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All publications of “Elvio Giasson”

5 results

Selection of Environmental Covariates for Classifier Training Applied in Digital Soil Mapping

Alcinei Ribeiro Campos, Elvio Giasson, José Janderson Ferreira Costa, Israel Rosa Machado, Elisângela Benedet da Silva, Benito Roberto Bonfatti

26/Nov/2018

ABSTRACT A large number of predictor variables can be used in digital soil mapping; however, the presence of irrelevant covariables may compromise the prediction of soil types. Thus, algorithms can be applied to select the most relevant predictors. This study aimed to compare three covariable selection systems (two filter algorithms and one wrapper algorithm) and assess their impacts on the predictive model. The study area was the Lajeado River Watershed in the state of Rio Grande do Sul, Brazil. We […]

Spatial Disaggregation of Multi-Component Soil Map Units Using Legacy Data and a Tree-Based Algorithm in Southern Brazil

Israel Rosa Machado, Elvio Giasson, Alcinei Ribeiro Campos, José Janderson Ferreira Costa, Elisângela Benedet da Silva, Benito Roberto Bonfatti

02/Mar/2018

ABSTRACT Soil surveys often contain multi-component map units comprising two or more soil classes, whose spatial distribution within the map unit is not represented. Digital Soil Mapping tools supported by information from soil surveys make it possible to predict where these classes are located. The aim of this study was to develop a methodology to increase the detail of conventional soil maps by means of spatial disaggregation of multi-component map units and to predict the spatial location of the derived […]

PRODUCTION OF A SOIL MAP ASSOCIATING COMMON DIGITAL SOIL MAPPING TECHNIQUES WITH HAND DELINEATION OF SOIL MAPPING UNITS

Rodrigo Teske, Elvio Giasson, Tatiane Bagatini

01/Jul/2015

The production of soil maps through digital soil mapping (DSM) techniques may be hampered due to the lack of traditional reference soil maps. In these situations, the tacit knowledge of the field soil scientist can be used for manual delineation of soil mapping units (MUs) based on generation of a map of occurrence of soil types predicted by DSM. The objective of this study was to evaluate and to compare soil maps generated by two methods. One method, called “direct […]

SELECTION OF SAMPLING DENSITY BASED ON DATA FROM AREAS ALREADY MAPPED FOR TRAINING DECISION TREE MODELS IN DIGITAL SOIL MAPPING

Tatiane Bagatini, Elvio Giasson, Rodrigo Teske

01/Jul/2015

In order to study sampling techniques useful for digital soil mapping (DSM), we evaluated the effect of changes in sampling density, based on data from areas already mapped by traditional methods, in regard to the accuracy of decision trees models for generating soil maps using DSM. In two watersheds in northwestern Rio Grande do Sul, Brazil, 1:50,000 scale conventional soils maps were used as reference maps. From the ASTER – GDEM Global Digital Elevation Model and the hydrographic network, maps […]

COMPARISON OF SAMPLING PROCEDURES FOR TRAINING PREDICTIVE MODELS IN DIGITAL SOIL CLASS MAPPING

Rodrigo Teske, Elvio Giasson, Tatiane Bagatini

01/Jan/2015

The predictive models used in digital soil mapping (DSM) need to be trained with data that most fully capture the variation of terrain and soil properties in order to generate adequate correlations between environmental variables and the occurrence of soil unities. Several methods have been used in DSM to evaluate the accuracy of these models. The aims of this study were to compare the use of three sampling procedures for training a classification and regression tree (CART) algorithm, and evaluate […]