GIS based landslide susceptibility mapping using a fuzzy logic approach: A case study from Ghurmi-Dhad Khola area, Eastern Nepal

Landslides cause extensive loss of life and property in the Nepal Himalaya. Since the late 1980s, different mathematical models have been developed and applied for landslide susceptibility mapping and hazard assessment in Nepal. The main goal of this paper is to apply fuzzy logic to landslide susceptibility mapping in the Ghurmi-Dhad Khola area, Eastern Nepal. Seven causative factors are considered: slope angle, slope aspect, distance from drainage, land use, geology, distance from faults and folds, soil and rock type. Likelihood ratios are obtained for each class of causative factors by comparison with past landslide occurrences. The ratios are normalized between zero and one to obtain fuzzy membership values. Further, different fuzzy operators are applied to generate landslide susceptibility maps. Comparison with the landslide inventory map reveals that the fuzzy gamma operator with a γ-value of 0.60 yields the best prediction accuracy. Consequently, this operator is used to produce the final landslide susceptibility zonation map.     

Evaluation of the consistency of landslide susceptibility mapping: a case study from the Kankai watershed in east Nepal

GIS-based landslide susceptibility maps for the Kankai watershed in east Nepal are developed using the frequency ratio method and the multiple linear regression technique. The maps are derived from comparing observed landslides with possible causative factors: slope angle, slope aspect, slope curvature, relative relief, distance from drainage, land use, geology, distance from faults and mean annual rainfall. The consistency of the maps is evaluated using landslide density analysis, success rate analysis and spatially agreed area approach. The first two analyses produce almost identical quantitative results, whereas the last approach is able to reveal spatial differences between the maps and also to improve predictions in the agreed high landslide-susceptible area.     

基于自组织特征映射网络-随机森林模型的滑坡易发性评价

为深入探讨评价单元和非滑坡样本选取对滑坡易发性预测的影响,构建了一种基于自组织特征映射网络-随机森林模型的滑坡易发性评价模型。该模型针对栅格单元和斜坡单元在滑坡易发性评价中的不足,结合栅格单元和斜坡单元的相互关系,提出了滑坡易发性指数的优化计算方法。在此基础上,基于随机森林Tree Bagger分类器构建滑坡易发性评价模型,通过对比分析自组织特征映射网络和随机方法选取非滑坡样本对评价结果的影响,探讨自组织特征映射网络、随机森林和自组织特征映射网络-随机森林三种评价模型的有效性;将评价模型应用于大余县滑坡易发性评价。结果显示,随机森林模型和自组织特征映射网络-随机森林模型的预测精度较高,分别达到91.19%和94.94%,成功率曲线的AUC值分别为0.822和0.849,表明自组织特征映射网络-随机森林模型具有更高的预测率和成功率, 自组织特征映射网络聚类的预测精度虽然有限,但作为非滑坡样本的选择方法,能够有效提高随机森林模型的评价精度。     

联合Sentinel-1与Landsat8影像的希夏邦马峰冰川三维运动反演

SAR偏移量技术和光学偏移量技术是冰川运动监测重要的技术手段,但目前对于融合不同平台的影像进行三维形变的研究较少。文章选取2019年11月至2021年1月西藏聂拉木县希夏邦马峰地区的大型冰川作为研究对象,基于方差分量估计融合该研究区的Sentinel-1与Landsat8两种数据进行冰川的三维位移解算,选取了同一时期的光学影像对偏移量估计结果进行对比分析,同时选取稳定区域进行精度评估,分析该方法在冰川运动监测中的适用性和精确性。结果表明,该冰川在2019年11月至2021年1月,联合解算的东西向最大流速为21 cm/d,南北向最大流速为68 cm/d,垂直向最大流速为17 cm/d。对比单一影像获取的冰川位移结果,多影像联合解算方法,能够弥补SAR数据的失相干和光学数据的低质量像元值的不足,获得更加完整和详细的冰川信息,监测结果精度更高。可为利用不同平台的数据联合监测山地冰川的多维度和高精度变化提供参考和技术支持。     

Slope stability analysis on a regional scale using GIS: a case study from Dhading,Nepal

