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1.
Landslide hazards are a major natural disaster that affects most of the hilly regions around the world. In India, significant damages due to earthquake induced landslides have been reported in the Himalayan region and also in the Western Ghat region. Thus there is a requirement of a quantitative macro-level landslide hazard assessment within the Indian subcontinent in order to identify the regions with high hazard. In the present study, the seismic landslide hazard for the entire state of Karnataka, India was assessed using topographic slope map, derived from the Digital Elevation Model (DEM) data. The available ASTER DEM data, resampled to 50 m resolution, was used for deriving the slope map of the entire state. Considering linear source model, deterministic seismic hazard analysis was carried out to estimate peak horizontal acceleration (PHA) at bedrock, for each of the grid points having terrain angle 10° and above. The surface level PHA was estimated using nonlinear site amplification technique, considering B-type NEHRP site class. Based on the surface level PHA and slope angle, the seismic landslide hazard for each grid point was estimated in terms of the static factor of safety required to resist landslide, using Newmark’s analysis. The analysis was carried out at the district level and the landslide hazard map for all the districts in the Karnataka state was developed first. These were then merged together to obtain a quantitative seismic landslide hazard map of the entire state of Karnataka. Spatial variations in the landslide hazard for all districts as well as for the entire state Karnataka is presented in this paper. The present study shows that the Western Ghat region of the Karnataka state is found to have high landslide hazard where the static factor of safety required to resist landslide is very high. 相似文献
2.
The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas. 相似文献
3.
Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model 总被引：10，自引：0，他引：10
The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks
with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in
the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as
well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing.
Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature;
2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage;
and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural
network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation
training method has been used for the selection of the five different random training sites in order to calculate the factor’s
weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard
maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide
test locations that were not used during the training phase of the neural network. Our findings of verification results show
an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently
analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis.
The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide
areas. 相似文献
4.
Assessment of Coastal Erosion Vulnerability around Midnapur-Balasore Coast, Eastern India using Integrated Remote Sensing and GIS Techniques 总被引：2，自引：0，他引：2
Adarsa Jana Amit K. Bhattacharya 《Journal of the Indian Society of Remote Sensing》2013,41(3):675-686
Digha coastal region in the northeastern part of the Bay of Bengal is potentially vulnerable to erosional hazard. The present study assessed the coastal erosion vulnerability along this 65 km long coastal stretch located between Rasulpur (Midnapur) and Subarnarekha (Balasore) estuarine complex, which had been subjected to anthropogenic intervention. Multi-resolution Landsat satellite imagery were used for shoreline change study from 1972 to 2010. During this period, accretion was recorded updrift of artificial structures, viz, seawall, groin, pylons and jetties; while, extensive erosion was recorded in downdrift areas of these structures. Assessment was subsequently divided into four categories ranging from “high erosion” to “accretion”. Data from several sources were compiled to map landuse and human activities in the coastal zone. This map was divided into four categories, ranging from “very high capital” to “no capital” landuse. Population density map of the surrounding coastal villages was generated using census data, and divided into four categories ranging from “high density area” to “very low density area”. Subsequently, coastal erosion vulnerability was assessed by combining coastal retreat with landuse type and population density in this study area using simple vector algebraic technique. Zones of vulnerability of different magnitude (viz., very high, high, moderate, and low) have been identified. Furthermore, calculation of “imminent collapse zone (ICZ)” shows that maximum values are around artificial structures and anthropogenic activities. The coastal erosion vulnerability map prepared from this study can be used for proper planning and management of this coastal region. 相似文献
5.
M. A. A. Patwary P. K. Champati ray Irshad Parvaiz 《Journal of the Indian Society of Remote Sensing》2009,37(3):493-509
Structurally disturbed zones of Himalaya are among the worst landslide affected regions in the world. Although landslides
are induced/triggered either by torrential rain during monsoon or by seismic activity in the region, the inherent terrain
conditions characterize the prevailing basic conditions susceptible to landslides. Using remotely sensed data and Geographic
Information System (GIS), geological and terrain factors can be integrated for preparation of factor maps and demarcation
of areas susceptible to landslides. Moderate to high resolution data products available from Indian Remote Sensing satellites
have been utilized for deriving geological and terrain factor maps, which were integrated using knowledge driven heuristic
approach in Integrated Land and Water Information System (ILWIS) GIS. The resultant map shows division of the area into landslide
susceptibility classes ranked in terms of hazard potential in one of the structurally disturbed zones in western Himalaya
around Rishikesh. 相似文献
6.
