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Ecological Indicators - Multivariate indicator Kriging approach using a GIS to classify soil degradation for Mediterranean agricultural lands

Ecological Indicators - Multivariate indicator Kriging approach using a GIS to classify soil degradation for Mediterranean agricultural lands | Geostatistics | Scoop.it

Land evaluation is sensitive to the effects of variability of ecologically complex phenomena. A probability map incorporating some of these phenomena is proposed to account for local uncertainty of areas affected by soil degradation in the Apennines of south Italy. To be useful, a method for assessing soil degradation should integrate several kinds of data. We present here an overview of the geostatistical approach to solving this problem: non-linear estimation. The following factors have been considered: the soil erosion by water (geomorphologic indicator), the station aridity (bioclimate indicator), and top-soil depth (pedologic indicator). We convert the continuous data values of each variable at each location using a binary variable indicator transform based on critical thresholds. The indicator transform values for individual variables are then integrated to form multiple variable indicator transform (MVIT) to evaluate the degree of soil degradation. Areas suited to soil degradation maps delineated by geographical information system (GIS), showed that the joint probabilities of meeting specific criteria indicator Kriging were influenced by the critical threshold values used to transform each individual variable and the method of integration. So, the understanding of soil vulnerability to degradation is increased to providing a way to classify degraded regions. On the basis of this information different land uses strategies could be identified to develop sustainable assessment models of soils. For example, many countries of these disadvantaged areas, should have agro-forestation programmes that increase the heterogeneity in vegetation cover contrasting hydrological properties, thus promoting a self-regulating system for runoff and erosional soil degradation control.

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Spatial structure of soil texture and its influence on spatial variability of nitrate leaching

Spatial structure of soil texture and its influence on spatial variability of nitrate leaching | Geostatistics | Scoop.it
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Field scale variability of soil texture can influence crop yield and movement of soil water in the field. The objective of this study was to investigate the spatial structure of soil texture in relation to the variability of nitrate-N leaching using geostatistics. Soil textural fractions showed strong spatial autocorrelations from surface to 60 cm depth. Random variability of soil texture increased with depth. Soil water content, as well as total carbon, total nitrogen and soil organic carbon of top 15 cm, also showed spatial autocorrelations similar to soil texture. Elevation, relative slope position and vertical distance to channel network showed significant influence on the distribution of soil texture. Soil texture at 90 cm depth correlated best with cumulative percolated water and cumulative nitrate leached in field lysimeters. Our results showed that soil layers with low hydraulic conductivity control the water and nitrate movement through the soil profile.

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T-9: Variogram Modeller Part 3 - Geostatistics Kitchen

T-9: Variogram Modeller Part 3 - Geostatistics Kitchen | Geostatistics | Scoop.it
“In this website you will find a set of Mathematica Notebooks & Packages developed by grad students of the portuguese research center CERENA.”
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ESTUDO SOBRE A UTILIZAÇÃO ADEQUADA DA KRIGAGEM NA REPRESENTAÇÃO COMPUTACIONAL DE SUPERFÍCIES BATIMÉTRICAS

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Model generalization of two different drainage patterns by self-organizing maps

Model generalization of two different drainage patterns by self-organizing maps | Geostatistics | Scoop.it

In this study, we develop a new method using self-organizing maps (SOMs) for the selection of hydrographic model generalization. The most suitable attributes of the stream objects are used as input variables to the SOM. The attributes were weighted using Pearson’s chi-square independence test. We used the Radical Law to determine how many features should be selected, and an incremental approach was developed to determine which clusters should be selected from the SOM. Two drainage patterns (dendritic and modified basic) were obtained from the National Hydrography Datasets of United States Geological Survey at 1:24,000-scale (high resolution) and used in order to derive stream networks at 1:100,000-scale (medium resolution). The 1:100,000-scale stream networks, derived in accordance with the proposed approach, are similar to those in the original maps in both quantity and visual aspects. Stream density and pattern were maintained in each subunit, and continuous and semantically correct networks were obtained.