A spatially distributed physically based slope stability model combined with a hydrological model is presented and applied to a 350-km² area located in Dhading district, Nepal. Land slide safety factor maps are generated for five cases, including three steady state conditions assuming either completely dry soils, half saturated soils, or fully saturated soils, and two quasi-dynamic conditions, i.e. soil wetness resulting from storm events with, respectively a 2 or 25-year return period. For the quasi-dynamic cases, two methods are used, one based on accumulation of groundwater flow from upstream areas, and the other on accumulation of soil water from direct infiltration. The methodology delineates areas most prone to shallow land sliding in function of readily available data as topography, land-use and soil types. For the study area only 29% of the soils are unconditionally stable, while 25% of the soils are found to be unstable under fully saturated conditions. The comparison between the methods based on contributing area or on infiltration for quasi-dynamic conditions show that the approach based on infiltration is more reliable for the study area. The proposed methodology for predicting landslide susceptibility on a regional scale, based on basic data in GIS form, may be useful for other remote regions where detailed information is not available.     

Prediction on landslide displacement using a new combination model: a case study of Qinglong landslide in China

Prediction on landslide displacement plays an important role in landslide early warning. Many models have been proposed for this purpose. However, the accuracy of the prediction results by these models often varies under different conditions. Rational evaluation and comprehensive consideration of these results still remain a scientific challenge. A new comprehensive combination model is proposed to predict the landslides displacement. The elementary displacement prediction is made by the support vector machine model, the exponential smoothing model, and the gray model (GM)(1,1). The results of the models are comprehensively evaluated by combining the results and introducing the accuracy matrix. The optimal weight in the evaluation work is obtained. A rational prediction result can be attained based on the so-called combination model. The proposed method has been tested by the application of Qinglong landslides in Guizhou Province, China. The comparison between the prediction results and in situ measurement shows that the prediction precision of the proposed model is satisfactory. The root-mean-square error (RMSE) of the combination model can be reduced to 1.4316 (monitoring site JCK2), 1.2623 (monitoring site JCK4), 2.3758 (monitoring site JCK6), 2.2704 (monitoring site JCK8), 1.4247 (monitoring site JCK11), and 0.9449 (monitoring site JCK12), which is much lower than the RMSE of the individual models.

     

Mapping tropical forest vegetation from Landsat TM images based on fusion of knowledge and geo-data

Tropical forests play a crucial role in the function of our planet and in the maintenance of life. Tropical forest vegetation maps are very important for managing tropical forests. Mapping tropical forest vegetation only by spectral-based remote sensing techniques has proven to be problematic. The objective of the study is to develop a rule-based model to identify different forest types using Landsat TM images and GIS. In this paper, we developed the rule-based model to identify different forest types in Xishuangbanna, P.R. of China, using two temporal Landsat TM images and geo-data such as DEM, rainfall and temperature. The results show that the method put forward is useful and effective in tropical forest vegetation mapping, which can effectively integrate multi-knowledge and multi-resource data to identify the tropical forest vegetation types with higher accuracy.     

D-InSAR与随机森林模型耦合的活动性滑坡识别方法探究

灾害的早期识别是防灾减灾领域的关键技术。文中以甘肃省舟曲县为例,利用2018年1月－2019年1月Sentinel-1A雷达卫星降轨数据和2021年5月Sentinel-2光学遥感影像数据,通过D-InSAR技术获取研究区地表形变信息,利用随机森林模型识别潜在的滑坡体。结果表明：使用已有的滑坡数据集,采用随机森林模型能够较好地识别出潜在滑坡体。潜在滑坡点分布位置均位于地表形变量大的区域。舟曲县整体形变沿东西向发生,主要分布于舟曲县东北和西南方向,与潜在滑坡点高度重合。识别出的潜在滑坡点（立节乡北山滑坡）,年形变量达到0.12 m,于2021年1月18日发生滑坡,该滑坡典型案例也印证了文中方法的有效性。     

The chaotic characteristics of landslide evolution: a case study of Xintan landslide

A new method based on the chaos theory is used to assess the evolution process of a slope system. The method is applied to the Xintan landslide and the results show: (1) the slope movement is a complex process of the slope going in and out of the stable and chaotic state; (2) the method reveals the evolution process of the slope pointing to the slope failure while the observed movement shows a simple monotonic increase with time; (3) the method is not sufficiently mature to precisely predict the time of failure but it has potential for improvement with further research and more field data for analysis.     

