基于GIS平台与证据权的地震滑坡易发性评价

汶川地震诱发了数以万计的滑坡灾害.应用地理信息系统与遥感技术,选取地震烈度、岩性、坡度、断层、高程、坡向、河流与公路8个因素作为汶川地震滑坡影响因子,采用证据权方法,对研究区内汶川地震滑坡进行灾害易发性研究.检验表明,易发性评价结果的正确率达到81.855％.不同因子组合评价结果表明,地震烈度对滑坡易发性分区结果影响最...     

2008年汶川地震滑坡详细编目及其空间分布规律分析

最新研究成果表明, 2008年5月12日汶川MS 8.0级地震触发了超过197000处滑坡。首先,基于GIS与遥感技术构建了汶川地震滑坡的3类编目图,分别为单体滑坡面分布数据、滑坡中心点位置和滑坡后壁点位置。构建方法为基于地震前后高分辨率遥感影像的目视解译方法,区分单体滑坡并圈定其边界,对滑坡后壁进行识别与定点,并开展了部分滑坡的野外验证工作。这些滑坡分布在一个面积大约为110000km2的区域内,滑坡总面积约为1160km2。选择一个面积约为44031km2的区域作为研究区,区内滑坡数量为196007个,滑坡面积为1150.622km2,这是最详细完整的汶川地震滑坡编录成果,也是单次地震事件触发滑坡最多的记录。其次,开展研究区内的地震滑坡空间分布规律的研究。基于滑坡面与滑坡中心点分别构建滑坡空间分布面积密度图与点密度图,结果表明:滑坡多沿着映秀北川断裂分布,多发生在断裂的上盘。滑坡的高密度区位于映秀北川同震地表破裂的南西段(映秀镇与北川县之间)的上盘区域,这一区域恰对应着逆冲分量为主的断裂上盘,表明逆冲断裂对上盘区域发生滑坡的极强烈的控制作用,而该区域正是形变最大的区域,因此说明是地震滑坡发生的强烈控制作用。基于滑坡面密度(LAP)、滑坡中心点密度(LCND)与滑坡后壁点密度(LTND)这3个衡量指标,使用统计分析方法,评价了汶川地震滑坡与地震参数、地质参数、地形参数的关系。结果表明:LAP、LCND与LTND这3个衡量指标与坡度、地震烈度与PGA存在明显的正相关关系; 与距离震中、距离映秀北川同震地表破裂存在负相关关系; 斜坡曲率越接近0,滑坡越不易发生; LAP、LCND与LTND的高值高程区间为1200～3000m; 滑坡发生的优势坡向为E、SE、S方向; 滑坡发育的易发岩性为砂岩与粉砂岩(Z)、花岗岩; 滑坡与坡位的相关关系不太明显。统计结果还表明LCND与LTND两个衡量指标的差异对地震与地质因子不敏感,而对地形因子较敏感。最后将本文的统计结果与以往的汶川地震滑坡空间分布规律统计成果进行了一些对比,对比结果表明,对于某些因子,如高程、岩性、距离震中、距离映秀北川断裂的统计分析结果,采用不完整的滑坡分布数据或点数据,与采用较完整的滑坡分布面数据会有一定的差异,这种差异并未出现在针对坡度与坡向等因子的统计对比结果中。总之,作者认为一个完备、详细的地震滑坡分布面要素编目图是地震滑坡空间分布规律定量分析、危险性定量分析与滑坡控制的地震区地貌演化研究的重要基础,否则,与实际情况相比,得到统计结果会有一定的偏差,本文的研究成果与以往成果的对比结果证明了这一点。     

