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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
20/08/2015 |
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Data da última atualização: |
26/02/2016 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
ZULLO JUNIOR, J.; COLTRI, P. P.; GONÇALVES, R. R. do V.; ROMANI, L. A. S. |
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Afiliação: |
JURANDIR ZULLO JUNIOR, Cepagri/Unicamp; PRISCILA PEREIRA COLTRI, Cepagri/Unicamp; RENATA RIBEIRO DO VALLE GONÇALVES, Cepagri/Unicamp; LUCIANA ALVIM SANTOS ROMANI, CNPTIA. |
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Título: |
Multi-resolution in remote sensing for agricultural monitoring: a review. |
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Ano de publicação: |
2014 |
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Fonte/Imprenta: |
Revista Brasileira de Cartografia, Rio de Janeiro, n. 66/7, p. 1517-1529, dez. 2014. |
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Idioma: |
Inglês |
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Notas: |
International Issue. Título equivalente: Múltiplas resoluções do sensoriamento remoto aplicado à agricultura: uma revisão. |
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Conteúdo: |
We present a review of literature on remote sensing spatial and temporal images resolution applied to agriculture purposes. In this paper, the defi nitions are reviewed and a focus is given on the applications of resolutions in agricultural studies. A few main applications with low, medium and high spatial resolution focus were selected for this review, especially in sugarcane and coffee crops, where remote sensing contributions are traditionally important. The paper starts with an overview of agriculture and remote sensing, focused on the history of spatial and temporal resolutions uses. This section is followed by a review of low spatial and high temporal resolution systems, with important results in sugarcane. In this section studies conclude that sensors like AVHRR/NOAA can significantly contribute to improve sugarcane monitoring, as well as help understanding of development and expansion. After that, we present medium and high spatial resolution systems, especially for coffee crops that have been showing good results in fi ne scale, with detailed information. Studies conclude that satellites like Geoeye and IKONOS can obtain biophysical information of the culture and satellites like Landsat can monitor the coffee growing. Finally the review presents a conclusion with some key recommendations. |
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Palavras-Chave: |
Cana-de-açúcar; Coffee; Resolução espacial; Resolução temporal; Spatial resolution; Temporal resolution. |
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Thesagro: |
Café; Sensoriamento remoto. |
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Thesaurus Nal: |
Remote sensing; Sugarcane. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/128288/1/Multiresolution-Alvim.pdf
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Marc: |
LEADER 02290naa a2200289 a 4500
001 2022403
005 2016-02-26
008 2014 bl uuuu u00u1 u #d
100 1 $aZULLO JUNIOR, J.
245 $aMulti-resolution in remote sensing for agricultural monitoring$ba review.$h[electronic resource]
260 $c2014
500 $aInternational Issue. Título equivalente: Múltiplas resoluções do sensoriamento remoto aplicado à agricultura: uma revisão.
520 $aWe present a review of literature on remote sensing spatial and temporal images resolution applied to agriculture purposes. In this paper, the defi nitions are reviewed and a focus is given on the applications of resolutions in agricultural studies. A few main applications with low, medium and high spatial resolution focus were selected for this review, especially in sugarcane and coffee crops, where remote sensing contributions are traditionally important. The paper starts with an overview of agriculture and remote sensing, focused on the history of spatial and temporal resolutions uses. This section is followed by a review of low spatial and high temporal resolution systems, with important results in sugarcane. In this section studies conclude that sensors like AVHRR/NOAA can significantly contribute to improve sugarcane monitoring, as well as help understanding of development and expansion. After that, we present medium and high spatial resolution systems, especially for coffee crops that have been showing good results in fi ne scale, with detailed information. Studies conclude that satellites like Geoeye and IKONOS can obtain biophysical information of the culture and satellites like Landsat can monitor the coffee growing. Finally the review presents a conclusion with some key recommendations.
650 $aRemote sensing
650 $aSugarcane
650 $aCafé
650 $aSensoriamento remoto
653 $aCana-de-açúcar
653 $aCoffee
653 $aResolução espacial
653 $aResolução temporal
653 $aSpatial resolution
653 $aTemporal resolution
700 1 $aCOLTRI, P. P.
700 1 $aGONÇALVES, R. R. do V.
