Landslide susceptibility mapping using LiDAR and DMC data: a case study in the Three Gorges area, China

The objective of this study is to map landslide susceptibility in Zigui segment of the Yangtze Three Gorges area that is known as one of the most landslide-prone areas in China by using data from light detection and ranging (LiDAR) and digital mapping camera (DMC). The likelihood ratio (LR) and logistic regression model (LRM) were used in this study. The work is divided into three phases. The first phase consists of data processing and analysis. In this phase, LiDAR and DMC data and geological maps were processed, and the landslide-controlling factors were derived such as landslide density, digital elevation model (DEM), slope angle, aspect, lithology, land use and distance from drainage. Among these, the landslide inventories, land use and drainage were constructed with both LiDAR and DMC data; DEM, slope angle and aspect were constructed with LiDAR data; lithology was taken from the 1:250,000 scale geological maps. The second phase is the logistic regression analysis. In this phase, the LR was applied to find the correlation between the landslide locations and the landslide-controlling factors, whereas the LRM was used to predict the occurrence of landslides based on six factors. To calculate the coefficients of LRM, 13,290,553 pixels was used, 29.5 % of the total pixels. The logical regression coefficients of landslide-controlling factors were obtained by logical regression analysis with SPSS 17.0 software. The accuracy of the LRM was 88.8 % on the whole. The third phase is landslide susceptibility mapping and verification. The mapping result was verified using the landslide location data, and 64.4 % landslide pixels distributed in “extremely high” zone and “high” zone; in addition, verification was performed using a success rate curve. The verification result show clearly that landslide susceptibility zones were in close agreement with actual landslide areas in the field. It is also shown that the factors that were applied in this study are appropriate; lithology, elevation and distance from drainage are primary factors for the landslide susceptibility mapping in the area, while slope angle, aspect and land use are secondary.     

Landslide vulnerability and risk assessment for multi-hazard scenarios using airborne laser scanning data (LiDAR)

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.     

Landslide susceptibility analysis with a bivariate approach and GIS in Northern Iran

Globally, landslides cause hundreds of billions of dollars in damage and hundreds of thousands of deaths and injuries each year. A landslide susceptibility map describes areas where landslides are likely to occur in the future by correlating some of the principal factors that contribute to landslides with the past distribution of landslides. A case study is conducted in the mountainous northern Iran. In this study, a landslide susceptibility map of the study area was prepared using bivariate method with the help of the geographic information system. Area density (bivariate) method was used to weight landslide-influencing data layers. An overlay analysis is carried out by evaluating the layers obtained according to their weight and the landslide susceptibility map is produced. The study area was classified into five hazard classes: very low, low, moderate, high, and very high. The percentage distribution of landslide susceptibility degrees was calculated. It was found that about 26% of the study area is classified as very high and high hazard classes.     

Landslide detection and susceptibility mapping using LiDAR and an artificial neural network approach: a case study in the Cuyahoga Valley National Park,Ohio

The purpose of this study was to detect shallow landslides using hillshade maps derived from light detection and ranging (LiDAR)-based digital elevation model (DEM) derivatives. The landslide susceptibility mapping used an artificial neural network (ANN) approach and backpropagation method that was tested in the northern portion of the Cuyahoga Valley National Park (CVNP) located in northeast Ohio. The relationship between landslides and predictor attributes, which describe landform classes using slope, profile and plan curvatures, upslope drainage area, annual solar radiation, and wetness index, was extracted from LiDAR-based DEM using geographic information system (GIS). The approach presented in this paper required a training study area for the development of the susceptibility model and a validation study area to test the model. The results from the validation showed that within the very high susceptibility class, a total of 42.6 % of known landslides that were associated with 1.56 % of total area were correctly predicted. In contrast, the very low susceptibility class that represented 82.68 % of the total area was associated with 1.20 % of known landslides. The results suggest that the majority of the known landslides occur within a small portion of the study area, consistent with field investigation and other studies. Sample probabilistic maps of landslide susceptibility potential and other products from this approach are summarized and presented for visualization to help park officials in effective management and planning.     