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Influence of incorporating geometric anisotropy on the construction of thematic maps of simulated data and chemical attributes of soil

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Spatial Variability of Heavy Metals in the Soils of Ahwaz Using Geostatistical Methods

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PLOS ONE: Spatial Distribution of Soil Organic Carbon and Total Nitrogen Based on GIS and Geostatistics in a Small Watershed in a Hilly Area of Northern China

PLOS ONE: Spatial Distribution of Soil Organic Carbon and Total Nitrogen Based on GIS and Geostatistics in a Small Watershed in a Hilly Area of Northern China | Geostatistics | Scoop.it

The spatial variability of soil organic carbon (SOC) and total nitrogen (STN) levels is important in both global carbon-nitrogen cycle and climate change research. There has been little research on the spatial distribution of SOC and STN at the watershed scale based on geographic information systems (GIS) and geostatistics. Ninety-seven soil samples taken at depths of 0–20 cm were collected during October 2010 and 2011 from the Matiyu small watershed (4.2 km2) of a hilly area in Shandong Province, northern China. The impacts of different land use types, elevation, vegetation coverage and other factors on SOC and STN spatial distributions were examined using GIS and a geostatistical method, regression-kriging. The results show that the concentration variations of SOC and STN in the Matiyu small watershed were moderate variation based on the mean, median, minimum and maximum, and the coefficients of variation (CV). Residual values of SOC and STN had moderate spatial autocorrelations, and the Nugget/Sill were 0.2% and 0.1%, respectively. Distribution maps of regression-kriging revealed that both SOC and STN concentrations in the Matiyu watershed decreased from southeast to northwest. This result was similar to the watershed DEM trend and significantly correlated with land use type, elevation and aspect. SOC and STN predictions with the regression-kriging method were more accurate than those obtained using ordinary kriging. This research indicates that geostatistical characteristics of SOC and STN concentrations in the watershed were closely related to both land-use type and spatial topographic structure and that regression-kriging is suitable for investigating the spatial distributions of SOC and STN in the complex topography of the watershed.

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WHAT IS GEOSTATISTICS? | statistics for all

WHAT IS GEOSTATISTICS? | statistics for all | Geostatistics | Scoop.it
You need not be a statistician to make good use of geostatistics, but you may need the assistance, support, guidance of a (geo?)statistician. A good engineer, ecologist, biologist, plant scientist, hydrologist, soil physicist already has a good ...
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Reservoir water quality monitoring using Landsat TM images and indicator kriging

Reservoir water quality monitoring using Landsat TM images and indicator kriging | Geostatistics | Scoop.it

Landsat TM images were used to study the trophic states of Te-Chi Reservoir located in central Taiwan. Water quality parameters such as total phoshate, ChlorophyII a concentration and secchi disk depth are found highly correlated with spectral parameters derived from Landsat TM images. Carlson trophic state index is adopted for evaluation of reservoir trophic states. The reservoir trophic states determined from field data and from satellite image are highly consistent, indicating great potential of using satellite image for reservoir water quality investigation. 
- See more at: http://www.geospatialworld.net/Paper/Application/ArticleView.aspx?aid=1310#sthash.Rsk8Q31q.dpuf

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QGIS 2.0: Layout de Mapas – Parte 03

QGIS 2.0: Layout de Mapas – Parte 03 | Geostatistics | Scoop.it
Construtor de Consultas
Durante a produção de Layout de Mapas no Quantum GIS, você pode topar com elementos que atrapalham a visualização e não podem ser exibidos.
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Geostatistical Entropy for Texture Analysis: An Indicator Kriging Approach - Pham - 2013 - International Journal of Intelligent Systems - Wiley Online Library

Geostatistical Entropy for Texture Analysis: An Indicator Kriging Approach - Pham - 2013 - International Journal of Intelligent Systems - Wiley Online Library | Geostatistics | Scoop.it

'Texture analysis is a major research topic in intelligent image processing. Its useful applications, including object detection and classification, to various fields of engineering, science, medicine, biology, and creative arts have been increasingly reported. Texture is a fundamental aspect of human vision and perception by which different types of objects can be distinguished through their appearance, ranging from distinctive to subtle roughness. Given tremendous efforts in terms of both theoretical developments and applications, texture analysis still remains a challenging area of research in image analysis and pattern recognition. This paper presents a novel and practical image texture analysis method using the fundamentals of geostatistics and the concept of entropy in information theory. Experimental results on medical and document image data have shown the superior performance of the proposed approach over its related texture analysis methods.

