Seleção De Densidade De Amostragem Com Base Em Dados De Áreas Já Mapeadas Para Treinamento De Modelos De Árvore De Decisão No Mapeamento Digital De Solos

Bagatini, Tatiane; Giasson, Élvio; Teske, Rodrigo

doi:10.1590/01000683rbcs20140289

Cited by 7 publications

(9 citation statements)

References 13 publications

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“…However, Giasson et al (2013) observed an average K of 0.37 in the Lajeado Grande watershed, which shows characteristics similar to the area of the present study. Likewise, Bagatini et al (2015) obtained a GA similar to those in the present study; in their study, depending on the sampling point density, GA varied between 60 and 76% in the Rio Santo Cristo watershed, and between 54% (lowest density, 0.1 points ha -1…”

Section: Resultssupporting

confidence: 88%

“…This GA behavior was similar to that observed in the Carajazinho chart, which belongs to the same physiographic region evaluated by Giasson et al (2013) and Bagatini et al (2015).…”

Section: Resultssupporting

confidence: 85%

“…Based on the assumptions derived from the soil-landscape relationships, these functions constitute a frame for empirical fitting among quantitative correlations between the soil and the environment (McBratney et al, 2003). This approach has been frequently used in order to predict soil classes and/ or properties in nonsampled locations at the regional, national, and global levels (ten Caten et al, 2012;Hengl et al, 2014;Dalmolin & ten Caten, 2015;Teske et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Digital soil mapping and its implications in the extrapolation of soil-landscape relationships in detailed scale

Wolski

Dalmolin

Flores

et al. 2017

Pesq. agropec. bras.

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-The objective of this work was to test the extrapolation of soil-landscape relationships in a reference area (RA) to a topographic map (scale 1:50,000), using digital soil mapping (DSM), and to compare these results to those obtained in similar studies previously conducted in Brazil. A soil survey in a 10 km 2 RA, using conventional mapping techniques (scale 1:10,000), was made in order to map a 678 km 2 physiographically similar area (scale 1:50,000) using DSM. The decision tree technique was employed to build a predictive extrapolation model based on soil classes and eight terrain attributes in the RA. The validation of DSM by application of field observation points resulted in a 66.1% global accuracy and in 0.36 kappa index. The most representative soils in the area were correctly predicted, whereas the less representative and less frequent soils in the landscape (and consequently with reduced sampling) had their prediction compromised. The RA proportion, which equals 1.5% of the total area, is a limiting factor in the formulation of soil-landscape relationships to precisely represent the mapped area by DSM.

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Section: Resultssupporting

confidence: 88%

“…This GA behavior was similar to that observed in the Carajazinho chart, which belongs to the same physiographic region evaluated by Giasson et al (2013) and Bagatini et al (2015).…”

Section: Resultssupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

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Digital soil mapping and its implications in the extrapolation of soil-landscape relationships in detailed scale

Wolski

Dalmolin

Flores

et al. 2017

Pesq. agropec. bras.

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“…De modo geral, a reprodutibilidade do mapa convencional de solos (Tabela 4) com os dados coletados apenas nos pixels dos perfis de solos alcançou valores próximos ao valor médio de 60% observado na literatura em estudos com outras estratégias de amostragem utilizada no mapeamento digital de solos (Höfig et al, 2014;Giasson et al, 2015;Bagatini et al, 2016;Dias et al, 2016;Pelegrino et al, 2016). Vale destacar que em ambas as áreas de estudo apenas duas unidades representam mais de 75% da composição dos respectivos mapas de solos, sendo estas as mais importantes na predição e as quais apresentaram maior concordância com os mapas legados utilizados na validação.…”

Section: /9unclassified

Índice de área foliar residual como estratégia para manejo de pasto: estrutura do capim Tanzânia

Lemos

Ruggieri

Silva

et al. 2019

Agraria

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AGRONOMIA (AGRONOMY) RESUMO: Os estudos de mapeamento digital de classes de solos (MDS) têm utilizado mapas legados como principal fonte de informação para calibração dos modelos preditores. Entretanto, são necessárias novas abordagem com técnicas que permitam o uso de informações contidas em perfis de solos georreferenciados, permitindo a aplicação do MDS em áreas amostradas que não disponibilizem de mapas convencionais de solos. O objetivo deste estudo foi avaliar o desempenho na predição de ocorrência de solos de amostras coletadas em pixels de perfis de solos georreferenciados e em pixels coletados em buffers com raio de 50, 100, 150, 200 e 250 m dos perfis de solos nas bacias dos rios Lajeado Grande e Santo Cristo. As duas áreas possuem dados de levantamento de solos publicados em escala 1:50.000. Para predição da ocorrência das classes de solos foram utilizadas dez variáveis preditoras geradas a partir de um modelo digital de elevação com resolução espacial de 30 m. Para a predição foi utilizado o algoritmo Random Forest. Os mapas preditos foram avaliados quanto à exatidão em relação aos perfis de solos e quanto a reprodutibilidade dos mapas convencionais. A utilização dos pixels amostrais coletados nos buffers não alterou de forma expressiva a acurácia geral dos mapas preditos na bacia do rio Lajeado Grande, mas permitiu um ganho de 15,6% de exatidão na bacia do rio Santo Cristo. O uso apenas dos perfis georreferenciados resultou em mapas preditos com exatidão superior a 75% e concordância de reprodutibilidade igual ou superior a 67% em relação ao mapa convencional.

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“…This approach allowed better distribution of points across polygons, as opposed to random sampling, which tends to provide greater density of points in large polygons, thus reducing the importance of the information contained in small polygons (Odgers et al, 2014). The point density chosen was approximately 2.5 points per hectare, as suggested by Sarmento et al (2012) and Bagatini et al (2015) for sampling density in DSM. The sampling process resulted in selection of approximately 50,000 sampling points in the WDS area and approximately 38,000 sampling points in the IPDS area for use in the predictive model.…”

Section: Assignment Of Soil-landscape Relationships and Creation Of Tmentioning

confidence: 99%

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

Machado

Giasson

Campos

et al. 2018

Rev. Bras. Ciênc. Solo

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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 soil classes. Three digital maps of terrain variables -slope, landforms, and topographic wetness indexwere correlated with the soil map and 72 georeferenced profiles from the Porto Alegre soil survey. Explicit rules that expressed regional soil-landscape relationships were formulated based on the resulting combinations. These rules were used to select typical areas of occurrence of each soil class and to train a decision tree model to predict the occurrence of individualized soil classes. Validation of the soil map predictions was conducted by comparison with available soil profiles. The soil map produced showed high agreement (80.5 % accuracy) with the soil classes observed in the soil profiles; Ultisols and Lithic Udorthents were predicted with greater accuracy. The soil variables selected in this study were suitable to represent the soil-landscape relationships, suggesting potential use in future studies. This approach developed a more detailed soil map relevant to current demands for soil information and has potential to be replicated in other areas in which data availability is similar.

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Seleção De Densidade De Amostragem Com Base Em Dados De Áreas Já Mapeadas Para Treinamento De Modelos De Árvore De Decisão No Mapeamento Digital De Solos

Cited by 7 publications

References 13 publications

Digital soil mapping and its implications in the extrapolation of soil-landscape relationships in detailed scale

Digital soil mapping and its implications in the extrapolation of soil-landscape relationships in detailed scale

Índice de área foliar residual como estratégia para manejo de pasto: estrutura do capim Tanzânia

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

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