基于GIS的概率比率模型的滑坡易发性评价

以中国1∶50万区域环境地质调查(以地质灾害为主)、700个县(市)地质灾害调查与区划调查等(成果)资料为基础,选取地形起伏度、地貌类型、工程地质岩组、地震动峰值加速度、年平均降雨量、土地利用程度综合指数6个评价因子,采用概率比率模型,1 km×1 km评价单元,计算得到全国滑坡易发性评价指数图,并验证了结果的可靠性。进行了易发程度分区,最终得到高易发区、中易发区、低易发区和不易发区4个分区,完成了全国滑坡易发程度分区图。研究表明,概率比率模型方法可以客观、定量地评价滑坡易发性,适用于大区域易发性评价。     

Multi-scenario Rockfall Hazard Assessment Using LiDAR Data and GIS

Transportation corridors that pass through mountainous or hilly areas are prone to rockfall hazard. Rockfall incidents in such areas can cause human fatalities and damage to properties in addition to transportation interruptions. In Malaysia, the North–South Expressway is the most significant expressway that operates as the backbone of the peninsula. A portion of this expressway in Jelapang was chosen as the site of rockfall hazard assessment in multiple scenarios. Light detection and ranging techniques are indispensable in capturing high-resolution digital elevation models related to geohazard studies. An airborne laser scanner was used to create a high-density point cloud of the study area. The use of 3D rockfall process modeling in combination with geographic information system (GIS) is a beneficial tool in rockfall hazard studies. In this study, a 3D rockfall model integrated into GIS was used to derive rockfall trajectories and velocity associated with them in multiple scenarios based on a range of mechanical parameter values (coefficients of restitution and friction angle). Rockfall characteristics in terms of frequency, height, and energy were determined through raster modeling. Analytic hierarchy process (AHP) was used to compute the weight of each rockfall characteristic raster that affects rockfall hazard. A spatial model that considers rockfall characteristics was conducted to produce a rockfall hazard map. Moreover, a barrier location was proposed to eliminate rockfall hazard. As a result, rockfall trajectories and their characteristics were derived. The result of AHP shows that rockfall hazard was significantly influenced by rockfall energy and then by frequency and height. The areas at risk were delineated and the hazard percentage along the expressway was observed and demonstrated. The result also shows that with increasing mechanical parameter values, the rockfall trajectories and their characteristics, and consequently rockfall hazard, were increased. In addition, the suggested barrier effectively restrained most of the rockfall trajectories and eliminated the hazard along the expressway. This study can serve not only as a guide for a comprehensive investigation of rockfall hazard but also as a reference that decision makers can use in designing a risk mitigation method. Furthermore, this study is applicable in any rockfall study, especially in situations where mechanical parameters have no specific values.     

滑坡风险评估GIS的设计与实现

对地理信息系统在评估过程中的作用进行了详细阐述,着重强调了GIS强大的数据管理、空间分析和空间量算功能在滑坡信息分析中的作用.基于模糊综合评判模型,进行了滑坡风险评估地理信息系统的设计与实现.针对地形地貌、地质条件、气候条件、水文条件、植被条件、人为活动、人口密度、工程设施和土地利用等信息,按致灾指标提取与分析、承灾指...     

Landslide detection based on height and amplitude differences using pre- and post-event airborne X-band SAR data

Natural Hazards - The recognition of landslides and making their inventory map are considered to be urgent tasks not only for damage estimation but also for planning rescue and restoration...     

基于GIS的重庆万州区滑坡灾害危险性评价

在充分调查万州区地质环境及滑坡灾害基本特征的基础上,根据资料的有效性和可获得性,选取地表高程、坡度、地层岩性、地质构造、土地利用类型、区域交通建设及河流侵蚀冲刷7个影响滑坡发生的因素作为评价指标,采用AHP法确定各个指标权重并建立滑坡灾害危险性指数模型,通过GIS系统的空间分析功能进行栅格运算,得出研究区滑坡灾害危险性分区。采用上述指标和方法将重庆市万州区的滑坡灾害划分为极高危险区、高危险区、中危险区、低危险区和极低危险区,划分结果符合该区滑坡灾害的实际情况。     

Environmental change detection in the central part of Iraq using remote sensing data and GIS