Binh Thai Pham Dieu Tien Bui Ha Viet Pham Hung Quoc Le Indra Prakash M. B. Dholakia 《Journal of the Indian Society of Remote Sensing》2017,45(4):673-683
Landslide hazard assessment at the Mu Cang Chai district; Yen Bai province (Viet Nam) has been done using Random SubSpace fuzzy rules based Classifier Ensemble (RSSCE) method and probability analysis of rainfall data. RSSCE which is a novel classifier ensemble method has been applied to predict spatially landslide occurrences in the area. Prediction of temporally landslide occurrences in the present study has been done using rainfall data for the period 2008–2013. A total of fifteen landslide influencing factors namely slope, aspect, curvature, plan curvature, profile curvature, elevation, land use, lithology, rainfall, distance to faults, fault density, distance to roads, road density, distance to rivers, and river density have been utilized. The result of the analysis shows that RSSCE and probability analysis of rainfall data are promising methods for landslide hazard assessment. Finally, landslide hazard map has been generated by integrating spatial prediction and temporal probability analysis of landslides for the land use planning and landslide hazard management. 相似文献
7.
阳泉市矿区地质环境复杂,人类活动频繁,存在塌陷、滑坡等地质灾害隐患。本文在充分调查地质灾害的基础上,构建了矿区风险评价体系,运用信息量法和层次分析法,选取了交通、居民地、矿山等8项指标开展易发性区划;在易发性评价的基础上叠加降雨因子,实现了矿区地质灾害的危险性评价;采用人口、建筑、交通因素构建了承载体易损性模型;并结合危险性和易损性构建了矿区的地质灾害风险评价模型。研究结果表明,矿区中地质灾害高风险地带主要分布在存在矿山活动的赛鱼、蔡洼街道且靠近人口聚集的地方,其影响范围较大,应及时采取监测预报等措施。 相似文献
8.
针对滑坡灾害易发性难以定量评价的问题,提出了以汇水域为基本统计单元,层次分析法与信息量法相结合的滑坡易发性评价模型。该模型是在综合分析已有监测数据的基础上,建立了坡度、黄土分布、土地表覆被、水系、断层、高程、地表粗糙度、坡向等8类要素与滑坡稳定性的相关性,根据评价结果将滑坡易发性划分为5个不同的等级。基于新疆新源县滑坡易发性评价的实验结果表明,该模型评价结果与滑坡实际分布情况相符,能够准确对不同汇水域灾害易发性进行分级评价,可以为相关部门进行防灾、预警提供一定的数据支持。提出了一种主观判断与客观分析相结合的方法,回答了"什么地方最容易发生地质灾害"的问题。 相似文献
9.
M. Kannan E. Saranathan R. Anbalagan 《Journal of the Indian Society of Remote Sensing》2011,39(4):485-496
Landslide is a common natural hazard that usually occurs in mountainous areas. Rapid urban development and high traffic intensity
movements have been hampered to a great extent by phenomenon of landslides. In Ghat section, vertical cuttings and steep slopes
are induced slope failures. An assessment of landslide hazards is therefore a prerequisite for sustainable development of
the hilly region. In the present study, Macro Landslide Hazard Zonation was carried out in the Bodi – Bodimettu ghats section,
Western Ghats, Theni district. The slope spreads over an area of about 10.09 sq km encompassing Puliuttu Ar. sub-watershed.
The study was made with help of different types of data including Survey of India topographic map, geology map, important
inherent factors like lithology, structure, slope morphometry, relative relief, land use/land cover and hydrogeological conditions
using Bureau of Indian Standard (BSI 14496 (Part 2):1998) and related thematic maps. Based on the thematic layers, landslide
hazard evaluation factor (LHEF) and total estimated hazard (TEHD) were calculated and the macro hazard zonation map was prepared.
Landslide Hazard Zonation (LHZ) of the terrain shows that out of 17 facets, facets 1 to 5 and 8 falls under Moderate Hazard
zone category and facets 6, 7 and 9 to 17 under the High Hazard zone category. The field study with further analysis for hazard
concluded that about 68% of the total area falls in the high hazard zone. 相似文献
10.
The Likelihood Ratio (LR) Model has been applied as an improvement upon the Frequency Ratio (FR) that computes the ratio of the percentage of the landslide pixels to the percentage of the non-landslide pixels instead of the total number of pixels used in the denominator as in case of the FR. The comparative assessment of the two techniques is made through spatial modelling of GIS vector data using the ArcGIS software. Two different Landslide Information Values were computed for each polygon element of the study area employing the two FR techniques that categorized the study area into five classes of vulnerability using natural breaks (Jenks) technique. Subsequently, vulnerability zonation maps were prepared showing the different levels of landslide vulnerability. The LR technique yielded significantly higher vulnerability assessment accuracy (77%) as compared to the standard FR (71%). 相似文献
11.
