岷江上游多级多期崩滑堵江事件初步研究

本文研究了岷江上游地区较场-茂县-汶川长约100km河段上形成多级、多期大型滑坡堵江事件。研究表明，这类特殊的地质灾害有其发生发展的特殊的地质背景，它们造成的灾害和环境效应较一般的滑坡灾害更严重。有针对性对岷江上游这类灾害进一步深入研究是很必要的。     

垦丁国家公园

正垦丁国家公园位于台湾本岛最南端的恒春半岛,三面环海,东面太平洋,西邻台湾海峡,南濒巴士海峡,陆地面积约18,084公顷,海域面积约15,206公顷,合计近33,290公顷,园区南北长约24公里,东西宽约24公里,全境属于热带性气候区,终年气温和暖,热带植物衍生,四周海域清澈,珊瑚生长繁盛,为台湾热门观光胜地之一。     

台湾屏东县狮子乡坡地灾害潜势分析

以屏东县狮子乡为例,利用GIS探讨近年来致灾降雨事件之特性及空间分布趋势,并配合崩塌潜势与土石流(即泥石流,编者注)潜势溪流范围.首先将筛选出之地文因子,以曼一惠特尼进行崩塌相关性验证,具相关性之因子再以不安定(即不稳定)指数法计算出权重,并求得影响崩塌之主要因子;对于土石流潜势溪流之危险度并不予以评估,仅说明其集水区之地文特性;然后绘制崩塌潜感图,最后配合致灾降雨事件之日最大降雨量频率分析、特性与主要降雨空间分布进行灾害潜势分析,提供疏散避难发布决策之先后顺序.期望藉由分析坡地灾害潜势,能够达防灾、减灾之功效.     

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％.     

金沙江向家坝库区崩滑灾害的环境效应

金沙江向家坝库区长约１５０ｋｍ的沿江地带，共发育１０２个变形破坏体（含滑坡、崩塌和变形体），总体积１５．６５×１０８ｍ３．据统计岸坡变形破坏密度Ｄ和模数Ｅ分别达０．３４个／ｋｍ和５２４．９３×１０４ｍ３／ｋｍ．对崩滑灾害所处的环境特征研究表明，滑坡、崩塌与所在地层岩性、地质构造、地形条件、岩体结构特征及近期河流地质作用等环境因素共同作用有关．     

甘肃魏家地煤矿矿山地质灾害特征及预测评估

目前魏家地煤矿发育地质灾害有崩塌4处、地面塌陷1处、泥石流沟1条。预测新建工业场地引发崩塌灾害对矿山地质环境的影响程度属较轻,面积21.2hm~2;预测引发地面塌陷对矿山地质环境影响程度属严重,面积约878hm2;预测加剧N1泥石流对矿山地质环境的影响程度属较轻,面积约18hm~2;预测地表变形加剧T1地面塌陷的影响程度严重、加剧B1-B4崩塌、N1泥石流灾害的影响程度较轻;对公路、矿区专用铁路的影响程度较严重。预测遭受4处崩塌灾害对矿山地质环境的影响程度属较轻,面积计约0.065hm~2。本文针对矿山地质灾害提出了相应的预防措施,研究结果为矿山地质环境保护与治理提供依据。     

台湾崩塌地调查研究方法综述

近年来，在经济的快速发展与人口不断增加的双重压力下，台湾平地开发已达饱和；而对于崩塌地之关注与研究，亦至少有三十几年的历史。本文首先针对崩塌地形成之种类与原因加以分类并探讨；其次，再就崩塌地传统地面之现场研究调查项目和结果分析加以说明；最后，则列举地理信息系统、遥感探测技术与航溯技术在崩塌地调查与研究上的应用。如何防止灾害的发生和避免生命财产的损失，以及防止自然灾害于未然，确立和应用此类调查研究方法，实为当务之急。     