This study aims to assess the potential of several ancillary input data for the improvement of unsupervised land cover change detection in arid environments. The study area is located in Central Iraq where desertification has been observed. We develop a new scheme based on known robust indices. We employ Landsat (multispectral scanner, thematic mapper, and enhanced thematic mapper) satellite data acquired in 1976, 1990, and 2002. We use the Normalized Deferential Vegetation Index, Normalized Differential Water Index (NDWI), Salinity Index (SI), and Eolian Mapping Index. Two new equations were applied for the SI and the NDWI indices. Validation was performed using ground truth data collected in 16 days. We show that such an approach allows a robust and low-cost alternative for preliminary and large-scale assessments. This study shows that desertification has increased in the study area since 1990.     

基于GIS与WOE-BP模型的滑坡易发性评价

区域滑坡易发性研究对地质灾害风险管理具有重要意义.以往研究中,将多元统计模型与机器学习方法相结合用于滑坡易发性评价的研究较少.以三峡库区万州区为例,首先选取9种指标因子（坡度、坡向、剖面曲率、地表纹理、地层岩性、斜坡结构、地质构造、水系分布及土地利用类型）作为滑坡易发性评价指标.基于证据权模型（weights of evidence,WOE）计算得到的对比度和滑坡面积比与分级面积比的相对大小,对各指标因子进行状态分级;再利用粒子群法优化的BP神经网络模型（PSO-BP）得到各指标因子权重.综合两种模型确定的状态分级权重和指标因子权重（WOE-BP）计算滑坡易发性指数（landslide susceptibility index,LSI）,基于GIS平台得到全区滑坡易发性分区图.结果表明:水系、地层岩性和地质构造是影响万州区滑坡发育的主要指标因子;WOE-BP模型的预测精度为80.8%,优于WOE模型的73.1%和BP神经网络模型的71.6%,可为定量计算指标因子权重和优化滑坡易发性评价提供有效途径.      

Landslide susceptibility zonation using GIS and evidential belief function model

The main goal of this paper is to generate a landslide susceptibility map through evidential belief function (EBF) model by using Geographic Information System (GIS) for Qianyang County, Shaanxi Province, China. At first, a detailed landslide inventory map was prepared, and the following ten landslide-conditioning factors were collected: slope angle, slope aspect, curvature, plan curvature, profile curvature, altitude, distance to rivers, geomorphology, lithology, and rainfall. The landslides were detected from the interpretation of aerial photographs and supported by field surveys. A total of 81 landslides were randomly split into the following two parts: the training dataset 70 % (56 landslides) were used for establishing the model and the remaining 30 % (25 landslides) were used for the model validation. The ArcGIS was used to analyze landslide-conditioning factors and evaluate landslide susceptibility; as a result, a landslide susceptibility map was generated by using EBF and ArcGIS 10.0, thus divided into the following five susceptibility classes: very low, low, moderate, high, and very high. Finally, when we validated the accuracy of the landslide susceptibility map, both the success-rate and prediction-rate curve methods were applied. The results reveal that a final susceptibility map has the success rate of 83.31 % and the prediction rate of 79.41 %.     

Use of GIS Technology in the Prediction and Monitoring of Landslide Hazard

Technologies such as Geographical Information Systems (GIS) have raised great expectations as potential means of coping with natural disasters, including landslides. However, several misconceptions on the potential of GIS are widespread. Prominent among these is the belief that a landslide hazard map obtained by systematic data manipulation within a GIS is assumed to be more objective than a comparable hand-made product derived from the same input data and founded on the same conceptual model. Geographical data can now be handled in a GIS environment by users who are not experts in either GIS or natural hazard process fields. The reality of the successful application of GIS within the landslide hazard domain seems to be somewhat less attractive than current optimistic expectations.In spite of recent achievements, the use of GIS in the domain of prevention and mitigation of natural catastrophes remains a pioneering activity. Diffusion of the technology is still hampered by factors such as the difficulty in acquiring appropriate raw data, the intrinsic complexity of predictive models, the lack of efficient graphical user interfaces, the high cost of digitisation, and the persistence of bottlenecks in hardware capabilities.In addition, researchers are investing more in tuning-up hazard models founded upon existing, often unreliable data than in attempting to initiate long-term projects for the acquisition of new data on the causes of catastrophic events. Governmental institutions are frequently involved in risk reduction projects whose design and implementation appear to be governed more by political issues than by technical ones. There is an unfortunate general tendency to search for data which can be collected at low cost rather than attempting to capture the information which most readily explains the causes of a disaster.If the technical, cultural, economic and political reasons for this unhealthy state cannot be adequately tackled, the International Decade for Natural Disaster Reduction will probably come to an end without achieving significant advances in the prediction and control of natural disasters.     

