RS & GIS-based landslide susceptibility mapping of the Balason River basin,Darjeeling Himalaya,using logistic regression (LR) model

The present study deals with the preparation of a landslide susceptibility map of the Balason River basin, Darjeeling Himalaya, using a logistic regression model based on Geographic Information System and Remote Sensing. The landslide inventory map was prepared with a total of 295 landslide locations extracted from various satellite images and intensive field survey. Topographical maps, satellite images, geological, geomorphological, soil, rainfall and seismic data were collected, processed and constructed into a spatial database in a GIS environment. The chosen landslide-conditioning factors were altitude, slope aspect, slope angle, slope curvature, geology, geomorphology, soil, land use/land cover, normalised differential vegetation index, drainage density, lineament number density, distance from lineament, distance to drainage, stream power index, topographic wetted index, rainfall and peak ground acceleration. The produced landslide susceptibility map satisfied the decision rules and ?2 Log likelihood, Cox &; Snell R-Square and Nagelkerke R-Square values proved that all the independent variables were statistically significant. The receiver operating characteristic curve showed that the prediction accuracy of the landslide probability map was 96.10%. The proposed LR method can be used in other hazard/disaster studies and decision-making.     

Qualitative landslide susceptibility assessment by multicriteria analysis: A case study from San Antonio del Sur,Guantánamo,Cuba

Landslide hazard mapping is essential for regional landslide hazard management. The main objective of this study is to construct a rainfall-induced landslide hazard map of Luhe County, China based on an automated machine learning framework (AutoGluon). A total of 2241 landslides were identified from satellite images before and after the rainfall event, and 10 impact factors including elevation, slope, aspect, normalized difference vegetation index (NDVI), topographic wetness index (TWI), lithology, land cover, distance to roads, distance to rivers, and rainfall were selected as indicators. The WeightedEnsemble model, which is an ensemble of 13 basic machine learning models weighted together, was used to output the landslide hazard assessment results. The results indicate that landslides were mainly occurred in the central part of the study area, especially in Hetian and Shanghu. Totally 102.44 s were spent to train all the models, and the ensemble model WeightedEnsemble has an Area Under the Curve (AUC) value of 92.36% in the test set. In addition, 14.95% of the study area was determined to be at very high hazard, with a landslide density of 12.02 per square kilometer. This study serves as a significant reference for the prevention and mitigation of geological hazards and land use planning in Luhe County.     

Landslide hazard mapping along national highway-154A in Himachal Pradesh,India using information value and frequency ratio

For the socio-economic development of a country, the highway network plays a pivotal role. It has therefore become an imperative to have landslide hazard assessment along these roads to provide safety. The current study presents landslide hazard zonation maps, based on the information value method and frequency ratio method using GIS on 1:50,000 scale by generating the information about the landslide influencing factors. The study was carried out in the year 2017 on a part of Ravi river catchment along one of the landslide-prone Chamba to Bharmour road corridor of NH-154A in Himachal Pradesh, India. A number of landslide triggering geo-environmental factors like “slope, aspect, relative relief, soil, curvature, Land Use and Land Cover (LULC), lithology, drainage density, and lineament density” were selected for landslide hazard mapping based on landslide inventory. The landslide inventory has been developed using satellite imagery, Google earth and by doing exhaustive field surveys. A digital elevation model was used to generate slope gradient, slope aspect, curvature, and relative relief map of the study area. The other information, i.e., soil maps, geological maps, and toposheets, have been collected from various departments. The landslide hazard zonation map was categorized namely “very high hazard, high hazard, medium hazard, low hazard, and very low hazard.” The results from these two methods have been validated using area under curve (AUC) method. It has been found that hazard zonation map prepared using frequency ratio model had a prediction rate of 75.37% while map prepared using information value method had prediction rate of 78.87%. Hence, on the basis of prediction rate, the landslide hazard zonation map, obtained using information value method, was experienced to be more suitable for the study area.     

Sensitivity analysis for the GIS-based mapping of the ground subsidence hazard near abandoned underground coal mines

Ground subsidence around abandoned underground coal mines can cause much loss of life and property. We analyze factors that can affect ground subsidence around abandoned mines in Jeongahm in Kangwon-do by sensitivity analysis in geographic information system (GIS). Spatial data for the subsidence area, topography and geology and various ground engineering data were collected and used to make a factor raster database for a ground subsidence hazard map. To determine the importance of extracted subsidence-related factors, frequency ratio model and sensitivity analysis were employed. Sensitivity analysis is a method for comparing the combined effects of all factors except one. Sensitivity analysis and its verification showed that using all factors provided 91.61% accuracy. The best accuracy was achieved by not considering the groundwater depth (92.77%) and the worst by not considering the lineament (85.42%). The results show that the distance from the lineament and the distance from the drift highly affected the occurrence of ground subsidence, and the groundwater depth, land use and rock mass rating had the least effects. Thus, we determined causes of ground subsidence in the study area and this information could help in the prediction of ground subsidence in other areas.     

