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DIGITAL MAPPING OF FARMLAND CLASSES IN THREE LANDSCAPE IN MEXICO J. Soil Sci. Plant Nutr.
Cruz-Cárdenas,G; Ortiz-Solorio,C.A; Ojeda-Trejo,E; Martinez-Montoya,J.F; Sotelo-Ruiz,E.D; Licona-Vargas,A.L.
The cartography of farmland classes allows generating land maps, using a methodology based on local knowledge, rapidly and at low cost, and with a greater number of cartographic units than conventional soil surveys maps. However, the results found when producing these maps with automated cartography techniques are contrasting. Precision and accuracy were evaluated in 324 computer generated farmland class (FLC) maps by applying the Inverse Distance Weighted (IDW) interpolation model. These maps were obtained by varying the sample size for the training, its spatial design, and the Power value of the interpolator. Moreover, the effort needed to obtain maps with acceptable reliability was quantified. The procedure was applied to FLC maps obtained from surveys...
Tipo: Journal article Palavras-chave: Map accuracy; IDW interpolator; Soil sampling strategies.
Ano: 2010 URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-95162010000200003
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Methodology to filter out outliers in high spatial density data to improve maps reliability Scientia Agricola
Maldaner,Leonardo Felipe; Molin,José Paulo; Spekken,Mark.
ABSTRACT The considerable volume of data generated by sensors in the field presents systematic errors; thus, it is extremely important to exclude these errors to ensure mapping quality. The objective of this research was to develop and test a methodology to identify and exclude outliers in high-density spatial data sets, determine whether the developed filter process could help decrease the nugget effect and improve the spatial variability characterization of high sampling data. We created a filter composed of a global, anisotropic, and an anisotropic local analysis of data, which considered the respective neighborhood values. For that purpose, we used the median to classify a given spatial point into the data set as the main statistical parameter and took...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Precision agriculture; Local analysis; Map accuracy.
Ano: 2022 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162022000100102
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