Comparison of two split-window methods for retrieving land surface temperature from MODIS data

Land surface temperature (LST) is a key parameter in environment and earth science study, especially for monitoring drought. The objective of this work is a comparison of two split-window methods: Mao method and Sobrino method, for retrieving LST using MODIS (Moderate-resolution Imaging Spectroradiometer) data in North China Plain. The results show that the max, min and mean errors of Mao method are 1.33K, 1.54K and 0.13K lower than the standard LST product respectively; while those of Sobrino method are 0.73K, 1.46K and 1.50K higher than the standard respectively. Validation of the two methods using LST product based on weather stations shows a good agreement between the standard and Sobrino method, with RMSE of 1.17K, whereas RMSE of Mao method is 1.85K. Finally, the study introduces the Sobmao method, which is based on Sobrino method but simplifies the estimation of atmospheric water vapour content using Mao method. The Sobmao method has almost the same accuracy with Sobrino method. With high accuracy and simplification of water vapour content estimation, the Sobmao method is recommendable in LST inversion for good application in Ningxia region, the northwest China, with mean error of 0.33K and the RMSE value of 0.91K.     

Evaluating the performance of artificial intelligence methods for estimation of monthly mean soil temperature without using meteorological data

Soil temperature has an important role in agricultural, hydrological, meteorological and climatological studies. In the present research, monthly mean soil temperature at four different depths (5, 10, 50 and 100 cm) was estimated using artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP). The monthly mean soil temperature data of 31 stations over Iran were employed. In this process, the data of 21 and 10 stations were used for training and testing stages of used models, respectively. Furthermore, the geographical information including latitude, longitude and altitude as well as periodicity component (the number of months) was considered as inputs in the mentioned intelligent models. The results demonstrated that the ANN and ANFIS models had good performance in comparison with the GEP model. Nevertheless, the ANFIS generally performed better than ANN model.     

An Experimental Comparison of Ordinary and Universal Kriging and Inverse Distance Weighting

A factorial, computational experiment was conducted to compare the spatial interpolation accuracy of ordinary and universal kriging and two types of inverse squared-distance weighting. The experiment considered, in addition to these four interpolation methods, the effects of four data and sampling characteristics: surface type, sampling pattern, noise level, and strength of small-scale spatial correlation. Interpolation accuracy was measured by the natural logarithm of the mean squared interpolation error. Main effects of all five factors, all two-factor interactions, and several three-factor interactions were highly statistically significant. Among numerous findings, the most striking was that the two kriging methods were substantially superior to the inverse distance weighting methods over all levels of surface type, sampling pattern, noise, and correlation.     

Comparison and integration of spatial data using covariance matrices

A differential approach to map comparison and integration is presented utilizing the variance-covariance matrices of polynomial trend surfaces. The approach is based on the statistics presented in the trend-surface computer program, CORSURF. Examples in south-central Kansas and the Maracaibo Basin in Venezuela are presented to show how the technique could be used in basinanalysis studies.     

Evaluation of spatial interpolation methods for groundwater level in an arid inland oasis,northwest China

The Wuwei oasis, situated in the upper reaches of the Shiyang River basin in the arid inland of northwest China, is intensively cultivated using both groundwater and irrigation water originating from the Qilian Mountains. Groundwater levels are declining due to overuse of irrigation water. To estimate the decline over the entire Wuwei oasis, eight different interpolation methods were used for interpolating groundwater levels over 3 years, i.e. starting in 1983, followed by 1988 and ending with 1992. Cross-validation and orthogonal-validation were applied to evaluate the accuracy of the different methods. Root mean squared error and the correlation coefficient (R ²) were calculated for each of the interpolation methods and years. Three kriging methods (simply, ordinary, and universal) gave the best fit. Modified ordinary kriging was found better than simple and universal kriging methods with a smaller number of points having large differences (>50 m) between estimated and predicted values. Based on the groundwater surfaces determined by the ordinary kriging as modified by Yamamoto, the groundwater decline was found from 1983 to 1992 to be a modest 2.1 m in average.     