Study of regional monsoonal effects on landslide hazard zonation in Cameron Highlands, Malaysia

In general, landslides in Malaysia mostly occurred during northeast and southwest periods, two monsoonal systems that bring heavy rain. As the consequence, most landslide occurrences were induced by rainfall. This paper reports the effect of monsoonal-related geospatial data in landslide hazard modeling in Cameron Highlands, Malaysia, using Geographic Information System (GIS). Land surface temperature (LST) data was selected as the monsoonal rainfall footprints on the land surface. Four LST maps were derived from Landsat 7 thermal band acquired at peaks of dry and rainy seasons in 2001. The landslide factors chosen from topography map were slope, slope aspect, curvature, elevation, land use, proximity to road, and river/lake; while from geology map were lithology and proximity to lineament. Landslide characteristics were extracted by crossing between the landslide sites of Cameron Highlands and landslide factors. Using which, the weighting system was derived. Each landslide factors were divided into five subcategories. The highest weight values were assigned to those having the highest number of landslide occurrences. Weighted overlay was used as GIS operator to generate landslide hazard maps. GIS analysis was performed in two modes: (1) static mode, using all factors except LST data; (2) dynamic mode, using all factors including multi-temporal LST data. The effect of addition of LST maps was evaluated. The final landslide hazard maps were divided into five categories: very high risk, high risk, moderate, low risk, and very low risk. From verification process using landslide map, the landslide model can predict back about 13–16% very high risk sites and 70–93% of very high risk and high risk combined together. It was observed however that inclusion of LST maps does not necessarily increase the accuracy of the landslide model to predict landslide sites.     

利用光学遥感数据、GIS及人工神经网络模型分析区域滑坡灾害

用光学遥感数据和地理信息系统(GIS)分析了马来西亚Selangor地区的滑坡灾害。通过遥感图像解译和野外调查,在研究区内确定出滑坡发生区。通过GIS和图像处理,建立了一个集地形、地质和遥感图像等多种信息的空间数据库。滑坡发生的因素主要为:地形坡度、地形方位、地形曲率及与排水设备距离;岩性及与线性构造距离;TM图像解译得到的植被覆盖情况;Landsat图像解译得到的植被指数;降水量。通过建立人工神经网络模型对这些因素进行分析后得到滑坡灾害图:由反向传播训练方法确定每个因素的权重值,然后用该权重值计算出滑坡灾害指数,最后用GIS工具生成滑坡灾害图。用遥感解译和野外观测确定出的滑坡位置资料验证了滑坡灾害图,准确率为82.92%。结果表明推测的滑坡灾害图与滑坡实际发生区域足够吻合。     

Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia

This paper deals with landslide hazards and risk analysis of Penang Island, Malaysia using Geographic Information System (GIS) and remote sensing data. Landslide locations in the study area were identified from interpretations of aerial photographs and field surveys. Topographical/geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for landslide hazard analysis. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide susceptibility was analyzed using landslide-occurrence factors employing the probability-frequency ratio model. The results of the analysis were verified using the landslide location data and compared with the probabilistic model. The accuracy observed was 80.03%. The qualitative landslide hazard analysis was carried out using the frequency ratio model through the map overlay analysis in GIS environment. The accuracy of hazard map was 86.41%. Further, risk analysis was done by studying the landslide hazard map and damageable objects at risk. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network.     