浙江省泥石流形成及成灾特点

浙江地处我国东南沿海,近几年频繁遭受泥石流等山地灾害的危害。由于所处地理地质和气候环境,发育的泥石流有其独特的地域特征。从泥石流的发育特征、形成过程等探讨了浙江泥石流形成的机理,分析了其成灾特点。结果表明,浙江省泥石流为滑坡、崩塌转化型泥石流,泥石流的形成机理及过程主要为:强降雨下滑坡、崩塌发生,持续强降雨使得滑坡、崩塌松散物流化从而形成泥石流。滑坡、崩塌转化型泥石流在流域特征上表现为形成区坡度大,易发滑坡、崩塌,水力条件充分等。泥石流灾害具有低频性、群发性、并发性、突发性、夜发性、危害大等特点。     

“4·20”芦山7.0级地震次生山地灾害活动特征与趋势

2013-04-20芦山县发生7.0级地震,震源深度13 km.截至4月29日地震共造成196人遇难,21人失踪,13484人受伤,200余万人受灾.地震发生后,我们立即开展了地震次生山地灾害的遥感解译、实地调查及危险性评估工作.作者定义了地震次生山地灾害,分析了次生山地灾害的活动特征、形成机制与模式以及发展趋势,并与汶川地震次生山地灾害进行了对比.初步查明芦山地震诱发了1460余处崩塌和滑坡,大量落石和4处堰塞湖.次生山地灾害具有规模小、群发性和高位破坏的特征.崩塌和落石成群发育于坚硬岩石形成的陡坡上,主要发育区段有:芦山县的宝盛乡金鸡峡、双石镇大岩峡以及省道S210线K317路段和灵关镇以北小关子段,对沿河公路及救援生命通道影响严重.滑坡数量较少,以中小规模为主,主要发生于砂岩、页岩和松散堆积层中,仅发现一处大型滑坡并转化为碎屑流.堰塞湖主要由崩塌、滑坡形成,均为低危险性小型堰塞湖.芦山地震次生灾害的主控因素为构造、岩性、结构面和地形,崩塌破坏主要表现为顺层滑动破坏型、切层倾倒破坏型和结构面控制破坏型3种模式.芦山地震诱发崩塌、滑坡的数量、分布范围和规模比汶川地震小得多,其数量仅为汶川地震的5.53％,造成的地表破坏面积(3.06 km2)仅为0.57％.芦山地震区两次地震扰动的叠加效应,大大降低坡体的稳定性,震后崩塌、滑坡和泥石流的活动性增加,增幅有限,活跃期也相对较短,但震区山地灾害的隐伏性和隐蔽性强,给隐患排查带来困难.     

四川地区地震崩塌滑坡的基本特征及危险性分区

四川地区地震崩塌及滑坡非常发育，时常造成严重灾害，本文在现场调查资料及已有研究的基础上，对四川地区地震崩塌，滑坡的分布规律，类型，特征及其灾害性进行了更深入的分析，并就其特征进行了分区。     

Spatial patterns of old, deep-seated landslides: A case-study in the northern Ethiopian highlands

During the last decade, slope failures were reported in a 500 km² study area in the Geba–Werei catchment, northern Ethiopia, a region where landslides were not considered an important hazard before. Field observations, however, revealed that many of the failures were actually reactivations of old deep-seated landslides after land use changes. Therefore, this study was conducted (1) to explore the importance of environmental factors controlling landslide occurrence and (2) to estimate future landslide susceptibility. A landslide inventory map of the study area derived from aerial photograph interpretation and field checks shows the location of 57 landslides and six zones with multiple landslides, mainly complex slides and debris flows. In total 14.8% of the area is affected by an old landslide. For the landslide susceptibility modelling, weights of evidence (WofE), was applied and five different models were produced. After comparison of the models and spatial validation using Receiver Operating Characteristic curves and Kappa values, a model combining data on elevation, hillslope gradient, aspect, geology and distance to faults was selected. This model confirmed our hypothesis that deep-seated landslides are located on hillslopes with a moderate slope gradient (i.e. 5°–13°). The depletion areas are expected on and along the border of plateaus where weathered basalts rich in smectite clays are found, and the landslide debris is expected to accumulate on the Amba Aradam sandstone and upper Antalo limestone. As future landslides are believed to occur on inherently unstable hillslopes similar to those where deep-seated landslides occurred, the classified landslide susceptibility map allows delineating zones where human interventions decreasing slope stability might cause slope failures. The results obtained demonstrate that the applied methodology could be used in similar areas where information on the location of landslides is essential for present-day hazard analysis.     