元素空间分布插值方法的对比研究:以铜陵地区土壤中的重金属元素为例

文中以铜陵地区As、Cd、Cu、Pb、Tl、Zn等6种土壤污染元素为例,选取常用且具有代表性的反距离加权法、径向基函数法、普通克里格法、多维分形法4种空间插值方法,进行土壤元素空间插值,并对其结果进行验证分析和评价。各方法均选取最优参数进行插值对比,土壤样本数共372个,其中337个用于插值计算,35个不参与插值计算而用于验证插值结果。对比研究显示,普通克里格法对刻画区域土壤元素的空间分布趋势效果最佳,但其半变异函数模型及参数的优选仍有待进一步研究;多维分形法对刻画土壤元素局部异常和污染效果最佳,但其对土壤元素分布普遍特征的反映仍需深入研究;反距离加权法和径向基函数法对土壤元素分布的空间插值精度一般,但其简单易用、插值最优参数易于选择。     

Hybrid spatial data mining methods for site selection of emergency response centers

Traditional methods for site selection fail to give reasonable weight to the potential risk grade of different locations. Consequently, site selection does not highlight locations with higher risk grades. Thus, a hybrid analytical method for the site selection of emergency response centers is proposed. First, spatial predicates are incorporated into emergency event analysis. These spatial predicates describe the spatial relationships between emergency locations and surrounding objects. A spatial data association mining method is then developed to identify the correlation rules that contain emergency information and geographical factors. Such rules act as the weight adding mechanism on different urban buildings, such that every spatial object is assigned a risk grade based on these rules. Furthermore, a simulated annealing algorithm is developed by incorporating the weight adding method to locate the optimal sites for emergency centers. A series of experiments is conducted, and the results demonstrate the efficiency of the proposed hybrid method.     

高分辨率遥感数据处理方法实验研究

近几年高分辨率遥感数据处理与应用研究越来越受到国内外学者的重视,已成为目前遥感应用研究领域的热点和难点之一。首先分析了基于像素的中分辨率卫星数据的遥感数据处理方法,包括:基于统计理论的分类方法和基于光谱信息的非参数理论方法的分类方法。在此基础上,针对高分辨率遥感图像的特点,分析了面向对象的高分辨率遥感数据处理方法。其中,图像分割是最关键的一步,即如何有效地提取出对象特征非常重要。最后,给出了高分辨率遥感数据处理方法相关算法实验,主要进行了几种图像分割算法实验,同时结合植被变化检测,进行了变化检测算法实验。     

岩盐三轴压缩试验方法及数据处理方式的对比研究

在能源地下储存及废弃物地下处置库的设计、施工、运行中,岩盐的强度和变形特征是极为重要的参数。正确地获得岩盐的强度参数对地下储存库的设计和安全运行具有重要意义。分别选定江苏金坛和湖北江汉岩样作为试验样本,通过三轴压缩试验获得岩盐短期强度和变形的力学参数,比较并总结了以德国为代表的欧美国家与国内试验方法及数据处理方式的异同。同时介绍了德国短期强度试验确定岩盐损伤起始点的方法及其不足,提出了更为精确地确定方法并确定了金坛岩盐损伤起始点。分析发现:对于大变形岩盐,国内测量的强度数据普遍偏高,应采用横截面积修正以及科学应变代替工程应变的度量方式。     

Comparison of rock discontinuity mean trace length and density estimation methods using discontinuity data from an outcrop in Wenchuan area,China

The equations that exist in the literature to estimate corrected mean trace length and corrected two-dimensional density of a rock discontinuity set using area sampling technique are critically reviewed. The discontinuity traces appearing in an outcrop in Yingxiu area in China are used along with rectangular windows to calculate the corrected mean trace length and two-dimensional density using Kulatilake and Wu’s equations. Similarly, circular windows are used along with Mauldon’s and Zhang and Einstein’s equation to calculate the mean trace length and Mauldon’s equation to calculate the two-dimensional density for the same discontinuity sets using the same outcrop discontinuity trace data. For both parameters, the predictions based on the rectangular window methods were found to be more accurate than that based on the circular window methods.     

An evaluation of two spatial interpolation techniques in global sea-surface temperature reconstructions: Last Glacial Maximum and Pliocene case studies

Global estimates of sea-surface temperature (SST) distributions represent an important prescribed boundary condition for atmosphere-only general circulation models (AGCMs) which aim to simulate the behaviour of the past climate system. We investigate the use of objective interpolation techniques, based on Delaunay triangulation and gridded-averaging, in translating point-based SST data into global maps. Existing global SST datasets generated by CLIMAP for the Last Glacial Maximum (LGM) and PRISM for the middle Pliocene are used as vehicles for evaluation. Results indicate that the sample quantity and array of the CLIMAP sites are sufficient to faithfully reconstruct major surface water patterns of the LGM. However, as problems with sample quantity and distribution become more acute in pre-Quaternary periods, interpolators are limited in their ability to produce reasonable estimates in data sparse regions. In such cases, potential biases associated with interpolation become significant enough to bring into doubt the overall validity of the global SST reconstruction. This limits the datasets use as a prescribed boundary condition in AGCM simulations. On a regional scale, objective methods offer a valuable means of assessing data quality and constraining future subjective analysis. Primary rather than interpolated SST data remain extremely valuable as a tool to evaluate output from coupled ocean–atmosphere models rather than a prescribed boundary condition in AGCM simulations.     