基于随机森林-特征递归消除模型的可解释性缓丘岭谷地貌滑坡易发性评价

研究旨在基于随机森林-特征递归消除模型,通过SHAP算法(SHapley Additive exPlanation, SHAP)与部分依赖图(Partial Dependence Plot, PDP)对缓丘岭谷地貌区域进行滑坡易发性评价与内部机制解释,以期为地质灾害防治研究提供参考。利用优化随机森林算法对典型缓丘岭谷地区滑坡易发性进行研究,建立缓丘岭谷滑坡易发性评价模型;利用特征递归消除算法剔除噪声因子,选取地形地貌、地质构造、环境条件、人类活动5个类型16个因子构建重庆合川区滑坡致灾因子数据库;结合合川区754个历史滑坡点,利用随机森林算法对因子重要性进行排序,并根据专家经验法对研究区的滑坡易发性进行划分,将研究区的滑坡易发性分为极低、低、中、高、极高5个等级;应用部分依赖图对合川区滑坡发生影响大的因子进行解释和SHAP算法对个体滑坡进行局部解释。结果表明：与原模型相比,随机森林-特征递归消除模型测试集AUC值提高了0.019,证明了特征递归消除算法的有效性;训练集以及测试集的AUC值分别为0.769、0.755,具有较高的预测精度;缓丘缓坡地区在起伏较大地区滑坡密度较大,历史滑坡多集中于高易发地区;滑坡的空间分布具有不均匀性与复杂性,各致灾因子对滑坡发生的影响有着明显的区域特征与空间异质性,在缓坡丘陵地区多年平均降雨、高程、岩性3个因子对滑坡发生的影响最大;由SHAP算法对合川白塔坪上山公路滑坡事件进行解释,岩性与高程对滑坡起抑制作用,起伏度、坡度、归一化植被指数(NDVI)与POI核密度促进滑坡发生。综上所述,基于随机森林-特征递归消除模型在缓丘岭谷区滑坡易发性评价中具有较高的准确性,通过部分依赖图与SHAP算法对全局滑坡与个体滑坡发生的内在机理进行解释分析,有利于构建与完善不同地貌环境下滑坡易发性评价因子体系并探究滑坡内部决策机理,可为区域滑坡易发性评估与地质灾害防治提供参考。     

The application of GIS-based bivariate statistical methods for landslide hazards assessment in the upper Tons river valley,Western Himalaya,India

Landslides are one of the most frequent and common natural hazards in many parts of Himalaya. To reduce the potential risk, the landslide susceptibility maps are one of the first and most important steps in the landslide hazard mitigation. Earth observation satellite and geographical information system-based techniques have been used to derive and analyse various geo-environmental parameters significant to landslide hazards. In this study, a bivariate statistics method was used for spatial modelling of landslide susceptibility zones. For this purpose, thematic layers including landslide inventory, geology, slope angle, slope aspect, geomorphology, slope morphology, drainage density, lineament and land use/land cover were used. A large number of landslide occurrences have been observed in the upper Tons river valley area of Western Himalaya. The result has been used to spatially classify the study area into zones of very high, high, moderate, low and very low landslide susceptibility zones. About 72% of active landslides have been observed to occur in very high and high hazard zones. The result of the analysis was verified using the landslide location data. The validation result shows significant agreement between the susceptibility map and landslide location. The result can be used to reduce landslide hazards by proper planning.     

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.     

遗传算法优化BP网络在滑坡灾害预测中的应用研究

在陕西省宝鸡市附近长寿沟地区滑坡详细调查和遥感解译的基础上,完成了1∶10000滑坡编目图。通过使用GIS的水文分析功能,运用正反DEM技术,将长寿沟地区划分为216个自然斜坡单元,其中包括123个滑坡单元和93个未发生滑坡单元,分析滑坡发生与坡高、坡度、坡向、坡形、人类工程活动和水文地质条件影响因子之间的统计规律。利用经遗传算法优化后的BP神经网络对80个滑坡样本和40个未滑坡样本进行训练学习,然后再利用训练好的网络对预测样本进行评价分析。结果表明:43个已滑坡单元中只有3个被误判为无滑坡,正确率为9302%,53个未滑坡单元中有10个被预测为滑坡,正确率为8113%,总体正确率为8646%。通过对被预测为滑坡的10个斜坡单元进行分析,发现这些单元在坡形、坡高等影响因素的组合上已经具备了发生滑坡的条件,虽然目前没有发生滑坡,但作为潜在的滑坡危险区,可以为滑坡灾害预测预报和防灾减灾工作提供参考。     

Relative effect-based landslide hazard zonation mapping in parts of Nilgiris, Tamil Nadu, South India

The Nilgiri massif, South India, is chronically prone to landslides due to deforestation and the resultant direct entry of rainwater and the final increases of pore pressure leading to landslides in the region. In order to understand such landslide causes, the relative effect method, a new technique, has been adopted for the study area. Among various methods, this is a statistical method developed within the framework of the Geographic Information System to map landslide hazard zones in a mountainous environment. To determine the relative effect (RE) of the factors influencing landslides, data layers of geology, land use/land cover, geomorphology, slope, lineament density, drainage density, and soil were analyzed by calculating the ratio of the unit portion in coverage and landslide, this function that is logarithmic. To quantify the magnitude of factors influencing each grid unit, REs were summed and classified into zones of low-, moderate-, and high-landslide hazard zones. It is also appropriate to follow suitable measures to prevent the landslides in the study area by involving all stockholders and with the active participation of local communities.     