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Characterization and delimitation of the terroir coffee in plantations in the municipal district of Araponga, Minas Gerais, Brazil

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AN INVESTIGATION OF ANNUAL RAINFALL SPATIAL DISTRIBUTION USING GEOSTATISTICAL METHODS (A CASE STUDY: QOM PROVINCE)

The rate of average precipitation specifically its regional average is one of the significant factors in the field of natural resources studies. There are different estimation methods to estimate the precipitation such as geostatistic technique. This method is important with concerning to correlation and data spatial structure. Spatial location of the samples can be analyzed with the purposed quantity together. In other word the relationship between different quantitative rates is required to the community, samples distance and their situation dime. This spatial relationship (distance and community) is possible to describe in mathematical method between the rates of quantities in sampled community. In this research Kriging and inverse distance methods with power of 1 to 3 used to investigate the annual precipitation rate in Qom province.

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Spatial Variability Analysis of Soil Nutrients Based on GIS and Geostatistics: A Case Study of Yisa Township, Yunnan, China

Spatial Variability Analysis of Soil Nutrients Based on GIS and Geostatistics: A Case Study of Yisa Township, Yunnan, China | Geostatistics | Scoop.it
Li Jing, Min Qingwen, Li Wenhua, Bai Yanying, Dhruba Bijaya G. C, and Yuan Zheng (2014) Spatial Variability Analysis of Soil Nutrients Based on GIS and Geostatistics: A Case Study of Yisa Township, Yunnan, China. Journal of Resources and Ecology: Vol. 5, No. 4, pp. 348-355. doi: http://dx.doi.org/10.5814/j.issn.1674-764x.2014.04.010
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GEOSTAT 2014 Bergen Day 4: Spatio-temporal geostatistics (screen 3)

“lecture by Ben Gräler http://www.geostat-course.org/Bergen_2014.”;
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Covariate selection with iterative principal component analysis for predicting physical soil properties

Covariate selection with iterative principal component analysis for predicting physical soil properties | Geostatistics | Scoop.it

Abstract

Local and regional soil data can be improved by coupling new digital soil mapping techniques with high resolution remote sensing products to quantify both spatial and absolute variation of soil properties. The objective of this research was to advance data-driven digital soil mapping techniques for the prediction of soil physical properties at high spatial resolution using auxiliary data in a semiarid ecosystem in southeastern Arizona, USA. An iterative principal component analysis (iPCA) data reduction routine of reflectance and elevation covariate layers was combined with a conditioned Latin Hypercube field sample design to effectively capture the variability of soil properties across the 6250 ha study area. We sampled 52 field sites by genetic horizon to a 30 cm depth and determined particle size distribution, percent coarse fragments, Munsell color, and loss on ignition. Comparison of prediction models of surface soil horizons using ordinary kriging and regression kriging indicated that ordinary kriging had greater predictive power; however, regression kriging using principal components of covariate data more effectively captured the spatial patterns of soil property–landscape relationships. Percent silt and soil redness rating had the smallest normalized mean square error and the largest correlation between observed and predicted values, whereas soil coarse fragments were the most difficult to predict. This research demonstrates the efficacy of coupling data reduction, sample design, and geostatistical techniques for effective spatial prediction of soil physical properties in a semiarid ecosystem. The approach applied here is flexible and data-driven, allows incorporation of wide variety of numerically continuous covariates, and provides accurate quantitative prediction of individual soil properties for improved land management decisions and ecosystem and hydrologic models.

