Mapping Soil Particle Size Fractions Using Compositional Kriging,Cokriging and Additive Log-ratio Cokriging in Two Case Studies

Information on the spatial distribution of soil particle-size fractions (psf) is required for a wide range of applications. Geostatistics is often used to map spatial distribution from point observations; however, for compositional data such as soil psf, conventional multivariate geostatistics are not optimal. Several solutions have been proposed, including compositional kriging and transformation to a composition followed by cokriging. These have been shown to perform differently in different situations, so that there is no procedure to choose an optimal method. To address this, two case studies of soil psf mapping were carried out using compositional kriging, log-ratio cokriging, cokriging, and additive log-ratio cokriging; and the performance of Mahalanobis distance as a criterion for choosing an optimal mapping method was tested. All methods generated very similar results. However, the compositional kriging and cokriging results were slightly more similar to each other than to the other pair, as were log-ratio cokriging and additive log-ratio cokriging. The similar results of the two methods within each pair were due to similarities of the methods themselves, for example, the same variogram models and prediction techniques, and the similar results between the two pairs were due to the mathematical relationship between original and log-ratio transformed data. Mahalanobis distance did not prove to be a good indicator for selecting an optimal method to map soil psf.     

基于GIS的证据权模型在秦岭～松潘金矿潜力预测中的应用

数据驱动的证据权法被用来进行金矿潜力制作。为了确定秦岭~松潘金矿的潜力区,需利用地质、地球化学、地球物理等数据。数据采集、图形处理、空间分析都是在GIS平台上进行的。预测结果表明,证据权法在综合不同空间数据上是有效的,最终的预测图件圈出了最有利的矿化区,可用于进一步勘查研究。     

Modelling techniques for volumetric reconstruction of earth structures

Visualisation of seismic and tomographic results is a crucial point to properly understand the models provided by seismic methods. We consider several geostatistical methods (inverse distance weighting, point kriging and mathematical wavelets) to map surface wave tomography in a sparsely sampled study area, and to compare their accuracy and efficiency with proper raypath methodologies (inversion and projection onto convex sets). A large set of synthetic data is used to estimate seismic velocities before application to real data. The contour maps of prediction errors indicate that spatial prediction and inversion perform similarly.     

Assessment of ground subsidence hazard near an abandoned underground coal mine using GIS

This study constructs a hazard map for ground subsidence around abandoned underground coal mines (AUCMs) at Samcheok City in Korea using a probability (frequency ratio) model, a statistical (logistic regression) model, and a Geographic Information System (GIS). To evaluate the factors related to ground subsidence, an image database was constructed from a topographical map, geological map, mining tunnel map, Global Positioning System (GPS) data, land use map, lineaments, digital elevation model (DEM) data, and borehole data. An attribute database was also constructed from field investigations and reports on the existing ground subsidence areas at the study site. Nine major factors causing ground subsidence were extracted from the probability analysis of the existing ground subsidence area: (1) depth of drift; (2) DEM and slope gradient; (3) groundwater level, permeability, and rock mass rating (RMR); (4) lineaments and geology; and (5) land use. The frequency ratio and logistic regression models were applied to determine each factor’s rating, and the ratings were overlain for ground subsidence hazard mapping. The ground subsidence hazard map was then verified and compared with existing subsidence areas. The verification results showed that the logistic regression model (accuracy of 95.01%) is better in prediction than the frequency ratio model (accuracy of 93.29%). The verification results showed sufficient agreement between the hazard map and the existing data on ground subsidence area. Analysis of ground subsidence with the frequency ratio and logistic regression models suggests that quantitative analysis of ground subsidence near AUCMs is possible.     

Combining Areal and Point Data in Geostatistical Interpolation: Applications to Soil Science and Medical Geography

