| |
|
|
| Registros recuperados : 23 | |
| 4. |  | PEREIRA, A. A.; BARROS, D. A. de; ACERBI JUNIOR, F. W.; PEREIRA, J. A. A.; REIS, A. A. dos. Análise da distribuição espacial de áreas queimadas através da função K de Ripley. Scientia Forestalis, Piracicaba, v. 41, n. 100, p. 445-455, dez. 2013.| Biblioteca(s): Embrapa Florestas. |
|   |
| 6. | | PROTÁSIO, T. de P.; COUTO, A. M.; REIS, A. A. dos; TRUGILHO, P. F.; GODINHO, T. P. Potencial siderúrgico e energético do carvão vegetal de clones de Eucalyptus spp. aos 42 meses de idade. Pesquisa Florestal Brasileira, Colombo, v. 33, n. 74, p. 137-149, abr./jun. 2013.| Biblioteca(s): Embrapa Florestas. |
|  |
| 7. | | GUEDES, I. C. de L.; MELLO, J. M. de; SILVEIRA, E. M. de O.; MELLO, C. R. de; REIS, A. A. dos; GOMIDE, L. R. Continuidade espacial de características dendrométricas em povoamentos clonais de eucalyptus sp. avaliada ao longo do tempo. Revista Cerne, Lavras, v. 21, n. 4, p. 527-534, out./dez. 2015.| Biblioteca(s): Embrapa Florestas. |
| |
| 8. | | REIS, A. A. dos; MELLO, J. M. de; RAIMUNDO, M. R.; ACERBI JUNIOR, F. W.; OLIVEIRA, M. S. de; DINIZ, J. M. F. de S. Estratificação de um povoamento de eucalipto por interpoladores geoestatísticos e sensoriamento remoto. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 51, n. 10, p. 1751-1761, out. 2016. Título em inglês: Stratification of an eucalyptus plantation through geostatistical interpolators and remote sensing.| Biblioteca(s): Embrapa Unidades Centrais. |
| |
| 9. | | REIS, A. A. dos; PROTÁSIO, T. de P.; MELO, I. C. N. A. de; TRUGILHO, P. F.; CARNEIRO, A. de C. O. Composição da madeira e do carvão vegetal de Eucalyptus urophylla em diferentes locais de plantio. Pesquisa Florestal Brasileira, Colombo, v. 32, n. 71, p. 277-290, jul./set. 2012.| Biblioteca(s): Embrapa Florestas. |
| |
| 11. | | BATISTA, A. P. B.; MELLO, J. M. de; RAIMUNDO, M. R.; SCOLFORO, H. F.; REIS, A. A. dos; SCOLFORO, J. R. S. Species richness and diversity in shrub savanna using ordinary kriging. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 51, n. 8, p. 958-966, ago. 2016. Título em português: Riqueza e diversidade de espécies em um fragmento de campo cerrado por meio de krigagem ordinária.| Biblioteca(s): Embrapa Unidades Centrais. |
| |
| 12. | | SILVA, S. T. da; MELLO, J. M. de; ARCEBI JUNIOR, F. W.; REIS, A. A. dos; RAIMUNDO, M. R.; SILVA, I. L. G.; SCOLFORO, J. R. S. Uso de imagens de sensoriamento remoto para estratificação do cerrado em inventários florestais. Pesquisa Florestal Brasileira, Colombo, v. 34, n. 80, p. 337-343, out./dez. 2014.| Biblioteca(s): Embrapa Florestas. |
| |
| 13. | | REIS, A. A. dos; SILVA, M. A. da; SANTOS, F. C. dos; ALBUQUERQUE FILHO, M. R. de; SILVEIRA, M. C. T. da; TEIXEIRA, E. C.; VASCONCELOS, A. de A.; BORGES, I. D.; SOUZA, W. G. Impacto do sistema de pastejo no estoque de carbono lábil do solo arenoso. In: CONGRESO INTERNACIONAL DE SISTEMAS SILVOPASTORILES, 12.; CONGRESO DE LA RED GLOBAL DE SISTEMAS SILVOPASTORILES, 2.; IV SEMINARIO NACIONAL DE SISTEMAS SILVOPASTORILES, 4., 2023, Montevideo; CONGRESO NACIONAL SISTEMAS SILVOPASTORILES, 5., 2023, Buenos Aires. Sistemas silvopastoriles: hacia una diversificación sostenible. Cali: CIPAV, 2023. p. 842-848.| Biblioteca(s): Embrapa Milho e Sorgo. |
| |
| 14. | | PEREIRA, F. R. da S.; REIS, A. A. dos; FREITAS, R. G.; OLIVEIRA, S. R. de M.; AMARAL, L. R. do; FIGUEIREDO, G. K. D. A.; ANTUNES, J. F. G.; LAMPARELLI, R. A. C.; MORO, E.; MAGALHÃES, P. S. G. Imputation of missing parts in UAV orthomosaics using PlanetScope and Sentinel-2 data: a case study in a grass-dominated área. International Journal of Geo-Information, v. 12, n. 2, 41, Feb. 2023.| Biblioteca(s): Embrapa Agricultura Digital. |