基于简化 Newmark位移模型的区域地震滑坡危险性快速评估以汶川MS 8.0级地震为例

以汶川MS8.0级地震重灾区的11县市为例,初步提出了基于简化Newmark位移模型的地震滑坡危险性应急快速评估方法。利用汶川地震即时地震动参数、工程地质岩性经验分组及地形坡度数据,借助ArcGIS空间数据建模工具编制了地震滑坡危险性快速评估流程模块。计算了区域浅表层饱和岩土体斜坡的静态安全系数Fs、临界加速度ac,并借此分析了地震滑坡易发性。利用经验式获得了汶川地震Arias强度和区域滑坡位移DN分布,实现了汶川地震重灾区地震滑坡危险性的快速评估,为应急救灾决策提供了参考。通过对比评估结果和震后滑坡调查成果,可知数十处灾难性滑坡绝大部分位于-高危险区的龙门山主中央断裂带两侧约20km地带中,显示了评估方法的可靠性; 同时,分析指出了空间数据精度及更新不足导致局部评估结果欠佳的局限性,并提出了改进建议。     

基于RS与GIS的层次分析法在滑坡危险性评估中的应用——以四川宣汉天台乡滑坡为例

基于遥感(RS)和地理信息系统(GIS)技术,采用多层次分析(AHP)法,以四川宣汉天台乡为研究区,根据该区实际情况,选取线性构造、道路、土地利用、坡度、坡向5种影响滑坡灾害发生的因素作为评价因子,进行区域滑坡危险性评估。在ArcGIS的空间分析环境中运行权重叠加,把研究区划分成滑坡极易发生区、易发生区、一般发生区、可能发生区、难发生区和极难发生区。通过实地调查和与研究区的滑坡灾害实证研究结果进行比较,发现评估结果与实际状况较为吻合,研究方法能够准确地评估区域滑坡灾害危险性的程度。     

逻辑回归模型在玉树地震滑坡危险性评价中的应用与检验

2010年4月14日07时49分(北京时间),青海省玉树县发生了Ms7.1级大地震。作者基于高分辨率遥感影像解译与现场调查验证的方法,圈定了2036处本次地震诱发滑坡。这些滑坡受地震地表破裂控制强烈,规模相对较小,常常密集成片分布。滑坡类型多样,以崩塌型滑坡为主,还包括滑动型、流滑型、碎屑流型、复合型等类型的滑坡。本文基于地理信息系统(GIS)与遥感(RS)技术,应用逻辑回归模型开展玉树地震滑坡危险性评价,并对结果合理性进行检验。应用GIS技术建立玉树地震滑坡灾害及相关滑坡影响因子空间数据库,选择高程、斜坡坡度、斜坡坡向、斜坡曲率、与水系距离、坡位、断裂、地层岩性、归一化植被指数(NDVI)、公路、同震地表破裂、地震动峰值加速度(PGA)共12个因子作为玉树地震滑坡影响因子,在GIS平台下将这些因子专题图层栅格化。应用逻辑回归模型得到每个因子分级的回归系数,然后建立滑坡危险性指数分布图。利用玉树地震滑坡空间分布图对滑坡危险性指数图进行检验,正确率达到83.21%。滑坡危险性分级结果表明,在占研究区总面积4.97%的\     

Frequency ratio model based landslide susceptibility mapping in lower Mae Chaem watershed, Northern Thailand

The purpose of this study is to produce a landslide susceptibility map for the lower Mae Chaem watershed, northern Thailand using a Geographic Information System (GIS) and remotely sensed images. For this purpose, past landslide locations were identified from satellite images and aerial photographs accompanied by the field surveys to create a landslide inventory map. Ten landslide-inducing factors were used in the susceptibility analysis: elevation, slope angle, slope aspect, lithology, distance from lineament, distance from drainage, precipitation, soil texture, land use/land cover (LULC), and NDVI. The first eight factors were prepared from their associated database while LULC and NDVI maps were generated from Landsat-5 TM images. Landslide susceptibility was analyzed and mapped using the frequency ratio (FR) model that determines the level of correlation between locations of past landslides and the chosen factors and describes it in terms of frequency ratio index. Finally, the output map was validated using the area under the curve (AUC) method where the success rate of 80.06% and the prediction rate of 84.82% were achieved. The obtained map can be used to reduce landslide hazard and assist with proper planning of LULC in the future.     