A common issue in spatial interpolation is the combination of data measured over different spatial supports. For example, information available for mapping disease risk typically includes point data (e.g. patients’ and controls’ residence) and aggregated data (e.g. socio-demographic and economic attributes recorded at the census track level). Similarly, soil measurements at discrete locations in the field are often supplemented with choropleth maps (e.g. soil or geological maps) that model the spatial distribution of soil attributes as the juxtaposition of polygons (areas) with constant values. This paper presents a general formulation of kriging that allows the combination of both point and areal data through the use of area-to-area, area-to-point, and point-to-point covariances in the kriging system. The procedure is illustrated using two data sets: (1) geological map and heavy metal concentrations recorded in the topsoil of the Swiss Jura, and (2) incidence rates of late-stage breast cancer diagnosis per census tract and location of patient residences for three counties in Michigan. In the second case, the kriging system includes an error variance term derived according to the binomial distribution to account for varying degree of reliability of incidence rates depending on the total number of cases recorded in those tracts. Except under the binomial kriging framework, area-and-point (AAP) kriging ensures the coherence of the prediction so that the average of interpolated values within each mapping unit is equal to the original areal datum. The relationships between binomial kriging, Poisson kriging, and indicator kriging are discussed under different scenarios for the population size and spatial support. Sensitivity analysis demonstrates the smaller smoothing and greater prediction accuracy of the new procedure over ordinary and traditional residual kriging based on the assumption that the local mean is constant within each mapping unit.     

数字高程模型在地表过程研究中的应用

数字高程模型(DEM)是人们研究地表过程、构造地貌的一种行之有效的方法和手段。DEM是地形表面的数字表达,易于三维可视化和统计分析。DEM作为一种空间数据,可以进行各种空间分析,编制平均高程图、山顶面图、谷地面图、局部地形图、平均坡度图和剖面图,从而有效地显示地形特征,预测地表侵蚀量和地形发展趋势。特定盆地充填面的DEM分析是探讨特定地质时期(新生代)以来地表由沉积转入侵蚀、沉积之后隆升过程、隆升速率和剥蚀量、剥蚀速率等地表过程的有效手段,它与大地构造、深部地球物理相结合,可以揭示隆升机制和大陆动力学背景。低起伏侵蚀面的DEM分析有利于定量揭示山脉夷平面分布,结合基岩地质等其他资料综合分析,可揭示侵蚀面的成因,揭示大地构造与地表过程相互关系是未来研究的主要目标之一。     

A GIS method for landslide inventory and susceptibility mapping in the Río El Estado watershed,Pico de Orizaba volcano,México

In volcanic terrains, dormant stratovolcanoes are very common and can trigger landslides and debris flows continually along stream systems, thereby affecting human settlements and economic activities. It is important to assess their potential impact and damage through the use of landslide inventory maps and landslide models. In Mexico, numerous geographic information systems (GIS)-based applications have been used to represent and assess slope stability. However, there is no practical and standardized landslide mapping methodology under a GIS. This work provides an overview of the ongoing research project from the Institute of Geography at the National Autonomous University of Mexico that seeks to conduct a multi-temporal landslide inventory and produce a landslide susceptibility map by using GIS. The Río El Estado watershed on the southwestern flank of Pico de Orizaba volcano, the highest mountain in Mexico, is selected as a study area. The geologic and geomorphologic factors in combination with high seasonal precipitation, high degree of weathering, and steep slopes predispose the study area to landslides. The method encompasses two main levels of analysis to assess landslide susceptibility. First, the project aims to derive a landslide inventory map from a representative sample of landslides using aerial orthophotographs and field work. Next, the landslide susceptibility is modelled by using multiple logistic regression implemented in a GIS platform. The technique and its implementation of each level in a GISs-based technology is presented and discussed.     

Comparison of statistical prediction methods for characterizing the spatial variability of apparent electrical conductivity in coastal salt-affected farmland

Soil salinity has been known to be problematic to land productivity and environment in the lower Yellow River Delta due to the presence of a shallow, saline water table and marine sediments. Spatial information on soil salinity has gained increasing importance for the demand of management and sustainable utilization of arable land in this area. Apparent electrical conductivity, as measured by electromagnetic induction instrument in a fairly quick manner, has succeeded in mapping soil salinity and many other soil physical and chemical properties from field to regional scales. This was done based on the correlation that existed between apparent electrical conductivity and many other soil properties. In this paper, four spatial prediction methods, i.e., local polynomial, inverse distance weighed, ordinary kriging and universal kriging, were employed to estimate field-scale apparent electrical conductivity with the aid of an electromagnetic induction instrument (type EM38). The spatial patterns estimated by the four methods using EM38 survey datasets of various sample sizes were compared with those generated by each method using the entire sample size. Spatial similarity was evaluated using difference index (DI) between the maps created using various sample sizes (i.e., target maps) and the maps generated with the entire sample size (i.e., the reference map). The results indicated that universal kriging had the best performance owing to the inclusion of residuals and spatial detrending in the kriging system. DI showed that spatial similarity between the target and reference maps of apparent electrical conductivity decreased with the reduction in sample size for each prediction method. Under the same reduction in sample size, the method retaining the most spatial similarity was universal kriging, followed by ordinary kriging, inverse distance weighed, and local polynomial. Approximately, 70 % of total survey data essentially met the need for retaining 90 % details of the reference map for universal kriging and ordinary kriging methods. This conclusion was that OK and UK were two most appropriate methods for spatial estimation of apparent electrical conductivity as they were robust with the reduction in sample size.     