| |
| 15. | | TORO, A. P. S. G. D.; WERNER, J. P. S.; REIS, A. A. dos; ESQUERDO, J. C. D. M.; ANTUNES, J. F. G.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; MAGALHÃES, P. S. G.; FIGUEIREDO, G. K. D. A. Evaluation of early season mapping of integrated crop livestock systems using Sentinel-2 data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 43, B3, p. 1335-1340, 2022. Edition of proceedings of the 2022 edition of the XXIVth ISPRS Congress, Nice, France.| Biblioteca(s): Embrapa Agricultura Digital. |
| |
| 16. | | BUENO, I. T.; ANTUNES, J. F. G.; REIS, A. A. dos; WERNER, J. P. S.; TORO, A. P.; FIGUEIREDO, G. K. D. A.; ESQUERDO, J. C. D. M.; LAMPARELLI, R. A. C.; COUTINHO, A. C.; MAGALHÃES, P. S. G. Mapping integrated crop-livestock systems in Brazil with planetscope time series and deep learning. Remote Sensing of Environment, v. 299, 113886, Dec. 2023.| Biblioteca(s): Embrapa Agricultura Digital. |
|  |
| 17. | | ALMEIDA, H. S. L.; REIS, A. A. dos; WERNER, J. P. S.; ANTUNES, J. F. G.; ZHONG, L.; FIGUEIREDO, G. K. D. A.; ESQUERDO, J. C. D. M.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; MAGALHÃES, P. S. G. Deep neural networks for mapping integrated crop-livestock systems using PlanetScope time series. IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2021, Brussels. Proceedings [...]. [S. l.]: IEEE, 2021. p. 4224-4227. IGARSS 2021. Paper WE2.MM-8.3.| Biblioteca(s): Embrapa Agricultura Digital. |
| |
| 18. | | REIS, A. A. dos; WERNER, J. P. S.; SILVA, B. C. da; ANTUNES, J. F. G.; ESQUERDO, J. C. D. M.; FIGUEIREDO, G. K. D. A.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; MAGALHÃES, P. S. G. Can canopy height of mixed pastures in integrated crop-livestock systems be estimated using planetscope imagery? In: WORLD CONGRESS ON INTEGRATED CROP-LIVESTOCK-FORESTRY SYSTEMS, 2., 2021. Proceedings reference. Brasília, DF: Embrapa, 2021. p. 658-663. WCCLF 2021. Evento online.| Biblioteca(s): Embrapa Agricultura Digital. |
| |
| 19. | | REIS, A. A. dos; WERNER, J. P. S.; SILVA, B. C.; FIGUEIREDO, G. K. D. A.; ANTUNES, J. F. G.; ESQUERDO, J. C. D. M.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; ROCHA, J. V.; MAGALHÃES, P. S. G. Monitoring pasture aboveground biomass and canopy height in an integrated crop-livestock system using textural information from PlanetScope imagery. Remote Sensing, v. 12, n. 16, p. 1-21, Aug. 2020. Article number: 2534.| Biblioteca(s): Embrapa Agricultura Digital. |
| |
| 20. | | ANTUNES, J. F. G.; REIS, A. A. dos; ALMEIDA, H. S. L.; WERNER, J. P. S.; FIGUEIREDO, G. K. D. A.; ESQUERDO, J. C. D. M.; BUENO, I. T.; TORO, A. P. S. G. D.; LAMPARELLI, R. A. C.; COUTINHO, A. C.; MAGALHÃES, P. S. G. Classification of integrated crop-livestock systems using PlanetScope time series. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 20., 2023, Florianópolis. Anais [...]. São José dos Campos: Instituto Nacional de Pesquisas Espaciais, 2023. p. 916-919. Editores: Douglas Francisco Marcolino Gherardi, Ieda Del´Arco Sanches, Luiz Eduardo Oliveira e Cruz de Aragão.| Biblioteca(s): Embrapa Agricultura Digital. |
| |
| Registros recuperados : 23 | |
|
|
|
 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
|
Biblioteca(s): |
Embrapa Agricultura Digital. |
|
Data corrente: |
21/09/2020 |
|
Data da última atualização: |
