陕南山地滑坡和泥石流分布特征及其相关性

滑坡和泥石流是陕南山地成灾的主要物理地质现象。本文根据野外调查资料和前人研究成果,从地质、地貌、水文工程地质特征、水文气象、人为因素诸方面,简要的分析了陕南山地滑坡、泥石流形成条件、区域分布规律滑坡对泥石流形成的影响和滑坡转化成泥石流的辩证关系。     

川藏公路海竹段地质灾害的遥感分析

本文重点是利用遥感地学分析原理研究了川藏公路海竹段的滑坡、崩塌和泥石流的地质灾害。同时 ,在遥感分析的基础上 ,对本区的工程地质进行了分区 ,为川藏公路改线及病害整治提供了基础资料.     

新书推介

近日,科学出版社出了一本地球信息科学基础与应用类的新书:《滑坡遥感》。该书是作者王治华研究员30余年滑坡遥感探索与实践积累的总结。近年来,国内外地质灾害频发,灾损严重,将现代空间信息技术应用于滑坡和地质环境调查是减灾防灾工作的迫切需要。王治华研究员自20世纪80年代初涉遥感领域,90年代涉入GIS领域,将该技术应用于灾害与环境调查研究。先后完成了我国大渡河铜街子、雅砻江二滩、长江三峡、     

章丘市垛庄镇一带泥石流遥感解译

文章采用高分辨率WorldView-2卫星数据对章丘市垛庄镇一带进行多波段组合与多功能信息提取,建立了影像解译标志及评判标准,在评判标准的基础上,结合地质、地形地貌及其它诱发泥石流的因素对泥石流隐患区进行了遥感解译,实现了研究区的泥石流遥感调查评价。     

滑坡图像自动识别浅议

识别滑坡须先了解什么是滑坡,广义滑坡包括崩塌、滑坡、碎屑流、泥石流等所有斜坡重力侵蚀现象;狭义滑坡指部分斜坡沿着斜坡内的一个或数个面在重力的作用下作剪切运动的现象。各类滑坡有自已特殊的地表形态特征,发育的基本地质环境条件和触发因素,据这些特征识别滑坡。利用数字滑坡技术进行滑坡识别大致分为2步:(1)通过RS和GIS技术将不同时间的调查区地物现场以不同分辨率展现在数字图像上,并与地理控制及地质环境信息配准、组合,建立解译基础;(2)在滑坡地学理论指导下,通过人机交互方式进行解译和时空分析,获取减灾防灾需要的信息。该方法尚未达到遥感自动识别滑坡的程度,但建立解译基础的过程已可由计算机通过多种程序软件完成,故认为滑坡模式识别的前2个步骤:数字化及预处理已由计算机实现。现需探索的是用计算机实现基于滑坡地学理论知识,以人机交互方式进行的滑坡识别及分析过程。就狭义滑坡而言,基于DEM的滑坡地形识别已可由计算机实现。如能确定地面滑坡壁及滑体与地下滑面、滑床的关系,了解它们的光谱特征并建立计算模型,便可构建遥感技术的滑坡模式识别。     

Quick Bird遥感数据在特大型滑坡调查中的应用——以黄河龙羊峡-寺沟峡段参果滩滑坡为例

应用具高分辨率、高清晰度、信息量丰富及数据时效性强等特点的Quick Bird遥感数据对黄河龙羊峡-寺沟峡段参果滩特大型滑坡进行遥感调查,对滑坡的形成条件、运动特征以及触发条件、成因机理等进行了遥感解译分析。     

QuickBird遥感数据在特大型滑坡调查中的应用——以黄河龙羊峡-寺沟峡段参果滩滑坡为例

应用具高分辨率、高清晰度、信息量丰富及数据时效性强等特点的QuickBird遥感数据对黄河龙羊峡-寺沟峡段参果滩特大型滑坡进行遥感调查，对滑坡的形成条件、运动特征以及触发条件、成因机理等进行了遥感解译分析。     

基于RS与GPS的汶川地震路基路面震害损毁调查——以都汶公路为例

文章以都汶公路为例,利用遥感解译技术快速获取地震诱发山地灾害类型及其对公路造成的严重损害类型,利用GPS对公路损毁灾害点准确定位,获得路基路面直接震害类型信息,同时对山地灾害遥感判识结果进行检验。通过分析认为公路沿线岩体岩性以及风化破碎程度的不同,对公路产生的损毁程度不相同;路基破坏程度与距离震中和断裂带的远近有密切关系;路基下方护坡的破坏与所处边坡的坡度有关;崩塌滑坡产生大量松散固体物质,雨季到来之后,极易暴发大规模泥石流,需要注意并防范泥石流对行车安全和道路交通的影响。     

青岛市崂山区建立地质灾害自动监测预警示范工作的探索

青岛市崂山区特殊的自然地理和地质环境条件,在降雨以及人类工程-经济活动的影响下,引发了崩塌、滑坡、泥石流等突发性地质灾害.在对区内地质灾害点全面调查、排查和勘测工作的基础上,通过学习总结其他省市经验,建立了青岛市崂山区青山滑坡地质灾害自动监测预警示范区,为该区地质灾害防治工作提供技术依据.     

