基于GIS的长江三峡库区滑坡影响因子分析

利用GIS技术和统计方法,对三峡库区选定的研究区域(面积4539km2)滑坡空间分布和地形、地质等滑坡内部因子之间相关性进行统计计算。在建立地质、地形数据库等滑坡因子空间数据库和滑坡空间分布数据库(数据比例尺均为1∶10000)基础上,从地形数据库提取25m分辨率DEM,再派生出高程、高差、坡度、坡向、平面曲率、剖面曲率等地形影响因子;从地质数据库提取地层和岩性组合影响因子。将各个定性的因子按一定规则进行重分类、转换为25m分辨率的栅格数据格式,在GIS中进行地图代数运算、统计计算滑坡和各影响因子相关性。结果表明,滑坡分布和Q4、J1x,J1z、S岩性岩组;90m以下、90～135m和135～175m三个高程带;15～20m局部高差;10°～25°坡度;北、南和西北方向及-1～1曲率范围等影响因子相关性等级都大于1,为滑坡发生的主要影响因子类属。研究的结果是进行滑坡易发性评价的基础,可以指导库区滑坡灾害管理、土地利用等。     

GIS支持下三峡库区秭归县滑坡灾害空间预测

基于GIS空间分析和统计模型相结合进行区域评价与空间预测是滑坡灾害研究的重要方向之一。以三峡库区秭归县为研究区,选择坡度、坡向、边坡结构、工程岩组、排水系统、土地利用和公路开挖作为评价因子。为提高模型的预测精度、可信度和推广能力,利用窗口采样规则降低训练样本之间的空间相关性。建立Logistic回归模型,对滑坡灾害与评价因子进行定量相关性分析。计算研究区滑坡灾害易发性指数,对其进行聚类分析,绘制滑坡易发性分区图,其中高、中易发区占整个研究区面积的38.9%,主要分布在人类工程活动频繁和靠近排水系统的区域。经过验证,该模型的预测精度达到77.57%。     

青海省柴达尔-木里地区道路沿线多年冻土分布模拟

以青海省柴达尔-木里铁路、热水-江仓公路沿线两侧约10 km缓冲区为研究区域,以冻土钻孔实测数据为基础,定量分析和评价了经度、纬度、高程、太阳辐射、坡度、坡向、地面曲率等地形-候因子对沿线区域多年冻土分布的影响,建立了以经度、高程、坡度为自变量、多年冻土发生概率为因变量的Logistic模型.借助于GIS软件和DEM数据,完成了道路沿线区域多年冻土分布概率图的绘制和多年冻土分布概率的特征分析.结果表明,极可能多年冻土(概率值为0.75～1)的分布面积为1983 km2,占整个研究区域面积的65%;可能多年冻土(概率值为0.5～0.75)的分布区面积为192 km2,占研究区域面积的6%;季节冻土(概率值＜0.5)的分布区面积为894 km2,占沿线区域面积的29%.     

三峡库区耕地质量评价

本文基于GIS与RS技术,结合三峡库区区域特点,从自然质量、区位条件、生态安全和空间形态四个层面选取指标,构建三峡库区耕地质量综合评价指标体系,对库区耕地质量进行评价与分类,并利用统计数据对研究结果进行验证。结果表明,三峡库区耕地质量整体较好。其中,一类、二类耕地分别占库区耕地面积的22.31%与34.18%,主要分布在库尾各区县及库中的涪陵区;三类、四类耕地分别占27.96%与15.55%,主要分布在库中各区县;单位面积实际粮食产量较高的区县内,一类、二类耕地面积所占比例较高,而单位面积实际粮食产量较低的区县,三类、四类耕地所占比例较高。该研究结果可为三峡库区耕地保护与土地规划提供参考依据。     

基于GIS的多因子分析法对兰州市大气环境功能区划的研究

兰州市是全国大气污染十分严重的城市之一。因地制宜,进行城市大气环境功能分区对全市污染总量控制以及污染治理具有非常重要的意义。研究通过对影响兰州市大气质量主要因子分析,应用层次分析法(AHP)建立了大气功能区划分的多因子评价模型。并且运用GIS空间分析功能对兰州市区进行了大气污染指数模拟与功能区划,结果表明:由于受到地形因子与盛行风影响,兰州市区大气质量较差,市区中西部大部分被三类功能区覆盖。三类功能区面积约32.9 km2,约占整个兰州市城区面积的20.5%,主要分布在西固区的西北部和七里河区的西南部。二类功能区面积约122.8 km2,主要分布区域是城关区、安宁区、七里河区的东南部和西固区的东南部。结果表明该方法能定量划分大气环境功能区,证明GIS具有较为准确的空间分析与模拟功能。     