金沙江上游特米古滑坡堰塞湖形成与溃决时间讨论

开展古滑坡堰塞湖形成演化过程研究,可以揭示古灾害地质环境效应,重建区域构造历史活动序列和古气候演变特征。特米古滑坡发育于金沙江上游巴塘段,滑坡堆积地貌和堰塞湖相沉积物保存较好,是研究区内古地质环境的良好载体。在遥感解译、无人机测绘、现场调查和地质测年的基础上,结合前人研究成果,分析探讨了特米古滑坡发育特征、堰塞湖形成时间与溃决演化过程。结果表明,特米古滑坡是特大型岩质历史堵江滑坡,滑坡堰塞湖实际形成时间应该远早于2.15 ka BP,历史上曾发生过多次溃决,完全溃决时间大约为1.08 ka BP,堰塞湖稳定保存时间大于1.07 ka。金沙江巴塘段大型堵江滑坡群并非由单次地质事件形成,而是由金沙江断裂带多次强烈地震诱发。     

基于信息量模型与Logistic回归模型耦合的地质灾害易发性评价

汶川地震在区内诱发了大量滑坡、崩塌等地质灾害,且孕育了大量松动岩体,这些松动岩体在降雨等因素诱发下将会产生大量次生地质灾害,危险性极大。故此对汶川县进行地质灾害易发性评价具有十分重要的现实意义。本文选取高程、坡度、坡向、起伏度、沟谷密度、工程岩组、断层、水系、道路9个影响因子,基于GIS的栅格数据模型,采用信息量模型、Logistic回归模型以及两种模型耦合分析进行地质灾害易发性评价。研究结果表明,采用耦合模型较信息量或Logistic单一模型评价结果更加合理、精度更高;易发性高与较高区域多集中于水系延展区域与断层集中区域。所计算得出的易发性分区结果与研究区实际情况相近,能在地质灾害风险评价中起到重要参考作用。     

安徽黄山市徽州区地质灾害危险性评价研究

皖南山区是安徽省地质灾害高发区域。本文选取黄山市徽州区为研究区,根据区内地形地貌和地质构造特点,选取了高程、坡度、坡向、断裂构造、水系、土地覆盖类型、工程地质岩组、人类活动强度等8项致灾因子作为地质灾害危险性评价指标。结合地质灾害野外实地调查成果,采用信息量模型法对研究区进行地质灾害危险性评价,探索建立适合皖南山区的地质灾害危险性评价模型。     

基于河长—坡降指数的滑坡堵江事件自动识别

由于本身特征的复杂差异性和背景环境信息干扰,目前地质灾害自动识别难度较大,尚无法满足应用需求。利用算法自动提取某类地质灾害普遍具备且明显区别于周围地物的特征,是实现地质灾害自动识别的有效途径。一定规模的滑坡堵江事件常通过链式过程在山间河谷形成裂点等诸多微地貌特征,成为其遥感解译和现场调查的重要标志。本文从河流地貌演化角度出发,以河流裂点作为滑坡堵江的普遍地貌特征,提出了基于河长-坡降指数（Stream Length-gradient index,SL) 的滑坡堵江事件自动识别方法,并通过GIS程序设计和编写提供了相应功能模块。以DEM、遥感影像和区域地质图为数据源,利用该方法在西藏亚东县康布麻曲上游流域开展应用分析,得到以下结论:针对1:5万DEM数据,采样点计算间隔设定为300m比较合理,能够同时满足降低数据误差和突出地形差异的要求;研究区中不同因素按照对河流地貌的改造程度,排序为滑坡堵江>基岩变化>构造活动;经验证研究区滑坡堵江事件自动提取的正确率达85.71%,表明在高山峡谷地区,基于河长—坡降指数开展滑坡堵江事件自动识别具有可行性,效果理想。     

逻辑回归和信息量模型在山区滑坡易发性评价中适宜性的分析

以万山区为例,在区域滑坡孕灾条件的基础上,筛选工程地质岩组、斜坡结构、平均坡度、地貌、距构造距离及距河流距离共6个易发条件因子,选取逻辑回归模型和信息量模型对山区滑坡进行易发性评价。结果显示逻辑回归模型中中高易发区面积占比分别为1578%和1970%,82%的地质灾害点落在该区域内;信息量模型中中高易发区面积占比为1241%、2519%,包含了区域88%的滑坡灾害点。最后通过实际发生的灾害点在各易发区的分布情况进行检验,逻辑回归模型中灾害点落在高易发区的比例远小于信息量模型,且高易发等级中灾害点实际发生的比值较小,说明针对山区区域滑坡地质灾害易发性评价结果预测上,信息量模型的评价结果更为客观准确。     