降雨资料Kriging与IDW插值对比分析—以漓江流域为例

降水空间化信息在很多领域都具有重要意义,而进行空间插值方法形成降水空间化信息是当代较为常用的方式。面对众多的插值方法其插值精度成为我们是否采用的关键,就kirging插值法与IDW(Inverse Distance Weighting)插值进行研究以探讨其插值效果,为此类科学研究提供依据。研究以漓江流域内各气象站点降水统计资料为基础分别采用上述两种方法进行插值处理,通过与预留实测站点比较评判方法的插值效果的优劣。结果表明,多年月平均降水量作为时间步长时,降水丰沛的月份使用kriging插值法较优于IDW插值法,而枯水月份则使用IDW插值法较优于kriging插值法。     

Comparison of probability distributions in extreme value analysis of rainfall and temperature data

Estimation of rainfall and temperature for a desired return period is a prerequisite for planning, design and operation of various hydraulic structures and for evaluation of technical and engineering appraisal of large infrastructure projects. This can be computed through Extreme Value Analysis (EVA) by fitting probability distributions to the annual series of 1-day maximum rainfall, maximum and minimum temperature. This paper details the study on adoption of Extreme Value Type-1, Extreme Value Type-2, 2-parameter Log Normal and Log Pearson Type-3 (LP3) probability distributions in EVA of rainfall and temperature for Hissar. Based on the applicability, standard parameter estimation procedures such as method of moments, maximum likelihood method (MLM) and order statistics approach are used for determination of parameters of distributions. The adequacy on fitting of probability distributions used in EVA of rainfall and temperature is evaluated by goodness-of-fit (GoF) tests, viz. Anderson–Darling and Kolmogorov–Smirnov and diagnostic test using D-index. The GoF and diagnostic tests results suggest the LP3 (MLM) is better suited amongst four probability distributions adopted in EVA of rainfall and temperature for Hissar.     

基于台湾集集地震数据的CPT与SPT液化判别方法比较

利用集集地震静力触探试验(CPT)数据,对基于CPT测试的Robertson液化判别方法和Olsen方法进行了检验,两个方法对液化点判别成功率分别为82.61%和80.43%,对非液化点判别成功率分别为31.82%和44.32%。CPT液化判别方法对液化点判别基本可靠,但对非液化点判别准确性较差。对集集地震标准贯入试验(SPT)数据,美国地震工程研究中心(NCEER)推荐的SPT液化判别方法对液化点和非液化点判别成功率分别达到92.41%和94.35%。SPT方法判别成功率非常高,整体准确性远高于CPT方法。另一方面,CPT的土分类图可以同时反映土的种类与强度,甚至可以对集集地震液化土与非液化土进行区分。对于细粒土的液化初判,CPT土分类图也优于SPT方法中的黏粒含量指标。因此,土分类图是CPT的优势所在。     

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.     

Non-parametric machine learning methods for interpolation of spatially varying non-stationary and non-Gaussian geotechnical properties

Spatial interpolation has been frequently encountered in earth sciences and engineering.A reasonable appraisal of subsurface heterogeneity plays a significant role in planning,risk assessment and decision making for geotechnical practice.Geostatistics is commonly used to interpolate spatially varying properties at un-sampled locations from scatter measurements.However,successful application of classic geostatistical models requires prior characterization of spatial auto-correlation structures,which poses a great challenge for unexperienced engineers,particularly when only limited measurements are available.Data-driven machine learning methods,such as radial basis function network(RBFN),require minimal human intervention and provide effective alternatives for spatial interpolation of non-stationary and non-Gaussian data,particularly when measurements are sparse.Conventional RBFN,however,is direction independent(i.e.isotropic)and cannot quantify prediction uncertainty in spatial interpolation.In this study,an ensemble RBFN method is proposed that not only allows geotechnical anisotropy to be properly incorporated,but also quantifies uncertainty in spatial interpolation.The proposed method is illustrated using numerical examples of cone penetration test(CPT)data,which involve interpolation of a 2D CPT cross-section from limited continuous 1D CPT soundings in the vertical direction.In addition,a comparative study is performed to benchmark the proposed ensemble RBFN with two other non-parametric data-driven approaches,namely,Multiple Point Statistics(MPS)and Bayesian Compressive Sensing(BCS).The results reveal that the proposed ensemble RBFN provides a better estimation of spatial patterns and associated prediction uncertainty at un-sampled locations when a reasonable amount of data is available as input.Moreover,the prediction accuracy of all the three methods improves as the number of measurements increases,and vice versa.It is also found that BCS prediction is less sensitive to the number of measurement data and outperforms RBFN and MPS when only limited point observations are available.     

