Digital Soil Mapping
Mostrando 1-12 de 50 artigos, teses e dissertações.
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1. Prediction of soil classes in a complex landscape in Southern Brazil
Resumo: O objetivo deste trabalho foi avaliar o uso da seleção de covariáveis por conhecimento especializado no desempenho de modelos de predição de classes de solos em uma paisagem complexa, para identificar o melhor modelo preditivo para o mapeamento digital de solos na região Sul do Brasil. Um total de 164 pontos foram amostrados em campo, com uso d
Pesq. agropec. bras.. Publicado em: 11/11/2019
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2. Digital mapping of soil attributes using machine learning
RESUMO O mapeamento de atributos químicos do solo em larga escala pode acarretar em ganhos no planejamento de uso e ocupação do mesmo. Existem diferentes técnicas disponíveis para tal fim, cujos desempenhos devem ser testados para diferentes situações de paisagem. Objetivou-se neste trabalho espacializar atributos químicos do solo, comparando oito m�
Rev. Ciênc. Agron.. Publicado em: 04/11/2019
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3. AlradSpectra: a Quantification Tool for Soil Properties Using Spectroscopic Data in R
ABSTRACT Soil reflectance spectroscopy has become an innovative method for soil property quantification supplying data for studies in soil fertility, soil classification, digital soil mapping, while reducing laboratory time and applying a clean technology. This paper describes the implementation of a Graphical User Interface (GUI) using R named AlradSpectra.
Rev. Bras. Ciênc. Solo. Publicado em: 29/07/2019
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4. Selection of Environmental Covariates for Classifier Training Applied in Digital Soil Mapping
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
Rev. Bras. Ciênc. Solo. Publicado em: 07/01/2019
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5. Digital Soil Mapping of Soil Properties in the “Mar de Morros” Environment Using Spectral Data
ABSTRACT Quantification of soil properties is essential for better understanding of the environment and better soil management. The conventional techniques of laboratory analysis are sometimes costly and detrimental to the environment. Thus, development of new techniques for soil analysis that do not generate residues, such as spectroscopy, is increasingly n
Rev. Bras. Ciênc. Solo. Publicado em: 07/01/2019
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6. Digital Soil Mapping Using Machine Learning Algorithms in a Tropical Mountainous Area
ABSTRACT: Increasingly, applications of machine learning techniques for digital soil mapping (DSM) are being used for different soil mapping purposes. Considering the variety of models available, it is important to know their performance in relation to soil data and environmental variables involved in soil mapping. This paper investigated the performance of
Rev. Bras. Ciênc. Solo. Publicado em: 14/11/2018
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7. Bibliometric Analysis for Pattern Exploration in Worldwide Digital Soil Mapping Publications
Abstract Bibliometric analyses provide a clear understanding of the scientific performance and relate them with standards of the global scientific production. Soil science is an outstanding and developing field among environmental sciences. Knowledge about soil characteristics and their distribution in the environment has been enriched by the use of new geot
An. Acad. Bras. Ciênc.. Publicado em: 25/10/2018
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8. Multinomial Logistic Regression and Random Forest Classifiers in Digital Mapping of Soil Classes in Western Haiti
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
Rev. Bras. Ciênc. Solo. Publicado em: 02/07/2018
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9. Prediction of Topsoil Texture Through Regression Trees and Multiple Linear Regressions
ABSTRACT: Users of soil survey products are mostly interested in understanding how soil properties vary in space and time. The aim of digital soil mapping (DSM) is to represent the spatial variability of soil properties quantitatively to support decision-making. The goal of this study is to evaluate DSM techniques (Regression Trees - RT and Multiple Linear R
Rev. Bras. Ciênc. Solo. Publicado em: 16/04/2018
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10. Knowledge-based digital soil mapping for predicting soil properties in two representative watersheds
ABSTRACT: The estimation of soil physical and chemical properties at non-sampled areas is valuable information for land management, sustainability and water yield. This work aimed to model and map soil physical-chemical properties by means of knowledge-based digital soil mapping approach as a study case in two watersheds representative of different physiogra
Sci. agric. (Piracicaba, Braz.). Publicado em: 2018-04
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11. Spatial Disaggregation of Multi-Component Soil Map Units Using Legacy Data and a Tree-Based Algorithm in Southern Brazil
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
Rev. Bras. Ciênc. Solo. Publicado em: 08/03/2018
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12. Environmental Correlation and Spatial Autocorrelation of Soil Properties in Keller Peninsula, Maritime Antarctica
ABSTRACT: The pattern of variation in soil and landform properties in relation to environmental covariates are closely related to soil type distribution. The aim of this study was to apply digital soil mapping techniques to analysis of the pattern of soil property variation in relation to environmental covariates under periglacial conditions at Keller Penins
Rev. Bras. Ciênc. Solo. Publicado em: 08/01/2018