Monitoring land use/land cover change using multi-temporal Landsat satellite images in an arid environment: a case study of El-Arish,Egypt

Environment in arid conditions is dynamic and needs more investigation to understand the complexity of change. This spatiotemporal study will help to assess and monitor the land use and land cover change in the arid region of El-Arish area, where the climate and human activities are the major threats to rural development. In the past 11 years, dramatic changes of environment have been recorded in case studies. The post-classification comparison method was used to observe the changes using multi-temporal satellite images which were captured in the years 1999, 2001, 2005, and 2010. The overall accuracy of the produced thematic images was assessed regarding to the quantity and allocation disagreements. Five classes were defined in this investigation: bare soil, vegetation, urban, sand dunes, and fertile soil. From the year 1999 to 2010, fertile soil was increased by 13 %. Bare soil class occupied more than 50 % of land in the case study during for over a decade. From year 1999 to 2010, vegetation cover witnessed a dramatic increase. Soil and water management are the keys of land development and positive land use and land cover dynamics. Changing agricultural policies of using the available water resources are needed in the case study to prevent severe food shortage in the future.     

Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics: a case study in southern Italy

In this article, the results of a study aimed to assess the landslide susceptibility in the Calaggio Torrent basin (Campanian Apennines, southern Italy) are presented. The landslide susceptibility has been assessed using two bivariate-statistics-based methods in a GIS environment. In the first method, widely used in the existing literature, weighting values (Wi) have been calculated for each class of the selected causal factors (lithology, land-use, slope angle and aspect) taking into account the landslide density (detachment zones + landslide body) within each class. In the second method, which is a modification of the first method, only the landslide detachment zone (LDZ) density has been taken into account to calculate the weighting values. This latter method is probably characterized by a major geomorphological coherence. In fact, differently from the landslide bodies, LDZ must necessarily occur in geoenvironmental classes prone to failure. Thus, the calculated Wi seem to be more reliable in estimating the propensity of a given class to generate failure. The thematic maps have been reclassified on the basis of the calculated Wi and then overlaid, with the purpose to produce landslide susceptibility maps. The used methods converge both in indicating that most part of the study area is characterized by a high–very high landslide susceptibility and in the location and extent of the low-susceptible areas. However, an increase of both the high–very high and moderate–high susceptible areas occurs in using the second method. Both the produced susceptibility maps have been compared with the geomorphological map, highlighting an excellent coherence which is higher using method-2. In both methods, the percentage of each susceptibility class affected by landslides increases with the degree of susceptibility, as expected. However, the percentage at issue in the lowest susceptibility class obtained using method-2, even if low, is higher than that obtained using method-1. This suggests that method-2, notwithstanding its major geomorphological coherence, probably still needs further refinements.     

基于斜坡单元与随机森林模型的元阳县崩滑地质灾害易发性评价

针对基于栅格单元与定性定量方法模型在地质灾害易发性评价中存在模型预测精度低且使用较为频繁的不足与弊端,采用斜坡单元与机器学习方法之一的随机森林模型相结合开展元阳县崩滑地质灾害易发性评价。在ArcGIS中,利用曲率分水岭法划分出7851个斜坡单元。经过大量统计研究与地质环境条件分析,选取工程地质岩组、地貌类型、高程、坡度、坡向、曲率、起伏度、河流距离、断层距离等9个因子作为评价指标,并通过SPSS软件,将9个评价指标与灾点发育特征的关系进行数据分析,得出各评价指标权重。在SPSS中,采用随机森林模型,建立易发性评价模型,将元阳县崩滑地质灾害易发性划分为低、中、高、极高4类,所占面积分别为410.06 km2、470.21 km2、550.02 km2和776.87 km2,分别占元阳县面积的18.58%、21.30%、24.92%和35.20%。经与详查结果对比,评价结果与实际高度吻合。利用ROC曲线得出区划结果精度AUC值为92.7%,区划结果相当好。研究显示,元阳县中部和西南两个部分地质灾害集中,易发性极高。     

A novel index to evaluate discretization methods:A case study of flood susceptibility assessment based on random forest