基于信息量法模型与GIS的滑坡地质灾害风险性评价

为查明理县滑坡地质灾害总体风险水平,根据滑坡高易发的特征,选取坡度、坡向、构造、岩性、水系、降雨、地震、人类活动等8个因子作为滑坡危险性评价指标,选取人口密度、单位面积GDP和土地资源作为易损性评价指标,基于层次分析法-信息量法模型,使用GIS技术开展理县滑坡地质灾害风险评价.地质灾害风险性评价结果符合理县滑坡地质灾害...     

汶川地震中擂鼓镇地区的滑坡崩塌规律及预测

地震滑坡影响因素与滑坡崩塌分布关系的研究,有助于认识地震滑坡崩塌的发育规律,进而对潜在的地震滑坡危险区段进行划分,为土地的合理使用提供支持.5.12汶川地震引发了大量的滑坡、崩塌、碎屑流等次生灾害,造成了严重的人员伤亡和经济财产损失.以受滑坡、崩塌灾害影响严重的北川县擂鼓镇约180 km2的地区作为研究对象,选取坡度、高程、坡向等影响因素进行确定性及面积发育率分析,探讨它们与滑坡、崩塌等灾害空间分布之间的关系.研究结果表明:在高程低于1 km的地段,滑坡崩塌的发生频率达13.5%,高于其它地段;坡向为东向、北东向、南东向的坡体的滑坡崩塌发生率较其它方向大;随着坡度的增大,滑坡、崩塌的分布也在增大,坡度大于30°的区域滑坡发生频率较高.采用2种方法对研究区进行地震滑坡危险性区划,获得大体一致的划分结果:①基于综合确定性系数与面积发育率方法分析的地震滑坡危险性区划结果中,约有66%的滑坡崩塌落入较高危险和高危险区域;②采用判别分析法获得的地震滑坡危险性区划结果中,约有73%的滑坡崩塌判定为不稳定区域.其中,判别分析法选用的地震动、坡度、曲率等因素在不同地区都对滑坡分布具普遍的影响作用.     

基于GIS汕头潮阳区崩塌、滑坡地质灾害危险性评价

以潮阳区1:5万地质灾害详细调查数据为基础,选取地质灾害点坡度、坡向、地形起伏度、工程地质岩组、地质构造、土地利用类型等6个因素评价指标,采用信息量法获取研究区易发性,基于GIS空间分析功能将10年一遇降雨工况和易发性分析计算,得出地质灾害危险性分区图。研究区综合地质灾害高危险区面积明显大于单灾种评价结果,高危险区主要位于崩塌、滑坡较发育的碎裂岩区域;对提高区域地质灾害风险预测能力及综合防治水平具有实际意义。     

Morphological analysis and features of the landslide dams in the Cordillera Blanca,Peru

Global warming in high mountain areas has led to visible environmental changes as glacial retreat, formation and evolution of moraine dammed lakes, slope instability, and major mass movements. Landslide dams and moraine dams are rather common in the Cordillera Blanca Mountains Range, Peru, and have caused large damages and fatalities over time. The environmental changes are influencing the rivers’ and dams’ equilibrium, and the potential induced consequences, like catastrophic debris flows or outburst floods resulting from dam failures, can be major hazards in the region. The studies of past landslide dam cases are essential in forecasting induced risks, and specific works on this topic were not developed in the study region. Reflecting this research gap, a database of 51 cases and an evolution study of landslide dams in the Cordillera Blanca Mountains is presented. The main morphometric parameters and information of the landslide, the dam body, the valley, and the lake, if any, have been determined through direct and indirect survey techniques. Low variability in some of the main morphometric parameter distributions (valley width and landslide volume) has been shown, most likely due to an environmental control connected to the regional tectonic and glacial history. In order to analyze present and future landslide dam evolution, a morphological analysis was carried out using two recently developed geomorphological indexes employed on the Italian territory. The results of the Cordillera Blanca analysis have been compared with a large Italian landslide dam inventory, highlighting as much the differences as the similarities between the two datasets. The long-term geomorphological evolution changes are evaluated. Many of the stable dams are in disequilibrium with their surrounding environment and their classification result is of “uncertain determination.”     

