RS & GIS-based landslide susceptibility mapping of the Balason River basin,Darjeeling Himalaya,using logistic regression (LR) model

The present study deals with the preparation of a landslide susceptibility map of the Balason River basin, Darjeeling Himalaya, using a logistic regression model based on Geographic Information System and Remote Sensing. The landslide inventory map was prepared with a total of 295 landslide locations extracted from various satellite images and intensive field survey. Topographical maps, satellite images, geological, geomorphological, soil, rainfall and seismic data were collected, processed and constructed into a spatial database in a GIS environment. The chosen landslide-conditioning factors were altitude, slope aspect, slope angle, slope curvature, geology, geomorphology, soil, land use/land cover, normalised differential vegetation index, drainage density, lineament number density, distance from lineament, distance to drainage, stream power index, topographic wetted index, rainfall and peak ground acceleration. The produced landslide susceptibility map satisfied the decision rules and ?2 Log likelihood, Cox &; Snell R-Square and Nagelkerke R-Square values proved that all the independent variables were statistically significant. The receiver operating characteristic curve showed that the prediction accuracy of the landslide probability map was 96.10%. The proposed LR method can be used in other hazard/disaster studies and decision-making.     

青藏高原东部表土磁化率特征与环境意义

现代表土磁化率与气候因子关系的研究是黄土古气候重建的重要内容,在黄土高原地区取得了重要进展,但在青藏高原地区相对不足。在青藏高原东部系统采集了106个表土样品,分析了其磁化率的变化特征;并通过表土磁化率与气候因子的相关分析,讨论了气候因子对高原东部现代表土低频磁化率和频率磁化率的影响。结果表明：研究区表土磁化率特征主要受到温度和降水量的影响,水热组合差异影响表土磁化率值的高低。低频磁化率与气候因子的相关性较弱,总体上与温度的相关性优于降水量,可能表明其与气候因子的关系比较复杂;频率磁化率百分比与降水呈较好的正相关关系,表明该指标对降水量的变化更为敏感,可以用于青藏高原东部的古降水定量重建。     

藏南泽当地区大反向逆冲断层变形研究——以磁组构与EBSD组构分析为例

仁布-泽当逆冲断层是喜马拉雅大反向逆冲断层（GCT）在藏南地区的重要延伸部分,也是喜马拉雅造山带北部边界新生代最为活动的构造单元之一。新生代以来特提斯喜马拉雅的构造变形组构特征的研究对于深入理解碰撞造山带演化与高原隆升具有重要构造意义。本文综合GCT泽当-琼结段断层的宏观与微观变形特征,对断裂带石英脉、围岩中石英和云母矿物的电子背散射（EBSD）组构及断层两侧岩石磁组构（AMS）特征进行对比分析。结果表明对AMS主要贡献来自顺磁性云母、绿泥石等,磁化率各向异性椭球体以压扁状为主,磁面理与构造面理（劈理、断层面）基本重合,显示较强的构造变形磁组构特征;磁线理优选方向近南北向,且与观测北向逆冲断层方向一致,揭示剪切作用在变形过程中的持续作用。研究发现泽当地区GCT附近石英微观结构从围岩至断层区,石英至少呈现3种不同类型的微观变形机制：围岩区溶解蠕变、断裂带石英以膨凸重结晶和亚颗粒旋转重结晶作用为主。断裂带石英的c轴EBSD组构指示变形为低温（300~400℃）环境,其中黑云母的结晶学优选（CPO）与磁组构主轴优选方向存在高度的一致性,进一步证实了顺磁性矿物黑云母对AMS的主要贡献。综合研究表明泽当地区GCT的韧性变形是断层处在中上地壳韧性带的活动阶段变形的结果,也代表了特提斯喜马拉雅在碰撞、高原隆升期的变形主要特征。     

Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks

In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory data.In this paper,a novel method that improves the performance of machine learning techniques is presented.The proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of landslides.In this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked on.Elevation,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands.To show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM models.These models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,subsequently.When using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,respectively.Using the additional samples improved the test accuracy of all the models except SVM.As a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is promising because it can improve the predictive capability of common landslide prediction models.     

Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments,but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT)model and the K-means cluster algorithm to produce a regional landslide susceptibility map.Yanchang County,a typical landslide-prone area located in northwestern China,was taken as the area of interest to introduce the proposed application procedure.A landslide inventory containing 82 landslides was prepared and subse-quently randomly partitioned into two subsets:training data(70％landslide pixels)and validation data(30％landslide pixels).Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means clus-ter algorithm.The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC)curve)of the proposed model was the highest,reaching 0.88,compared with traditional models(support vector machine(SVM)=0.85,Bayesian network(BN)=0.81,frequency ratio(FR)=0.75,weight of evidence(WOE)=0.76).The landslide frequency ratio and fre-quency density of the high susceptibility zones were 6.76/km2 and 0.88/km2,respectively,which were much higher than those of the low susceptibility zones.The top 20％interval of landslide occurrence probability contained 89％of the historical landslides but only accounted for 10.3％of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without contain-ing more"stable"pixels.Therefore,the obtained susceptibility map is suitable for application to landslide risk management practices.     

