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Biblioteca(s): |
Embrapa Solos. |
Data corrente: |
09/07/2015 |
Data da última atualização: |
11/11/2021 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
SAMUEL-ROSA, A.; HEUVELINK, G. B. M.; VASQUES, G. M.; ANJOS, L. H. C. |
Afiliação: |
ALESSANDRO SAMUEL-ROSA, CAPES/UFRRJ/ISRIC; GERARD B. M. HEUVELINK, ISRIC; GUSTAVO DE MATTOS VASQUES, CNPS; LUCIA HELENA CUNHA DOS ANJOS, UFRRJ. |
Título: |
Do more detailed environmental covariates deliver more accurate soil maps? |
Ano de publicação: |
2015 |
Fonte/Imprenta: |
Geoderma, v. 243/244, p. 214-227, Apr. 2015. |
DOI: |
https://doi.org/10.1016/j.geoderma.2014.12.017 |
Idioma: |
Inglês |
Conteúdo: |
In this study we evaluated whether investing in more spatially detailed environmental covariates improves the accuracy of digital soil maps. We used a case study from Southern Brazil to map clay content (CLAY), organic carbon content (SOC), and effective cation exchange capacity (ECEC) of the topsoil for a ~ 2000 ha area located on the edge of the plateau of the Paraná Sedimentary Basin. Five covariates, each with two levels of spatial detail were used: area-class soil maps, digital elevation models (DEM), geologic maps, land use maps, and satellite images. Thirty-two multiple linear regression models were calibrated for each soil property using all spatial detail combinations of the covariates. For each combination, stepwise regression was used to select predictor variables incorporated in the model. Model evaluation was done using the adjusted R-square of the regression. The baseline model, calibrated with the less detailed version of each covariate, and the best performing model were used to calibrate two linear mixed models for each soil property. Model parameters were estimated using restricted maximum likelihood. Spatial prediction was performed using the empirical best linear unbiased predictor. Validation of baseline and best performing linear multiple regression and linear mixed models was done using cross-validation. Results show that for CLAY the prediction accuracy did not considerably improve by using more detailed covariates. The amount of variance explained increased only ~ 2 percentage points (pp), less than that obtained by including the kriging step, which explained 4 pp. On the other hand, prediction of SOC and ECEC improved by ~ 13 pp when the baseline model was replaced by the best performing model. Overall, the increase in prediction performance was modest and may not outweigh the extra costs of using more detailed covariates. It may be more efficient to spend extra resources on collecting more soil observations, or increasing the detail of only those covariates that have the strongest improvement effect. In our case study, the latter would only work for SOC and ECEC, by investing in a more detailed land use map and possibly also a more detailed geologic map and DEM. MenosIn this study we evaluated whether investing in more spatially detailed environmental covariates improves the accuracy of digital soil maps. We used a case study from Southern Brazil to map clay content (CLAY), organic carbon content (SOC), and effective cation exchange capacity (ECEC) of the topsoil for a ~ 2000 ha area located on the edge of the plateau of the Paraná Sedimentary Basin. Five covariates, each with two levels of spatial detail were used: area-class soil maps, digital elevation models (DEM), geologic maps, land use maps, and satellite images. Thirty-two multiple linear regression models were calibrated for each soil property using all spatial detail combinations of the covariates. For each combination, stepwise regression was used to select predictor variables incorporated in the model. Model evaluation was done using the adjusted R-square of the regression. The baseline model, calibrated with the less detailed version of each covariate, and the best performing model were used to calibrate two linear mixed models for each soil property. Model parameters were estimated using restricted maximum likelihood. Spatial prediction was performed using the empirical best linear unbiased predictor. Validation of baseline and best performing linear multiple regression and linear mixed models was done using cross-validation. Results show that for CLAY the prediction accuracy did not considerably improve by using more detailed covariates. The amount of variance explained in... Mostrar Tudo |
Palavras-Chave: |
