结合信息熵改进的证据权法与滑坡危险度区划

滑坡是一种常见的地质灾害,其危害性极大,科学地划分滑坡危险度是防灾减灾工作中主动预测预警的重要环节.该文结合GIS技术与信息熵理论,充分利用熵值法特别适合于确定同一个指标下不同因素相对权重的特点,开展了滑坡危险度区划模型中证据权法的改进研究,并以台湾地区为实验区,在综合考虑滑坡的形成要素并提取滑坡危险度评价因子后,实现了研究区滑坡危险度区划.通过定性和定量等精度检验和对比分析,证明了结合信息熵改进的证据权法所获得的滑坡危险度区划结果更准确且更符合实际.     

Rock Kriging with the Microscope

An idea to consider rock textures from a geostatistical viewpoint is suggested. Mineral grains are coded by indicator functions. Four metrics are shown of interest for petrographic applications. The simplest one is used to calculate covariograms of indicators for platinum-bearing gabbronorite from the Pansky rock massif (Kola Peninsula, Russia) with maximal range of 2 units. This is generalized in the concept of a minimal cluster of mineral grains for the given rock. The theory allows us to combine grain-by-grain and cluster-by-cluster considerations of rock texture. It may be used to classify monotonous lithological series using nuances of rock textures.     

Soil depth estimation through soil-landscape modelling using regression kriging in a Himalayan terrain

Soil formation depends upon several factors such as parent material, soil biota, topography and climate. It is difficult to use conventional soil survey methods for mapping the depth of soil in complex mountainous terrains. In this context, the present study aimed to estimate the soil depth for a large area (330.35 km²) using different geo-environmental factors through a soil-landscape regression kriging (RK) model in the Darjeeling Himalayas. RK with seven predictor variables such as elevation, slope, aspect, general curvature, topographic wetness index, distance from the streams and land use, was used to estimate the soil depth. While topographic parameters were derived from an 8-m resolution digital elevation model, the ortho-rectified Cartosat-1 satellite image was used to prepare the land use map. Soil depth measured at 148 sites within the study area was used to calibrate and validate the RK model. The result showed that the RK model with the seven predictors could explain 67% spatial variability of soil depth with a prediction variance between 0.23 and 0.42 m at the test site. In the regression analysis, land use (0.133) and slope (–0.016) were identified as significant determinants of soil depth. The prediction map showed higher soil depth in south-facing slopes and near valleys in comparison to other areas. Mean, mean absolute and root mean-square errors were used to access the reliability of the prediction, which indicated a goodness-of-fit of the RK model.     

运用证据权重法进行阳性钉螺-地理因子的空间关系研究

Schistosomiasis is a serious public health problem in the middle-lower Yangtze River Basin in China. Study of spatial variation of snail distribution that is related to microgeographic factors can help to choose pertinent measures for snail extinguishment and environment rebuilding. This paper studied the theoretical architecture of weights-of-evidence approach. The case study was made for spatial relation between the occurrence of infected snails and geographic factor combinations in Waijiazhou marshland of Poyang Lake region in China. The multievidence data came from the geographical factor combinations by crossing operation of vegetation coverage grade layer, cattle route distance grade layer, and special environment layer (181 combinations in total) in GIS. The calculation of weight contrast index shows that high vegetation coverage, cattle route distance of <45 meters, and special geographic factor “ground depression” had direct spatial relation with the occurrence of infected snails. The verification by crossing operation in GIS indicated 72.45% of the infected snails concentrated on the areas of positive weight contrast index (sequenced in an order of weight contrast index from high to low), demonstrating the high efficiency of the model established in finding infected snails according to the geographic factor combinations that can be explicitly discerned in the study area. Supported by a the National Natural Science Fundation of China (No. 30590370), the Research Project “Spatial Simulation of Schistosomiasis Susceptible Areas in the Poyang Lake Region” Sponsored by Science Research Plan 2007 of Jiangxi Normal University (Natural Science Category).     