Pece V Gorsevski Paul E Gessler Randy B Foltz William J Elliot 《Transactions in GIS》2006,10(3):395-415
An empirical modeling of road related and non‐road related landslide hazard for a large geographical area using logistic regression in tandem with signal detection theory is presented. This modeling was developed using geographic information system (GIS) and remote sensing data, and was implemented on the Clearwater National Forest in central Idaho. The approach is based on explicit and quantitative environmental correlations between observed landslide occurrences, climate, parent material, and environmental attributes while the receiver operating characteristic (ROC) curves are used as a measure of performance of a predictive rule. The modeling results suggest that development of two independent models for road related and non‐road related landslide hazard was necessary because spatial prediction and predictor variables were different for these models. The probabilistic models of landslide potential may be used as a decision support tool in forest planning involving the maintenance, obliteration or development of new forest roads in steep mountainous terrain. 相似文献
12.
滑坡灾害是最常见的地质灾害之一，无人机遥感和虚拟现实（virtual reality，VR）技术的快速发展为滑坡灾害沉浸式模拟与可视化分析提供了重要的数据资源和技术支持。拟重点开展滑坡灾害VR场景动态构建与探索分析研究，探讨了滑坡灾害数据多样化组织、VR场景动态融合表达等关键技术，提出了基于手柄射线的VR场景交互方法，在此基础上进行了原型系统研发与案例试验分析。试验结果表明，所提方法在无人机遥感数据支持下能够动态构建滑坡灾害VR场景，并且能够支持用户沉浸式交互与滑坡灾情信息分析。 相似文献
13.
以滑坡蠕变阶段坡体的蠕变会引起环境条件的改变，进而影响植被生长状况的野外考察客观现实为依据，提出一种间接监测滑坡变化的新方法。利用高分辨率光学遥感技术，对滑坡蠕变阶段遥感影像上坡体上覆植被的异常特征进行判识，建立遥感影像上植被异常与滑坡蠕变的关系，反映滑坡的演化过程，弥补GPS技术、InSAR技术及部分地面监测手段在地势高、地形陡峭、植被茂盛等条件下监测工作的不足，为后续的滑坡预测研究提供帮助。以植被覆盖度较高的新磨村山体高位滑坡为例，首先，对研究区域进行分区；其次，计算各分区的植被覆盖度；最后，利用植被覆盖度分析遥感影像上的植被异常与滑坡蠕变的关系，并根据滑后遥感影像和实地考察情况进行验证。结果显示，2014年—2016年，滑坡的主要物源区、变形体上方细长局部崩滑区和泉眼及冲沟周边的植被覆盖度出现明显的下降，即随着滑坡发生时间的临近，植被受滑坡蠕变的影响变大，植被生长状况变差；而且随着距裸地等滑坡风险较大区域的距离增大，植被受滑坡蠕变的影响变小，植被生长状况变好。这表明，植被异常与滑坡蠕变存在明显的时空相关性，体现了滑坡蠕变阶段遥感影像上植被异常与滑坡蠕变的内在联系，反映了滑坡逐步失稳的演化过程，为进一步预测滑坡的发生提供依据。 相似文献
14.
A robust method for spatial prediction of landslide hazard in roaded and roadless areas of forest is described. The method is based on assigning digital terrain attributes into continuous landform classes. The continuous landform classification is achieved by applying a fuzzy k-means approach to a watershed scale area before the classification is extrapolated to a broader region. The extrapolated fuzzy landform classes and datasets of road-related and non road-related landslides are then combined in a geographic information system (GIS) for the exploration of predictive correlations and model development. In particular, a Bayesian probabilistic modeling approach is illustrated using a case study of the Clearwater National Forest (CNF) in central Idaho, which experienced significant and widespread landslide events in recent years. The computed landslide hazard potential is presented on probabilistic maps for roaded and roadless areas. The maps can be used as a decision support tool in forest planning involving the maintenance, obliteration or development of new forest roads in steep mountainous terrain. 相似文献
15.
16.
Multi-Temporal Interferometric Synthetic Aperture Radar (MTInSAR) data offer a valuable support to landslide mapping and to landslide activity estimation in mountain environments, where in situ measures are sometimes difficult to gather. Nowadays, the interferometric approach is more and more used for wide-areas analysis, providing useful information for risk management actors but at the same time requiring a lot of efforts to correctly interpret what satellite data are telling us. In this context, hot-spot-like analyses that select and highlight the fastest moving areas in a region of interest, are a good operative solution for reducing the time needed to inspect a whole interferometric dataset composed by thousands or millions of points. In this work, we go beyond the concept of MTInSAR data as simple mapping tools by proposing an approach whose final goal is the quantification of the potential loss experienced by an element at risk hit by a potential landslide. To do so, it is mandatory to evaluate landslide intensity. Here, we estimate intensity using Active Deformation Areas (ADA) extracted from Sentinel-1 MTInSAR data. Depending on the localization of each ADA with respect to the urban areas, intensity is derived in two different ways. Once exposure and vulnerability of the elements at risk are estimated, the potential loss due to a landslide of a given intensity is calculated. We tested our methodology in the Eastern Valle d’Aosta (north-western Italy), along four lateral valleys of the Dora Baltea Valley. This territory is characterized by steep slopes and by numerous active and dormant landslides. The goal of this work is to develop a regional scale methodology based on satellite radar interferometry to assess the potential impact of landslides on the urban fabric. 相似文献
17.
