Aridity indices to assess desertification susceptibility: a methodological approach using gridded climate data and cartographic modeling

Natural Hazards - Desertification is a land degradation phenomenon with dire and irreversible consequences, affecting different regions of the world. Assessment of spatial climate susceptibility to...     

基于WBS-RBS的地铁基坑故障树风险识别与分析

针对地铁基坑工程环境复杂、影响因素多的特点,提出以故障树分析为基础结合工作分解结构（WBS）-风险分解结构（WBS）进行风险识别的方法。此方法引入WBS-RBS将地铁基坑工程工作分解结构和风险源分解结构耦合判断并说明相应风险因素或风险事件,并按照故障树建树原则根据彼此间的逻辑关系,用逻辑门连接上下层事件,形成地铁基坑工程主要故障树。在此基础上,对风险因素进行敏感性分析并提出相应的预防措施。     

Landslide susceptibility assessment using object mapping units,decision tree,and support vector machine models in the Three Gorges of China

Due to the particular geographical location and complex geological conditions, the Three Gorges of China suffer from many landslide hazards that often result in tragic loss of life and economic devastation. To reduce the casualty and damages, an effective and accurate method of assessing landslide susceptibility is necessary. Object-based data mining methods were applied to a case study of landslide susceptibility assessment on the Guojiaba Town of the Three Gorges. The study area was partitioned into object mapping units derived from 30 m resolution Landsat TM images using multi-resolution segmentation algorithm based on the landslide factors of engineering rock group, homogeneity, and reservoir water level. Landslide locations were determined by interpretation of Landsat TM images and extensive field surveys. Eleven primary landslide-related factors were extracted from the topographic and geologic maps, and satellite images. Those factors were selected as independent variables using significance testing and correlation coefficient analysis, including slope, profile curvature, engineering rock group, slope structure, distance from faults, land cover, tasseled cap transformation wetness index, reservoir water level, homogeneity, and first and second principal components of the images. Decision tree and support vector machine (SVM) models with the optimal parameters were trained and then used to map landslide susceptibility, respectively. The analytical results were validated by comparing them with known landslides using the success rate and prediction rate curves and classification accuracy. The object-based SVM model has the highest correct rate of 89.36 % and a kappa coefficient of 0.8286 and outperforms the pixel-based SVM, object-based C5.0, and pixel-based SVM models.     

The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area,China

Landslide susceptibility mapping is an indispensable prerequisite for landslide prevention and reduction. At present, research into landslide susceptibility mapping has begun to combine machine learning with remote sensing and geographic information system (GIS) techniques. The random forest model is a new integrated classification method, but its application to landslide susceptibility mapping remains limited. Landslides represent a serious threat to the lives and property of people living in the Zigui–Badong area in the Three Gorges region of China, as well as to the operation of the Three Gorges Reservoir. However, the geological structure of this region is complex, involving steep mountains and deep valleys. The purpose of the current study is to produce a landslide susceptibility map of the Zigui–Badong area using a random forest model, multisource data, GIS, and remote sensing data. In total, 300 pre-existing landslide locations were obtained from a landslide inventory map. These landslides were identified using visual interpretation of high-resolution remote sensing images, topographic and geologic data, and extensive field surveys. The occurrence of landslides is closely related to a series of environmental parameters. Topographic, geologic, Landsat-8 image, raining data, and seismic data were used as the primary data sources to extract the geo-environmental factors influencing landslides. Thirty-four layers of causative factors were prepared as predictor variables, which can mainly be categorized as topographic, geological, hydrological, land cover, and environmental trigger parameters. The random forest method is an ensemble classification technique that extends diversity among the classification trees by resampling the data with replacement and randomly changing the predictive variable sets during the different tree induction processes. A random forest model was adopted to calculate the quantitative relationships between the landslide-conditioning factors and the landslide inventory map and then generate a landslide susceptibility map. The analytical results were compared with known landslide locations in terms of area under the receiver operating characteristic curve. The random forest model has an area ratio of 86.10%. In contrast to the random forest (whole factors, WF), random forest (12 major factors, 12F), decision tree (WF), decision tree (12F), the final result shows that random forest (12F) has a higher prediction accuracy. Meanwhile, the random forest models have higher prediction accuracy than the decision tree model. Subsequently, the landslide susceptibility map was classified into five classes (very low, low, moderate, high, and very high). The results demonstrate that the random forest model achieved a reasonable accuracy in landslide susceptibility mapping. The landslide hazard zone information will be useful for general development planning and landslide risk management.     

