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Relating crop yield to soil properties at the field scale is a tool in site-specific management planning for sustainable crop production. This study investigated the relationship between some soil ...

Application of geostatistical techniques when evaluating mineral deposits could reflect some geological characteristics which help through the stage of planning and production. In the present study, an attempt has been done on two phosphate deposits at Elsebaiya area on both sides of the River Nile namely: Um Tondoba mine at Elsebaiya East area and Western River Nile mine in Elsebaiya West area. Depending on the available data, statistical analysis illustrated differences in the distribution of P2O5 % and ore thickness within the studied areas. Geostatistics used to start with constructing variograms for P2O5 % and thickness for the two phosphate deposits to be used with ordinary kriging models, also constructing cross variograms between P2O5 % and thickness to be used with cokriging models where the difference in the variogram parameters reflected a specific variation for each deposit horizontally and vertically. The ordinary kriging models and cokriging models illustrated different distribution behavior through both the two kriging techniques.

In this article, we propose an algorithm that generates data on the entire sphere following the given axially symmetric covariance. We demonstrate that the axially symmetric data can be decomposed ...

Site characterization based on measurements is essential for geological and geotechnical engineering. However, measurements are usually limited and sparse because of many limitations, which can hardly be utilized to perform a well site characterization. Therefore, some data fusion methods are commonly utilized to integrate correlated data to improve the performance of site characterization. Among data fusion methods, cokriging is widely utilized to improve the performance of site characterization by integrating measurements of correlated variables. The correlation between correlated variables is expressed by a cross-variogram, which can only be calculated using co-located measurements between correlated variables. However, the measurements in geological and geotechnical engineering are commonly obtained by destructive sampling, which are usually not co-located and cannot be utilized to calculate the cross-variogram. In this study, a Bayesian inference method is developed to tackle this difficulty. The proposed method is illustrated and validated by two real datasets. The results show that the proposed method can estimate a well cross-variogram model, no matter whether the measurements of correlated variables are co-located or not. Moreover, the uncertainty of variogram models and cokriging estimation can be quantified by the proposed method. The proposed method can improve the wide utilization of the cokriging method, which can help characterize geology conditions of geological and geotechnical engineering.

Having in mind the importance of knowing the variability and spatial correlation of soil properties in Indian Dark Earth (IDE), we evaluated in this study the variability and the spatial correlation of aggregates and carbon in an Ultisol under coffee cultivation in southern Amazonas. It was established a 48 x 88 m sampling grid spaced 06 x 08 m, totalling 88 sampling points. Then soil samples were collected at: 0.0-0.05, 0.05-0.10, and 0.10-0.20 m layers. The spatial variability of the Mean Weighted Diameter (MWD) attributes, aggregates > 2 mm, < 2 mm, bulk density (BD) and organic carbon (OC) was analyzed by adjusting the simple semivariograms, while spatial correlations of the OC with aggregates and BD were analyzed by cross-semivariogram. We could conclude that there was spatial dependence in the variables, wherein the largest ones were observed at 0.0-0.05 m, except for Mean Weighted Diameter (MWD) and aggregates greater than 2.00 mm with larger range of values in depth from 0.05-0.10 and 0.10-0.20 m. The mean weight diameter and aggregate class attributes greater than 2.00 mm had negative spatial correlation with organic carbon at 0.0-0.05 m, while the smaller aggregates than 2.00 mm classes and bulk density correlated positively with organic carbon at 0.0-0.05 m and 0.10-0.20 m. Considerando a importância do conhecimento da variabilidade e correlação espacial dos atributos do solo em Terra Preta de Índio (TPIs), avaliou-se neste trabalho a variabilidade e a correlação espacial de agregados e carbono em um Argissolo Amarelo eutrófico sob cultivo de café na região sul do Amazonas. Foi estabelecido um grid amostral com dimensões de 48 x 88 m e espaçamentos de 06 x 08 m, totalizando 88 pontos amostrais. Em seguida, foram coletadas amostras de solos nas profundidades: 0,0-0,05, 0,05-0,10, e 0,10-0,20 m. A variabilidade espacial dos atributos diâmetro médio ponderado (DMP), agregados > 2 mm, < 2 mm, densidade do solo (Ds) e carbono orgânico (CO) foi analisada ajustando os semivariogramas simples, enquanto as correlações espaciais do CO com agregados e Ds foram analisadas por semivariogramas cruzados. Concluiu-se que as variáveis apresentaram dependência espacial, e os maiores alcances são constatados na profundidade 0,0-0,05 m, exceto para DMP e agregados maiores que 2,00 mm com maioresvalores de alcance na profundidade 0,05-0,10 e 0,10-0,20 m. Os atributos diâmetro médio ponderado e classes de agregados maior que 2,00 mm apresentam correlação espacial negativa com carbono orgânico na profundidade 0,0-0,05 m, enquanto a classes de agregados menor que 2,00 mm e densidade do solo apresentam correlação positiva com carbono orgânico nas profundidades 0,0-0,05 m e 0,10-0,20 m.

