Selection of Environmental Covariates for Classifier Training Applied in Digital Soil Mapping
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 […]
Mapping Soil Cation Exchange Capacity in a Semiarid Region through Predictive Models and Covariates from Remote Sensing Data
15/Oct/2018
ABSTRACT: Planning sustainable use of land resources requires reliable information about spatial distribution of soil physical and chemical properties related to environmental processes and ecosystemic functions. In this context, cation exchange capacity (CEC) is a fundamental soil quality indicator; however, it takes money and time to obtain this data. Although many studies have been conducted to spatially quantify soil properties on various scales and in different environments, not much is known about interactions between soil properties and environmental covariates in […]
Multinomial Logistic Regression and Random Forest Classifiers in Digital Mapping of Soil Classes in Western Haiti
18/Jun/2018
ABSTRACT Digital soil mapping (DSM) has been increasingly used to provide quick and accurate spatial information to support decision-makers in agricultural and environmental planning programs. In this study, we used a DSM approach to map soils in western Haiti and compare the performance of the Multinomial Logistic Regression (MLR) with Random Forest (RF) to classify the soils. The study area of 4,300 km2 is mostly composed of diverse limestone rocks, alluvial deposits, and, to a lesser extent, basalt. A soil […]
Digital pedological mapping of Botucatu sheet (SF-22-Z-B-VI-3): data training on conventional maps and field validation
01/Aug/2013
Digital soil mapping allows predicting patterns of soil classes on the basis of well-known reference areas and of data mining techniques to model soil-landscape relationships. The purpose of this study was to (1) generate a digital pedological map using data mining techniques to associate geomorphometric and geology variables with soil classes of traditional soil maps in reference areas and (2) validate these maps by different field techniques. The mapping was carried out using the 1:50.000 Botucatu sheet (SF-22-Z-B-VI-3), and 1:50.000 […]