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1.
基于数字高程模型(DEM)计算得到的坡度、坡向等地形属性是滑坡危险性评价模型的重要输入数据, DEM误差会导致地形属性计算结果不确定性, 进而影响滑坡危险性评价模型的结果。本文选择基于专家知识的滑坡危险性评价模型和逻辑斯第回归模型, 采用蒙特卡洛模拟方法, 研究DEM误差所导致的滑坡危险性评价模型结果不确定性。研究区位于长江中上游的重庆开县, 采用5 m分辨率的DEM, 以序贯高斯模拟方法模拟了不同大小(误差标准差为1 m、7.5 m、15 m)和空间自相关性(变程为0 m、30 m、60 m、120 m)的12 类DEM误差场参与滑坡危险性评价。每次模拟包括100 个实现, 通过对每次模拟分别计算滑坡危险性评价结果的标准差图层和分类一致性百分比图层, 用以评价结果不确定性。评价结果表明, 在不同的DEM精度下, 两个滑坡危险性评价模型所得结果的总体不确定性随空间自相关程度的变化趋势并不相同。当DEM空间自相关性程度不同时, 基于专家知识的滑坡危险性评价模型的评价结果总体不确定随着DEM误差增加而呈现不同的变化趋势, 而逻辑斯第回归模型的评价结果总体不确定性随着DEM误差大小增加而单调增加。从评价结果总体不确定性角度而言, 总体上逻辑斯第回归模型比基于专家知识的滑坡危险性评价模型更加依赖于DEM数据质量。  相似文献   

2.
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.  相似文献   

3.
GIS and ANN model for landslide susceptibility mapping   总被引：1，自引：0，他引：1  
XU Zeng-wang 《地理学报》2001,11(3):374-381
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.  相似文献   

4.
GIS and ANN model for landslide susceptibility mapping   总被引：4，自引：0，他引：4  
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…  相似文献   

5.
This paper proposes a statistical decision-tree model to analyze landslide susceptibility in a wide area of the Akaishi Mountains, Japan. The objectives of this study were to validate the decision-tree model by comparing landslide susceptibility and actual landslide occurrence, and to reveal the relationships among landslide occurrence, topography, and geology. Landslide susceptibility was examined through ensemble learning with a decision tree. Decision trees are advantageous in that estimation processes and order of important explanatory variables are explicitly represented by the tree structures. Topographic characteristics (elevation, slope angle, profile curvature, plan curvature, and dissection and undissection height) and geological data were used as the explanatory variables. These topographic characteristics were calculated from digital elevation models (DEMs). The objective variables were landslide occurrence and reactivation data between 1992 and 2002 that were depicted by satellite image analysis. Landslide susceptibility was validated by comparing actual data on landslides that occurred and reactivated after the model was constructed (between 2002 and 2004).This study revealed that, from 2002 to 2004, landslides tended to occur and reactivate in catchments with high landslide susceptibility. The landslide susceptibility map thus depicts the actual landslide occurrence and reactivation in the Akaishi Mountains. This result indicates that the decision-tree model has appropriate accuracy for estimating the probabilities of future landslides. The tree structure indicates that landslides occurred and reactivated frequently in the catchments that had an average slope angle exceeding ca. 29° and a mode of slope angle exceeding 33°, which agree well with previous studies. A decision tree also quantitatively expresses important explanatory variables at the higher order of the tree structure.  相似文献   

6.
The Iranian Soil and Water Research Institute has been involved in mapping the soils of Iran and classifying landforms for the last 60 years. However, the accuracy of traditional landform maps is very low (about 55%). To date, aerial photographs and topographic maps have been used for landform classification studies. The principal objective of this research is to propose a quantitative approach for landform classification based on a 10-m resolution digital elevation model (DEM) and some use of an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image. In order to extract and identify the various landforms, slope, elevation range, and stream network pattern were used as basic identifying parameters. These are extractable from a DEM. Further, ASTER images were required to identify the general outline shape of a landform type and the presence or absence of gravel. This study encompassed a relatively large watershed of 451 183 ha with a total elevation difference of 2445 m and a variety of landforms from flat River Alluvial Plains to steep mountains. Classification accuracy ranged from 91.8 to 99.6% with an average of 96.7% based upon extensive ground-truthing. Since similar digital and ASTER image information is available for Iran, an accurate landform map can now be produced for the whole country. The main advantages of this approach are accuracy, lower demands on time and funds for field work and ready availability of required data for many regions of the world.  相似文献   