Estimation of core permeability as a function of well logging records is a pivotal task in reservoir characterization as it is affected by data sparseness, distinct scales, various sources, and lithology structures. In this paper, three specific dataset formats of a well logging and core measurements were considered for the core permeability modeling and prediction using the ordinary kriging and co-kriging interpolation algorithms. The dataset were used as complete dataset of multi-facies and two split datasets of shale- and sand-based measurements. More specifically, the ordinary kriging was first used to model the core permeability as a function of the well logging records only through constructing the variogram given the three mentioned dataset types. The same procedure was repeated by adopting the cross-variogram approach to model the core permeability as a primary factor along with core porosity as a secondary factor, both as a function of the well logging records. The well logging attributes, which were obtained from an oil well, include neutron porosity and water saturation given the well depth. The cross-variogram was adopted to quantify the dissimilarity between known and unknown data for the co-kriging interpolation to provide linear coregionalization modeling of core permeability and porosity. The modeling and prediction accuracy of core permeability through the ordinary kriging and co-kriging algorithms were assessed by applying the leave-one-out cross validation in addition to the visual mismatch between predicted and observed core permeability. Results illustrated that co-kriging interpolation led to a more accurate permeability prediction than the ordinary kriging by achieving the highest adjusted R-square and the lowest root mean square error. The integrated workflow of ordinary kriging and co-kriging was applied on a dataset from an oil well in sandstone reservoir in the South Rumaila oil field, Southern Iraq.

A mammogram is an examination of the breast intended to prevent and diagnose breast cancer. In this work we propose a methodology for detecting masses by determining certain asymmetric regions between pairs of mammograms of the left and the right breast. The asymmetric regions are detected by means of structural variations between corresponding regions, defined by a spatial descriptor called cross-variogram function. After determining the asymmetric regions of a pair of images, the variogram function is applied to each asymmetric region separately, for classification as either mass or non-mass. The first stage of the methodology consists in preprocessing the images to make them adequate for registration. The following step performs the bilateral registration of pairs of left and right breasts. Pairs of corresponding regions are listed and their variations are measured by means of the cross-variogram spatial descriptor. Next, a model is created to train a Support Vector Machine (SVM) using the values of the cross-variogram function of each pair of windows as features. The pairs of breasts containing lesions are classified as asymmetric regions; the remaining ones are classified as symmetric regions. From the asymmetric regions, features are extracted from the variogram function to be used as tissue texture descriptors. The regions containing masses are classified as mass regions, and the other ones as non-mass regions. Stepwise linear discriminant analysis is used to select the most statistically significant features. Tests are performed with new cases for the final classification as either mass or non-mass by the trained SVM. The best results presented in the final classification were 96.38% of accuracy, 100% of sensitivity and 95.34% of specificity. The worst case presented 70.21% of accuracy, 100% of sensitivity and 67.56% of specificity. The average values for all tests were 90.26% of accuracy, 100% of sensitivity and 85.37% of specificity.

RESUMO Estimar a variabilidade espacial de uma cultura em campo auxilia no entendimento de alguns fenômenos que podem estar correlacionados com a sua produtividade. Este trabalho teve, como objetivo, estimar, pelo método da cokrigagem, a produtividade (kg ha-1) do cafeeiro conilon, em três safras consecutivas, tendo como covariável o número de ramos produtivos (plagiotrópicos) por planta. No centro de uma lavoura, demarcou-se uma malha amostral, com 109 pontos georreferenciados, sendo cada ponto constituído por cinco plantas. Fez-se o acompanhamento, por três safras agrícolas consecutivas, contando-se os ramos produtivos e realizando-se a colheita manual. O café colhido foi separado, para cada estádio de maturação (cereja, verdoengo e verde), e secado para umidade padrão de 12%, determinando-se a produtividade de café seco em coco (kg ha-1). A produtividade e o número de ramos produtivos por ponto amostral apresentaram correlação linear e dependência espacial nas três safras. As estimativas das produtividades pelos números de ramos produtivos apresentaram similaridades na cokrigagem, mostrando ser a covariável ramo produtivo eficiente na estimativa da produtividade do café conilon (Robusta Tropical). ABSTRACT Knowing the spatial variability of a coffee-crop helps to understand some phenomena which can be correlated with its productivity. This study aimed estimating the productivity (kg ha-1) of conilon coffee-tree in three consecutive harvests using the cokriging method, considering the number of plagiotropic branches per plant as a covariable. In the center of a coffee-crop we settled a sampling grid with 109 georeferenced points, each point consisted of five plants. Subsequently the vegetative branches were counted for three consecutive growing seasons before performing manual harvesting of coffee beans. The harvested coffee was separated into cherry, yellowish and green and dried to a standard moisture of 12%. The dry cherry productivity (kg ha-1) was then evaluated. The three harvests evaluated showed linear correlation and spatial dependence among productivity and number of vegetative branches. Estimation of productivity by the number of vegetative branches showed similarities in cokriging, indicating that vegetative branches is an efficient covariable to estimate conilon coffee productivity.

