与滑坡、泥石流的易发程度分区,最后通过叠加得到全区易发程度综合区划,划分出高、中、低和非易发区,为地质灾害防治提供依据。     

基于分形理论的南江县滑坡敏感性分析与易发性评价

定量分析滑坡发生的敏感性,能为易发性区划、危险性评价、风险性评估等提供定量依据,对研究滑坡的成灾背景、发育规律具有重要意义。文章基于ArcGIS技术应用“累计和分形理论”对滑坡的敏感性进行了分析,得到各致灾因子的累计和分维值及滑坡发生对各致灾因子的敏感性;基于滑坡对致灾因子的敏感性绘制南江县易发性区划图,将南江县滑坡易发性等级分为极高易发区、高易发区、中易发区、较低易发区、极低易发区五个等级。     

利用证据权法实现滑坡易发性区划

依托“5.12”特大地震的抗震救灾工作,以汶川地震12个极重灾县市为研究对象,在1:5万滑坡详细调查、编录和遥感影像解译的基础上,利用DEM数据,ETM影像及基础地质数据,使用证据权法完成了研究区滑坡易发性评价因子的提取与制图以及相关性统计分析,实现了1:5万的滑坡易发性区划。     

吉林省地质灾害易发程度分区方法研究

地质灾害易发程度分区是地质灾害防治工作中的重要组成部分,是编制地质灾害防治规划的基础。本文所述地质灾害是地质灾害防治条例中规定的山体崩塌、滑坡、泥石流、地面塌陷、地裂缝、地面沉降等。这些地质灾害的发生与地形地貌、岩体类型、密度、降水强度等地质环境条件及人为工程活动等密切相关,本文对吉林省地质灾害易发地区进行了研究。     

基于RS和GI S的三峡库区滑坡易发程度与土地利用变化的关系研究

三峡工程建设以来,库区土地利用类型发生重大变化.为深入研究三峡库区蓄水以来滑坡发育与土地利用类型变化的关系,本文利用不同时期(1987年、2000年和2010年)卫星影像,研究三峡库首区秭归至巴东段长江干流土地利用类型变化,采用滑坡面积模数比(Rsi)分析滑坡发育与土地类型及变化的关联性.结果表明:(1)在1987～2...     

海南省保亭县地质灾害易发程度区划

结合海南省保亭县1：5万地质灾害详细调查项目,根据野外实际调查与遥感成果,选取地质构造、地形地貌及人类活动3个大类,构造、工程地质岩组、坡度、坡高、海拔高度、植被覆盖率、河流及公路等8个小类作为地质灾害影响评价因子.在ArcGIS平台上运用AHP法,进行评价因子权重计算并建立评价模型,完成保亭县地质灾害易发程度区划,划分出高、中、低易发区,可为今后地质灾害预测与防治提供科学依据.     

基于GIS的哀牢山戛洒镇小流域滑坡易发性评价

根据研究区的基本情况,选择坡度、坡向、地层岩性、距断层距离、降雨、土地利用等6个评价因子,采用滑坡灾害易发性评价的GIS与AHP耦合模型进行戛洒镇滑坡灾害易发性评价,并将滑坡灾害分为极高、高、中、低和极低易发区5个区域进行了滑坡灾害易发性评价结果分析,以期为后期的小流域滑坡风险评估研究服务。     

基于GIS空间分析的吉林省辉南县地质灾害易发程度评价

辉南县地质环境条件复杂,人类工程活动强烈,崩塌、滑坡、泥石流、地面塌陷等地质灾害频发,严重威胁当地居民安全,影响旅游业和当地的经济发展,本文针对影响以上4类地质灾害的易发条件,分布选取适合的评价因子进行打分,然后进行网格剖分,再采用综合指数法并利用Mapgis空间分析功能,得出崩塌滑坡、泥石流、地面塌陷的易发程度分区图,最后通过叠加得到全区地质灾害易发程度综合区划,划分出高、中、低三类易发区,为地质灾害防治提供依据。     

基于层次分析法和GIS的甘肃省武山县地质灾害易发性区划研究

武山县地质构造复杂,岩土体破碎,泥石流、滑坡、崩塌等地质灾害发育。以GIS平台的空间分析和栅格计算功能为基础,选取区内对地质环境影响较大的和易量化的因素作为评价因子,通过层次分析法确定各评价因子的权重,通过栅格化各评价因子形成叠加运算图层,结合计算分析结果和野外调查认识,形成最终的地质灾害易发性分区图,较之传统的评价和分区方法具有半定量评价与定性认识相结合的改进,在具体实践中具有较明显的优点。     

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.     