14/12/2021 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
B - 2 |
|
Autoria: |
REIS, A. A. dos; SILVA, B. C.; WERNER, J. P. S.; SILVA, Y. F.; ROCHA, J. V.; FIGUEIREDO, G. K. D. A.; ANTUNES, J. F. G.; ESQUERDO, J. C. D. M.; COUTINHO, A. C.; LAMPARELLI, R. A. C; MAGALHÃES, P. S. G. |
|
Afiliação: |
Feagri, Nipe/Unicamp; Feagri/Unicamp; Feagri/Unicamp; Feagri/Unicamp; Feagri/Unicamp; Feagri/Unicamp; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; ALEXANDRE CAMARGO COUTINHO, CNPTIA; Nipe/Unicamp; Nipe/Unicamp. |
|
Título: |
Exploring the potential of high-resolution PlanetScope imagery for pasture biomass estimation in an integrated crop-livestock system. |
|
Ano de publicação: |
2020 |
|
Fonte/Imprenta: |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 42-3, W12, p. 419-424, 2020. |
|
DOI: |
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-419-2020 |
|
Idioma: |
Inglês |
|
Notas: |
Publicado também em: IEEE LATIN AMERICAN GRSS; ISPRS REMOTE SENSING CONFERENCE, Santiago, 2020. Proceedings... [Piscataway]: IEEE, 2020. p. 675-680. LAGIRS 2020. |
|
Conteúdo: |
ABSTRACT: Pasture biomass information is essential to monitor forage resources in grazed areas, as well as to support grazing management decisions. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely-sensed data. In a preliminary study, we investigated the potential of spectral variables derived from PlanetScope imagery to predict pasture biomass in an area of Integrated Crop-Livestock System (ICLS) in Brazil. Satellite and field data were collected during the same period (May - August 2019) for calibration and validation of the relation between predictor variables and pasture biomass using the Random Forest (RF) regression algorithm. We used as predictor variables 24 vegetation indices derived from PlanetScope imagery, as well as the four PlanetScope bands, and field management information. Pasture biomass ranged from approximately 24 to 656 g.m-2, with a coefficient of variation of 54.96%. Near Infrared Green Simple Ratio (NIR/Green), Green Leaf Algorithm (GLA) vegetation indices and days after sowing (DAS) are among the most important variables as measured by the RF Variable Importance metric in the best RF model predicting pasture biomass, which resulted in Root Mean Square Error (RMSE) of 52.04 g.m-2 (32.75%). Accurate estimates of pasture biomass using spectral variables derived from PlanetScope imagery are promising, providing new insights into the opportunities and limitations related to the use of PlanetScope imagery for pasture monitoring. MenosABSTRACT: Pasture biomass information is essential to monitor forage resources in grazed areas, as well as to support grazing management decisions. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely-sensed data. In a preliminary study, we investigated the potential of spectral variables derived from PlanetScope imagery to predict pasture biomass in an area of Integrated Crop-Livestock System (ICLS) in Brazil. Satellite and field data were collected during the same period (May - August 2019) for calibration and validation of the relation between predictor variables and pasture biomass using the Random Forest (RF) regression algorithm. We used as predictor variables 24 vegetation indices derived from PlanetScope imagery, as well as the four PlanetScope bands, and field management information. Pasture biomass ranged from approximately 24 to 656 g.m-2, with a coefficient of variation of 54.96%. Near Infrared Green Simple Ratio (NIR/Green), Green Leaf Algorithm (GLA) vegetation indices and days after sowing (DAS) are among the most important variables as measured by the RF Variable Importance metric in the best RF model predicting pasture biomass, which resulted in Root Mean Square Error (RMSE) of 52.04 g.m-2 (32.75%). Accurate estimates of pasture biomass using spectral variables derived from PlanetScope imagery are promisi... Mostrar Tudo |