Most existing multi-temporal classification studies use spectral information alone and ignore the temporal correlation between two-date images. This article proposes a new method to characterize the local temporal correlation using multi-temporal texture measured with a geostatistical function called the pseudo cross variogram (PCV). The derived multi-temporal texture, as an additional band, was combined with the spectral information in multi-temporal classification. The performance of the multi-temporal texture was evaluated and compared with the use of multi-temporal spectral data alone and plus the traditional variogram texture in land cover classification using bitemporal hyperspectral Compact High Resolution Imaging Spectrometer/Project for On Board Autonomy (CHRIS/PROBA) images. The results show that although land cover classification using spectral information from bitemporal CHRIS/PROBA data alone had an acceptable overall accuracy of 85.66%, the inclusion of multi-temporal texture in land cover c...

We present and test a new method to correct for the static shift affecting magnetotelluric (MT) apparent resistivity sounding curves. We use geostatistical analysis of apparent resistivity and phase data for selected periods. For each period, we first estimate and model the experimental variograms and cross variogram between phase and apparent resistivity. We then use the geostatistical model to estimate, by cokriging, the corrected apparent resistivities using the measured phases and apparent resistivities. The static shift factor is obtained as the difference between the logarithm of the corrected and measured apparent resistivities. We retain as final static shift estimates the ones for the period displaying the best correlation with the estimates at all periods. We present a 3D synthetic case study showing that the static shift is retrieved quite precisely when the static shift factors are uniformly distributed around zero. If the static shift distribution has a nonzero mean, we obtained best results when an apparent resistivity data subset can be identified a priori as unaffected by static shift and cokriging is done using only this subset. The method has been successfully tested on the synthetic COPROD-2S2 2D MT data set and on a 3D-survey data set from Las Cañadas Caldera (Tenerife, Canary Islands) severely affected by static shift.

The variance-based cross-variogram between two spatial processes, Z1 (·) and Z2 (·), is var (Z1 ( u ) − Z2 ( v )), expressed generally as a bivariate function of spatial locations uandv. It characterizes the cross-spatial dependence between Z1 (·) and Z2 (·) and can be used to obtain optimal multivariable predictors (cokriging). It has also been called the pseudo cross-variogram; here we compare its properties to that of the traditional (covariance-based) cross-variogram, cov (Z1 ( u ) − Z1 ( v ), Z2 ( u ) − Z2 ( v )). One concern with the variance-based cross-variogram has been that Z1 (·) and Z2 (·) might be measured in different units (“apples” and “oranges”). In this note, we show that the cokriging predictor based on variance-based cross-variograms can handle any units used for Z1 (·) and Z2 (·); recommendations are given for an appropriate choice of units. We review the differences between the variance-based cross-variogram and the covariance-based cross-variogram and conclude that the former is more appropriate for cokriging. In practice, one often assumes that variograms and cross-variograms are functions of uandv only through the difference u − v. This restricts the types of models that might be fitted to measures of cross-spatial dependence.

