Influence of incorporating geometric anisotropy on the construction of thematic maps of simulated data and chemical attributes of soil
The study on spatial variability of soil properties performed through geostatistical techniques allow us to identify the spatial distribution of phenomena by means of a spatial model that considers degree of dependence among observed data, depending on distance and also the direction that separate t...
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Veröffentlicht in: | Chilean journal of agricultural research 2013-10, Vol.73 (4), p.414-423 |
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creator | Guedes, Luciana Pagliosa Carvalho Uribe-Opazo, Miguel Angel Ribeiro, Paulo Justiniano |
description | The study on spatial variability of soil properties performed through geostatistical techniques allow us to identify the spatial distribution of phenomena by means of a spatial model that considers degree of dependence among observed data, depending on distance and also the direction that separate them, if there is geometric anisotropy, in other words, a directional trend in spatial continuity. However, the main difficulty in decision making regarding the use of anisotropic spatial model focuses on its relevance to the parameters that express the geometric anisotropy in a spatial model exercise in relation to the estimation space. This study aims at identifying the degree of influence of geometric anisotropy on the accuracy of spatial estimation using simulated data sets with different sample sizes and soil chemical properties such as: Fe, potential acidity (H + Al), organic matter and Mn. Comparing the isotropic and anisotropic models, especially for smaller sample sizes (100 and 169) showed an increased sum of squares of differences between predictions anisotropy F^sub α^ctor (F^sub α^) equals 2. Furthermore, from F^sub α^ equals 2.5, over 50% of the simulations showed values of overall accuracy (OA) of less than 0.80 and values for the concordance index Kappa (K) and Tau (T) from 0.67 to 0.80, indicating differences between thematic maps. Similar conclusions were obtained for chemical properties of the soil, from F^sub α^ equals 2, showing that there are relevant differences regarding the inclusion or not of geometric anisotropy. [PUBLICATION ABSTRACT] |
doi_str_mv | 10.4067/S0718-58392013000400013 |
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However, the main difficulty in decision making regarding the use of anisotropic spatial model focuses on its relevance to the parameters that express the geometric anisotropy in a spatial model exercise in relation to the estimation space. This study aims at identifying the degree of influence of geometric anisotropy on the accuracy of spatial estimation using simulated data sets with different sample sizes and soil chemical properties such as: Fe, potential acidity (H + Al), organic matter and Mn. Comparing the isotropic and anisotropic models, especially for smaller sample sizes (100 and 169) showed an increased sum of squares of differences between predictions anisotropy F^sub α^ctor (F^sub α^) equals 2. Furthermore, from F^sub α^ equals 2.5, over 50% of the simulations showed values of overall accuracy (OA) of less than 0.80 and values for the concordance index Kappa (K) and Tau (T) from 0.67 to 0.80, indicating differences between thematic maps. Similar conclusions were obtained for chemical properties of the soil, from F^sub α^ equals 2, showing that there are relevant differences regarding the inclusion or not of geometric anisotropy. 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However, the main difficulty in decision making regarding the use of anisotropic spatial model focuses on its relevance to the parameters that express the geometric anisotropy in a spatial model exercise in relation to the estimation space. This study aims at identifying the degree of influence of geometric anisotropy on the accuracy of spatial estimation using simulated data sets with different sample sizes and soil chemical properties such as: Fe, potential acidity (H + Al), organic matter and Mn. Comparing the isotropic and anisotropic models, especially for smaller sample sizes (100 and 169) showed an increased sum of squares of differences between predictions anisotropy F^sub α^ctor (F^sub α^) equals 2. Furthermore, from F^sub α^ equals 2.5, over 50% of the simulations showed values of overall accuracy (OA) of less than 0.80 and values for the concordance index Kappa (K) and Tau (T) from 0.67 to 0.80, indicating differences between thematic maps. Similar conclusions were obtained for chemical properties of the soil, from F^sub α^ equals 2, showing that there are relevant differences regarding the inclusion or not of geometric anisotropy. 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However, the main difficulty in decision making regarding the use of anisotropic spatial model focuses on its relevance to the parameters that express the geometric anisotropy in a spatial model exercise in relation to the estimation space. This study aims at identifying the degree of influence of geometric anisotropy on the accuracy of spatial estimation using simulated data sets with different sample sizes and soil chemical properties such as: Fe, potential acidity (H + Al), organic matter and Mn. Comparing the isotropic and anisotropic models, especially for smaller sample sizes (100 and 169) showed an increased sum of squares of differences between predictions anisotropy F^sub α^ctor (F^sub α^) equals 2. Furthermore, from F^sub α^ equals 2.5, over 50% of the simulations showed values of overall accuracy (OA) of less than 0.80 and values for the concordance index Kappa (K) and Tau (T) from 0.67 to 0.80, indicating differences between thematic maps. 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source | Bioline International; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
subjects | Accuracy AGRICULTURE, MULTIDISCIPLINARY AGRONOMY Anisotropy Datasets Geostatistics Monte Carlo simulation Soils Studies |
title | Influence of incorporating geometric anisotropy on the construction of thematic maps of simulated data and chemical attributes of soil |
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