7.
The purpose of this study is to develop and apply the technique for landslide susceptibility analysis using geological structure in a Geographic Information System (GIS). In the study area, the Janghung area of Korea, landslide locations were detected from Indian Remote Sensing (IRS) satellite images by change detection, where the geological structure of foliation was surveyed and analysed. The landslide occurrence factors (location of landslide, geological structure and topography) were constructed into a spatial database. Then, strike and dip of the foliation and the aspect and slope of the topography were compared and the results, which were verified using landslide location data, show that foliation of gneiss has a geometrical relation to the joint or fault that leads to a landslide. Using the geometrical relations, the landslide susceptibility was assessed and verified. The verification results showed satisfactory agreement between the susceptibility map and the landslide location data.  相似文献   

8.
基于GIS的斜坡单元划分方法改进与实现   总被引：5，自引：0，他引：5  
颜阁  梁收运  赵红亮 《地理科学》2017,37(11):1764-1770
斜坡单元已广泛应用于滑坡易发性制图和地质灾害评价。然而在山间盆地或大型宽谷处,常规方法划分出的斜坡单元与地貌背景难以匹配。依据高程及其衍生变量的基本形态系统和曲率的流域分割原理,基于ArcGIS技术,通过叠加曲率和反转曲率的流域边界,改进了斜坡单元划分方法。结果表明：与常规方法相比,改进方法不仅能够使用山脊线和山谷线以划分斜坡单元,还能利用台地边界和宽谷边界以分割水平地表与倾斜地表;划分出的单元大小相对均匀,单元形状总体介于圆形和正三角形之间。对于水平成分较多的地区,如黄土塬区和水库库区,该方法与传统方法相比,具有较好的应用前景。  相似文献   

9.
在GIS技术的支持下,以三峡库区忠县-石柱河段为研究区域(面积260.9km2,滑坡分布面积5.3km2),建立了地质、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∶10万);在进行滑坡影响因子敏感性分析的基础上;对双变量分析模型进行了改进应用,对滑坡影响定量因子采用滑坡种子网格数据驱动的分级新方法。在GIS系统中进行了滑坡危险度评价成果图制图,将评价结果分为很低、低、中等、高、很高5个等级,依次占研究区域19.9%、31.69%、27.95%、17.1%和3.6%。评价结果显示危险性高和很高的区域主要分布在长江两岸,这与实际的滑坡分布吻合。研究结果对在三峡库区推广应用、防灾减灾具有实际指导意义。  相似文献   

10.
A landslide susceptibility map is proposed for the Pays de Herve (E Belgium), where large landslides affect Cretaceous clay outcrop areas. Based on a Bayesian approach, this GIS-supported probabilistic map identifies the areas most susceptible to deep landslides. The database is comprised of the source areas of ten pre-existing landslides (i.e. a sample of 154 grid cells) and of six environmental data layers, namely lithology, proximity to active faults, slope angle and aspect, elevation and distance to the nearest valley-floor. A 30-m-resolution DEM from the Belgian National Geographical Institute is used for the analysis. Owing to the small size of the sample, a special cross-validation procedure of the susceptibility map is performed, which uses in an iterative way each of the landslides to test the predictive power of the map derived from the other landslides. Four different sets of variables are used to produce four susceptibility maps, whose prediction curves are compared. While the prediction rates associated with the models not involving the “proximity to active fault” criterion are comparable to those of the models considering this variable, strong weaknesses inherent in the fault data on which the latter rely suggest that the final susceptibility map should be based on a model that excludes any reference to fault. This highlights the difference between a triggering factor and determining factors, and in the same time broadens the scope of the produced map. A single reactivated slide is also used to test the possibility of predicting future reactivation of existing landslides in the area. Finally, the need for geomorphological control over the mathematical treatment is underlined in order to obtain realistic prediction maps.  相似文献   