Submitted by Guilherme Lemeszenski (guilherme@nead.unesp.br) on 2013-08-22T19:01:18Z No. of bitstreams: 1 S0103-90162013000300008.pdf: 1075726 bytes, checksum: 350db5e96da54d1ae5772bd485ea8bdd (MD5) Made available in DSpace on 2013-08-22T19:01:18Z (GMT). No. of bitstreams: 1 S0103-90162013000300008.pdf: 1075726 bytes, checksum: 350db5e96da54d1ae5772bd485ea8bdd (MD5) Previous issue date: 2013-06-01 Made available in DSpace on 2013-09-30T20:01:11Z (GMT). No. of bitstreams: 2 S0103-90162013000300008.pdf: 1075726 bytes, checksum: 350db5e96da54d1ae5772bd485ea8bdd (MD5) S0103-90162013000300008.pdf.txt: 32928 bytes, checksum: b7dc84a9ce8cd0a3fa163634c029a6ea (MD5) Previous issue date: 2013-06-01 Submitted by Vitor Silverio Rodrigues (vitorsrodrigues@reitoria.unesp.br) on 2014-05-20T13:13:31Z No. of bitstreams: 2 S0103-90162013000300008.pdf: 1075726 bytes, checksum: 350db5e96da54d1ae5772bd485ea8bdd (MD5) S0103-90162013000300008.pdf.txt: 32928 bytes, checksum: b7dc84a9ce8cd0a3fa163634c029a6ea (MD5) Made available in DSpace on 2014-05-20T13:13:31Z (GMT). No. of bitstreams: 2 S0103-90162013000300008.pdf: 1075726 bytes, checksum: 350db5e96da54d1ae5772bd485ea8bdd (MD5) S0103-90162013000300008.pdf.txt: 32928 bytes, checksum: b7dc84a9ce8cd0a3fa163634c029a6ea (MD5) Previous issue date: 2013-06-01 Soil CO2 emission (FCO2) is governed by the inherent properties of the soil, such as bulk density (BD). Mapping of FCO2 allows the evaluation and identification of areas with different accumulation potential of carbon. However, FCO2 mapping over larger areas is not feasible due to the period required for evaluation. This study aimed to assess the quality of FCO2 spatial estimates using values of BD as secondary information. FCO2 and BD were evaluated on a regular sampling grid of 60 m × 60 m comprising 141 points, which was established on a sugarcane area. Four scenarios were defined according to the proportion of the number of sampling points of FCO2 to those of BD. For these scenarios, 67 (F67), 87 (F87), 107 (F107) and 127 (F127) FCO2 sampling points were used in addition to 127 BD sampling points used as supplementary information. The use of additional information from the BD provided an increase in the accuracy of the estimates only in the F107, F67 and F87 scenarios, respectively. The F87 scenario, with the approximate ratio between the FCO2 and BD of 1.00:1.50, presented the best relative improvement in the quality of estimates, thereby indicating that the BD should be sampled at a density 1.5 time greater than that applied for the FCO2. This procedure avoided problems related to the high temporal variability associated with FCO2, which enabled the mapping of this variable to be elaborated in large areas. UNESP FCAV Depto. de Ciências Exatas USP ESALQ Depto. de Ciência do Solo UNESP FCAV Depto. de Ciências Exatas

In remote sensing is the concept of spectral signature in multispectral imagery to recognize different land uses in the area; This study proposes the existence of a characteristic variogram for land use in multispectral images. To test this idea we proceeded to work with a sector of a scene image of multispectral Landsat 7 ETM +, in 6 of their bands (1- 450nm to 520nm, 2 - 520nm to 600nm, 3 - 630nm to 690nm, 4 - 760nm to 900nm 5 - over 1550nm to 1.750nm and 7 - 2.080nm to 2.350nm), corresponding to two uses of urban land and agricultural, the omnidirectional variogram for each band was analyzed and modal variogram for each land use was established in the stripe set. Of the analyzed claims data for each land use is a model characteristic and modal cross variogram how their wavelengths.

Buscou-se, com este estudo, estimar, através do uso da cokrigagem, valores de cálcio e magnésio, utilizando como co-variável valores de pH. Em um Latossolo Vermelho Distroférrico, amostrou-se a camada de 0-0,2 m, utilizando grade de 25 x 25 m, totalizando 60 pontos em 5,02 ha. Realizou-se, inicialmente, análise descritiva e de correlação e, posteriormente, análise geoestatística, para identificar a dependência espacial das variáveis estudadas. Utilizando a técnica da cokrigagem, os valores de pH mostraram-se eficientes na estimativa de teores de Ca e Mg, devido a elevada correlação entre esta co-variável e as variáveis a serem estimadas. Sought, with this study, estimating, through the use of cokriging, values of calcium and magnesium, using as covariate pH. In a Rhodic Hapludox, sampled the layer of 0-0.2 m, using the grid of 25 x 25 m, totaling 60 points from 5.02 ha. Was held, initially, descriptive analysis and correlation, and subsequently, geostatistical analysis to identify the spatial dependence of variables. Using the technique of cokriging pH values were efficient in the estimation of Ca and Mg, due to the high correlation between this covariate and the variables to be estimated.