扎龙保护区丹顶鹤栖息地适宜性评价

结合TM影像和ENVISAT ASAR(HH/HV)数据,以扎龙保护区丹顶鹤栖息地为研究对象,在分布式定量提取评价因子(植被类型、生境结构、巢下水深、植被盖度、人为干扰)的基础上,建立HSI模型对丹顶鹤栖息地质量进行评价,并根据实测巢址空间分布情况对评价结果进行验证。结果表明:基于主观专家知识建立的HSI模型得到丹顶鹤高适宜栖息地面积为48km2,适宜栖息地面积为265km2,低适宜栖息地面积为385km2,不适宜栖息地面积为1 403km2,结合实测数据验证,得出高适宜、适宜和低适宜栖息地巢址共占实测巢址的92.86%,不适宜栖息地巢址占7.14%。经检验HSI模型对淡水沼泽湿地典型水禽栖息地的质量评价有良好的适用性。     

基于GIS甘肃中南部滑坡泥石流活动强度评价

以甘肃中南部为研究区,采用GIS、MATLAB、FUZZY相结合的评价方法,收集筛选评价指标数据,基于GIS进行数据处理、转换,将各层对应的2 km×2 km栅格数据转入MATLAB,建立成因性指标与滑坡泥石流活动强度的模糊隶属关系,基于GIS进行空间分析,确定评价指标划分等级,设计不同权重方案,在MATLAB中编程试算,试算结果用重点(样)区特征性数据所反映的强度大小与相对等级,进行拟合检验与灵敏性检验,进一步调整指标等级与权重参数,最终得到符合成因机制的滑坡泥石流活动强度评价等级分布。结果表明该评价方法高效实用、精度高,可以进行高分辨率区域评价、区域仿真模拟、区划等工作。     

准噶尔盆地东部土壤风蚀危险度评价

以准噶尔盆地东部露天矿区为研究区,基于GIS技术和土壤风蚀理论,结合研究区自然环境现状,选取植被覆盖度、土地利用类型、土壤可蚀性指数（K值）、地形起伏度为土壤风蚀危险度评价因子,建立土壤风蚀危险度模型,对研究区土壤风蚀危险度进行评价分析。结果表明：研究区土壤风蚀无险型区域面积28.99 km²（0.13%）,轻险型区域面积为2 100.66 km²（9.42%）,危险型区域面积为5 066.56 km²（22.72%）,强险型区域面积为14 593.12 km²（65.44%）,极险型区域面积为646.7 km²（2.29%）。在各个因子的影响下,研究区的风蚀危险度极高,强险型为研究区内最主要的等级程度。研究区土壤风蚀危险度从南向北危险度有增加的趋势,且成片状分布,与实际情况相吻合,说明基于GIS技术的土壤风蚀危险度评价可宏观地揭示新疆准东地区土壤风蚀危险度空间格局分异特征。     

芦山地震重灾区崩塌滑坡易发性评价

2013-04-20 T08:02,四川省芦山县发生7.0级大地震,地震诱发了大量的次生山地灾害.在芦山、宝兴、天全三个地震重灾县6651.35 km2的区域内,采用震后遥感影像解译并结合野外调查的方法,共解译出1379处崩塌(含落石)滑坡.应用GIS技术,建立了芦山地震诱发崩塌滑坡灾害及相关地形、地质空间数据库,分析了岩性、断层、地震动加速度、高程、坡度等5个因素与崩塌滑坡分布的关系,应用崩塌滑坡数量百分比这一标准来分别衡量每个因素中各个级别对崩塌滑坡的影响程度;然后使用层次分析法对这5个参数进行权重分析;在GIS平台下对这些参数进行综合分析,以此将研究区内的崩塌滑坡按易发程度分为极高易发区、高易发区、中易发区、低易发区4类,极高易发区与高易发区面积约2149.89 km2,占研究区总面积的32.32％.     