11.
Terrain attributes such as slope gradient and slope shape, computed from a gridded digital elevation model (DEM), are important input data for landslide susceptibility mapping. Errors in DEM can cause uncertainty in terrain attributes and thus influence landslide susceptibility mapping. Monte Carlo simulations have been used in this article to compare uncertainties due to DEM error in two representative landslide susceptibility mapping approaches: a recently developed expert knowledge and fuzzy logic-based approach to landslide susceptibility mapping (efLandslides), and a logistic regression approach that is representative of multivariate statistical approaches to landslide susceptibility mapping. The study area is located in the middle and upper reaches of the Yangtze River, China, and includes two adjacent areas with similar environmental conditions – one for efLandslides model development (approximately 250 km2) and the other for model extrapolation (approximately 4600 km2). Sequential Gaussian simulation was used to simulate DEM error fields at 25-m resolution with different magnitudes and spatial autocorrelation levels. Nine sets of simulations were generated. Each set included 100 realizations derived from a DEM error field specified by possible combinations of three standard deviation values (1, 7.5, and 15 m) for error magnitude and three range values (0, 60, and 120 m) for spatial autocorrelation. The overall uncertainties of both efLandslides and the logistic regression approach attributable to each model-simulated DEM error were evaluated based on a map of standard deviations of landslide susceptibility realizations. The uncertainty assessment showed that the overall uncertainty in efLandslides was less sensitive to DEM error than that in the logistic regression approach and that the overall uncertainties in both efLandslides and the logistic regression approach for the model-extrapolation area were generally lower than in the model-development area used in this study. Boxplots were produced by associating an independent validation set of 205 observed landslides in the model-extrapolation area with the resulting landslide susceptibility realizations. These boxplots showed that for all simulations, efLandslides produced more reasonable results than logistic regression.  相似文献   

12.
X. Yao  L.G. Tham  F.C. Dai 《Geomorphology》2008,101(4):572-582
The Support Vector Machine (SVM) is an increasingly popular learning procedure based on statistical learning theory, and involves a training phase in which the model is trained by a training dataset of associated input and target output values. The trained model is then used to evaluate a separate set of testing data. There are two main ideas underlying the SVM for discriminant-type problems. The first is an optimum linear separating hyperplane that separates the data patterns. The second is the use of kernel functions to convert the original non-linear data patterns into the format that is linearly separable in a high-dimensional feature space. In this paper, an overview of the SVM, both one-class and two-class SVM methods, is first presented followed by its use in landslide susceptibility mapping. A study area was selected from the natural terrain of Hong Kong, and slope angle, slope aspect, elevation, profile curvature of slope, lithology, vegetation cover and topographic wetness index (TWI) were used as environmental parameters which influence the occurrence of landslides. One-class and two-class SVM models were trained and then used to map landslide susceptibility respectively. The resulting susceptibility maps obtained by the methods were compared to that obtained by the logistic regression (LR) method. It is concluded that two-class SVM possesses better prediction efficiency than logistic regression and one-class SVM. However, one-class SVM, which only requires failed cases, has an advantage over the other two methods as only “failed” case information is usually available in landslide susceptibility mapping.  相似文献   