兰州市滑坡泥石流灾害与防治

本文在研究兰州市滑坡、泥石流类型、分布与发展趋势的基础上，论述了滑坡、泥石流防治现状和存在问题，指出了加强防治工作的紧迫性，提出了滑坡、泥石流灾害防治的指导思想、目标和对策，强调了开展系统防灾的重要性。     

Investigation and Assessment of Landslides and Debris Flows in Sichuan Province of China by Remote Sensing Technique

Taking TM images, ETM images, SPOT images, aerial photos and other remote sensing data as fundamental sources, this research makes a thorough investigation on landslides and debris flows in Sichuan Province, China, using the method of manual interpretation and taking topography maps as references after the processes of terrain correction, spectral matching, and image mosaic. And then, the spatial characteristics of landslides and debris flows in the year of 2005 are assessed and made into figures. The environmental factors which induce landslides and debris flows such as slope, vegetation coverage, lithology, rainfall and so on are obtained by GIS spatial analysis method. Finally, the relationships of landslides or debris flows with some environmental factors are analyzed based on the grade of each environmental factor. The results indicate： 1） The landslides and debris flows are mainly in the eastern and southern area of Sichuan Province, however, there are few landslides and debris flows in the western particularly the northwestern Sichuan. 2） The landslides and debris flows of Sichuan Province are mostly located in the regions with small slope degree. The occurring rate of debris flow reduces with the increase of the vegetation coverage degree, but the vegetation coverage degree has little to do with the occurrence of landslide. The more rainfall a place has, the easier the landslides and debris flows take place.     

Debris flow susceptibility assessment using a probabilistic approach: A case study in the Longchi area,Sichuan province,China

The Longchi area with the city of Dujiangyan, in the Sichuan province of China, is composed of Permian stone and diorites and Triassic sandstones and mudstones intercalated with slates. An abundance of loose co-seismic materials were present on the slopes after the May 12, 2008 Wenchuan earthquake, which in later years served as source material for rainfall-induced debris flows or shallow landslides. A total of 48 debris flows, all triggered by heavy rainfall on 13th August 20l0, are described in this paper. Field investigation, supported by remote sensing image interpretation, was conducted to interpret the co-seismic landslides in the debris flow gullies. Specific characteristics of the study area such as slope, aspect, elevation, channel gradient, lithology, and gully density were selected for the evaluation of debris flow susceptibility. A score was given to all the debris flow gullies based on the probability of debris flow occurrence for the selected factors. In order to get the contribution of the different factors, principal component analyses were applied. A comprehensive score was obtained for the 48 debris flow gullies which enabled us to make a susceptibility map for debris flows with three classes. Twenty-two gullies have a high susceptibility, twenty gullies show a moderate susceptibility and six gullies have a low susceptibility for debris flows.     

Empirical-statistical models based on remote sensing for estimating the volume of landslides induced by the Wenchuan earthquake

The Wenchuan Ms 8.0 earthquake on May 12, 2008 induced a huge number of landslides. The distribution and volume of the landslides are very important for assessing risks and understanding the landslide - debris flow - barrier lake - bursts flood disaster chain. The number and the area of landslides in a wide region can be easily obtained by remote sensing technique, while the volume is relatively difficult to obtain because it requires some detailed geometric information of slope failure surface and sub-surface. Different empirical models for estimating landslide volume were discussed based on the data of 107 landslides in the earthquake-stricken area. The volume data of these landslides were collected by field survey. Their areas were obtained by interpreting remote sensing images while their apparent friction coefficients and height were extracted from the images unifying DEM (digital elevation model). By analyzing the relationships between the volume and the area, apparent friction coefficients, and the height, two models were established, one for the adaptation of a magnitude scale landslide events in a wide range of region, another for the adaptation in a small scope. The correlation coefficients (R2) are 0.7977 and 0.8913, respectively. The results estimated by the two models agree well with the measurement data.     