|
Palavras-Chave: |
Aprendizado de máquina; Dove satellites; Floresta aleatória; Índice de vegetação; Integração lavoura-pecuária; Integrated crop-livestock system; Machine Learning; Nano-Satellites; Pastureland; Random Forest; Vegetation Indices. |
|
Thesagro: |
Biomassa; Pastagem. |
|
Thesaurus NAL: |
Biomass; Pasture management; Vegetation index. |
|
Categoria do assunto: |
-- |
|
Marc: |
LEADER 03286naa a2200457 a 4500
001 2125045
005 2021-12-14
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-419-2020$2DOI
100 1 $aREIS, A. A. dos
245 $aExploring the potential of high-resolution PlanetScope imagery for pasture biomass estimation in an integrated crop-livestock system.$h[electronic resource]
260 $c2020
500 $aPublicado também em: IEEE LATIN AMERICAN GRSS; ISPRS REMOTE SENSING CONFERENCE, Santiago, 2020. Proceedings... [Piscataway]: IEEE, 2020. p. 675-680. LAGIRS 2020.
520 $aABSTRACT: Pasture biomass information is essential to monitor forage resources in grazed areas, as well as to support grazing management decisions. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely-sensed data. In a preliminary study, we investigated the potential of spectral variables derived from PlanetScope imagery to predict pasture biomass in an area of Integrated Crop-Livestock System (ICLS) in Brazil. Satellite and field data were collected during the same period (May - August 2019) for calibration and validation of the relation between predictor variables and pasture biomass using the Random Forest (RF) regression algorithm. We used as predictor variables 24 vegetation indices derived from PlanetScope imagery, as well as the four PlanetScope bands, and field management information. Pasture biomass ranged from approximately 24 to 656 g.m-2, with a coefficient of variation of 54.96%. Near Infrared Green Simple Ratio (NIR/Green), Green Leaf Algorithm (GLA) vegetation indices and days after sowing (DAS) are among the most important variables as measured by the RF Variable Importance metric in the best RF model predicting pasture biomass, which resulted in Root Mean Square Error (RMSE) of 52.04 g.m-2 (32.75%). Accurate estimates of pasture biomass using spectral variables derived from PlanetScope imagery are promising, providing new insights into the opportunities and limitations related to the use of PlanetScope imagery for pasture monitoring.
650 $aBiomass
650 $aPasture management
650 $aVegetation index
650 $aBiomassa
650 $aPastagem
653 $aAprendizado de máquina
653 $aDove satellites
653 $aFloresta aleatória
653 $aÍndice de vegetação
653 $aIntegração lavoura-pecuária
653 $aIntegrated crop-livestock system
653 $aMachine Learning
653 $aNano-Satellites
653 $aPastureland
653 $aRandom Forest
653 $aVegetation Indices
700 1 $aSILVA, B. C.
700 1 $aWERNER, J. P. S.
700 1 $aSILVA, Y. F.
700 1 $aROCHA, J. V.
700 1 $aFIGUEIREDO, G. K. D. A.
700 1 $aANTUNES, J. F. G.
700 1 $aESQUERDO, J. C. D. M.
700 1 $aCOUTINHO, A. C.
700 1 $aLAMPARELLI, R. A. C
700 1 $aMAGALHÃES, P. S. G.
773 $tThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences$gv. 42-3, W12, p. 419-424, 2020.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