Flood assessment and early warning of the reoccurrence of river blockage at the Baige landslide

On 10th Oct.and 3rd Nov.2018,two successive landslides occurred in the Jinsha River catchment at Baige Village,Tibet Autonomous Region,China.The landslides blocked the major river and formed the barrier lake,which finally caused the huge flood disaster loss.The hillslope at Baige landslide site has been still deforming after the 2018 slidings,which is likely to fail and block the Jinsha River again in the future.Therefore the investigation of 2018 flood disaster at the Baige landslide is of a great significance to provide a classic case for flood assessment and early warning for the future disaster.The detailed survey revealed that the outstanding inundations induced bank collapse disasters upstream the Baige landslide dams,and the field investigations and hydrological simulation suggested that the downstream of the Baige landslide were seriously flooded due to the two periods of the outburst floods.On these bases,the early warning process of potential outburst floods at the Baige landslide was advised,which contains four stages:Outburst Flood Simulating Stage,Outburst Flood Fore-casting Stage,Emergency Plan and Emergency Evacuation Stage.The study offers a con-ceptual model for the mitigation of landslides and flood disasters in the high-relief mountain-ous region in Tibet.     

Determining landslide susceptibility in Central Taiwan from rainfall and six site factors using the analytical hierarchy process method

This work provides a landslide susceptibility assessment model for rainfall-induced landslides in Central Taiwan based on the analytical hierarchy process method. The model considers rainfall and six site factors, including slope, geology, vegetation, soil moisture, road development and historical landslides. The rainfall factor consists of 10-day antecedent rainfall and total rainfall during a rainfall event. Landslide susceptibility values are calculated for both before and after the beginning of a rainfall event. The 175 landslide cases with detailed field surveys are used to determine a landslide-susceptibility threshold value of 9.0. When a landslide susceptibility assessment value exceeds the threshold value, slope failure is likely to occur. Three zones with different landslide susceptibility levels (below, slightly above, and far above the threshold) are identified. The 9149 landslides caused by Typhoon Toraji in Central Taiwan are utilized to validate the study's result. Approximately, 0.2%, 0.4% and 15.3% of the typhoon-caused landslides are located in the three landslide susceptibility zones, respectively. Three villages with 6.6%, 0.4% and 4.9% of the landslides respectively are used to validate the accuracy of the landslide susceptibility map and analyze the main causes of landslides. The landslide susceptibility assessment model can be used to evaluate susceptibility relative to accumulated rainfall, and is useful as an early warning and landslide monitoring tool.     

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.     

基于遥感与地理信息系统的分布式斜坡稳定性定量评估模型

文章介绍一种进行斜坡稳定性定量研究的分布式模型——SINMAP模型。该模型以水文学理论为基础,耦合稳定状态水文模型TOPMODEL与大范围斜坡稳定性模型,在充分考虑各种影响因素的基础上,对研究区域进行斜坡稳定性评价。选取汉江江口流域作为试验研究区,以DEM、遥感影象、各种专题图件及地面考察资料作为信息源,利用SINMAP方法获得可视化的研究区地表稳定性指数专题图。经实际资料检验表明,该模型可获取较高的预测精度,尤其在流域尺度上具有极大的应用价值。     

Modeling typhoon- and earthquake-induced landslides in a mountainous watershed using logistic regression

Landslides can be caused by storms and earthquakes. Most logistic regression models proposed in recent years have been targeted at rainfall-induced landslides. In areas such as Taiwan, where landslides can be triggered by typhoons (tropical cyclones) and earthquakes, a rainfall-induced model is insufficient because it provides only a partial explanation of landslide occurrence and overlooks the potential effect of earthquakes on typhoon-triggered landslides. This study used landslides triggered by a major earthquake and a typhoon prior to the earthquake to develop an earthquake-induced model and a typhoon-induced model. The models were then validated by using landslides triggered by three typhoons after the earthquake. According to the results, typhoon-triggered landslides tended to be near stream channels and earthquake-triggered landslides were more likely to be near ridge lines. Moreover, a major earthquake could still affect the locations of typhoon-triggered landslides 6 years after the earthquake. This study therefore demonstrates that an earthquake-induced model both sheds light on the environmental factors for triggering landslides, and augments a rainfall-induced model in its predictive capability in areas such as Taiwan.     