Application of frequency ratio,weights of evidence and evidential belief function models in landslide susceptibility mapping 总被引：2，自引：1，他引：1
The landslide hazard occurred in Taibai County has the characteristics of the typical landslides in mountain hinterland. The slopes mainly consist of residual sediments and locate along the highway. Most of them are in the less stable state and in high risk during rainfall in flood season especially. The main purpose of this paper is to produce landslide susceptibility maps for Taibai County (China). In the first stage, a landslide inventory map and the input layers of the landslide conditioning factors were prepared in the geographic information system supported by field investigations and remote sensing data. The landslides conditioning factors considered for the study area were slope angle, altitude, slope aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, normalized difference vegetation index, lithological unit, rainfall and land use. Subsequently, the thematic data layers of conditioning factors were integrated by frequency ratio (FR), weights of evidence (WOE) and evidential belief function (EBF) models. As a result, landslide susceptibility maps were obtained. In order to compare the predictive ability of these three models, a validation procedure was conducted. The curves of cumulative area percentage of ordered index values vs. the cumulative percentage of landslide numbers were plotted and the values of area under the curve (AUC) were calculated. The predictive ability was characterized by the AUC values and it indicates that all these models considered have relatively similar and high accuracies. The success rate of FR, WOE and EBF models was 0.9161, 0.9132 and 0.9129, while the prediction rate of the three models was 0.9061, 0.9052 and 0.9007, respectively. Considering the accuracy and simplicity comprehensively, the FR model is the optimum method. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose. 相似文献
18.
以三峡库区万州段为研究区,从多源空间数据中提取29个致灾因子作为区域滑坡易发性分析的评价指标,在数字高程模型基础上采用集水区重叠法划分斜坡单元,构建旋转森林集成学习模型,定量预测滑坡空间易发性,并生成滑坡易发性分区图。在易发性分区图中,高易发区占11.6%,主要分布在万州主城区和长江及支流两岸;不易发区占45.6%,主要分布在人类工程活动低、植被覆盖度高的区域。采用受访者工作特征曲线和曲线下面积对旋转森林模型的滑坡易发性进行评价,结果显示该模型的预测精度为90.7%,其预测能力优于C4.5决策树。研究表明,应用旋转森林进行滑坡易发性评价具有预测能力强、精度高等优点。 相似文献
19.
P. Rajakumar S. Sanjeevi S. Jayaseelan G. Isakkipandian M. Edwin P. Balaji G. Ehanthalingam 《Journal of the Indian Society of Remote Sensing》2007,35(1):31-42
Remote sensing and Geographic Information System (GIS) are well suited to landslide studies. The aim of this study is to prepare
a landslide susceptibility map of a part of Ooty region, Tamil Nadu, India, where landslides are common. The area of the coverage
is approximately 10 × 14 km in a hilly region where planting tea, vegetables and cash crops are in practice. Hence, deforestation,
formation of new settlements and changing land use practices are always in progress. Land use and land cover maps are prepared
from Indian Remote Sensing Satellite (IRS 1C - LISS III) imagery. Digital Elevation Model (DEM) was developed using 20 m interval
contours, available in the topographic map. Field studies such as local enquiry, land use verification, landslide location
identification were carried out. Analysis was carried out with GIS software by assigning rank and weights for each input data.
The output shows the possible landslide areas, which are grouped for preparation of landslide susceptibility maps. 相似文献
20.
In this study, landslide susceptibility assessments were achieved using logistic regression, in a 523 km2 area around the Eastern Mediterranean region of Southern Turkey. In reliable landslide susceptibility modeling, among others, an appropriate landslide sampling technique is always essential. In susceptibility assessments, two different random selection methods, ranging 78–83% for the train and 17–22% validation set in landslide affected areas, were applied. For the first, the landslides were selected based on their identity numbers considering the whole polygon while in the second, random grid cells of equal size of the former one was selected in any part of the landslides. Three random selections for the landslide free grid cells of equal proportion were also applied for each of the landslide affected data set. Among the landslide preparatory factors; geology, landform classification, land use, elevation, slope, plan curvature, profile curvature, slope length factor, solar radiation, stream power index, slope second derivate, topographic wetness index, heat load index, mean slope, slope position, roughness, dissection, surface relief ratio, linear aspect, slope/aspect ratio have been considered. The results showed that the susceptibility maps produced using the random selections considering the entire landslide polygons have higher performances by means of success and prediction rates. 相似文献