Rating curve modelling with Manning's equation to manage instability and improve extrapolation

Abstract

The rating curve is the most frequently used methodology for continuous river flow measurement. However, to establish a reliable rating curve is difficult, takes time, and is often impossible when the measurement cross-section is unstable. To overcome this difficulty, the use of a modified form of the Manning equation is suggested to express flow rate as a function of hydraulic radius and longitudinal water surface slope rather than of water level alone, as is the case with the classical stage-discharge relationship. This formulation permits one to integrate the development of the river bed geometry and the hydraulic characteristics of the reach explicitly within the rating curve, in a simple way. The procedure is used and evaluated on the Leysse River in Chambéry, France. The results demonstrate the validity and potential of this approach, particularly for extrapolation, when the hydraulic and geometric characteristics are evolving.     

Using landscape characteristics to define an adjusted distance metric for improving kriging interpolations

Interpolation of point measurements using geostatistical techniques such as kriging can be used to estimate values at non-sampled locations in space. Traditional geostatistics are based on the spatial autocorrelation concept that nearby things are more related than distant things. In this study, additional information was used to modify the traditional Euclidean concept of distance into an adjusted distance metric that incorporates similarity in terms of quantifiable landscape characteristics such as topography or land use. This new approach was tested by interpolating soil moisture content, pH and carbon-to-nitrogen (C:N) ratio measured in both the mineral and the organic soil layers at a field site in central Sweden. Semivariograms were created using both the traditional distance metrics and the proposed adjusted distance metrics to carry out ordinary kriging (OK) interpolations between sampling points. In addition, kriging with external drift (KED) was used to interpolate soil properties to evaluate the ability of the adjusted distance metric to incorporate secondary data into interpolations. The new adjusted distance metric typically lowered the nugget associated with the semivariogram, thereby better representing small-scale variability in the measured data compared to semivariograms based on the traditional distance metric. The pattern of the resulting kriging interpolations using KED and OK based on the adjusted distance metric were similar because they represented secondary data and, thus, enhanced small-scale variability compared to traditional distance OK. This created interpolations that agreed better with what is expected for the real-world spatial variation of the measured properties. Based on cross-validation error, OK interpolations using the adjusted distance metric better fit observed data than either OK interpolations using traditional distance or KED.     

An Alternative Measure of the Reliability of Ordinary Kriging Estimates

This paper presents an interpolation variance as an alternative to the measure of the reliability of ordinary kriging estimates. Contrary to the traditional kriging variance, the interpolation variance is data-values dependent, variogram dependent, and a measure of local accuracy. Natural phenomena are not homogeneous; therefore, local variability as expressed through data values must be recognized for a correct assessment of uncertainty. The interpolation variance is simply the weighted average of the squared differences between data values and the retained estimate. Ordinary kriging or simple kriging variances are the expected values of interpolation variances; therefore, these traditional homoscedastic estimation variances cannot properly measure local data dispersion. More precisely, the interpolation variance is an estimate of the local conditional variance, when the ordinary kriging weights are interpreted as conditional probabilities associated to the n neighboring data. This interpretation is valid if, and only if, all ordinary kriging weights are positive or constrained to be such. Extensive tests illustrate that the interpolation variance is a useful alternative to the traditional kriging variance.     

空间信息统计学在环境科学领域的应用进展

空间信息统计学,又称为地质统计学,是研究多孔介质空间变异性的有效方法,在环境科学领域有很好的应用前景。本文简单介绍了空间信息统计学的产生与发展现状,回顾了其在环境科学领域应用的一些成果,并就空间信息统计学在环境科学领域的应用前景进行了展望。     

Predictive GIS model for potential mapping of Cu, Pb, Zn mineralization

IntroductionChina is the third richest country in the world ofmineral resources with a total potential value of 11trillion USdollars. Yunnan Provincce is one of theimportant metallogenic belts for nonferrous and pre-cious metal deposits[1].Mineral source …     

Solving the kriging problem by using the Gram-Schmidt orthogonalization

This paper presents an approach to solve the kriging problem, defined in terms of projections, by using Gram-Schmidt orthogonalization. The Gram-Schmidt orthogonalization allows us to find an optimal approximationY* in then-dimensional subspaceH _n of any vectorY element of a Hilbert spaceH. This approach requiresO(n ³) multiplication operations to obtain an orthogonal basis, andO(n ²) multiplications needed to calculate kriging solution for a given point.