Automated derivation and spatio-temporal analysis of landslide properties in southern Kyrgyzstan

The study area located in southern Kyrgyzstan is affected by high and ongoing landslide activity. To characterize this activity, a multi-temporal landslide inventory containing over 2800 landslide polygons was generated from multiple data sources. The latter include the results of automated landslide detection from multi-temporal satellite imagery. The polygonal representation of the landslides allows for characterization of the landslide geometry and determination of further landslide attributes in a way that accounts for the diversity of conditions within the landslide, e.g., at the landslide main scarp opposed to its toe. To perform such analyses, a methodology for efficient geographic information system (GIS)-based attribute derivation was developed, which includes both standard and customized GIS tools. We derived a number of landslide attributes, including area, length, compactness, slope, aspect, distance to stream and geology. The distributions of these attributes were analyzed to obtain a better understanding of landslide properties in the study area as a preliminary step for probabilistic landslide hazard assessment. The obtained spatial and temporal attribute variations were linked to differences in the environmental characteristics within the study area, in which the geological setting proved to be the most important differentiating factor. Moreover, a significant influence of the different data sources on the distribution of the landslide attribute values was found, indicating the importance of a critical evaluation of the landslide data to be used in landslide hazard assessments.     

Landslide vulnerability mapping using frequency ratio model: a geospatial approach in Bodi-Bodimettu Ghat section, Theni district, Tamil Nadu, India

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.     

http://www.sciencedirect.com/science/article/pii/S167498711400022X

The governing factors that influence landslide occurrences are complicated by the different soil conditions at various sites.To resolve the problem,this study focused on spatial information technology to collect data and information on geology.GIS,remote sensing and digital elevation model(DEM) were used in combination to extract the attribute values of the surface material in the vast study area of SheiPa National Park,Taiwan.The factors influencing landslides were collected and quantification values computed.The major soil component of loam and gravel in the Shei-Pa area resulted in different landslide problems.The major factors were successfully extracted from the influencing factors.Finally,the discrete rough set(DRS) classifier was used as a tool to find the threshold of each attribute contributing to landslide occurrence,based upon the knowledge database.This rule-based knowledge database provides an effective and urgent system to manage landslides.NDVI(Normalized Difference Vegetation Index),VI(Vegetation Index),elevation,and distance from the road are the four major influencing factors for landslide occurrence.The landslide hazard potential diagrams(landslide susceptibility maps) were drawn and a rational accuracy rate of landslide was calculated.This study thus offers a systematic solution to the investigation of landslide disasters.     

Recognition of the regional lineaments of Iran: Using geospatial data and their implications for exploration of metallic ore deposits

The relationship between major structural lineaments and locations of ore deposits in Iran has been investigated using geospatial data. In the course of lineament extraction, satellite images, aeromagnetic data, digital elevation model (DEM) and structural maps were processed and the lineaments and large-scale faults were identified. The extracted lineaments, based on subjective assessment, from each dataset were imported into GIS software and the “lineament map of Iran” was prepared by data integration. The analysis for selecting significant lineament was mainly based on fault correlated lineament and lineament with field map fractures, which was sets as benchmarks for compiling a final output map. Four major regional lineament trends of N–S, E–W, NW–SE and NE–SW were identified in the data of all images, which are corresponded to the structural zones and the major fault systems of Iran. The mineral deposits (active and abandoned) and mineral indications database compiled are based on the published maps, papers, reports and the ore deposits data files of Geological Survey of Iran. Integrating the output of these two datasets by GIS software resulted in the “Combined Map of Lineaments and Gold, Copper, Lead, Zinc and Iron Deposits of Iran”. The number and distance of ore deposits toward the lineaments were processed by the counting and cumulative methods in the GIS software's. Approximately, over 90% of the ore deposits of Iran are located in the central part of the lineaments (15 km on each side) which are concordant with a definition of large lineament. About 50% of these mineral deposits are closer than 5 km to the lineaments. There are significant correlations between lineament density and intersections with ore deposits occurrences. The observed associations at this scale are informative in establishing exploration strategy and decreasing exploration risks for detailed work on ore deposit scale.