金沙江流域(云南境内)山地灾害危险性评价

云南境内的金沙江流域是斜坡不稳定的敏感区,根据1988-2000年的区域调查和统计,区内发育山地灾害点1697处,其中流域面积大于1km2的泥石流沟808条,体积大于1×104m3的滑坡580处,体积大于1000m3的崩塌309处。用于山地灾害危险性评价的主要敏感因子包括岩土体类型、山坡坡度、降雨、土地利用、地震烈度和人类活动。在对这些因子进行了敏感性评价的基础上,应用GIS对敏感因子集成评价而产生了云南金沙江流域山地灾害危险性评价图。评价结果表明:高危险区面积占全区面积6464km2的8 77%,中危险区占全区总面积的41 51%,低危险区占41 12%,无危险区占8 60%。山地灾害危险性评价图可以帮助规划者或工程师在土地发展规划中选择最佳建设场所,以减轻灾害的影响。     

Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN)

An efficient and accurate method of generating landslide susceptibility maps is very important to mitigate the loss of properties and lives caused by this type of geological hazard. This study focuses on the development of an accurate and efficient method of data integration, processing and generation of a landslide susceptibility map using an ANN and data from ASTER images. The method contains two major phases. The first phase is the data integration and analysis, and the second is the Artificial Neural Network training and mapping. The data integration and analysis phase involve GIS based statistical analysis relating landslide occurrence to geological and DEM (digital elevation model) derived geomorphological parameters. The parameters include slope, aspect, elevation, geology, density of geological boundaries and distance to the boundaries. This phase determines the geological and geomorphological factors that are significantly correlated with landslide occurrence. The second phase further relates the landslide susceptibility index to the important geological and geomorphological parameters identified in the first phase through ANN training. The trained ANN is then used to generate a landslide susceptibility map. Landslide data from the 2004 Niigata earthquake and a DEM derived from ASTER images were used. The area provided enough landslide data to check the efficiency and accuracy of the developed method. Based on the initial results of the experiment, the developed method is more than 90% accurate in determining the probability of landslide occurrence in a particular area.     

GIS and ANN model for landslide susceptibility mapping

Landslide hazard is as the probability of occurrence of a potentially damaging landslide phenomenon within specified period of time and within a given area. The susceptibility map provides the relative spatial probability of landslides occurrence. A study is presented of the application of GIS and artificial neural network model to landslide susceptibility mapping, with particular reference to landslides on natural terrain in this paper. The method has been applied to Lantau Island, the largest outlying island within the territory of Hong Kong. A three-level neural network model was constructed and trained by the back-propagate algorithm in the geographical database of the study area. The data in the database includes digital elevation modal and its derivatives, landslides distribution and their attributes, superficial geological maps, vegetation cover, the raingauges distribution and their 14 years 5-minute observation. Based on field inspection and analysis of correlation between terrain variables and landslides frequency, lithology, vegetation cover, slope gradient, slope aspect, slope curvature, elevation, the characteristic value, the rainstorms corresponding to the landslide, and distance to drainage line are considered to be related to landslide susceptibility in this study. The artificial neural network is then coupled with the ArcView3.2 GIS software to produce the landslide susceptibility map, which classifies the susceptibility into three levels: low, moderate, and high. The results from this study indicate that GIS coupled with artificial neural network model is a flexible and powerful approach to identify the spatial probability of hazards.     

GIS and ANN model for landslide susceptibility mapping

1 IntroductionThe population growth and the expansion of settlements and life-lines over hazardous areas exert increasingly great impact of natural disasters both in the developed and developing countries. In many countries, the economic losses and casualties due to landslides are greater than commonly recognized and generate a yearly loss of property larger than that from any other natural disasters, including earthquakes, floods and windstorms. Landslides in mountainous terrain often occur a…     