A comparative study on the landslide susceptibility mapping using logistic regression and statistical index models

The logistic regression and statistical index models are applied and verified for landslide susceptibility mapping in Daguan County, Yunnan Province, China, by means of the geographic information system (GIS). A detailed landslide inventory map was prepared by literatures, aerial photographs, and supported by field works. Fifteen landslide-conditioning factors were considered: slope angle, slope aspect, curvature, plan curvature, profile curvature, altitude, STI, SPI, and TWI were derived from digital elevation model; NDVI was extracted from Landsat ETM7; rainfall was obtained from local rainfall data; distance to faults, distance to roads, and distance to rivers were created from a 1:25,000 scale topographic map; the lithology was extracted from geological map. Using these factors, the landslide susceptibility maps were prepared by LR and SI models. The accuracy of the results was verified by using existing landslide locations. The statistical index model had a predictive rate of 81.02%, which is more accurate prediction in comparison with logistic regression model (80.29%). The models can be used to land-use planning in the study area.     

Evaluation of Interpolation Accuracy of Neural Kriging with Application to Temperature-Distribution Analysis

An interpolation method based on a multilayer neural network (MNN), has been examined and tested for the data of irregular sample locations. The main advantage of MNN is in that it can deal with geoscience data with nonlinear behavior and extract characteristics from complex and noisy images. The training of MNN is used to modify connection weights between nodes located in different layers by a simulated annealing algorithm (one of the optimization algorithms of the network). In this process, three types of errors are considered: differences in values, semivariograms, and gradients between sample data and outputs from the trained network. The training is continued until the summation of these errors converges to an acceptably small value. Because the MNN trained by this learning criterion can estimate a value at an arbitrary location, this method is a form of kriging and termed Neural Kriging (NK). In order to evaluate the effectiveness of NK, a problem on restoration ability of a defined reference surface from randomly chosen discrete data was prepared. Two types of surfaces, whose semivariograms are expressed by isotropic spherical and geometric anisotropic gaussian models, were examined in this problem. Though the interpolation accuracy depended on the arrangement pattern of the sample locations for the same number of data, the interpolation errors of NK were shown to be smaller than both those of ordinary MNN and ordinal kriging. NK can also produce a contour map in consideration of gradient constraints. Furthermore, NK was applied to distribution analysis of subsurface temperatures using geothermal investigation loggings of the Hohi area in southwest Japan. In spite of the restricted quantity of sample data, the interpolation results revealed high temperature zones and convection patterns of hydrothermal fluids. NK is regarded as an interpolation method with high accuracy that can be used for regionalized variables with any structure of spatial correlation.     

基于DEM的地层界线V字型法则实现

地质人员经常依照V字型法则连接地层出露界线,但因其随意性和局限性容易出现误差和错误.基于GIS的机助地质填图系统中,基于等高线求取界线由于等高线的不连续性有着明显的局限性.为此,在DEM形成的三维空间中通过求解DEM与空间平面交线的方法来获取地质界线,可以有效地提高精度.文章分析探讨了基于不规则三角形网的地层界线求取思路,并通过实际例子对V字型法则给以实现,结果证明该方法执行速度较快、精度高.     

Karst unit mapping using geographic information system technology,Mower County,Minnesota, USA

Mower County is in southeastern Minnesota in an area underlain by sedimentary bedrock. These rocks are karsted Middle Devonian and Middle Ordovician limestone and dolomite. The karst features of the county were inventoried. These features include sinkholes, disappearing streams, caves, dry valleys, and springs. Previous karst mapping efforts for other counties in Minnesota have produced sinkhole probability maps and a springshed map. In Mower County, we have developed a new type of karst map using a geographic information system (GIS) to produce a karst unit map. Karst units are discrete three-dimensional bodies in which solution of the bedrock has resulted in the integration of surface water and groundwater. The field mapping and hydrologic investigations were done with conventional methods. The karst unit delineation was done using GIS technology, which allowed us to examine the county's karst using two-dimensional and three-dimensional views. Many different overlays of the karst elements were combined to better understand the landscape dynamics. Ultimately, it was the overlay of the karst features, hydrologic information, and depth-to-bedrock mapping on a shaded relief landscape morphology map which allowed us to best delineate the individual karst units.     