Application and verification of fuzzy algebraic operators to landslide susceptibility mapping

The aim of this study was to apply and to verify the use of fuzzy logic to landslide susceptibility mapping in the Gangneung area, Korea, using a geographic information system (GIS). For this aim, in the study, a data-derived model (frequency ratio) and a knowledge-derived model (fuzzy operator) were combined. Landslide locations were identified by changing the detection technique of KOMPSAT-1 images and checked by field studies. For landslide susceptibility mapping, maps of the topography, lineaments, soil, forest, and land cover were extracted from the spatial data sets, and the eight factors influencing landslide occurrence were obtained from the database. Using the factors and the identified landslide, the fuzzy membership values were calculated. Then fuzzy algebraic operators were applied to the fuzzy membership values for landslide susceptibility mapping. Finally, the produced map was verified by comparing with existing landslide locations for calculating prediction accuracy. Among the fuzzy operators, in the case in which the gamma operator (λ = 0.975) showed the best accuracy (84.68%) while the case in which the fuzzy or operator was applied showed the worst accuracy (66.50%).     

空间三维滑坡敏感性分区工具及其应用

对于滑坡敏感性分区目前有三种方法:定性法、统计法和基于岩土定量模型的确定性方法。定性法基于对滑坡敏感性或灾害评估的人为判断;统计法用一个来源于结合了权重因子的预测函数或指标;而确定性法,或者说是物理定量模型法以质量、能量和动量守恒定律为基础。二维确定性模型广泛用于土木工程设计,而无限边坡模型(一维)也用于滑坡灾害分区的确定性模型。文中提出了一个新的基于GIS(地理信息系统)的滑坡敏感性分区系统,这个系统可用于从复杂地形中确认可能的危险三维(3-D)滑坡体。所有与滑坡相关的空间数据(矢量或栅格数据)都被集成到这个系统中。通过把研究区域划分为边坡单元并假定初始滑动面是椭球的下半部分,并使用Monte Carlo随机搜索法,三维滑坡稳定性分析中的三维最危险滑面是三维安全系数最小的地方。使用近似方法假定有效凝聚力、有效摩擦角和三维安全系数服从正态分布,可以计算出滑坡失稳概率。3DSlopeGIS是一个计算机程序,它内嵌了GIS Developer kit(ArcObjects of ESRI)来实现GIS空间分析功能和有效的数据管理。应用此工具可以解决所有的三维边坡空间数据解问题。通过使用空间分析、数据管理和GIS的可视化功能来处理复杂的边坡数据,三维边坡稳定性问题很容易用一个友好的可视化图形界面来解决。将3DSlopeGIS系统应用到3个滑坡敏感性分区的实例中:第一个是一个城市规划项目,第二个是预测以往滑坡灾害对临近区域可能的影响,第三个则是沿着国家主干道的滑坡分区。基于足够次数的Monte Carlo模拟法,可以确认可能的最危险滑坡体。这在以往的传统边坡稳定性分析中是不可能的。     

GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping

Landslide susceptibility mapping is a vital tool for disaster management and planning development activities in mountainous terrains of tropical and subtropical environments. In this paper, the weights-of-evidence modelling was applied, within a geographical information system (GIS), to derive landslide susceptibility map of two small catchments of Shikoku, Japan. The objective of this paper is to evaluate the importance of weights-of-evidence modelling in the generation of landslide susceptibility maps in relatively small catchments having an area less than 4 sq km. For the study area in Moriyuki and Monnyu catchments, northeast Shikoku Island in west Japan, a data set was generated at scale 1:5,000. Relevant thematic maps representing various factors (e.g. slope, aspect, relief, flow accumulation, soil depth, soil type, land use and distance to road) that are related to landslide activity were generated using field data and GIS techniques. Both catchments have homogeneous geology and only consist of Cretaceous granitic rock. Thus, bedrock geology was not considered in data layering during GIS analysis. Success rates were also estimated to evaluate the accuracy of landslide susceptibility maps and the weights-of-evidence modelling was found useful in landslide susceptibility mapping of small catchments.     