A hybrid model using machine learning methods and GIS for potential rockfall source identification from airborne laser scanning data

The main objectives of this paper are to design and evaluate a hybrid approach based on Gaussian mixture model (GMM) and random forest (RF) for detecting rockfall source areas using airborne laser scanning data. The former model was used to calculate automatically slope angle thresholds for different type of landslides such as shallow, translational, rotational, rotational-translational, complex, debris flow, and rockfalls. After calculating the slope angle thresholds, a homogenous morphometric land use area (HMLA) was constructed to improve the performance of the model computations and reduce the sensitivity of the model to the variations in different conditioning factors. After that, the support vector machine (SVM) was applied in addition to backward elimination (BE) to select and rank the conditioning factors considering the type of landslides. Then, different machine learning methods [artificial neural network (ANN), logistic regression (LR), and random forest (RF) were trained with the selected best factors and previously prepared inventory datasets. The best fit method (RF) was then used to generate the probability maps and then the source areas were detected by combining the slope raster (reclassified according to the thresholds found by the GMM model) and the probability maps. The accuracy assessment shows that the proposed hybrid model could detect the potential rockfalls with an accuracy of 0.92 based on training data and 0.96 on validation data. Overall, the proposed model is an efficient model for identifying rockfall source areas in the presence of other types of landslides with an accepted generalization performance.     

Investigation of lead and zinc dispersion from an abandoned mine site at Tyndrum, Scotland, using tree bark measurements

Tyndrum was an industrial mining area from 1741 until the 1920s where the main ores extracted were lead sulphide （galena） and zinc sulphide （sphalarite）. Situated in central Scotland, the now deserted site is identified as a highly polluted, barren area where the original waste residues from the mine, an ore processing and a smelting site are clearly evident. Previous studies have suggested that high levels of lead and zinc contaminants are still dispersed from this former mine site, both atmospherically and through fluvial transport. For example, high concentrations of Pb, identified by its isotopic signature as originating in Tyndrum, have been found in sediments in Loch Tay, 25 km east of this area. This paper presents the results of a study of the occurrence and magnitude of airborne contamination by dispersion from the former mine site using measurements of metal concentrations in tree bark. These naturally occurring ‘bio monitors＇, which accumulate airborne materials including heavy metals, have become an effective alternative to the more costly air-filter system, which is restricted in scope and coverage. Bark samples were acquired from Scots pine （Pinus sylvestris） trees, which are abundant around the derelict mine site. Suitable locations were chosen to investigate atmospheric transport and deposition of contaminants within the area.     

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.     

Early Albian marine environments in Madagascar: An integrated approach based on oxygen,carbon and strontium isotopic data

New palaeotemperature reconstructions have been obtained on the basis of oxygen isotopic analysis of 178 aragonitic shell samples taken from specimens of three ammonoid orders (and some corresponding families): Phylloceratida (Phylloceratidae), Lytoceratida (Tetragonitidae) and Ammonitida (Oppeliidae, Desmoceratidae, Silesitidae, Cleoniceratidae and Douvilleiceratidae). Those obtained from aragonite shells, secreted in the lower epipelagic and in the middle mesopelagic zones during coolest season (winter), range from 15.4 to 16.8 °C, and from 11.8 to 12.0 °C, respectively. Presumed spring/autumn palaeotemperatures obtained from aragonite shells, secreted apparently in the upper and lower epipelagic, upper and middle mesopelagic zones, are somewhat higher. Presumed summer palaeotemperatures, calculated apparently for the upper and lower epipelagic, and upper mesopelagic zones range from 19.4 to 21.7 °C, from 17.7 to 19.4 °C, and from 14.4 to 16.1 °C, respectively. The predominant part of investigated ammonoids from Madagascar inhabited the epipelagic zone, but some phylloceratid, tetragonitid and silesitid ammonoids preferred deeper, cooler conditions (upper-middle mesopelagic zone). The study supports the hypothesis that Madagascar was located in middle latitudes within the tropical-subtropical climatic zone during the early Albian. Available carbon and strontium isotope data allow us to assume a more or less expressed carbon and strontium isotope stratification of the water column in this region in the early Albian. On the basis of the stable isotope data, following partly Lukeneder (2015), two large ethological groups can be recognised mainly in mid-aged and adult ammonoids. Some ammonoids (group 1) preferred apparently mesopelagic conditions, and to a lesser degree the epipelagic zone, being mainly cool-requiring animals. However, a significant part of the isotopically investigated ammonoids (group 2) preferred, on the contrary, only epipelagic conditions, being mainly thermophilic dwellers.     

