| |
|
|
| Registros recuperados : 504 | |
| 50. |  | FIDALGO, E. C. C.; CARVALHO JUNIOR, W. de; PEDREIRA, B. da C. C. G.; CHAGAS, C. da S. Adequação de uso da terra e detecção de conflitos na bacia hidrográfica do Rio Guapi-Macacu, RJ. In: CONGRESSO BRASILEIRO DE CIÊNCIA DO SOLO, 32., 2009, Fortaleza. O solo e a produção de bioenergia: perspectivas e desafios. [Viçosa, MG]: SBCS; Fortaleza: UFC, 2009. 1 CD-ROM.| Biblioteca(s): Embrapa Solos. |
|    |
| 51. | | CARVALHO JUNIOR, W. de; SCHAEFER, C. E. G. R.; CHAGAS, C. da S.; FERNANDES FILHO, E. I. Análise multivariada de argissolos da faixa atlântica brasileira. Revista Brasileira de Ciência do Solo, Viçosa, MG, v. 32, n. 5, p. 2081-2090, set./out. 2008.| Biblioteca(s): Embrapa Solos. |
| |
| 52. | | CALDERANO FILHO, B.; CARVALHO JUNIOR, W. de; POLIVANOV, H.; CHAGAS, C. da S.; BHERING, S. B.; CALDERANO, S. B. Avaliação da vulnerabilidade ambiental do médio alto Curso do Rio Grande (RJ), subsídios ao planejamento de paisagens montanhosas da Serra do Mar. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 17., 2015, João Pessoa. Anais... São José dos Campos: INPE, 2015. p. 207-214.| Biblioteca(s): Embrapa Solos. |
| |
| 53. | | CHAGAS, C. S.; FERNANDES FILHO, E. I.; ROCHA, M. F.; CARVALHO JÚNIOR, W. de; SOUZA NETO, N. C. Avaliação de modelos digitais de elevação para aplicação em um mapeamento digital de solos. Revista Brasileira de Engenharia Agrícola e Ambiental, v.14, n.2, p. 218-226, fev., 2010.| Biblioteca(s): Embrapa Algodão. |
| |
| 54. | | PINHEIRO, H. S. K.; BARBOSA, A. M.; ANJOS, L. H. C. dos; CARVALHO JUNIOR, W. de; CHAGAS, C. da S. Avaliação de diferentes fontes de dados na obtenção do modelo digital de elevação para mapeamento digital dos solos da bacia hidrográfica do Rio Guapi-Macacu, RJ. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 15., 2011, Curitiba. Anais [...]. São José dos Campos: INPE, 2011. p. 9136-9143.| Biblioteca(s): Embrapa Solos. |
| |
| 55. | | MOTTA, P. E. F. da; CARVALHO FILHO, A. de; PEREIRA, N. R.; CARVALHO JUNIOR, W. de; BLANCANEAUX, P. Aptidao agricola das terras dos municipios de Silvania e Sao Miguel do Passa Quatro, GO. In: CONGRESSO BRASILEIRO DE CIENCIA DO SOLO, 24., 1993, Goiania, GO. Resumos... Goiania: Sociedade Brasileira de Ciencia do Solo, 1993. p.399.| Biblioteca(s): Embrapa Cerrados; Embrapa Solos. |
| |
| 56. | | CALDERANO FILHO, B.; POLIVANOV, H.; GUERRA, A. J. T.; CHAGAS, C. da S.; CARVALHO JUNIOR, W. de; CALDERANO, S. B. Estudo geoambiental do município de Bom Jardim - RJ, com suporte de geotecnologias: subsídios ao planejamento de paisagens rurais montanhosas. Sociedade & Natureza, Uberlândia, v. 22, n. 1, p. 55-73, abr. 2010.| Biblioteca(s): Embrapa Solos. |
| |
| 57. | | PINHEIRO, H. S. K.; BARBOSA, A. M.; ANJOS, L. H. C. dos; CHAGAS, C. da S.; CARVALHO JUNIOR, W. de. Efeitos da resolução espacial em modelos digitais de elevação oriundos de interpolação de dados no mapeamento digital de solos da bacia do rio Guapi-Macacu, RJ. In: CONGRESSO BRASILEIRO DE CIÊNCIA DO SOLO, 33., 2011, Uberlândia. Solos nos biomas brasileiros: sustentabilidade e mudanças climáticas: anais. [Uberlândia]: SBCS: UFU, ICIAG, 2011. 1 CD-ROM.| Biblioteca(s): Embrapa Solos. |
|  |
| 58. | | CALDERANO FILHO, B.; POLIVANOV, H.; CARVALHO JUNIOR, W. de; GUERRA, A. J. T.; CHAGAS, C. da S.; CALDERANO, S. B. Diagnóstico físico-biótico do município de Bom Jardim - RJ, com auxílio de geotecnologias, para fins de planejamento de paisagens rurais montanhosas. In: CONGRESSO BRASILEIRO DE CIÊNCIA DO SOLO, 32., 2009, Fortaleza. O solo e a produção de bioenergia: perspectivas e desafios. [Viçosa, MG]: SBCS; Fortaleza: UFC, 2009. 1 CD-ROM.| Biblioteca(s): Embrapa Solos. |
| |
| 60. | | BARBOSA, A. M.; PINHEIRO, H. S. K.; ANJOS, L. H. C. dos; CARVALHO JUNIOR, W. de; CHAGAS, C. da S. Determinação de pontos amostrais através de atributos do terreno para mapeamento digital dos solos da bacia hidrográfica do Rio Guapi-Macacu, RJ. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 15., 2011, Curitiba, PR. Anais... Curitiba: INPE, 2011. 1690p. 8 p.| Biblioteca(s): Embrapa Solos. |
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Registro Completo