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...     

The effectiveness of high-resolution LiDAR data combined with PSInSAR data in landslide study

The spatial resolution of digital elevation models (DEMs) is an important factor for reliable landslide studies. Multi-interferometric techniques such as persistent scatterer interferometric synthetic aperture radar (PSInSAR) are used to evaluate the landslide state of activity and its ground deformation velocity, which is commonly measured along the satellite line of sight (LOS). In order to compare velocities measured by different satellites in different periods, their values can be projected along the steepest slope direction, which is the most probable direction of real movement. In order to achieve this result, DEM-derived products are needed. In this paper, the effectiveness of different DEM resolutions was evaluated in order to project ground deformation velocities measured by means of PSInSAR technique in two different case studies in the Messina Province (Sicily, southern Italy): San Fratello and Giampilieri. Three DEMs were used: (i) a 20-m resolution DEM of the Italian Military Geographic Institute (IGM), (ii) a 2-m resolution DEM derived from airborne laser scanning (ALS) light detection and ranging (LiDAR) data for the San Fratello 2010 landslide, and (iii) a 1-m resolution DEM derived from ALS LiDAR data for the area of Giampilieri. The evaluation of the applied method effectiveness was performed by comparing the DEMs elevation with those of each single permanent scatterer (PS) and projecting the measured velocities along the steepest slope direction. Results highlight that the higher DEM resolution is more suitable for this type of analysis; in particular, the PS located nearby the watershed divides is affected by geometrical problems when their velocities are projected along the steepest slope.     

地理信息系统（GIS）中的数据分层和应用

大千世界万物众生,具有物以类聚、人以群分的客观规律,因而促使对客观对象的科学研究皆可采用分门别类的方法去进行,这一理念同样可以延伸到地图制图及地理信息系统等以地物、地图为研究对象的学科中。由此衍生的数据分层是地理信息系统的关键词之一,科学有效的数据分层,是数据管理、GIS地图编制、制图综合、专题制图、空间分析的前提,国内、外著名的CAD、GIS软件都充分利用了层概念和其技术。在讨论层概念的基础上,利用两个在地学界应用比较广泛的和著名的GIS软件即我国具有自主版权的MapGIS和来自美国的ArcGIS,仔细研究和分析了层概念在软件中的应用;经研究进一步表明,数据分层是GIS技术得以进行空间数据管理、表达及进行空间分析的基础;在此基础上,对主题层、数据层、图层进行了概念上的界定,以此帮助对GIS技术的理解和应用。     

GIS中矢量与栅格数据模型比较

由于GIS软件的多样性，每种软件都有自己特定的数据模型，从数据结构上来说，矢量和栅格是地理信息系统中两种主要的空间数据结构。本文通过对栅格数据与矢量数据模型的应用比较．对于空间数据从需求分析，以满足对数据信息进行更改、更新、增加或者为了某种特定的需要。     

基于 GIS和 AHP的安康市滑坡地质灾害危险性评估

安康市滑坡崩塌频繁发生,对当地造成了不可估量的损失。以安康市地震小区划为依托,对当地滑坡地质灾害的易发性进行研究。在研究区进行野外踏勘和搜集资料的基础上,借助magis和arcgis平台强大的绘图、分析功能,运用层次分析法对滑坡灾害因子进行加权叠加计算,绘制滑坡灾害危险性区划图。区划结果分为四个等级：无危险区、低危险区、中危险区、高危险区。无危险区、低危险区滑坡灾害易发性较低,为适宜城市规划和项目实施工作地带,中危险区、高危险区则要加以治理以减少滑坡地质灾害对居民生命和财产的威胁。