Determining the equivalent permeability tensor for fractured rock masses using a stochastic REV approach: Method and application to the field data from Sellafield,UK

A numerical procedure to determine the equivalent permeability tensor of a fractured rock is presented, using a stochastic REV (Representative Elementary Volume) concept that uses multiple realizations of stochastic DFN (Discrete Fracture Network) models. Ten square DFN models are generated using the Monte Carlo simulations of the fracture system based on the data obtained from a site characterization program at Sellafield, Cumbria, UK. Smaller models with varying sizes of from 0.25 m×0.25 m to 10 m×10 m are extracted from the generated DFN models and are used as two-dimensional geometrical models for calculation of equivalent permeability tensor. The DFN models are also rotated in 30º intervals to evaluate the tensor characteristics of calculated directional permeability. Results show that the variance of the calculated permeability values decreases significantly as the side lengths of the DFN models increase, which justifies the existence of a REV. The REV side length found in this analysis is about 5 m and 8 m with 20% and 10% acceptable variations, respectively. The calculated directional permeability values at the REV size have tensor characteristic that is confirmed by a close approximation of an ellipse in a polar plot of the reciprocal of square roots of the directional permeability.

A study on the identification of liquefaction-induced failures on ground surface based on the data from the 1999 Kocaeli and Chi-Chi earthquakes

One of the major causes of earthquake damage is liquefaction. However, it doesn't result in severe harm unless it leads to ground surface damage or ground failure. Therefore, prediction of potential for ground surface damage due to liquefaction is one of the important issues in microzonation studies for liquefaction-induced damage in areas with high seismicity. In 1985, based on a database compiled from Chinese and Japanese earthquakes, Ishihara considered the influence of the non-liquefied cap soil on the occurrence or non-occurrence of ground failure (mainly sand boiling), and proposed an empirical approach to predict the potential for ground surface damage at sites susceptible to liquefaction. However, some investigators indicated that this approach is not generally valid for sites susceptible to lateral spread or ground oscillation. In this study, a contribution to improve the approach by Ishihara is made. For the purpose, an index called liquefaction severity index (LSI) and data from two devastating earthquakes, which occurred in Turkey and Taiwan in 1999, were employed. The data from liquefied and non-liquefied sites were grouped and then analysed. Based on the observations reported by reconnaissance teams who visited both earthquake sites and the results of the liquefaction potential analyses using the filed-performance data, a chart to assess the potential for ground surface disruption at liquefaction-prone areas was produced. The analyses suggest that the procedure proposed by Ishihara is quite effective particularly for the occurrence of sand boils, while the bounds suggested in this method generally may not be valid for the prediction of liquefaction-induced ground surface disruption at sites susceptible to lateral spreading. The chart proposed in this study shows an improvement over the Ishihara's approach for predicting the liquefaction-induced ground surface damage. The microzonation maps comparing the liquefaction sites observed along the southern shore of Izmit Bay and in Yuanlin, and the surface damage and non-damage zones predicted from the proposed chart can identify accurately the liquefaction (sand boiling and lateral spreading) and no-liquefaction sites.     

Extracting of prospective groundwater potential zones using remote sensing data,GIS, and a probabilistic approach in Bojnourd basin,NE of Iran

Various groundwater potential zones for the assessment of groundwater availability in the Bojnourd basin have been investigated using remote sensing, GIS, and a probabilistic approach. Five independent groundwater factors, including topography, ground slope, stream density, geology units, lineament density, and a groundwater productivity factor, i.e., springs’ discharge, were applied. Discharge rates of 226 springs over the area were collected, and the probabilistic model was designed by the discharge rates of springs as the dependent variable. For training the probabilistic model, a ratio of 70/30% of springs’ discharge was applied and discharge rates of 151 springs were selected to randomly train the model. The frequency ratio for each factor was calculated, and the groundwater potential zones were extracted by summation of frequency ratio maps. The groundwater potential map was also classified into four classes, viz., “very good” (with a frequency ratio of >6.75), “good” (5.5FR6.75), “moderate” (4.75FR5.5), and “poor” (FR4.75). Then, the model was verified based on a success-rate curve method which resulted in obtaining an accuracy ratio of 75.77%. Finally, sensitivity analysis was applied by a factor removal method in five steps. Results reveal that topography factor has the biggest effect on the groundwater potential map and removing this factor eventuates in the lowest accuracy of the final map (AUC = 63. 73%). The groundwater potential map is fairly affected by removing the lineament density factor with an accuracy of 68.80%. Removing the lineament density factor has the lowest effect on the final map with accuracy of 68.80%.     