Custo do mapeamento de solo; Informações auxiliares; Mapeamento digital do solo; Modelo de precisão; Modelo linear misto; Seleção de variáveis. |
Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
Marc: |
LEADER 03002naa a2200241 a 4500 001 2019572 005 2021-11-11 008 2015 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.1016/j.geoderma.2014.12.017$2DOI 100 1 $aSAMUEL-ROSA, A. 245 $aDo more detailed environmental covariates deliver more accurate soil maps?$h[electronic resource] 260 $c2015 520 $aIn this study we evaluated whether investing in more spatially detailed environmental covariates improves the accuracy of digital soil maps. We used a case study from Southern Brazil to map clay content (CLAY), organic carbon content (SOC), and effective cation exchange capacity (ECEC) of the topsoil for a ~ 2000 ha area located on the edge of the plateau of the Paraná Sedimentary Basin. Five covariates, each with two levels of spatial detail were used: area-class soil maps, digital elevation models (DEM), geologic maps, land use maps, and satellite images. Thirty-two multiple linear regression models were calibrated for each soil property using all spatial detail combinations of the covariates. For each combination, stepwise regression was used to select predictor variables incorporated in the model. Model evaluation was done using the adjusted R-square of the regression. The baseline model, calibrated with the less detailed version of each covariate, and the best performing model were used to calibrate two linear mixed models for each soil property. Model parameters were estimated using restricted maximum likelihood. Spatial prediction was performed using the empirical best linear unbiased predictor. Validation of baseline and best performing linear multiple regression and linear mixed models was done using cross-validation. Results show that for CLAY the prediction accuracy did not considerably improve by using more detailed covariates. The amount of variance explained increased only ~ 2 percentage points (pp), less than that obtained by including the kriging step, which explained 4 pp. On the other hand, prediction of SOC and ECEC improved by ~ 13 pp when the baseline model was replaced by the best performing model. Overall, the increase in prediction performance was modest and may not outweigh the extra costs of using more detailed covariates. It may be more efficient to spend extra resources on collecting more soil observations, or increasing the detail of only those covariates that have the strongest improvement effect. In our case study, the latter would only work for SOC and ECEC, by investing in a more detailed land use map and possibly also a more detailed geologic map and DEM. 653 $aCusto do mapeamento de solo 653 $aInformações auxiliares 653 $aMapeamento digital do solo 653 $aModelo de precisão 653 $aModelo linear misto 653 $aSeleção de variáveis 700 1 $aHEUVELINK, G. B. M. 700 1 $aVASQUES, G. M. 700 1 $aANJOS, L. H. C. 773 $tGeoderma$gv. 243/244, p. 214-227, Apr. 2015.
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Embrapa Solos (CNPS) |
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| Acesso ao texto completo restrito à biblioteca da Embrapa Meio Ambiente. Para informações adicionais entre em contato com cnpma.biblioteca@embrapa.br. |
Registro Completo
Biblioteca(s): |
Embrapa Meio Ambiente. |
Data corrente: |
22/01/2008 |
Data da última atualização: |
19/11/2020 |
Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
Autoria: |
IRIAS, L. J. M.; KITAMURA, P. C. |
Afiliação: |
Luiz José Maria Irias, Embrapa Meio Ambiente; Paulo Choji Kitamura, Embrapa Meio Ambiente. |
Título: |
Pesquisa, desenvolvimento & inovação para o rural brasileiro. |
Ano de publicação: |
2007 |
Fonte/Imprenta: |
In: BARBIERI, J.C.; SIMANTOB, M. A. (Org.). Organizações inovadoras sustentáveis: uma reflexão sobre o futuro das organizações. São Paulo: Atlas, 2007. Cap.2, p.24-38. |
Idioma: |
Português |
Thesagro: |
Pesquisa Agrícola. |
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
Marc: |
LEADER 00536naa a2200133 a 4500 001 1015708 005 2020-11-19 008 2007 bl uuuu u00u1 u #d 100 1 $aIRIAS, L. J. M. 245 $aPesquisa, desenvolvimento & inovação para o rural brasileiro.$h[electronic resource] 260 $c2007 650 $aPesquisa Agrícola 700 1 $aKITAMURA, P. C. 773 $tIn: BARBIERI, J.C.; SIMANTOB, M. A. (Org.). Organizações inovadoras sustentáveis: uma reflexão sobre o futuro das organizações. São Paulo: Atlas, 2007. Cap.2, p.24-38.
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