利用GIS与遥感数据通过空间统计模型进行滑坡灾害分析

本文介绍了利用地理信息系统（GIS）和遥感数据分析马来西弧Cameron地区的滑坡灾害。基于航窄照片解译和野外调查结果确定滑坡位置。利用GIS和图像处理技术,存窄问数据库中对地形和地质数据,以及卫星图像进行收集、处理与汇编。选择的影响滑坡发生概牢的要素为地形坡度、方位、地形曲率,以及距河流的距离,所有这些要素均来自于地形数据库。岩性和断层之间距离提取自地质数据库：七地覆盖数据来自于TM卫星图像;植被指数值来自于陆地卫星图像;降雨量分布数据来自于气象资料。通过利用频率比模型和二元罗吉斯回归模型获得的滑坡诱发要素,对滑坡灾害区进行分析和填图。利用滑坡位置数据验证这些分析结果并与概率模型相比较。验证结果表明,与二元罗吉斯回归模型（准确度为85．73％）相比,频率比模型的预测（准确度为89．25％）结果更佳。     

基于Worldview2数据的岫岩县地质灾害遥感解译与分析

岫岩县地处低山丘陵地区,地质灾害频发。以Worldview2数据为依据,在建立遥感解译标志基础上,采用遥感解译与野外验证相结合、人机交互解译与计算机自动信息提取相结合的方法,共解译出崩塌点504处、滑坡点8处、泥石流114处;结合区域地理地质环境特征,总结了研究区地质灾害点的基本特征,并利用GIS技术对灾害点进行点地质灾害发育程度进行分析,归纳了灾害点发育程度的空间分布特征及规律;分析了岫岩县地质灾害与地形地貌、水文气象以及坡度等影响因素的关系,得出地形地貌是发生地质灾害的主控因素,气象水文以及坡度是影响岫岩县地质灾害发生的重要因素。     

矿山遥感编图中的高分辨率遥感数据选择与比例尺计算方法

通过对青海调查矿区多种高分辨率卫星数据处理后形成的图像进行解译和编图,掌握了合理选择遥感数据信息源、利用制图软件计算遥感影像图比例尺及编制野外调查和成果解译图的技术方法。得出在选择遥感数据时要考虑不同数据的技术参数、性价比及调查区面积;明确了当图像的出图分辨率确定后,其比例尺与图像的空间分辨率、像素大小、文档大小有直接关系,同时建立了计算遥感图像最佳比例尺和最佳出图比例尺的公式和方法;介绍了利用Photoshop和Mapgis软件,编制矿山野外调查用图和遥感解译成果图件的流程及方法。这些工作的完成对青海矿业开发地质环境效应调查起到了积极的指导作用,也充分体现了高分辨率遥感图像在工作条件艰苦地区的优势作用。     

SPOT 5影像数据在伊犁谷地地质灾害遥感调查中的应用

遥感技术已成为区域地质灾害及发育环境宏观调查不可缺少的技术之一,在滑坡、崩塌、泥石流等地质灾害调查、监测和研究工作中发挥重要作用．本文简要介绍应用法国SPOT5卫星影像数据进行地质灾害遥感解译调查与监测,按照建立地质灾害遥感解译标志、室内遥感解译、野外实地验证、室内再解译的技术路线,共解译出滑坡、泥石流灾害点1505处,并对灾害重点片区进行详细遥感调查．通过野外验证取得较好应用效果．SPOT5影像数据不但满足l：5万地质灾害遥感解译,且完全满足1：1万重点片区地质灾害遥感解译．     

攀枝花大河流域仁和街幅地质灾害遥感调查与分布规律分析

通过对攀枝花大河流域仁和街幅地质灾害的遥感解译和现场调查,共获得地质灾害点61处,其中以崩塌滑坡为主,泥石流次之。在此基础上,利用GIS空间分析方法对灾害点的空间分布与距水系距离、距断层距离、地形坡度、海拔高程、地层岩性的关系进行统计分析。结果表明:地质灾害点分布较少;崩塌滑坡主要受水系控制,沿水系呈线状分布,在距离水系300m范围内分布密度最大;海拔高程、地层岩性和距断层距离都是影响崩塌滑坡分布的重要因素;崩塌滑坡主要分布在海拔高度1 300~1 600m范围内;距断层1km范围内,崩塌滑坡分布密度最大;软弱半成砂岩、泥岩出露的地方更易发生地质灾害;在坡度0°~15°范围内,分布密度最大。     

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.     

