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1. | | VIEIRA, R. F.; SILVA, C. M. S.; FERREIRA, A. P.; INFANTE, C. S. Biomassa e atividade microbiana em solos suplementados com lodo de esgoto e lodo de esgoto compostado. In: REUNIÃO BRASILEIRA DE FERTILIDADE DO SOLO E NUTRIÇÃO DE PLANTAS, 28.; REUNIÃO BRASILEIRA SOBRE MICORRIZAS, 12.; SIMPÓSIO BRASILEIRO DE MICROBIOLOGIA DO SOLO, 10.; REUNIÃO BRASILEIRA DE BIOLOGIA DO SOLO, 7., 2008, Londrina. FertBio 2008: desafios para o uso do solo com eficiência e qualidade ambiental: resumos. Londrina: Embrapa Soja: SBCS: IAPAR, UEL, 2008. 1 CD-ROM. Biblioteca(s): Embrapa Meio Ambiente. |
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8. | | DITA, M. A.; HERAI, R.; WAALWIJK, C.; YAMAGISHI, M.; GIACHETTO, P.; FERREIRA, G.; SOUZA, M.; KEMA, G. H. J. Comparative transcriptome analysis and genome assembly of Fusarium oxysporum f. sp. cubense. In: INTERNATIONAL PROMUSA SYMPOSIUM, 2011, Salvador. Bananas and plantains: toward sustainable global production and improved uses: abstracts. [S.l.]: ISHS, 2011. p. 58. Biblioteca(s): Embrapa Agricultura Digital. |
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11. | | CRUZ, J. C.; PEREIRA FILHO, I. A.; PEREIRA, F. T. F.; ALVARENGA, R. C. Avaliação de variedades de milho em diferentes densidades de plantio em sistema orgânico de produção. In: CONGRESSO BRASILEIRO DE AGROECOLOGIA, 1.; SEMINÁRIO INTERNACIONAL SOBRE AGROECOLOGIA, 4.; SEMINÁRIO ESTADUAL SOBE AGROECOLOGIA, 5., 2003, Porto Alegre. Conquistando a soberania alimentar: anais... Pelotas: Embrapa Clima Temperado; Emater-RS, 2003. 1 CD-ROM. Biblioteca(s): Embrapa Milho e Sorgo. |
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12. | | LANZER, E. A.; AMBROSI, I.; DOSSA, D.; FREIRE, L. M. de M.; GIROTO, A. F.; HOEFLICH, V. A.; REIS, P.; OSORIO, V. A.; PORTO, V. H. da F.; SALLES, P. A.; SOUZA, S. X. de; TRINDADE, A. M. Avaliação sócio-econômica das pesquisas da EMBRAPA na Região Sul. Brasília, DF: EMBRAPA-DPU: EMBRAPA-SEP, 1989. 40 p. (EMBRAPA-SEP. Documentos, 45). Biblioteca(s): Embrapa Agroindústria Tropical; Embrapa Amapá; Embrapa Amazônia Oriental; Embrapa Arroz e Feijão; Embrapa Florestas; Embrapa Gado de Leite; Embrapa Meio Norte / UEP-Parnaíba; Embrapa Meio-Norte; Embrapa Pantanal; Embrapa Semiárido; Embrapa Soja; Embrapa Solos; Embrapa Trigo; Embrapa Unidades Centrais. MenosEmbrapa Agroindústria Tropical; Embrapa Amapá; Embrapa Amazônia Oriental; Embrapa Arroz e Feijão; Embrapa Florestas; Embrapa Gado de Leite; Embrapa Meio Norte / UEP-Parnaíba; Embrapa Meio-Norte; Embrapa Pantanal; Embrapa Semiárido... Mostrar Todas |
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13. | | SANCHES, I. D.; FEITOSA, R. Q.; ACHANCCARAY, P.; MONTIBELLER, B.; LUIZ, A. J. B.; SOARES, M. D.; PRUDENTE, V. H. R.; VIEIRA, D. C.; MAURANO, L. E. P. Lem benchmark database for tropical agricultural remote sensing application. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 42, n. 1, p. 387-392, 2018. Edition of the proceedings ISPRS TC I Mid-term Symposium ?Innovative Sensing ? From Sensors to Methods and Applications?, 10-12 October 2018, held a Karlsruhe, Germany. 387-392. Biblioteca(s): Embrapa Meio Ambiente. |
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17. | | SILVA, G. O. da; BORTOLETTO, A. C.; STOKER, G.; PONIJALEKI, R. S.; PEREIRA, A. da S.; SUINAGA, F. A. Desempenho de cultivares de batata sob condições ambientais de estiagem. In: CONGRESO DE LA ASOCIACION LATINOAMERICANA DE LA PAPA - ALAP, 25.; ENCONTRO NACIONAL DE PRODUÇÃO E ABASTECIMENTO DE BATATA - ENB, 14., 2012, Uberlândia. [Anais...]. Uberlândia: Asociación Latinoamericana de la Papa, 2012. Biblioteca(s): Embrapa Clima Temperado. |
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18. | | LOURENÇO JUNIOR, J. de B.; COSTA, N. A. da; RODRIGUES FILHO, J. A.; CAMARAO, A. P.; MARQUES, J. R. F.; CARVALHO, L. O. D. de M.; NASCIMENTO, C. N. B. do; HANTANI, A. K. Desempenho produtivo e reprodutivo de búfalas em sistema integrado de pastagens nativa e cultivada. Belém, PA: EMBRAPA-CPATU, 1993. 29 p. il. (EMBRAPA-CPATU. Boletim de pesquisa, 141). Biblioteca(s): Embrapa Amapá; Embrapa Amazônia Ocidental; Embrapa Amazônia Oriental; Embrapa Arroz e Feijão; Embrapa Meio-Norte; Embrapa Roraima; Embrapa Unidades Centrais. |