Comparing landslide inventory maps

Landslide inventory maps are effective and easily understandable products for both experts, such as geomorphologists, and for non experts, including decision-makers, planners, and civil defense managers. Landslide inventories are essential to understand the evolution of landscapes, and to ascertain landslide susceptibility and hazard. Despite landslide maps being compiled every year in the word at different scales, limited efforts are made to critically compare landslide maps prepared using different techniques or by different investigators. Based on the experience gained in 20 years of landslide mapping in Italy, and on the limited literature on landslide inventory assessment, we propose a general framework for the quantitative comparison of landslide inventory maps. To test the proposed framework we exploit three inventory maps. The first map is a reconnaissance landslide inventory prepared for the Umbria region, in central Italy. The second map is a detailed geomorphological landslide map, also prepared for the Umbria region. The third map is a multi-temporal landslide inventory compiled for the Collazzone area, in central Umbria. Results of the experiment allow for establishing how well the individual inventories describe the location, type and abundance of landslides, to what extent the landslide maps can be used to determine the frequency-area statistics of the slope failures, and the significance of the inventory maps as predictors of landslide susceptibility. We further use the results obtained in the Collazzone area to estimate the quality and completeness of the two regional landslide inventory maps, and to outline general advantages and limitations of the techniques used to complete the inventories.     

GIS支持下的黄土高原地震滑坡区划研究

分析了影响黄土滑坡的各项影响因子,利用层次分析法(AHP)确定各影响因子的权重。在GIS支持下,建立包括各因子图的空间数据库,对各因子进行分级赋值,然后进行因子加权叠加分析,完成三种超越概率下(50年超越概率2%、10%和63.5%)黄土高原地震滑坡区划图。黄土地震滑坡灾害最严重地区一个是宁夏南部及与其相邻的甘肃白银地区,另一个是甘肃天水地区。     

Evaluation and comparison of landslide susceptibility mapping methods: a case study for the Ulus district,Bartın,northern Turkey

The purpose of this study was to investigate the capabilities of different landslide susceptibility methods by comparing their results statistically and spatially to select the best method that portrays the susceptibility zones for the Ulus district of the Bart?n province (northern Turkey). Susceptibility maps based on spatial regression (SR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression (LR) method, and artificial neural network method (ANN) were generated, and the effect of each geomorphological parameter was determined. The landslide inventory map digitized from previous studies was used as a base map for landslide occurrence. All of the analyses were implemented with respect to landslides classified as rotational, active, and deeper than 5 m. Three different sets of data were used to produce nine explanatory variables (layers). The study area was divided into grids of 90 m × 90 m, and the ‘seed cell’ technique was applied to obtain statistically balanced population distribution over landslide inventory area. The constructed dataset was divided into two datasets as training and test. The initial assessment consisted of multicollinearity of explanatory variables. Empirical information entropy analysis was implemented to quantify the spatial distribution of the outcomes of these methods. Results of the analyses were validated by using success rate curve (SRC) and prediction rate curve (PRC) methods. Additionally, statistical and spatial comparisons of the results were performed to determine the most suitable susceptibility zonation method in this large-scale study area. In accordance with all these comparisons, it is concluded that ANN was the best method to represent landslide susceptibility throughout the study area with an acceptable processing time.     

基于信息量与逻辑回归模型的次生滑坡灾害敏感性评价——以汶川县北部为例

次生滑坡灾害的影响是震后较长时间里人们持续关注的焦点,对其开展敏感性评价具有重要意义。选取5.12地震的重灾区汶川县北部作为研究区,利用遥感与地理信息技术提取地震滑坡信息,在全面分析滑坡与高程、坡度、坡向、岩性、断裂带、地震烈度以及水系等7个影响因子相关特性的基础上,采用信息量法与逻辑回归模型进行灾害敏感性评价,将研究区划分为极轻度、轻度、中度、高度和极高危险5个级别,并对不同模型的适用性开展分析和对比。结果表明,逻辑回归模型在描述区域滑坡灾害危险度总体特征方面稍具优势。     

Susceptibility assessment of shallow landslides on Oahu, Hawaii, under extreme-rainfall events