正射遥感影像地图制作技术在岷江上游滑坡研究中的应用

正射遥感影像地图是对遥感数学字图像进行几何校正和投影差改正,并与数字化的简化地形图复合的一种新图,或是直接用遥感图像采用特定方法生成的具有地形地素的一种新的图种。该文介绍了陆地卫星ＴＭ正射影像地图的制作;利用卫星正射遥感影像地图,分析、解译出岷江上游地区８７个滑坡和崩塌体。采纳数据融合技术,对该区正射遥感影像进行重建,突出地形细节,提高了滑坡和崩塌体的解译精度。３Ｓ技术结合,对岷江上游地区进行了三     

浙江庆元地区滑坡灾害的多要素评价

利用地理信息系统（GIS）与遥感技术结合进行油坡灾害评价与预测是当前滑坡研究的热点，在对浙江省庆元县进行遥感滑坡研究中，通过对滑坡各影响因素的详细分析。结合TM图像，利用GIS技术分析主要影响因素，确定影响因素集及其间的朴素关系。从而建立庆元县油坡灾害多要素综合评价的初步模型。结果有81.25%滑坡点落在危险区域。令人比较满意，因此，可以利用此模型进行庆元县滑坡灾害评价。     

遥感技术在东天山大黑山地区地质填图中的应用

利用不同地质体在同一波段或相同地质体在不同波段具有不同色调的变化规律,对东天山大黑山地区的ETM+图像和Spot图像进行了详细的地质解译,建立了研究区地层、侵入体和构造的解译标志。解译结果表明,在遥感影像图上可以准确的勾绘不同地质体的界线、确定不同构造的位置。通过野外地质调查,划分出不同的地层单位、侵入体和构造现象。遥感地质解译和野外验证结果对比表明,遥感技术在地质填图工作中可以发挥重要作用。     

基于GIS与确定性系数分析方法的汶川地震滑坡易发性评价

汶川Ms 80级大地震诱发了数以万计的滑坡灾害。在大约48678 km2的滑坡影响区域内,作者采用震后遥感影像解译并结合野外调查的方法,共解译出48007处滑坡。应用GIS技术,建立了汶川地震诱发滑坡灾害及相关地形、地质空间数据库。采用地震滑坡确定性系数分析方法,分析了地震滑坡关于地震烈度、岩性、坡度、断层、高程、坡向、河流与公路等8个因素的易发程度。基于GIS栅格分析方法,分别对16种不同影响因子组合类型进行地震滑坡易发性评价。最后,应用AUC（Area Under Curve,评价曲线下面积）方法得到最佳因子组合及其对应的评价结果,使用自然分类法则方法将研究区按滑坡易发程度分为极高易发区、高易发区、中易发区、低易发区与极低易发区5类,极高易发区与高易发区面积之和约1169046km2,占研究区总面积的2402%,其中发育滑坡面积为52484 km2,占滑坡总面积的7373%。结果表明了极高与高易发区与实际滑坡之间有着良好的一致性,方法的评价结果成功率（AUC值）达到82107%。     

Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland,Malaysia

This paper presents landslide susceptibility analysis around the Cameron Highlands area, Malaysia using a geographic information system (GIS) and remote sensing techniques. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten landslide occurrence factors were selected as: topographic slope, topographic aspect, topographic curvature and distance from drainage, lithology and distance from lineament, soil type, rainfall, land cover from SPOT 5 satellite images, and the vegetation index value from SPOT 5 satellite image. These factors were analyzed using an advanced artificial neural network model to generate the landslide susceptibility map. Each factor’s weight was determined by the back-propagation training method. Then, the landslide susceptibility indices were calculated using the trained back-propagation weights, and finally, the landslide susceptibility map was generated using GIS tools. The results of the neural network model suggest that the effect of topographic slope has the highest weight value (0.205) which has more than two times among the other factors, followed by the distance from drainage (0.141) and then lithology (0.117). Landslide locations were used to validate the results of the landslide susceptibility map, and the verification results showed 83% accuracy. The validation results showed sufficient agreement between the computed susceptibility map and the existing data on landslide areas.     

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.     

Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models

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.     

Probabilistic landslide susceptibility and factor effect analysis

The susceptibility of landslides and the effect of landslide-related factors at Penang in Malaysia using the geographic information system (GIS) and remote sensing data have been evaluated. Landslide locations were identified in the study area from interpretation of aerial photographs and from 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 were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land use from Landsat Thermatic Mapper (TM) satellite images; and the vegetation index value from SPOT HRV (High-Resolution Visible) satellite images. Landslide hazardous areas were analyzed and mapped using the landslide-occurrence factors employing the probability–frequency ratio method using the all factors. To assess the effect of these factors, each factor was excluded from the analysis, and its effect verified using the landslide location data. As a result, all factors had relatively positive effects, except lithology, on the landslide susceptibility maps in the study area.     

Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models

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.     