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19. | | GARCIA, P. M.; ARCURI, E. F.; BRITO, M. A. V. P.; LANGE, C. C.; BRITO, J. R. F.; CERQUEIRA, M. M. O. P. Detecção de Escherichia coli O157:H7 inoculada experimentalmente em amostras de leite cru por método convencional e PCR multiplex. Arquivo Brasileiro de Medicina Veterinária e Zootecnia, Belo Horizonte, v. 60, n. 5, p. 1241-1249, 2008. Biblioteca(s): Embrapa Gado de Leite. |
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Registros recuperados : 34 | |
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Registro Completo
Biblioteca(s): |
Embrapa Meio Ambiente. |
Data corrente: |
02/01/2019 |
Data da última atualização: |
17/01/2023 |
Tipo da produção científica: |
Artigo em Anais de Congresso |
Autoria: |
SANCHES, I. D.; FEITOSA, R. Q.; ACHANCCARAY, P.; MONTIBELLER, B.; LUIZ, A. J. B.; SOARES, M. D.; PRUDENTE, V. H. R.; VIEIRA, D. C.; MAURANO, L. E. P. |
Afiliação: |
IEDA DEL'ARCO SANCHES, INPE; RAUL QUEIROZ FEITOSA, PUC Rio; PEDRO ACHANCCARAY, PUC Rio; BRUNO MONTIBELLER, INPE; ALFREDO JOSE BARRETO LUIZ, CNPMA; MARINALVA DIAS SOARES, PUC Rio; VICTOR HUGO ROHDEN PRUDENTE, INPE; D C VIEIRA, INPE; LUIS EDUARDO PINHEIRO MAURANO, INPE. |
Título: |
Lem benchmark database for tropical agricultural remote sensing application. |
Ano de publicação: |
2018 |
Fonte/Imprenta: |
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 42, n. 1, p. 387-392, 2018. Edition of the proceedings ISPRS TC I Mid-term Symposium ?Innovative Sensing ? From Sensors to Methods and Applications?, 10-12 October 2018, held a Karlsruhe, Germany. |
Páginas: |
387-392. |
Idioma: |
Inglês |
Conteúdo: |
Abstract: The monitoring of agricultural activities at a regular basis is crucial to assure that the food production meets the world population demands, which is increasing yearly. Such information can be derived from remote sensing data. In spite of topic?s relevance, not enough efforts have been invested to exploit modern pattern recognition and machine learning methods for agricultural land-cover mapping from multi-temporal, multi-sensor earth observation data. Furthermore, only a small proportion of the works published on this topic relates to tropical/subtropical regions, where crop dynamics is more complicated and difficult to model than in temperate regions. A major hindrance has been the lack of accurate public databases for the comparison of different classification methods. In this context, the aim of the present paper is to share a multi-temporal and multi-sensor benchmark database that can be used by the remote sensing community for agricultural land-cover mapping. Information about crops in situ was collected in Luís Eduardo Magalhães (LEM) municipality, which is an important Brazilian agricultural area, to create field reference data including information about first and second crop harvests. Moreover, a series of remote sensing images was acquired and pre-processed, from both active and passive orbital sensors (Sentinel-1, Sentinel-2/MSI, Landsat-8/OLI), correspondent to the LEM area, along the development of the main annual crops. In this paper, we describe the LEM database (crop field boundaries, land use reference data and pre-processed images) and present the results of an experiment conducted using the Sentinel-1 and Sentinel-2 data. MenosAbstract: The monitoring of agricultural activities at a regular basis is crucial to assure that the food production meets the world population demands, which is increasing yearly. Such information can be derived from remote sensing data. In spite of topic?s relevance, not enough efforts have been invested to exploit modern pattern recognition and machine learning methods for agricultural land-cover mapping from multi-temporal, multi-sensor earth observation