Validated surface mineralogy from high-spectral resolution remote sensing: a review and a novel approach applied to gold exploration using AVIRIS data

Remote sensing's main limitation to date was that geological surface information lacked detail due to the broad bandwidth of the operational scanners. Imaging spectrometers now allow acquisition of image data in many contiguous spectral bands, with the aim of producing laboratory-like reflectance spectra for each pixel in the scene which can be directly compared with spectra of known materials measured in the field or laboratory to allow mineral identification on the Earth surface. The advent of satellite-based imaging spectrometer systems justifies the surge for techniques that allow the automatic preparation of validated surface mineralogy maps. We show an extension of a procedure known as cross-correlogram spectral matching to retrieve accuracy assessed surface mineralogy from Airborne Visible/InfraRed Imaging Spectrometer data (AVIRIS). The procedure is based on the spectral cross-correlation of known mineral spectra with unknown pixel spectra from AVIRIS. The validation is performed through a root-mean square calculation of expected correlograms and pixel correlograms. Results obtained in the study outlined allow an accurate delineation of zones of hydrothermal alteration in an area of active gold exploration.     

Fractal analysis based on the continuous wavelet transform and lithofacies classification from well-logs data using the self-organizing map neural network

The main goal of this paper is to show that the fractal analysis based on the continuous wavelet transform is not able to improve lithofacies classification using the self-organizing map (SOM) neural network model from well-logs data. The proposed idea consists to inject many inputs in SOM neural network machines and to choose the best map. These inputs are: data set 1: the five raw well-logs data which are: the gamma ray, density, neutron porosity, photoelectric absorption coefficient and sonic well-log; data set 2: the estimated Hölder exponents using the continuous wavelet transform of the data set 1; data set 3: data set 1 and the three radioactive elements concentrations; data set 4: the estimated Hölder exponents of the data set 1 and the Hölder exponents of the radioactive elements concentrations; data set 5: the estimated Hölder exponents of the data set 1 and the three radioactive elements concentrations logs. Application of the proposed idea at two boreholes located in the Algerian Sahara shows that the Hölder exponents estimated with the continuous wavelet transform as an input of the SOM neural network are not able to give geological details. However, the raw well-logs as an input give more details and precision especially when they are enhanced with the natural gamma ray spectrometry data.     

Urban development and flooding in Houston Texas, inferences from remote sensing data using neural network technique

The effects of urban development on the natural ecosystem and its link to the increased flooding in Houston, Texas were evaluated. Houston is suitable for this type of analysis due to its 1.95 million population, large geographic area and fast growth rate. Using neural network techniques, four Landsat Thematic Mapper images were grouped into five land use classes for the period 1984 to 2003: vegetation, bare ground, water, concrete and asphalt. Results show that asphalt and concrete increased 21% in the time period 1984–1994, 39% in 1994–2000 and 114%, from 2000 to 2003, while vegetation suffered an overall decrease. When change detection data are compared with runoff ratio data, a relationship between increased runoff and urban development is apparent, which indicates increased chances of flooding. Initial results of this work are made available to the public in GIS format via internet using Arc Internet Map Server (ArcIMS) at .     

Delineation of groundwater prospective zones from a delta region of India,using geoelectrical and water quality approach

Geoelectrical survey was carried out in the western delta region of River Vasista Godavari, Andhra Pradesh, India, for delineation of groundwater prospective zones due to acute shortage of water supply for various purposes. Forty-six vertical electrical soundings (VES) were done, employing the Schlumberger configuration with a maximum AB/2 of 160 m. The interpreted results of VES show four to five layers with variable thicknesses, such as topsoil zone (1.5–3 m), clay zone (0.84–32 m), finer sand zone (2–72 m), medium to coarse sand zone (4 to 28.8 m) and clay zone (1.2–∞ m), indicating a multi-aquifer system. These results are corroborated with the known lithologs of the study area. Further, the resistivity is also compared with electrical conductivity (EC) of groundwater observed nearby shallow wells representing buried channel (BC), flood plain (FP) and coastal (C) zones, which indicate slightly brackish to brackish water (EC: 1470–6010 µS/cm), whereas the groundwater observed from deep wells shows the fresh (EC: 726–1380 µS/cm), fresh to brackish (EC: 1010 to 3250 µS/cm), and brackish water (EC: 3020 to 4170 µS/cm) located in BC, FP and C zones, respectively. This survey reveals the prospective aquifer zones with potable water at VES locations of 4–6, 8, 10, 11, 14, 16–28, 33–36, 39 and 42–44, where the resistivity values vary from 10 to 40 Ω m. The slightly brackish and brackish water zones are also observed from the resistivity of less than 10 Ω m at shallow depth in BC (VES-22, 37, 38 and 46), FP (VES-1, 2, 7, 29, 30 and 40) and C (VES-3, 4, 9, 12, 13, 15, 31, 32, 41 and 45) zones. As a result, the present investigation has delineated the freshwater zones at shallow (<?12 m) and also at deeper depths (30–45 m) as prospective areas, where BC zone occurs. Freshwater pockets also identified in FP (VES-8 and 39) and C (VES-11, 14 and 15) zones. Thus, this study helps to solve the drinking and irrigation water problems.     