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
26/07/2023 |
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Data da última atualização: |
26/07/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
BASTOS, B. P.; PINHEIRO, H. S. K.; FERREIRA, F. J. F.; CARVALHO JUNIOR, W. de; ANJOS, L. H. C. dos. |
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Afiliação: |
BLENDA PEREIRA BASTOS, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; HELENA SARAIVA KOENOW PINHEIRO, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO; FRANCISCO JOSÉ FONSECA FERREIRA, UNIVERSIDADE FEDERAL DO PARANÁ; WALDIR DE CARVALHO JUNIOR, CNPS; LÚCIA HELENA CUNHA DOS ANJOS, UNIVERSIDADE FEDERAL RURAL DO RIO DE JANEIRO. |
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Título: |
Could airborne geophysical data be used to improve predictive modeling of agronomic soil properties in tropical hillslope area? |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Remote Sensing, v. 15, n. 15, 3719, 2023. |
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DOI: |
https://doi.org/10.3390/rs15153719 |
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Idioma: |
Inglês |
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Conteúdo: |
Airborne geophysical data (AGD) have great potential to represent soil-forming factors. Because of that, the objective of this study was to evaluate the importance of AGD in predicting soil attributes such as aluminum saturation (ASat), base saturation (BS), cation exchange capacity (CEC), clay, and organic carbon (OC). The AGD predictor variables include total count (uR/h), K (potassium), eU (uranium equivalent), and eTh (thorium equivalent), ratios between these elements (eTh/K, eU/K, and eU/eTh), factor F or F-parameter, anomalous potassium (Kd), anomalous uranium (Ud), anomalous magnetic field (AMF), vertical derivative (GZ), horizontal derivatives (GX and GY), and mafic index (MI). The approach was based on applying predictive modeling techniques using (1) digital elevation model (DEM) covariates and Sentinel-2 images with AGD; and (2) DEM covariates and Sentinel-2 images without the AGD. The study was conducted in Bom Jardim, a county in Rio de Janeiro-Brazil with an area of 382,430 km², with a database of 208 soil samples to a predefined depth (0-30 cm). Non-explanatory covariates for the selected soil attributes were excluded. Through the selected covariables, the random forest (RF) and support vector machine (SVM) models were applied with separate samples for training (75%) and validation (25%). The model's performance was evaluated through the R-squared (R2), root mean square error (RMSE), and mean absolute error (MAE), as well as null model values and coefficient of variation (CV%). The RF algorithm showed better performance with AGD (R2 values ranging from 0.15 to 0.23), as well as the SVM model (R2 values ranging from 0.08 to 0.23) when compared to RF (R2 values ranging from 0.10 to 0.20) and SVM (R2 values ranging from 0.04 to 0.10) models without AGD. Overall, the results suggest that AGD can be helpful for soil mapping. Nevertheless, it is crucial to acknowledge that the accuracy of AGD in predicting soil properties could vary depending on various common factors in DSM, such as the quality and resolution of the covariates and available soil data. Further research is needed to determine the optimal approach for using AGD in soil mapping. MenosAirborne geophysical data (AGD) have great potential to represent soil-forming factors. Because of that, the objective of this study was to evaluate the importance of AGD in predicting soil attributes such as aluminum saturation (ASat), base saturation (BS), cation exchange capacity (CEC), clay, and organic carbon (OC). The AGD predictor variables include total count (uR/h), K (potassium), eU (uranium equivalent), and eTh (thorium equivalent), ratios between these elements (eTh/K, eU/K, and eU/eTh), factor F or F-parameter, anomalous potassium (Kd), anomalous uranium (Ud), anomalous magnetic field (AMF), vertical derivative (GZ), horizontal derivatives (GX and GY), and mafic index (MI). The approach was based on applying predictive modeling techniques using (1) digital elevation model (DEM) covariates and Sentinel-2 images with AGD; and (2) DEM covariates and Sentinel-2 images without the AGD. The study was conducted in Bom Jardim, a county in Rio de Janeiro-Brazil with an area of 382,430 km², with a database of 208 soil samples to a predefined depth (0-30 cm). Non-explanatory covariates for the selected soil attributes were excluded. Through the selected covariables, the random forest (RF) and support vector machine (SVM) models were applied with separate samples for training (75%) and validation (25%). The model's performance was evaluated through the R-squared (R2), root mean square error (RMSE), and mean absolute error (MAE), as well as null model values and coefficient ... Mostrar Tudo |
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Palavras-Chave: |
Digital soil mapping; Gamma-ray spectrometry data; Hillslope areas; Machine learning; Magnetic data; Mapeamento digital do solo; Parent material. |
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Thesagro: |
Sensoriamento Remoto. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1155276/1/Could-airborne-geophysical-data-be-used-to-improve-predictive-modeling-2023.pdf
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Marc: |
LEADER 03095naa a2200277 a 4500
001 2155276
005 2023-07-26
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3390/rs15153719$2DOI
100 1 $aBASTOS, B. P.
245 $aCould airborne geophysical data be used to improve predictive modeling of agronomic soil properties in tropical hillslope area?$h[electronic resource]
260 $c2023
520 $aAirborne geophysical data (AGD) have great potential to represent soil-forming factors. Because of that, the objective of this study was to evaluate the importance of AGD in predicting soil attributes such as aluminum saturation (ASat), base saturation (BS), cation exchange capacity (CEC), clay, and organic carbon (OC). The AGD predictor variables include total count (uR/h), K (potassium), eU (uranium equivalent), and eTh (thorium equivalent), ratios between these elements (eTh/K, eU/K, and eU/eTh), factor F or F-parameter, anomalous potassium (Kd), anomalous uranium (Ud), anomalous magnetic field (AMF), vertical derivative (GZ), horizontal derivatives (GX and GY), and mafic index (MI). The approach was based on applying predictive modeling techniques using (1) digital elevation model (DEM) covariates and Sentinel-2 images with AGD; and (2) DEM covariates and Sentinel-2 images without the AGD. The study was conducted in Bom Jardim, a county in Rio de Janeiro-Brazil with an area of 382,430 km², with a database of 208 soil samples to a predefined depth (0-30 cm). Non-explanatory covariates for the selected soil attributes were excluded. Through the selected covariables, the random forest (RF) and support vector machine (SVM) models were applied with separate samples for training (75%) and validation (25%). The model's performance was evaluated through the R-squared (R2), root mean square error (RMSE), and mean absolute error (MAE), as well as null model values and coefficient of variation (CV%). The RF algorithm showed better performance with AGD (R2 values ranging from 0.15 to 0.23), as well as the SVM model (R2 values ranging from 0.08 to 0.23) when compared to RF (R2 values ranging from 0.10 to 0.20) and SVM (R2 values ranging from 0.04 to 0.10) models without AGD. Overall, the results suggest that AGD can be helpful for soil mapping. Nevertheless, it is crucial to acknowledge that the accuracy of AGD in predicting soil properties could vary depending on various common factors in DSM, such as the quality and resolution of the covariates and available soil data. Further research is needed to determine the optimal approach for using AGD in soil mapping.
650 $aSensoriamento Remoto
653 $aDigital soil mapping
653 $aGamma-ray spectrometry data
653 $aHillslope areas
653 $aMachine learning
653 $aMagnetic data
653 $aMapeamento digital do solo
653 $aParent material
700 1 $aPINHEIRO, H. S. K.
700 1 $aFERREIRA, F. J. F.
700 1 $aCARVALHO JUNIOR, W. de
700 1 $aANJOS, L. H. C. dos
773 $tRemote Sensing$gv. 15, n. 15, 3719, 2023.
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