Made available in DSpace on 2016-04-01T18:46:40Z (GMT). No. of bitstreams: 0 Previous issue date: 2011. Added 1 bitstream(s) on 2016-04-01T18:51:28Z : No. of bitstreams: 1 ISSN0102-9169-2011-26-01-113-125.pdf: 371033 bytes, checksum: 7f0d372b92cbf2fa8d3c7eea3ddafbdd (MD5) O objetivo do trabalho foi estimar a produtividade (PRODUT) em sc/ha do café conilon por meio da técnica de cokrigagem, usando como covariável a produção de café úmido (PROD) em kg e comparar o resultado com a estimativa obtida por krigagem ordinária. O estudo foi realizado em uma área comercial de café conilon, Coffea canephora Pierre, Var. conilon, localizada no Município de São Mateus-Espirito Santo. Para o trabalho foi amostrada em campo a produção de café úmido numa malha amostral irregular de 18,5 ha, totalizando 87 pontos amostrais. Foram determinados também, a produção de café seco e café pilado a 12% de umidade, para obtenção da variável PRODUT. Após a análise exploratória dos dados, que mostrou a correlação entre as variáveis da ordem de 0,899, foi efetuada a análise variográfica. Foram ajustados variogramas teóricos para PROD e PRODUT e variograma cruzado entre as duas variáveis. Finalmente foram estimados os valores para PRODUT, tanto por krigagem ordinária como por cokrigagem. A validação da estimativa por cokrigagem não mostrou, porém, ganhos significativos em relação à validação por krigagem ordinária. The objective of this research was to estimate the productivity (PRODUT) in sc/ha of coffee conilon through the technique of cokriging, using as covariate the production of humid coffee (PROD) in kg and compare the results with estimates obtained by kriging ordinary. The study was conducted in a commercial area of conilon coffee, Coffea canephora Pierre var. conilon, located in São Mateus Municipality, Espirito Santo State. For the field work was sampled the humid coffee production in a sampling grid irregular of 18.5 ha, 87 sampling points in the total. We also determined the production of dry coffee beans and coffee benefited 12% moisture, to obtain the PRODUT variable. After exploratory data analysis, which showed the correlation between variables in the order of 0.899, was performed variogram analysis. Were adjusted theoretical variograms to PROD and PRODUT and cross variogram between two variables. Finally we estimated the value of productivity, both by ordinary kriging as per cokriging. The validation of the estimation by cokriging not shows, however, significant gains in relation to validation by ordinary kriging. Universidade Estadual Paulista Júlio de Mesquita Filho, Departamento de Solos e Recursos Ambientais, Faculdade de Ciências Agronômicas de Botucatu, Botucatu, Fazenda Experimental Lageado - Rua José Barbosa de Barros, nº1780, Lageado, CEP 18610307, SP, Brasil Universidade Estadual Paulista Júlio de Mesquita Filho, Departamento de Geologia Aplicada, Instituto de Geociências e Ciências Exatas (IGCE), Rua da Tranquilidade, n 120 - Recanto Azul, Botucatu-SP, CEP: 18603-010, SP, Brasil Universidade Estadual Paulista Júlio de Mesquita Filho, Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas de Botucatu, Botucatu, Fazenda Experimental Lageado - Rua José Barbosa de Barros, nº1780, Lageado, CEP 18610307, SP, Brasil Universidade Estadual Paulista Júlio de Mesquita Filho, Departamento de Solos e Recursos Ambientais, Faculdade de Ciências Agronômicas de Botucatu, Botucatu, Fazenda Experimental Lageado - Rua José Barbosa de Barros, nº1780, Lageado, CEP 18610307, SP, Brasil Universidade Estadual Paulista Júlio de Mesquita Filho, Departamento de Geologia Aplicada, Instituto de Geociências e Ciências Exatas (IGCE), Rua da Tranquilidade, n 120 - Recanto Azul, Botucatu-SP, CEP: 18603-010, SP, Brasil Universidade Estadual Paulista Júlio de Mesquita Filho, Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas de Botucatu, Botucatu, Fazenda Experimental Lageado - Rua José Barbosa de Barros, nº1780, Lageado, CEP 18610307, SP, Brasil

The assessment of potential volcanic eruptions is a critical aspect when evaluating the safety of populated areas. A stochastic approach has been developed for the analysis and simulation of data sampled at active volcanoes. This approach allows for the detection and quantification of time correlation, cross correlation and volcanic tremor decomposition in order to identify potential precursors of major eruptions. The stochastic approach has been applied to data gathered at Stromboli volcano. Multivariate kriging analysis of the tremor intensity and the number of events per day has enabled time component estimation which could furnish additional monitoring variables for the forecast of paroxysmal phases at Stromboli, i.e., phases of significantly increased energy output. This multivariate approach is sufficiently general to be applicable to the analysis of multiparametric datasets recorded at volcanoes with different types of eruptive behaviour.