Spatial association between residential radon concentration and bedrock types in New Hampshire

Using a large database of residential short-term radon measurements in New Hampshire, this study evaluated the ability of expert-assigned bedrock radon potential for predicting residential radon concentration. First, each bedrock type was assigned a radon potential level by a geologist familiar with the local geology. Then, using residential radon measurements, a continuous surface of radon concentration was generated through a kriging process. The mean residential radon concentration within the spatial extent of each bedrock type was then calculated based on that surface. The Spearman Rank Correlation Coefficient was calculated between the two ranks of the bedrock types, one based on the expert-assigned potential level and the other based on the mean residential concentration. A strong correlation between the rank correlation and the area of the bedrock type was found. When only the 15 largest bedrock types were used, the Spearman Correlation Coefficient reached 0.6. Geological knowledge is concluded to be useful in predicting and mapping residential radon concentration, but the prediction should be interpreted with caution, especially for areas in which the underlying bedrocks are highly localized.     

Identification of suitable sites for groundwater recharge in Awaspi watershed using GIS and remote sensing techniques

Groundwater is a valuable natural resource for drinking, domestic, livestock use, and irrigation, especially in arid and semi-arid regions like the Garmiyan belt in Kurdistan region. The Awaspi watershed is located 50 km east of Kirkuk city, south Kurdistan, Iraq; and covers an area of 2146 km². The paper presents result of a study aimed at: (1) mapping and preparing thematic layers of factors that control groundwater recharge areas, and (2) determination of sites suitable for groundwater recharge. We used available data such as geological map, groundwater depth map, digital elevation model (DEM), Landsat 8 imagery, and tropical rainfall measuring mission (TRMM) data for this study. These data, supplemented by slope features, lithology, land use land cover, rainfall, groundwater depth, drainage density, landform, lineament density, elevation and topographic position index, were utilized to create thematic maps to identify suitable areas of groundwater recharge, using GIS and remote sensing techniques. Analytic hierarchy process (AHP) was applied to weight, rank, and reclassify these maps in the ArcGIS 10.3 environment, to determine the suitable sites for groundwater recharge within the Awaspi watershed. Fifty-five percent of the total area of the watershed was found to be suitable for groundwater recharge; whereas 45% of the area was determined to have poor suitability for groundwater recharge, but can be used for surface water harvesting.     

Digital elevation model (DEM) generation using the SAR interferometry technique

The development of satellite technology is rapidly increasing the evolution of remote sensing. Satellite images give extensive useful information about the land structure that is easily manageable in the process of generating true, high-speed information which allows the forecasting of future environmental and urban planning. Remote sensing comprises active and passive systems. Passive sensors detect natural radiation that is emitted or reflected by the object or surrounding area being observed. Active systems which produce their own electromagnetic energy and their main properties are their ability of collecting data in nearly all atmospheric conditions, day or night. These systems are frequently used to generate a digital elevation model (DEM) because they cover large areas. DEM supplies essential data for applications that are concerned with the Earth’s surface and DEMs derived from survey data are accurate but very expensive and time consuming to create. However, the use of satellite remote sensing to provide images to generate a DEM is considered to be an efficient method of obtaining data. Interferometric Synthetic Aperture Radar (InSAR) is a new geodetic technique for determining earth topography. InSAR measurements are highly dense and they only give information in Line of Sight of Radar. In the study, interferograms were produced from the InSAR images taken by ERS satellites in 1992 and 2007 and we developed the methods to generate a DEM using the InSAR technique and present the results relating to Kayseri Province in Turkey. The accuracy of the DEM derived from the InSAR technique is evaluated in comparison with a reference DEM generated from contours in a topographical map.     