data. Furthermore, only a small proportion of the works published on this topic relates to tropical/subtropical regions, where crop dynamics is more complicated and difficult to model than in temperate regions. A major hindrance has been the lack of accurate public databases for the comparison of different classification methods. In this context, the aim of the present paper is to share a multi-temporal and multi-sensor benchmark database that can be used by the remote sensing community for agricultural land-cover mapping. Information about crops in situ was collected in Luís Eduardo Magalhães (LEM) municipality, which is an important Brazilian agricultural area, to create field reference data including information about first and second crop harvests. Moreover, a series of remote sensing images was acquired and pre-processed, from both active and passive orbital sensors (Sentinel-1, Sentinel-2/MSI, Landsat-8/OLI), correspondent to the LEM area, along the development of the main annual crops. In this paper, we describe t... Mostrar Tudo |
Palavras-Chave: |
Agricultura tropical; Agricultural mapping/monitoring; C-band SAR data; Double gropping systems; Free available database; Mapeamento; Multispectral instrument. |
Thesagro: |
Agricultura; Base de dados; Sensoriamento remoto. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/189604/1/2018AA07.pdf
|
Marc: |
LEADER 02926nam a2200337 a 4500 001 2102815 005 2023-01-17 008 2018 bl uuuu u00u1 u #d 100 1 $aSANCHES, I. D. 245 $aLem benchmark database for tropical agricultural remote sensing application.$h[electronic resource] 260 $aInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 42, n. 1, p. 387-392, 2018. Edition of the proceedings ISPRS TC I Mid-term Symposium ?Innovative Sensing ? From Sensors to Methods and Applications?, 10-12 October 2018, held a Karlsruhe, Germany.$c2018 300 $a387-392. 520 $aAbstract: The monitoring of agricultural activities at a regular basis is crucial to assure that the food production meets the world population demands, which is increasing yearly. Such information can be derived from remote sensing data. In spite of topic?s relevance, not enough efforts have been invested to exploit modern pattern recognition and machine learning methods for agricultural land-cover mapping from multi-temporal, multi-sensor earth observation data. Furthermore, only a small proportion of the works published on this topic relates to tropical/subtropical regions, where crop dynamics is more complicated and difficult to model than in temperate regions. A major hindrance has been the lack of accurate public databases for the comparison of different classification methods. In this context, the aim of the present paper is to share a multi-temporal and multi-sensor benchmark database that can be used by the remote sensing community for agricultural land-cover mapping. Information about crops in situ was collected in Luís Eduardo Magalhães (LEM) municipality, which is an important Brazilian agricultural area, to create field reference data including information about first and second crop harvests. Moreover, a series of remote sensing images was acquired and pre-processed, from both active and passive orbital sensors (Sentinel-1, Sentinel-2/MSI, Landsat-8/OLI), correspondent to the LEM area, along the development of the main annual crops. In this paper, we describe the LEM database (crop field boundaries, land use reference data and pre-processed images) and present the results of an experiment conducted using the Sentinel-1 and Sentinel-2 data. 650 $aAgricultura 650 $aBase de dados 650 $aSensoriamento remoto 653 $aAgricultura tropical 653 $aAgricultural mapping/monitoring 653 $aC-band SAR data 653 $aDouble gropping systems 653 $aFree available database 653 $aMapeamento 653 $aMultispectral instrument 700 1 $aFEITOSA, R. Q. 700 1 $aACHANCCARAY, P. 700 1 $aMONTIBELLER, B. 700 1 $aLUIZ, A. J. B. 700 1 $aSOARES, M. D. 700 1 $aPRUDENTE, V. H. R. 700 1 $aVIEIRA, D. C. 700 1 $aMAURANO, L. E. P.
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