Landslide dam formation susceptibility analysis based on geomorphic features

Global climate change has increased the frequency of abnormally high rainfall; such high rainfall events in recent years have occurred in the mountainous areas of Taiwan. This study identifies historical earthquake- and typhoon-induced landslide dam formations in Taiwan along with the geomorphic characteristics of the landslides. Two separate groups of landslides are examined which are classified as those that were dammed by river water and those that were not. Our methodology applies spatial analysis using geographic information system (GIS) and models the geomorphic features with 20?×?20 m digital terrain mapping. The Spot 6 satellite images after Typhoon Morakot were used for an interpretation of the landslide areas. The multivariate statistical analysis is also used to find which major factors contribute to the formation of a landslide dam. The objective is to identify the possible locations of landslide dams by the geomorphic features of landslide-prone slopes. The selected nine geomorphic features include landslide area, slope, aspect, length, width, elevation change, runout distance, average landslide elevation, and river width. Our four geomorphic indexes include stream power, form factor, topographic wetness, and elevation–relief ratio. The features of the 28 river-damming landslides and of the 59 non-damming landslides are used for multivariate statistical analysis by Fisher discriminant analysis and logistic regression analysis. The principal component analysis screened out eleven major geomorphic features for landslide area, slope, aspect, elevation change, length, width, runout distance, average elevation, form factor, river width, stream power, and topography wetness. Results show that the correctness by Fisher discriminant analysis was 68.0 % and was 70.8 % by logistic regression analysis. This study suggests that using logistic regression analysis as the assessment model for identifying the potential location of a landslide dam is beneficial. Landslide threshold equations applying the geomorphic features of slope angle, angle of landslide elevation change, and river width (H _L/W _R) to identify the potential formation of natural dams are proposed for analysis. Disaster prevention and mitigation measures are enhanced when the locations of potential landslide dams are identified; further, in order to benefit such measures, dam volume estimates responsible for breaches are key.     

考虑河床坡度和泄流槽横断面影响的堰塞坝溃决过程试验研究

针对缺乏地形条件和工程处置措施对堰塞坝溃决过程影响研究的现状,采用4种河床坡度（0°、1°、2°、3°）和3种泄流槽横断面型式（三角形、梯形、复合型）,开展了堰塞坝溃决的模型试验。通过分析堰塞坝的溃决流量、溃决历时、溃口发展和坝体纵截面演变过程,研究了不同河床坡度和泄流槽横断面对堰塞坝溃决过程的影响规律。试验结果表明：...     

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.     

Deriving landslide dam geometry from remote sensing images for the rapid assessment of critical parameters related to dam-breach hazards

Dam-breaches that cause outburst floods may induce downstream hazards. Because landslide dams can breach soon after they are formed, it is critical to assess the stability quickly to enable prompt action. However, dam geometry, an essential component of hazard evaluation, is not available in most cases. Our research proposes a procedure that utilizes post-landslide orthorectified remote sensing images and the pre-landslide Digital Terrain Model in the Geographic Information System to estimate the geometry of a particular dam. The procedure includes the following three modules: (1) the selection of the reference points on the dam and lake boundaries, (2) the interpolation of the dam-crest elevation, and (3) the estimation of dam-geometry parameters (i.e., the height, length, and width), the catchment area, the volumes of barrier lake and landslides dam. This procedure is demonstrated through a case study of the Namasha Landslide Dam in Taiwan. It was shown the dam-surface elevation estimated from the proposed procedure can approximate the elevation derived from profile leveling after the formation of the landslide dam. Thus, it is feasible to assess the critical parameters required for the landslide dam hazard assessment rapidly once the ortho-photo data are available. The proposed procedure is useful for quick and efficient decision making regarding hazard mitigation.     