The selection of a suitable discretization method(DM)to discretize spatially continuous variables(SCVs)is critical in ML-based natural hazard susceptibility assessment.However,few studies start to consider the influence due to the selected DMs and how to efficiently select a suitable DM for each SCV.These issues were well addressed in this study.The information loss rate(ILR),an index based on the informa-tion entropy,seems can be used to select optimal DM for each SCV.However,the ILR fails to show the actual influence of discretization because such index only considers the total amount of information of the discretized variables departing from the original SCV.Facing this issue,we propose an index,infor-mation change rate(ICR),that focuses on the changed amount of information due to the discretization based on each cell,enabling the identification of the optimal DM.We develop a case study with Random Forest(training/testing ratio of 7:3)to assess flood susceptibility in Wanan County,China.The area under the curve-based and susceptibility maps-based approaches were presented to compare the ILR and ICR.The results show the ICR-based optimal DMs are more rational than the ILR-based ones in both cases.Moreover,we observed the ILR values are unnaturally small(＜1％),whereas the ICR values are obviously more in line with general recognition(usually 10％-30％).The above results all demonstrate the superiority of the ICR.We consider this study fills up the existing research gaps,improving the ML-based natural hazard susceptibility assessments.     

复合型滑坡固液耦合过程数值模拟分析——以无山坪滑坡为例

固液耦合作用是碎屑流向泥石流转化形成复合型滑坡灾害的关键因素, 会导致成灾范围和规模放大, 是防灾减灾领域研究中的热点和难点问题之一。文中采用自主研发的滑坡后破坏数值模拟平台(LPF3D, Landslides post failure 3D), 以2014年9月强降雨诱发的重庆奉节无山坪滑坡为例, 探讨了滑坡在水动力作用下远程成灾的动力过程, 揭示了固液耦合影响机制。研究结果显示: 水动力作用在滑坡运动过程中主要体现为液化和拖曳两种, 两种力学作用的增程效应明显, 往往使得碎屑流转化为泥石流, 导致远程成灾; 基于光滑粒子流体动力学(SPH)方法的两相耦合计算模型, 考虑流体状态方程、固体黏塑性本构方程和相间作用力的共同影响, 基本还原了强降雨条件下重庆奉节无山坪滑坡两相运动过程; 数值计算结果显示无山坪滑坡最大运动速度为34 m/s, 最大堆积厚度为21.5 m, 堆积面积为0.12 km2, 最远运动距离为1300 m, 模拟结果同实际滑坡的堆积形态基本一致。综上认为, 在高位远程滑坡风险调查与预测过程中, 需充分考虑强降雨工况下孔隙水压力和固液相间作用, 基于LPF3D方法的数值模拟为高位远程滑坡的风险定量评估提供了依据。     

Implication of subsurface flow on rainfall-induced landslide: a case study

Post-failure field investigation, instrumentation, monitoring, and numerical simulation were performed to give insights into the failure mechanism of a 13-h-delayed rainfall-induced landslide. A conceptual hydrological model was postulated based on the findings obtained from the investigation works. The results showed that subsurface flow was recharged by intense and prolonged rainfall through outcrops of fissured bedrock. The recharged water was mounded in the moderately weathered granite layer and caused an increase in hydraulic head. The groundwater seeped gradually upward into the overlying fill layer even after the rain has ceased, and eventually triggered the landslide when the water table was raised to a critical state. As most of the existing hydrologic-slope stability models were developed on the basis of soil-impermeable bedrock model, this could result in great discrepancies between the simulated results and the real hydrological responses of the slope. The findings from the present study highlighted the importance of considering subsurface flow and hydro-geological features in assessing the mechanism of rainfall-induced landslide.     

基于随机森林模型的城市不透水面提取研究 ——以呼和浩特市为例

城市不透水面信息对于城市生态环境动态演化过程研究具有重要意义。以Landsat 8遥感影像为数据源,以呼和浩特市为实证区域,进行了随机森林模型应用于城市不透水面的提取研究,并与目前应用广泛的支持向量机模型进行了对比分析。研究表明：在不同的抽样比例训练样本条件下,随机森林模型对于城市不透水面的提取精度均优于支持向量机的提取精度;对于随机森林模型和支持向量机模型,70%的训练样本比例均为最佳训练样本抽样比例。在该抽样比例下,随机森林模型提取城市不透水面的总体分类精度为93.29%,Kappa系数为0.9051,支持向量机模型的总体分类精度为91.26%,Kappa系数为0.8757;随机森林模型对于城市裸土的识别度较高,能更好地将城市裸土和不透水面进行区分,而支持向量机模型对于城市裸土、不透水面和绿地的区分能力均弱于随机森林模型。综合而言,随机森林模型对城市不透水面的提取精度优于支持向量机模型,随机森林模型可以有效应用于城市不透水面提取领域,进一步丰富了城市不透水面提取方法体系构成。