Characteristics of clustering debris flows in Wenchuan earthquake zone

Clustering debris-flow events, namely many debris flows simultaneously triggered by a regional rainstorm in a large-scale mountainous area, occurred in four regions of Wenchuan earthquake stricken areas in 2008 and 2010. The characteristics of the clustering debris flows are examined with regard to triggering rainfall, formation process, and relationship with the earthquake by field survey and remote sensing interpretation. It is found that the clustering events occurred nearly at the same time with the local peak rainstorms, and the rainfall intensity-duration bottom limit line for clustering debris flows is higher than the worldwide line. It means that more rainfall is needed for the occurrence of the clustering debris flows. Four kinds of major formation processes for these debris flows are summarized： tributary-dominated, mainstream- dominated, transformation from slope failures, and mobilization or liquefaction of landslide. The four regions has a spatial correlation with the strong- quake-influenced zone with the peak ground acceleration = 0.2 g and the seismic intensity 〉 X.     

Monitoring and warning of landslides and debris flows using an optical fiber sensor technology

Landslides and debris flows are typical geo-hazards which occur in hilly or mountainous regions. Debris flows may result from landslides. Geotechnical instrumentation plays an important role in monitoring and warning of landslides and resulted debris flows. Traditional technologies for monitoring landslides and debris flows have certain limitations. The new optical fiber sensors presented in this paper can overcome those limitations. This paper presents two new optical fiber sensor systems: one is the Fiber Bragg Grating (FBG)-based in-place inclinometer for monitoring landslides and the other is the FBG-based column-net system for monitoring debris flows. This paper presents the calibration results of FBG-based in-place inclinometers in laboratory. It is found that the calibration results are in good agreement with theoretical results. Both the FBG-based in-place inclinometers and the FBG-based column-net system have been installed at a site in Weijiagou valley, Beichuan County, Sichuan Province of China. Some preliminary results have been obtained and reported in the paper. The advantages of the FBG monitoring systems and their potential applications are also presented.     

Susceptibility assessment of landslides caused by the wenchuan earthquake using a logistic regression model

The Wenchuan earthquake on May 12, 2008 caused numerous collapses, landslides, barrier lakes, and debris flows. Landslide susceptibility mapping is important for evaluation of environmental capacity and also as a guide for post-earthquake reconstruction. In this paper, a logistic regression model was developed within the framework of GIS to map landslide susceptibility. Qingchuan County, a heavily affected area, was selected for the study. Distribution of landslides was prepared by interpretation of multi-temporal and multi-resolution remote sensing images (ADS40 aerial imagery, SPOT5 imagery and TM imagery, etc.) and field surveys. The Certainly Factor method was used to find the influencial factors, indicating that lithologic groups, distance from major faults, slope angle, profile curvature, and altitude are the dominant factors influencing landslides. The weight of each factor was determined using a binomial logistic regression model. Landslide susceptibility mapping was based on spatial overlay analysis and divided into five classes. Major faults have the most significant impact, and landslides will occur most likely in areas near the faults. Onethird of the area has a high or very high susceptibility, located in the northeast, south and southwest, including 65.3% of all landslides coincident with the earthquake. The susceptibility map can reveal the likelihood of future failures, and it will be useful for planners during the rebuilding process and for future zoning issues.     

Characteristics of earthquake-triggered landslides and post-earthquake debris flows in Beichuan County

Field investigations and aerial photography after the earthquake of May 12,2008 show a large number of geo-hazards in the zone of extreme earthquake effects.In particular,landslides and debris flows,the geo-hazards that most threaten post-disaster reconstruction,are widely distributed.We describe the characteristics of these geo-hazards in Beichuan County using high-resolution remote sensing of landslide distribution,and the relationships between the area and volume of landslides and the peak-discharges of debris flows both pre-and post-earthquake.The results show:1) The concentration(defined as the number of landslide sources per unit area:Lc) of earthquaketriggered landslides is inversely correlated with distance from the earthquake(DF) fault.The relationship is described by the following equation:Lc = 3.2264exp(-0.0831DF)(R2 = 0.9246);2) 87 % of the earthquake-triggered landslides were less than 15×104 m2 in area,and these accounted only for 50% of the total area;84% of the landslide volumes were less than 60×104 m3,and these accounted only for 50% of the total volume.The probability densities of the area and volume distributions are correlated:landslide abundance increases with landslide area and volume up to maximum values of 5 × 104 m2 and 30 × 104 m3,respectively,and then decreases exponentially.3) The area(AL) and volume(VL) of earthquake-triggered landslides are correlated as described with the following equation:VL=6.5138AL1.0227(R2 = 0.9131);4) Characteristics of the debris flows changed after the earthquake because of the large amount of landslide material deposited in the gullies.Consequently,debris flow peak-discharge increased following the earthquake as described with the following equation:Vpost = 0.8421Vpre1.0972(R2 = 0.9821)(Vpre is the peak discharge of pre-earthquake flows and the Vpost is the peak discharge of post-earthquake flows).We obtained the distribution of the landslides based on the above analyses,as well as the magnitude of both the landslides and the post-earthquake debris flows.The results can be useful for guiding post-disaster reconstruction and recovery efforts,and for the future mitigation of these geo-hazards.However,the equations presented are not recommended for use in site-specific designs.Rather,we recommend their use for mapping regional seismic landslide hazards or for the preliminary,rapid screening of sites.     