Comparison of mineralization pattern of geochemical data in spatial and position-scale domain using new DWT- PCA approach

In the current research to determine the mineralization pattern and discuss the mineralization components, the information of position - scale domain of geochemical data has been analyzed. A new method is proposed based on coupling discrete wavelet transforms (DWT) and principal component analysis (PCA) for mineralization elements forecasting applications. The results of this study indicate the potential of DWT–PCA method for geochemical data processing. Wavelet transform (WT), as a multi-spectral analysis method, can decompose the spatial and temporal signals into different frequencies. The features of mineralization can be identified using the position - scale domain of geochemical data that may not be achievable in spatial domain. The geochemical data from the Dalli region have been processed in the spatial domain using PCA. The surface geochemical data of 30 elements have been transformed to position–scale domain using two-dimensional discrete wavelet transform (2DDWT). Wavelet functions (WFs) of Haar, Coiflet2, Biorthogonal3.3 and Symlet7 have been applied separately to decompose the geochemical data to high and low frequencies in one level. To obtain more accurate and complete information of mineralization, a new index has been presented based on wavelet coefficients. Based on this new index, significant results have been obtained by using PCA of the index. The coefficients distribution map (CDM) as a new exploratory criterion has been generated based on 2DDWT to show the geochemical distribution map (GDM). Finally, the results of WT have been compared with the results of spatial domain and the best method of wavelet for interpretation of geochemical data has been introduced. The results of geochemical data analysis by DWT–PCA approach have been confirmed by the exploratory drillings in the study area.     

高温高压下岩石电导率不同测量方法的实验对比——以二辉橄榄岩为例

利用YJ-3000t紧装式六面顶压机,在1.0GPa、500～900℃和2.0GPa、500～950℃的实验条件下,同时采用交流阻抗谱法(AC0.05～10-6Hz)、直流法(DC)和单频交流法(0.1Hz)三种方法对采自河北大麻坪玄武岩中的尖晶石二辉橄榄岩进行了电导率的实验测量.实验结果表明:在实验的温度和压力范围内...     

Geostatistical modelling of spatial and depth variability of SPT data for Bangalore

The purpose of this study is to develop a geostatistical model to evaluate the spatial and depth variability of Standard Penetration Test (SPT) data from Bangalore, India. The database consists of 766 boreholes spread over a 220 km² area, with several SPT values (N) in each of them. The geostatistical analysis is done for N corrected (N _corrected) values. The N _corrected value has been corrected for different parameters such as overburden stress, size of the bore hole, type of sampler, hammer energy and length of the connecting rod. The knowledge of the semivariogram of the SPT data is used with kriging theory to estimate the values at points in the subsurface of Bangalore where field measurements are not available. The model is used to compute the variance of estimated data. The model predicts reasonably well the SPT data. The geostatistical model provides valuable results that can be used for seismic hazard analysis, site response and liquefaction studies for the development of microzonation maps. The predicted N values from the developed model can also be used to estimate the subsurface information, allowable bearing pressure of soils and elastic modulus of soils.     

From registration to visualization of geological data with MO2GEO: applications of the FieldModule,interpolation methods,and computer visualization

The registration and screen visualization of geological data are basic steps in modelling of 3D structures and geoscientific processes. Therefore, a standardized coding of geological field observations is reasonable to enable an independent modelling approach. The field module of MO2GEO provides diverse standards for data coding and was tested in the frame of a mapping project. The visualization of profiles and the construction of cross-sections improve the database for a variety of interpolation methods. The subsequently developed 3D models, constructed with diverse modelling tools, can be visualized and serve as the base for diverse applied scientific projects.