13.
This paper deals with the heuristic approach used for landslide hazard zonation along the coastal slopes and cliffs of the Cilento region between Agropoli and Sapri (Italy). This sector of coastline (about 118 km in length) is formed mainly of Mesozoic carbonates and Miocene flysch; Quaternary marine sandstones together with beach sands also crop out. Due to the destructive force of the waves, the coastline is affected by several landslides (mainly rock-falls and slides). The major geomorphological, geological and structural features of about 154 slopes and cliffs have been analysed and several parameters affecting the rock-masses were detected and measured. These parameters deal with topographical, geological, geomechanical, environmental and wave hydraulic characteristics of the studied area. In order to perform the heuristic approach, the Rock Engineering Systems (RES) proposed by Hudson was adopted with several modifications. The main steps of this work were: (1) the choice of parameters relevant to landslide hazard zonation, (2) the analysis of binary interaction between parameters, (3) the weighting of interaction importance, (4) the rating assignment to different classes of parameter values and (5) the final computation of an “Instability Index” (I.I.). A database containing the measured parameters was prepared, and using an interaction matrix, the outputs were linked into a Geographic Information System. It contains the following elements: geological and geomorphological features, historical data regarding landslides, images and values of I.I. for the studied slopes and cliffs. If new landslides occur or near-shore engineered structures are built, then the I.I. values will be automatically upgraded.Values of the I.I. were grouped into 3 classes marking low, medium and high landslide hazard. Both carbonatic rock-masses and flysch were distinguished with respect to I.I. values to show the differences in landslide susceptibility. In fact, rapid but small rock-falls can cause more casualties than moderate speed but large slides. High landslide hazard affects about 41% of carbonate cliffs and about 53% of slopes in arenaceous-marly flysch.  相似文献   

14.
This paper is focused primarily on how to represent landslide scarp areas, how to analyze results achieved by the application of specific strategies of representation and how to compare the outcomes derived by different tests, within a general framework related to landslide susceptibility assessment. These topics are analyzed taking into account the scale of data survey (1:10,000) and the role of a landslide susceptibility map into projects targeted toward the definition of prediction, prevention, and mitigation measures, in a wider context of civil protection planning. These aims are achieved by using ArcSDM (Arc Spatial Data Modeler), a software extension to ArcView GIS useful for developing spatial prediction models using regional datasets. This extension requires a representation by points of the investigated problems (landslide susceptibility, aquifer vulnerability, detection of mineral deposits, identification of natural habitats of animals, and plants, etc.). Maps of spatial evidence from regional geological and geomorphological datasets were used to generate maps showing susceptibility to slope failures in two different study areas, located in the northern Apennines and in the central Alps (Italy), respectively. The final susceptibility maps for both study areas were derived by the application of the weights-of-evidence (WofE) modeling technique. By this method a series of subjective decisions were required, strongly dependent on an understanding of the natural processes under study, supported by statistical analysis of the spatial associations between known landslides and evidential themes. Except for maps of attitude, permeability, and structure, that were not available for both study areas, the other data were the same and comprised geological, land use, slope, and internal relief maps. The paper illustrates how different representations of scarp areas by points (in terms of different number of points) did not greatly influence the final response map, considering the scale of this work. On the contrary, some differences were observed in the capability of the model to describe the relations between predictor variables and landslides. In effect, a representation of the scarp areas using one point every 50 m led to a more efficient model able to better define relationships of this type. It avoided both problems of redundancy of information, deriving by the use of too many points, and problems related to a random positioning of the centroid. Moreover, it permitted to minimize the uncertainty related with identification and mapping of landslides.  相似文献   

15.
本文分别利用光学立体和In SAR技术生成了东南极Grove山地区15 m分辨率的ASTER DEM和20 m分辨率的In SAR DEM。在利用ASTER立体像对生成DEM的过程中引入ICESat测高数据作为高程控制以减少错误匹配,提高DEM垂直精度;而在利用ERS tandem数据生成DEM后,选取ICESat测高数据对In SAR DEM进行倾斜面纠正,以消除基线不精确估计等带来的影响。通过与未作控制的ICESat测高数据进行比较,评价了两种DEM的精度并对误差进行了分析。同时,比较了两种DEM的差异,并分析了造成这些差异的原因,探讨了两种技术生成南极冰盖DEM的优势和不足。最后结合两DEM的优势,融合生成了Grove山地区高精度的DEM。  相似文献   