泰青威天然气管道临朐段水毁灾害分布特征与风险评价

对泰青威天然气管道临朐段地质灾害发育特征进行调查研究发现,临朐段地质灾害类型主要为水毁灾害,具体可分为坡面水毁、河沟道水毁和台田地水毁。野外地质灾害实地调查临朐段管道沿线共发现地质灾害15处,其中坡面水毁点6处,河沟道水毁点4处,台田地水毁点5处。采用定性与半定量相结合的评价方法对其进行地质灾害风险评价,结果表明: 地质灾害风险等级较高的有1处,占6.66%; 风险等级中等的有4处,占26.67%; 风险等级较低的有10处,占66.67%。根据管道沿线地形地貌、地质灾害发育密度、风险等级等因素,划分地质灾害中易发区61 km,低易发区10 km,管道沿线以地质灾害中易发区为主。最后,针对不同类型、不同风险等级地质灾害提出了相应的防治消减措施,为后期管道安全运营和风险整治决策提供了有效的技术依据。     

渭河流域东部全新世黄土-古土壤剖面光释光测年及其记录的土壤侵蚀事件

在渭河流域东部老官台(LGT)全新世黄土-古土壤剖面中,系统采集了15个黄土释光测年样品,应用单片再生剂量法(SAR)对这些样品的细颗粒混合矿物进行了光释光(OSL)测年,测定结果表明:红外之后蓝光释光([post—IR]OSL)的年龄偏大,红外释光(IRSL)的年龄偏小或与蓝光释光(BLSL)的年龄基本一致,应用IRSL和BLSL信号测定的OSL年龄整体偏小可能与测定样品中长石矿物的异常衰退有关,而[post—IR]OSL信号由于很少发生异常衰退问题,使得其年龄可靠性更高。基于测定的[post-IR]OSL年龄,结合该剖面的磁化率和粒度指标,进一步探讨了渭河流域东部自全新世以来发生的两期土壤侵蚀事件,第1期土壤侵蚀事件发生在约12.40～9.34 kaBP,即全新世大暧期来临之前的气候转暖期间;第2期土壤侵蚀事件发生在约4.60～3.76 kaBP,即全新世大暖期即将结束、气候开始出现恶化、黄河中游地区夏文化发展的时期,这为深入探索夏代发生的大洪水事件及其气候背景提供了科学依据,本区两期显著的土壤侵蚀事件的发现,揭示了东亚季风系统的不稳定性,表现为千年到百年尺度上的快速变化。     

西北干旱区黄土-古土壤磁化率变化特征

对祁连山东段黄土-古土壤环境磁学的研究表明,在这一地区黄土-古土壤沉积中主要的磁性矿物是磁铁矿型矿物,而磁化率的增强与黄土-古土壤序列并不匹配,且存在着由剖面底部向上逐渐升高的趋势。我们认为在铁磁性矿物种类没有变化的情况下,西北干旱-半干旱区磁化率的这种增强很可能是磁性颗粒物粒度变化或者来源相关的磁性颗粒含量的变化所引起。     

临沂市地质灾害现状及易发程度分区评价

2016年,临沂市地质灾害排查共查明了391处地质灾害隐患点,主要类型为崩塌、滑坡、泥石流、采空塌陷和岩溶塌陷,崩塌数量最多,其次为采空塌陷和岩溶塌陷。地质灾害规模以小型为主。该文详细介绍了临沂市地质环境背景条件,各类地质灾害发育现状及分布特征。在此基础上,采用"地质灾害综合危险性指数法"进行地质灾害易发性评价,运用栅格数据处理方法将临沂市划分成1350个面积相等的单元,对影响地质灾害发育的地质条件、地形地貌条件、气候植被条件、地质灾害隐患点、地质灾害规模、分布密度、活动频次和险情等要素进行量化,根据实际情况对地质灾害易发程度分级进行定性修正,将临沂市地质灾害易发程度划分为高易发区、中易发区、低易发区及不易发区4个区。预测了地质灾害发展趋势,划分了地质灾害防治分区,建议完善地质灾害预警预报系统,为临沂市应急管理局的地质灾害应急救援提供地质灾害基础背景资料。     

Farmland Abandonment Research Progress: Influencing Factors and Simulation Model

Farmland abandonment is a global problem and considered one of the most important areas in land use change research. Farmland abandonment research currently focuses on understanding the factors that affect farmland abandonment and developing scientific models to simulate farmland abandonment. The study reviewed the natural and political factors driving farmland abandonment and summarized the main models for farmland abandonment simulation together with their advantages and disadvantages. We discuss the main ecological effects of farmland abandonment and propose farmland abandonment research directions. The study found that: (1) the influence of labor cost change and ageing labor force on farmland abandonment needs further investigation, (2) simulation models for farmland abandonment must include the decision-making mechanism of individual farmers and focus on macro large-scale abandonment prediction models, and (3) the influence of farmland abandonment on landscape culture must be investigated in detail.