三维地质图制作方法——以1∶50万海南岛地质图为例

将Global Mapper、ArcGIS、MapGIS三款GIS软件有效结合起来,以海南岛为例进行基于DEM的三维晕渲地质图的制作方法研究。首先将SRTM数据和矢量地理数据在不同地图参数下进行自动配准,然后实现地质要素与DEM模型的精准匹配,最后根据DEM对第四系地质界线进行微调,实现了1∶50万海南岛三维地质图的制作。同时总结了不同椭球体与坐标系统下的数据转换、DEM空白区数据填补、晕渲地质图生产的方法。三维地质图制作所采用的DEM数据完全满足1∶50万三维地质图的制图要求;与传统地质图相比,其立体感和可读性明显增强,图件信息量显著增大,图面呈现清晰的三维地质结构,很好地提高了制图精度。该方法为我国三维地质图的制作提供了一个新的方法和思路,并为其他三维图件制作提供了参考借鉴。     

Indicator-Based Geostatistical Models For Mapping Fish Survey Data

Marine research survey data on fish stocks often show a small proportion of very high-density values, as for many environmental data. This makes the estimation of second-order statistics, such as the variance and the variogram, non-robust. The high fish density values are generated by fish aggregative behaviour, which may vary greatly at small scale in time and space. The high values are thus imprecisely known, both in their spatial occurrence and order of magnitude. To map such data, three indicator-based geostatistical methods were considered, the top-cut model, min–max autocorrelation factors (MAF) of indicators, and multiple indicator kriging. In the top-cut and MAF approaches, the variable is decomposed into components and the most continuous ones (those corresponding to the low and medium values) are used to guide the mapping. The methods are proposed as alternatives to ordinary kriging when the variogram is difficult to estimate. The methods are detailed and applied on a spatial data set of anchovy densities derived from a typical fish stock acoustic survey performed in the Bay of Biscay, which show a few high-density values distributed in small spatial patches and also as solitary events. The model performances are analyzed by cross-validating the data and comparing the kriged maps. Results are compared to ordinary kriging as a base case. The top-cut model had the best cross-validation performance. The indicator-based models allowed mapping high-value areas with small spatial extent, in contrast to ordinary kriging. Practical guidelines for implementing the indicator-based methods are provided.     

Creating an advanced backpropagation neural network toolbox within GIS software

An artificial neural network (ANN) toolbox is created within GIS software for spatial interpolation, which will help GIS users to train and test ANNs, perform spatial analysis, and display results as a single process. The performance is compared to that of the open source Fast Artificial Neural Network library and conventional interpolation methods by creating digital elevation models (DEMs) given that nearly exact solutions exist. Simulation results show that the advanced backpropagations such as iRprop speed up the learning, while they can get stuck in a local minimum depending on initial weight sets. Besides, the division of input–output examples into training and test data affects the accuracy, particularly when the distribution of the examples is skewed and peaked, and the number of data is small. ANNs, however, show the similar performance to inversed distance weighted or kriging and outperform polynomial interpolations as a global interpolation method in high-dimensional data. In addition, the neural network residual kriging (NNRK) model, which combines the ANN toolbox and kriging within GIS software, is performed. The NNRK outperforms conventional methods and well captures global trends and local variations. A key outcome of this work is that the ANN toolbox created within the de facto standard GIS software is applicable to various spatial analysis including hazard risk assessment over a large area, in particular when there are multiple potential causes, the relationship between risk factors and hazard events is not clear, and the number of available data is small given its performance for DEM generation.     

Utilization of laser range finder and differential GPS for high‐resolution topographic measurement at Hacituğrul Tepe,Turkey

Topographic maps are fundamental for geoarchaeological field studies and archaeological excavations. However, traditional methods of topographic mapping, as well as modern high‐tech methods such as airborne laser scanning and photogrammetry of high‐resolution satellite images, are often cost‐ineffective for field studies in terms of time, money, and labor. We here propose a method to measure topography for archaeological sites and surrounding areas quickly and accurately, using a laser range finder (LRF) and differential global positioning system DGPS. Three‐dimensional coordinates of points on land surfaces are measured through the LRF, targeted from multiple measuring locations whose positions are acquired with the DGPS. The point data are then interpolated to produce a digital elevation model (DEM) using a geographic information system (GIS). High‐resolution DEMs can be obtained with this method, with horizontal and vertical accuracies on the order of 10 cm. We here demonstrate the method for measuring detailed topography of the Hacituğrul Tepe in central Turkey. Digital topography data incorporated in GIS can also be part of an archaeological database, providing opportunities for quantitative analyses of topography and archaeological materials. © 2009 Wiley Periodicals, Inc.