The deterministic Stability INdex MAPping (SINMAP) model, which integrates a mechanistic infinite-slope stability model and a hydrological model, was applied to assess susceptibility of slopes in 32 shallow-landslide-prone watersheds of the eastern to southern areas of Oahu, Hawaii, USA. Input to the model includes a 10-m Digital Elevation Model (DEM), an inventory of storm-induced landslides that occurred from 1949 to 2006, and listings of soil-strength and hydrological parameters including transmissivity and steady-state recharge. The study area of ca. 384 km² was divided into four calibration regions with different geotechnical and hydrological characteristics. All parameter values were separately calibrated using observed landslides as references. The study used a quasi-dynamic scenario of soil wetness resulting from extreme daily rainfall events with a return period of 50 years. The return period was based on almost-90-year-long (1919–2007) daily rainfall records from 26 raingauge stations in the study area. Output of the SINMAP model includes slope-stability-index-distribution maps, slope-versus-specific-catchment-area charts, and statistical summaries for each region.The SINMAP model assessed susceptibility at the locations of all 226 observed shallow landslides and classified these susceptible areas as unstable. About 55% of the study area was predicted as highly unstable, highlighting a critical island problem. The SINMAP predictions were compared to an existing debris-flow-hazard map. Areas classified as unstable in the current study were classified as low-to-moderate and moderate-to-high debris-flow hazard risks by the prior mapping. The slope-stability maps provided by this study will aid in explaining the causes of known landslides, making emergency decisions, and, ultimately mitigating future landslide risks. The maps may be further improved by incorporating heterogeneous and anisotropic soil properties and spatial and temporal variation of rainfalls as well as by improving the accuracy of the DEM and the locations of shallow landslide initiation.     

Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium)

In this article a statistical multivariate method, i.e., rare events logistic regression, is evaluated for the creation of a landslide susceptibility map in a 200 km² study area of the Flemish Ardennes (Belgium). The methodology is based on the hypothesis that future landslides will have the same causal factors as the landslides initiated in the past. The information on the past landslides comes from a landslide inventory map obtained by detailed field surveys and by the analysis of LIDAR (Light Detection and Ranging)-derived hillshade maps. Information on the causal factors (e.g., slope gradient, aspect, lithology, and soil drainage) was extracted from digital elevation models derived from LIDAR and from topographical, lithological and soil maps. In landslide-affected areas, however, we did not use the present-day hillslope gradient. In order to reflect the hillslope condition prior to landsliding, the pre-landslide hillslope was reconstructed and its gradient was used in the analysis. Because of their limited spatial occurrence, the landslides in the study area can be regarded as “rare events”. Rare events logistic regression differs from ordinary logistic regression because it takes into account the low proportion of 1s (landslides) to 0s (no landslides) in the study area by incorporating three correction measures: the endogenous stratified sampling of the dataset, the prior correction of the intercept and the correction of the probabilities to include the estimation uncertainty. For the study area, significant model results were obtained, with pre-landslide hillslope gradient and three different clayey lithologies being important predictor variables. Receiver Operating Characteristic (ROC) curves and the Kappa index were used to validate the model. Both show a good agreement between the observed and predicted values of the validation dataset. Based on a qualified judgement, the created landslide susceptibility map was classified into four classes, i.e., very high, high, moderate and low susceptibility. If interpreted correctly, this classified susceptibility map is an important tool for the delineation of zones where prevention measures are needed and human interference should be limited in order to avoid property damage due to landslides.     

Torrent risk zonation in the Upstream Red River Basin based on GIS

This paper explores the methodology for compiling the torrent hazard and risk zonation map by means of GIS technique for the Red River Basin in Yunnan province of China, where is prone to torrent. Based on a 1:250,000 scale digital map, six factors including slope angle, rainstorm days, buffer of river channels, maximum runoff discharge of standard area, debris flow distribution density and flood disaster history were analyzed and superimposed to create the torrent risk evaluation map. Population density, farmland percentage, house property, and GDP as indexes accounting for torrent hazards were analyzed in terms of vulnerability mapping. Torrent risk zonation by means of GIS was overlaid on the two data layers of hazard and vulnerability. Then each grid unit with a resolution of 500 m × 500 m was divided into four categories of the risk: extremely high, high, moderate and low. Finally the same level risk was combined into a confirmed zone, which represents torrent risk of the study area. The risk evaluation result in the upper Red River Basin shows that the extremely high risk area of 13,150 km2 takes up 17.9% of the total inundated area, the high risk area of 33,783 km2 is 45.9%, the moderate risk area of 18,563 km2 is 25.2% and the low risk area of 8115 km2 is 11.0%.