基于GEE和U-net模型的同震滑坡识别方法

地震滑坡解译是震后重建的重要基础工作,主要通过室内人工遥感解译和室外野外调查确定。地震滑坡相比其他地物来说更为复杂,很难通过简单指数识别。室内遥感解译通过滑坡后壁、侧壁和堆积等纹理特征进行识别,大面积同震滑坡解译工作往往耗费大量人力和物力,且耗时长,难以满足灾害应急需求。本研究利用U-net神经网络模型,结合Google Earth Engine（GEE）云平台和人工智能学习平台Tensorflow,以地震局解译的汶川滑坡作为样本数据,以震后30 m分辨率的Landsat影像、高程、坡度以及NDVI数据作为模型输入参数,自动识别并获取了汶川地震后的同震滑坡数据,同时比较了不同参数组合情况下U-net神经网络模型的分割识别精度。研究表明：① U-net模型可以用于以Landsat影像为基础数据的同震滑坡快速自动识别;② 随着高程、坡度以及NDVI等输入参数增加,模型分割精度在逐渐提高,但假阳性结果也会出现增多,震后滑坡影像+高程+坡度+NDVI的输入参数组合精度最高;③ 在细节上,模型在多参数组合的情况下,大型滑坡能够很好被识别,一些较小型的滑坡受制于影像分辨率的影响,分割精度较差。为了更好识别小型滑坡,后续研究可能需提高影像的分辨率。此外,GEE云平台大大提高了训练样本获取的效率,为科研人员快速进行基于神经网络与遥感数据的地物识别研究提供了条件。     

Effects of loose deposits on debris flow processes in the Aizi Valley,southwest China

Loose deposits, rainfall and topography are three key factors that triggering debris flows.However, few studies have investigated the effects of loose deposits on the whole debris flow process.On June 28, 2012, a catastrophic debris flow occurred in the Aizi Valley, resulting in 40 deaths.The Aizi Valley is located in the Lower Jinsha River,southwestern Sichuan Province, China. The Aizi Valley debris flow has been selected as a case for addressing loose deposits effects on the whole debris flow process through remote sensing, field investigation and field experiments. Remote sensing interpretation and laboratory experiments were used to obtain the distribution and characteristics of the loose deposits, respectively. A field experiment was conducted to explore the mechanics of slope debris flows, and another field investigation was conducted to obtain the processes of debris flow formation, movement and amplification. The results showed that loose deposits preparation, slope debris flow initiation,gully debris flow confluence and valley debris flow amplification were dominated by the loose deposits.Antecedent droughts and earthquake activities may have increased the potential for loose soil sources in the Aizi Valley, which laid the foundation for debris flow formation. Slope debris flow initiated under rainfall, and the increase in the water content as well as the pore water pressure of the loose deposits were the key factors affecting slope failure. The nine gully debris flows converged in the valley, and the peak discharge was amplified 3.3 times due to a blockage and outburst caused by a large boulder. The results may help in predicting and assessing regional debris flows in dry-hot and seismic-prone areas based on loose deposits, especially considering large boulders.     

Co-seismic landslide inventory and susceptibility mapping in the 2008 Wenchuan earthquake disaster area, China

The Ms 8.0 May 12,2008 Wenchuan earthquake triggered tens of thousands of landslides.The widespread landslides have caused serious casualties and property losses,and posed a great threat to post-earthquake reconstruction.A spatial database,inventoried 43,842 landslides with a total area of 632 km 2,was developed by interpretation of multi-resolution remote sensing images.The landslides can be classified into three categories:swallow,disrupted slides and falls;deep-seated slides and falls,and rock avalanches.The correlation between landslides distribution and the influencing parameters including distance from co-seismic fault,lithology,slope gradient,elevation,peak ground acceleration(PGA) and distance from drainage were analyzed.The distance from co-seismic fault was the most significant parameter followed by slope gradient and PGA was the least significant one.A logistic regression model combined with bivariate statistical analysis(BSA) was adopted for landslide susceptibility mapping.The study area was classified into five categories of landslide susceptibility:very low,low,medium,high and very high.92.0% of the study area belongs to low and very low categories with corresponding 9.0% of the total inventoried landslides.Medium susceptible zones make up 4.2% of the area with 17.7% of the total landslides.The rest of the area was classified into high and very high categories,which makes up 3.9% of the area with corresponding 73.3% of the total landslides.Although the susceptibility map can reveal the likelihood of future landslides and debris flows,and it is helpful for the rebuilding process and future zoning issues.