The Application of Artificial Neural Networks to Landslide Susceptibility Mapping at Janghung, Korea

The purpose of this study was to develop techniques for landslide susceptibility using artificial neural networks and then to apply these to the selected study area at Janghung in Korea. Landslide locations were identified from interpretation of satellite images and field survey data, and a spatial database of the topography, soil, forest, and land use. Thirteen landslide-related factors were extracted from the spatial database. These factors were then used with an artificial neural network to analyze landslide susceptibility. Each factor's weight was determined by the back-propagation training method. Five different training sets were applied to analyze and verify the effect of training. Then the landslide susceptibility indices were calculated using the back-propagation weights, and susceptibility maps were constructed from Geographic Information System (GIS) data for the five cases. Landslide locations were used to verify results of the landslide susceptibility maps and to compare them. The artificial neural network proved to be an effective tool for analyzing landslide susceptibility.     

Landslide susceptibility mapping using rough sets and back-propagation neural networks in the Three Gorges, China

In the Three Gorges of China, there are frequent landslides, and the potential risk of landslides is tremendous. An efficient and accurate method of generating landslide susceptibility maps is very important to mitigate the loss of lives and properties caused by these landslides. This paper presents landslide susceptibility mapping on the Zigui-Badong of the Three Gorges, using rough sets and back-propagation neural networks (BPNNs). Landslide locations were obtained from a landslide inventory map, supported by field surveys. Twenty-two landslide-related factors were extracted from the 1:10,000-scale topographic maps, 1:50,000-scale geological maps, Landsat ETM + satellite images with a spatial resolution of 28.5 m, and HJ-A satellite images with a spatial resolution of 30 m. Twelve key environmental factors were selected as independent variables using the rough set and correlation coefficient analysis, including elevation, slope, profile curvature, catchment aspect, catchment height, distance from drainage, engineering rock group, distance from faults, slope structure, land cover, topographic wetness index, and normalized difference vegetation index. The initial, three-layered, and four-layered BPNN were trained and then used to map landslide susceptibility, respectively. To evaluate the models, the susceptibility maps were validated by comparing with the existing landslide locations according to the area under the curve. The four-layered BPNN outperforms the other two models with the best accuracy of 91.53 %. Approximately 91.37 % of landslides were classified as high and very high landslide-prone areas. The validation results show sufficient agreement between the obtained susceptibility maps and the existing landslide locations.     

Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models

The aim of this study is to evaluate the landslide hazards at Selangor area, Malaysia, using Geographic Information System (GIS) and Remote Sensing. Landslide locations of the study area were identified from aerial photograph interpretation and field survey. Topographical maps, geological data, and satellite images were collected, processed, and constructed into a spatial database in a GIS platform. The factors chosen that influence landslide occurrence were: slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, land cover, vegetation index, and precipitation distribution. Landslide hazardous areas were analyzed and mapped using the landslide-occurrence factors by frequency ratio and logistic regression models. The results of the analysis were verified using the landslide location data and compared with probability model. The comparison results showed that the frequency ratio model (accuracy is 93.04%) is better in prediction than logistic regression (accuracy is 90.34%) model.     

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.     

The 2010 Yushu earthquake triggered landslide hazard mapping using GIS and weight of evidence modeling

The Yushu County, Qinghai Province, China, April 14, 2010, earthquake triggered thousands of landslides in a zone between 96°20′32.9″E and 97°10′8.9″E, and 32°52′6.7″N and 33°19′47.9″N. This study examines the use of geographic information system (GIS) technology and Bayesian statistics in creating a suitable landslide hazard-zone map of good predictive power. A total of 2,036 landslides were interpreted from high-resolution aerial photographs and multi-source satellite images pre- and post-earthquake, and verified by selected field checking before a final landslide-inventory map of the study area could be established using GIS software. The 2,036 landslides were randomly partitioned into two subsets: a training dataset, which contains 80 % (1,628 landslides), for training the model; and a testing dataset 20 % (408 landslides). Twelve earthquake triggered landslide associated controlling parameters, such as elevation, slope gradient, slope aspect, slope curvature, topographic position, distance from main surface ruptures, peak ground acceleration, distance from roads, normalized difference vegetation index, distance from drainages, lithology, and distance from all faults were obtained from variety of data sources. Landslide hazard indices were calculated using the weight of evidence model. The landslide hazard map was compared with training data and testing data to obtain the success rate and predictive rate of the model, respectively. The validation results showed satisfactory agreement between the hazard map and the existing landslide distribution data. The success rate is 80.607 %, and the predictive rate is 78.855 %. The resulting landslide hazard map showed five classes of landslide hazard, i.e., very high, high, moderate, low and very low. The landslide hazard evaluation map should be useful for environmental recovery planning and reconstruction work.