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。     

Landslide scar/soil erodibility mapping using Landsat TM/ETM+ bands 7 and 3 Normalised Difference Index: A case study of central region of Kenya

Landsat series multispectral remote sensing imagery has gained increasing attention in providing solutions to environmental problems such as land degradation which exacerbate soil erosion and landslide disasters in the case of rainfall events. Multispectral data has facilitated the mapping of soils, land-cover and structural geology, all of which are factors affecting landslide occurrence. The main aim of this research was to develop a methodology to visualize and map past landslides as well as identify land degradation effects through soil erosion and land-use using remote sensing techniques in the central region of Kenya. The study area has rugged terrain and rainfall has been the main source of landslide trigger. The methodology comprised visualizing landslide scars using a False Colour Composite (FCC) and mapping soil erodibility using FCC components applying expert based classification. The components of the FCC were: the first independent component (IC1), Principal Component (PC) with most geological information, and a Normalised Difference Index (NDI) involving Landsat TM/ETM+ band 7 and 3.The FCC components formed the inputs for knowledge-based classification with the following 13 classes: runoff, extreme erosions, other erosions, landslide areas, highly erodible, stable, exposed volcanic rocks, agriculture, green forest, new forest regrowth areas, clear, turbid and salty water. Validation of the mapped landslide areas with field GPS locations of landslide affected areas showed that 66% of the points coincided well with landslide areas mapped in the year 2000. The classification maps showed landslide areas on the steep ridge faces, other erosions in agricultural areas, highly erodible zones being already weathered rocks, while runoff were mainly fluvial deposits. Thus, landuse and rainfall processes play a major role in inducing landslides in the study area.     

基于空间密度函数的哈尼梯田世界遗产地滑坡时空格局变化

为探究哈尼梯田世界文化景观遗产地核心区滑坡灾害时空分布规律，以Google Earth 0.55 m分辨率的2005、2009、2015年3期遥感影像为基础，结合实地走访调查，建立滑坡数据库，在ArcGIS 10.2平台上计算滑坡点的最邻近指数、K函数曲线及密度分布。结果显示：1）哈尼梯田遗产核心区2005、2009、2015年的滑坡数量分别为184、337和285个，对应最邻近指数为0.556、0.603、0.628；最显著聚集的空间尺度为1 000 m，从聚集向离散分布转变的空间尺度阈值分别为2.9、3.9、3.6 km。2）3个年份滑坡点高密度区占比逐渐增加（2.3%→5.8%→8.3%），中密度区占比亦逐渐增大（15.7%→21.8%→27.9%），低密度区占比逐渐减小（82.0%→72.5%→66.8%）。3）需要重点防范滑坡灾害风险的区域为森林区的西段和东段，村寨区的多依树、硐浦、勐品、水卜龙等地，以及阿勐控河和碧猛河流域内的梯田区。综上，研究区2005－2015年滑坡空间格局发生了显著变化，随着人类活动对地表景观干预程度不断加大，滑坡灾害风险增加了更多的不确定性。     

基于信息量模型和机器学习方法的滑坡易发性评价研究——以四川理县为例

以阿坝藏族羌族自治州地质灾害频发的理县为研究区,从地形地貌、地质环境、水文条件和人类工程活动等方面选取11个影响因子,通过皮尔森相关系数研究各因子之间的相关性,从而构建滑坡易发性评价指标体系。利用信息量模型计算各影响因子的信息量值,从信息量模型得出的极低和低易发性分区中选取非滑坡样本,在此基础上将样本数据代入随机森林和径向基函数神经网络2种机器学习模型开展滑坡易发性评价,并通过接收灵敏度（Receiver Operating Characteristic,ROC）曲线进行精度验证。结果显示：随机森林模型预测出的高易发区单位面积内分布的滑坡点数量更为集中,在仅占6.666%的区域分布了74.026%的灾害点,评价结果优于径向基函数神经网络模型。ROC曲线中两模型AUC（Area Under Curve）值分别为0.893、0.874,说明随机森林模型具有更高的可靠性,比径向基函数神经网络在该区域地质灾害易发性评价中更具优势。