基于资源一号02D卫星高光谱数据的甘肃头吊泉-南大滩地区蚀变矿物填图及金矿化带识别

高光谱遥感能够根据光谱指纹特征提取蚀变矿物,在地质领域应用成效十分显著.针对最新的资源一号02D卫星高光谱数据特点,首先进行了高光谱数据预处理,选取蚀变矿物光谱特征显著波段进行数据降维,并应用三次样条插值函数对短波红外数据进行光谱波段增值,将光谱采样间隔提高到了2 nm.在优选出矿物填图端元光谱后,应用改进的SAM填图...     

Monitoring runoff coefficients and groundwater levels using data from GRACE,GLDAS, and hydrometeorological stations: analysis of a Colombian foreland basin

The determination of space–time variation in groundwater accumulation in Colombia’s Eastern Llanos foreland basin from 2003 to 2014 was done using terrestrial water storage (TWS) anomalies identified in two versions of the Gravity Recovery and Climate Experiment (GRACE) data—from the Global Data Center for Space Research (CSR) at the University of Texas at Austin (USA) and from the Institute of Geodesy at the Graz University of Technology (ITSG, Austria)—and also soil moisture storage (SMS) data from the Global Land Data Assimilation System (GLDAS). These data were compared to changes in groundwater storage obtained using the water-budget equation, calculated based on recorded data from hydrometeorological stations. This study confirmed the viability of using satellite information to understand and monitor temporal variation in groundwater recharge in the study area. Temporal variations in TWS, SMS, and groundwater level were shown to correspond to regional rain and drought periods, which are sensitive to climate phenomena such as El Niño and La Niña. Comparing changes in TWS and groundwater level to changes in infiltration and recharge revealed correlation coefficients of 0.56 and 0.98 with CSR data and 0.71 and 0.86 with ITSG data, respectively.     

Recharge areas and hydrochemistry of carbonate springs issuing from Semmering Massif, Austria, based on long-term oxygen-18 and hydrochemical data evidence

Oxygen-18 content and hydrochemistry of the springs issuing from Semmering Massif was intensively monitored with the aim of characterizing the recharge areas and hydrochemical evolution. The δ¹⁸O-altitude effect was determined using isotopic and hydrogeological data for small, mainly crystalline reference springs; it was approximated at –0.27 and –0.21‰/100 m respectively for the northern and southern side of the massif. Applying these values the mean recharge altitude of the springs was calculated. For the large-capacity carbonate springs it ranges between 1,100–1,410 m, compatible with the topographic and hydrogeologic frame work of the Mesozoic limestones and dolomites comprised in the Lower Austroalpine feeding the springs. Hydrochemical composition of the carbonate springs is dominated by Ca²⁺, Mg²⁺, HCO₃ ^– and SO₄ ^2– ions. With respect to calcite, the springs are nearly saturated, but undersaturated with dolomite (except for some springs that seemed close to saturation). As is typical for carbon dioxide influx from soils in a mountainous region, the mean equilibrium P_CO2 is low, within 10^–3.0 and 10^–2.5 atm (0.1–0.3 vol%). On a long-term scale, the pH and the calculated SI_c, SI_d and equilibrium P_CO2 show a strong seasonality, whereas Ca²⁺, Mg²⁺ and HCO₃ ^– concentration is almost time invariant. By integrating the results of δ¹⁸O and chemical data, altitudinal variability of the chemistry of carbonate groundwater is demonstrated. Reflecting the systematic change of biotic activity and recharge conditions in the catchment areas, a negative co-variation results between the recharge altitude and P_CO2 and HCO₃ ^– concentration (HCO₃ ^– is not modified by any source/sink terms, thus rendering the change on the carbonate chemistry). P_CO2 and HCO₃ ^– drop by approx. 0.22 log units (atm) and 38.6 mg/l, respectively, for every 100-m gain in recharge altitude. Electronic Publication