4 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 […]

Mapping Soil Cation Exchange Capacity in a Semiarid Region through Predictive Models and Covariates from Remote Sensing Data

César da Silva Chagas, Waldir de Carvalho Júnior, Helena Saraiva Koenow Pinheiro, Pedro Armentano Mudado Xavier, Silvio Barge Bhering, Nilson Rendeiro Pereira, [...]

15/out/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

Wesly Jeune, Márcio Rocha Francelino, Eliana de Souza, Elpídio Inácio Fernandes Filho, Genelício Crusoé Rocha

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 […]

Mapeamento pedológico digital da folha Botucatu (SF-22-Z-B-VI-3): treinamento de dados em mapa tradicional e validação de campo

Cristiano Cassiano da Silva, Ricardo Marques Coelho, Stanley Robson de Medeiros Oliveira, Samuel Fernando Adami

01/ago/2013

O mapeamento digital de solos permite prever padrões de ocorrência de solos com base em áreas de referência e no uso de técnicas de mineração de dados para modelar associações solo-paisagem. Os objetivos deste trabalho foram produzir um mapa pedológico digital por meio de técnicas de mineração de dados aplicadas a variáveis geomorfométricas e de geologia, com base em áreas de referência; e testar a confiabilidade desse mapa por meio de validação em campo com diferentes sistemas de amostragem. O […]