天然土石坝稳定性初步研究

滑坡堵塞江河形成的天然土石坝是自然作用的产物，不同于人工土石坝，天然土石坝形成后有些存在几十年，几百年，有些形成后不久就溃决，这与坝体本身的性质和河水入流量有关，依据野外实测资料，证了土石坝的稳定性的主要是同土石坝的物质组成，几何形状和堰塞湖入流量等因素决定的，这一研究为天然土石坝的稳定性预测奠定了基础。     

A subjective and objective integrated weighting method for landslides susceptibility mapping based on GIS

The purpose of this study is to present a weighting method, integrating subjective weight with objective weight, for landslides susceptibility mapping based on geographical information system (GIS). First, the landslide inventory, aspect, slope, proximity to streams of drainage network, proximity to railway, proximity to road, topography, elevation, lithology, tectonic activity and annual precipitation, including their subclasses, were taken as independent landslide causal factors. Second, objective weights of the causal factors were calculated according to the landslide area density based on entropy weighting method, and key factors were selected according to the rank of the objective weights. Third, trapezoidal fuzzy number weighting approach was used to assess the sub-classes of each key factor. Finally, a case study was carried out in Guizhou province, China. A landslide susceptibility map was created using weighted linear combination model based on GIS. Using a predicted map of probability, the study area was classified into four categories of landslide susceptibility: low, moderate, moderate-high, and high.     

Logistic regression model for predicting the failure probability of a landslide dam

Landslides may obstruct river flow and result in landslide dams; they occur in many regions of the world. The formation and disappearance of natural lakes involve a complex earth–surface process. According to the lessons learned from many historical cases, landslide dams usually break down rapidly soon after the formation of the lake. Regarding hazard mitigation, prompt evaluation of the stability of the landslide dam is crucial. Based on a Japanese dataset, this study utilized the logistic regression method and the jack-knife technique to identify the important geomorphic variables, including peak flow (or catchment area), dam height, width and length in sequence, affecting the stability of landslide dams. The resulting high overall prediction power demonstrates the robustness of the proposed logistic regression models. Accordingly, the failure probability of a landslide dam can also be evaluated based on this approach. Ten landslide dams (formed after the 1999 Chi-Chi Earthquake, the 2008 Wenchuan Earthquake and 2009 Typhoon Morakot) with complete dam geometry records were adopted as examples of evaluating the failure probability. The stable Tsao-Ling landslide dam, which was induced by the Chi-Chi earthquake, has a failure probability of 27.68% using a model incorporating the catchment area and dam geometry. On the contrary, the Tangjiashan landslide dam, which was artificially breached soon after its formation during the Wenchuan earthquake, has a failure probability as high as 99.54%. Typhoon Morakot induced the Siaolin landslide dam, which was breached within one hour after its formation and has a failure probability of 71.09%. Notably, the failure probability of the earthquake induced cases is reduced if the catchment area in the prediction model is replaced by the peak flow of the dammed stream for these cases. In contrast, the predicted failure probability of the heavy rainfall-induced case increases if the high flow rate of the dammed stream is incorporated into the prediction model. Consequently, it is suggested that the prediction model using the peak flow as causative factor should be used to evaluate the stability of a landslide dam if the peak flow is available. Together with an estimation of the impact of an outburst flood from a landslide-dammed lake, the failure probability of the landslide dam predicted by the proposed logistic regression model could be useful for evaluating the related risk.     

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.