Rainfall event-based landslide susceptibility zonation mapping

Landslide susceptibility assessment is a major research topic in geo-disaster management. In recent days, various landslide susceptibility and landslide hazard assessment methodologies have been introduced with diverse thoughts of assessment and validation method. Fundamentally, in landslide susceptibility zonation mapping, the susceptibility predictions are generally made in terms of likelihoods and probabilities. An overview of landslide susceptibility zoning practices in the last few years reveals that susceptibility maps have been prepared to have different accuracies and reliabilities. To address this issue, the work in this paper focuses on extreme event-based landslide susceptibility zonation mapping and its evaluation. An ideal terrain of northern Shikoku, Japan, was selected in this study for modeling and event-based landslide susceptibility mapping. Both bivariate and multivariate approaches were considered for the zonation mapping. Two event-based landslide databases were used for the susceptibility analysis, while a relatively new third event landslide database was used in validation. Different event-based susceptibility zonation maps were merged and rectified to prepare a final susceptibility zonation map, which was found to have an accuracy of more than 77 %. The multivariate approach was ascertained to yield a better prediction rate. From this study, it is understood that rectification of susceptibility zonation map is appropriate and reliable when multiple event-based landslide database is available for the same area. The analytical results lead to a significant understanding of improvement in bivariate and multivariate approaches as well as the success rate and prediction rate of the susceptibility maps.     

Predictive landslide susceptibility mapping using spatial information in the Pechabun area of Thailand

For predictive landslide susceptibility mapping, this study applied and verified probability model, the frequency ratio and statistical model, logistic regression at Pechabun, Thailand, using a geographic information system (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys, and maps of the topography, geology and land cover were constructed to spatial database. The factors that influence landslide occurrence, such as slope gradient, slope aspect and curvature of topography and distance from drainage were calculated from the topographic database. Lithology and distance from fault were extracted and calculated from the geology database. Land cover was classified from Landsat TM satellite image. The frequency ratio and logistic regression coefficient were overlaid for landslide susceptibility mapping as each factor’s ratings. Then the landslide susceptibility map was verified and compared using the existing landslide location. As the verification results, the frequency ratio model showed 76.39% and logistic regression model showed 70.42% in prediction accuracy. The method can be used to reduce hazards associated with landslides and to plan land cover.     

Determination and application of the weights for landslide susceptibility mapping using an artificial neural network

The purpose of this study is the development, application, and assessment of probability and artificial neural network methods for assessing landslide susceptibility in a chosen study area. As the basic analysis tool, a Geographic Information System (GIS) was used for spatial data management and manipulation. Landslide locations and landslide-related factors such as slope, curvature, soil texture, soil drainage, effective thickness, wood type, and wood diameter were used for analyzing landslide susceptibility. A probability method was used for calculating the rating of the relative importance of each factor class to landslide occurrence. For calculating the weight of the relative importance of each factor to landslide occurrence, an artificial neural network method was developed. Using these methods, the landslide susceptibility index (LSI) was calculated using the rating and weight, and a landslide susceptibility map was produced using the index. The results of the landslide susceptibility analysis, with and without weights, were confirmed from comparison with the landslide location data. The comparison result with weighting was better than the results without weighting. The calculated weight and rating can be used to landslide susceptibility mapping.     

A multi-method approach to study the stability of natural slopes and landslide susceptibility mapping

In this paper, a multi-method approach for the assessment of the stability of natural slopes and landslide hazard mapping applied to the Dakar coastal region is presented. This approach is based on the effective combination of geotechnical field and laboratory works, of GIS, and of mechanical (deterministic and numerical) stability analysis. By using this approach, valuable results were gained regarding instability factors, landslide kinematics, simulation of slope failure and coastal erosion. This led to a thorough assessment and strong reduction in the subjectivity of the slope stability and hazard assessment and to the development of an objective landslide danger map of the SW coast of Dakar. Analysis of the results shows that the slides were influenced by the geotechnical properties of the soil, the weathering, the hydrogeological situation, and the erosion by waves. The landslide susceptibility assessment based on this methodological approach has allowed for an appropriate and adequate consideration of the multiple factors affecting the stability and the optimization of planning and investment for land development in the city.     

A heuristic approach to global landslide susceptibility mapping

Landslides can have significant and pervasive impacts to life and property around the world. Several attempts have been made to predict the geographic distribution of landslide activity at continental and global scales. These efforts shared common traits such as resolution, modeling approach, and explanatory variables. The lessons learned from prior research have been applied to build a new global susceptibility map from existing and previously unavailable data. Data on slope, faults, geology, forest loss, and road networks were combined using a heuristic fuzzy approach. The map was evaluated with a Global Landslide Catalog developed at the National Aeronautics and Space Administration, as well as several local landslide inventories. Comparisons to similar susceptibility maps suggest that the subjective methods commonly used at this scale are, for the most part, reproducible. However, comparisons of landslide susceptibility across spatial scales must take into account the susceptibility of the local subset relative to the larger study area. The new global landslide susceptibility map is intended for use in disaster planning, situational awareness, and for incorporation into global decision support systems.