16.
数字高程模型（DEM）在表达地貌形态、认知地表过程、揭示地学机理等研究中发挥着基础性的作用,是重要的地理空间数据模型,广泛地应用于地学分析与建模中。但是,传统DEM具有属性单一的天然缺陷,难以支撑面向地学过程与机理挖掘的地球系统科学研究。亟待在传统DEM的基础上实现其数据模型的增值,服务于新地貌学研究范式和新对地观测技术背景下的数字地形建模与分析。立足于以上问题,本文构建了DEM增值的理论框架,主要包括DEM增值的概念、内涵、内容、类别、不同增值类别之间的相互关系,以及此理论框架的研究意义和应用范畴。提出了DEM增值的构建方法,包含：① 强调地上地下一体化、时间空间相耦合的DEM空间维度和时间维度增值方法;② 重视地下、地表和地上物质构成,形态属性耦合的物质属性和形态属性增值方法;③ 顾及自然过程、人工作用的地物对象、地貌形态的地物要素和形体要素增值方法。最后,分别以数字阶地模型、数字坡地模型和数字流域模型为例,阐释DEM在面向地貌学本源问题时的不同增值方法及应用场景。期望通过对DEM进行维度、属性和要素3个层面的增值,实现现代对地观测技术背景下数字高程模型表达方法的突破,并支撑知识驱动的数字地貌问题分析。  相似文献   

17.
数字地形分析在滑坡研究中的应用综述   总被引：2，自引：0，他引：2  
高效的数字地形分析（Digital Terrain Analysis,DTA）是滑坡预测与评估研究的重要手段。文章综述了DTA在滑坡研究中的应用现状,基本内容包括地形因子分析、地形形态分析、地形单元划分以及DEM与滑坡模型的结合分析。地形因子分析的应用多而广,主要思路是在地形因子与滑坡发育的关系研究基础上分析其滑坡敏感性,进而构建滑坡预测和评估模型;地形形态分析是滑坡识别的重要手段,加强地貌形态和滑坡发育的关系研究有助于对潜在滑坡地形的识别;地形单元划分能为滑坡研究提供统计和分析单元;DEM与滑坡专业模型的结合方式多样,程度各异。同时,从尺度选择与转换的角度探讨了DTA滑坡研究的尺度问题,分析了DTA的局限性,指出DEM不能提供完备无误的地形信息,DTA不能完全取代常规的地形分析。最后,基于以上论述对未来的研究趋势提出了展望。  相似文献   

18.
Comparison of satellite and air photo based landslide susceptibility maps   总被引：4，自引：1，他引：4  
Landslide susceptibility maps can be prepared in a variety of ways. Many geoscientists favour the use of an overlay model approach in which several map layers are combined by some arithmetic rules to determine the potential for sliding in an area or region. The resulting susceptibility maps, although based on a subjective weighting of relevant factors, can often be of high accuracy and utility. In order to obtain the relevant input data for this type of analysis, remotely sensed data are often used. To date, susceptibility mapping, just as the mapping of historic and individual landslides, has tended to require higher-resolution imagery. This has somewhat limited the application of landslide susceptibility mapping. While high-resolution air photo or satellite imagery is superior to lower resolution imagery for the purpose of mapping of historic and individual landslides, such higher levels of resolution may not be required for the development of landslide susceptibility maps. In order to determine if medium-resolution satellite imagery, such as SPOT or ASTER, could provide the needed data for landslide susceptibility mapping, a comparison was undertaken of landslide susceptibility model output resulting from the use of stereo NAPP aerial photography versus the use of data obtained from stereo SPOT imagery. The test area selected for this study consisted of two watersheds, Pena Canyon and Big Rock Canyon, situated west of Santa Monica, California, USA, along the Pacific Coast Highway. Both watersheds have a long and well-documented history of landslide activity and sufficient geologic variability and complexity to provide a good test site. The specific overlay model used in this evaluation required input data consistent with the needs of many other models of this type. The model output derived from the two different data sources and presented here in the form of susceptibility maps were virtually identical. Statistical and difference analysis confirmed that both methods of obtaining input data provide similar results and successfully identified landslide prone areas. These results suggest that satellite imagery, in this instance, SPOT images, could potentially be used in lieu of conventional air photos, to evaluate landslide susceptibility. In many situations, especially in the case of remote locations and/or developing countries, this capability should result in substantial savings in terms of time, financial resources, and overall viability.  相似文献   

19.
Comparing landslide inventory maps   总被引：10，自引：1，他引：9  
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.  相似文献   

20.
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。  相似文献   

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