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short announcement: new R learning material

short announcement: new R learning material | Geostatistics | Scoop.it

Coursera, hail to Coursera. Despite the uprising criticism on MOOCs and their footprint in the educational landscape at universities Coursera created an interesting R learning course. It is divided and scheduled for 4 weeks and has video-tutorials as well as written material. The guys over at RevolutionAnalytics packed it all together:

Content:
Setting working directory and getting helpHow to get helpData TypesSubsettingVectorized OperationsReading/Writing DataControl Structures in RWriting FunctionsAvoiding loops using xapplyPlottingRegular expressionsRegular expressions in RClasses and methods in R
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Spatial Distribution of Soil Organic Carbon and Total Nitrogen Based on GIS and Geostatistics in a Small Watershed in a Hilly Area of Northern China

Spatial Distribution of Soil Organic Carbon and Total Nitrogen Based on GIS and Geostatistics in a Small Watershed in a Hilly Area of Northern China | Geostatistics | Scoop.it
PLOS One, Published Online 31 December 2013 By Gao Peng, Wang Bing, Geng Guangpo, and Zhang Guangcan "The spatial variability of soil organic carbon (SOC) and total nitrogen (STN) levels is importa...
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On the estimation of scale of fluctuation in geostatistics

On the estimation of scale of fluctuation in geostatistics | Geostatistics | Scoop.it
(2014). On the estimation of scale of fluctuation in geostatistics. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards. ???aop.label???.
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Coleta, processamento e análise de dados batimétricos visando a representação computacional do relevo submerso utilizando interpoladores determinísticos e probabilísticos

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Predictive risk mapping of schistosomiasis in Brazil using Bayesian geostatistical models

Schistosomiasis is one of the most common parasitic diseases in tropical and subtropical areas, including Brazil. A national control programme has been instigated in Brazil in the mid-1970s and proved successful in terms of morbidity control, as the number of cases with hepato-splenic involvement has been reduced significantly. To consolidate control and move towards elimination, there is a need for reliable maps on the spatial distribution of schistosomiasis, so that interventions can target communities at highest risk. The purpose of this study was to map the distribution of Schistosoma mansoni in Brazil. We utilized readily available prevalence data from the national schistosomiasis control programme for the years 2005–2009, derived remotely sensed climatic and environmental data and obtained socioeconomic data from various sources. Data were collated into a geographical information system and Bayesian geostatistical models were developed. Model-based maps identified important risk factors related to the transmission of S. mansoni and confirmed that environmental variables are closely associated with indices of poverty. Our smoothed predictive risk map, including uncertainty, highlights priority areas for intervention, namely the northern parts of North and Southeast regions and the eastern part of Northeast region. Our predictive risk map provides a useful tool for to strengthen existing surveillance-response mechanisms.

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RELAÇÃO ENTRE NÚMERO DE AMOSTRAS E VARIABILIDADE ESPACIAL DA DENSIDADE DO SOLO EM ÁREA DE CANA-DE-AÇÚCAR

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R-GIS | > R-GIS

R-GIS | > R-GIS | Geostatistics | Scoop.it
Interesting R script for addressing an important aspect of ENM: errors in georreference http://t.co/5lNLLOsQet
#GIS #R #ENM #SDM
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A multivariate approach for anomaly separation of potentially toxic trace elements in urban and peri-urban soils

Purpose
Geogenic soil enrichment and anthropogenic pollution by potentially toxic trace elements (PTEs) are two processes acting together. Although it is often difficult, it is necessary to separate the two processes for risk assessment and understanding the environmental implications. The aim of this study was to analyse the soil concentrations of various PTEs in a southern Italy area in order to: (1) determine their different correlation structure to isolate sources of variation acting at different spatial scales and (2) to define potential anomalies based on the correlation structure.


Materials and methods
In the urban and peri-urban area of Cosenza-Rende, 149 topsoil samples were collected (0.10 m) and analysed for different elements by X-ray fluorescence spectrometry. Principal component analysis and factorial kriging analysis were used to map the spatial distribution of PTEs in topsoil and to identify the main factors influencing their spatial variability.


Results and discussion
Two groups of PTEs were identified: the first group included As, Pb and Zn; and the second one Al, Co, Cr, Fe, La, Nb, Ni, Ti and V. The first group was related to anthropogenic causes, while the second one was more related to parent rock composition. The regionalized factors at different scales of variability allowed to aggregate and summarize the joint variability in the PTEs and consider the probable causes of soil pollution.


Conclusions
The study allowed analysing and quantifying the sources (environmental or anthropogenic) of variation of PTEs acting at different spatial scale and defining the spatial anomalies based on the correlation structure associated at the different spatial scales.

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