700 1 $aROMANI, L. A. S.
773 $tRevista Brasileira de Cartografia, Rio de Janeiro$gn. 66/7, p. 1517-1529, dez. 2014.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Solos. Para informações adicionais entre em contato com cnps.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
13/10/2008 |
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Data da última atualização: |
11/04/2024 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
MENDONÇA-SANTOS, M. de L.; SANTOS, H. G. dos; DART, R. de O.; PARES, J. G. |
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Afiliação: |
MARIA DE LOURDES MENDONÇA SANTOS BREFIN, CNPS; HUMBERTO GONCALVES DOS SANTOS, CNPS; RICARDO DE OLIVEIRA DART, CNPS; JERÔNIMO GUEDES PARÉS. |
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Título: |
Digital mapping of soil classes in Rio de Janeiro State, Brazil: data, modelling and prediction. |
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Ano de publicação: |
2008 |
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Fonte/Imprenta: |
In: HARTEMINK, A. E.; McBRATNEY, A.; MENDONÇA-SANTOS, M. de L. (ed.). Digital soil mapping with limited data. Dordrecht: Springer, 2008. cap. 34, p. 381-396. |
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DOI: |
https://doi.org/10.1007/978-1-4020-8592-5_34 |
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Idioma: |
Inglês |
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Conteúdo: |
A soil database for Rio de Janeiro State was collated in Access, for a project on quantifying the magnitude, spatial distribution and organic carbon in the soils of Rio de Janeiro State (Projeto Carbono_RJ). The main activities were the search, selection, analysis and review of the data for each soil profile already described in the study area, the georeferencing of each soil profile (when spatial coordinates were not available) and the input of new soil profiles into a new interface. The Rio de Janeiro soil dataset now contains 731 soil profiles, 2744 soil horizons, and 48 soil attributes usually described at the soil survey process. From this soil dataset, only 431 soil profiles that were adequately geo-located have been used in this application. The dataset contains limited data for bulk density and hydraulic soil properties, among others. From this dataset, quantitative modelling and digital soil mapping have been completed experimentally at 90 m resolution, using soil data and predictor variables, such as satellite images, lithology, a prior soil map and a DEM and its derivates. This dataset, which is one of the more complete soil datasets in Brazil, is being used as a testbed for learning and teaching DSM, using a variety of methods based on the scorpan model (Embrapa, 2006). In the first instance, the soil dataset was used to predict soil classes at the Order level of the Brazilian Soil Classification System ? SiBCS (Embrapa, 2006). Five models were built and their results were compared and mapped. MenosA soil database for Rio de Janeiro State was collated in Access, for a project on quantifying the magnitude, spatial distribution and organic carbon in the soils of Rio de Janeiro State (Projeto Carbono_RJ). The main activities were the search, selection, analysis and review of the data for each soil profile already described in the study area, the georeferencing of each soil profile (when spatial coordinates were not available) and the input of new soil profiles into a new interface. The Rio de Janeiro soil dataset now contains 731 soil profiles, 2744 soil horizons, and 48 soil attributes usually described at the soil survey process. From this soil dataset, only 431 soil profiles that were adequately geo-located have been used in this application. The dataset contains limited data for bulk density and hydraulic soil properties, among others. From this dataset, quantitative modelling and digital soil mapping have been completed experimentally at 90 m resolution, using soil data and predictor variables, such as satellite images, lithology, a prior soil map and a DEM and its derivates. This dataset, which is one of the more complete soil datasets in Brazil, is being used as a testbed for learning and teaching DSM, using a variety of methods based on the scorpan model (Embrapa, 2006). In the first instance, the soil dataset was used to predict soil classes at the Order level of the Brazilian Soil Classification System ? SiBCS (Embrapa, 2006). Five models were built and their re... Mostrar Tudo |
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Palavras-Chave: |
Brasil; Mapeamento digital; Rio de Janeiro. |
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Thesagro: |
Solo. |
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Thesaurus NAL: |
Soil map. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 02339naa a2200229 a 4500
001 1337609
005 2024-04-11
008 2008 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1007/978-1-4020-8592-5_34$2DOI
100 1 $aMENDONÇA-SANTOS, M. de L.
245 $aDigital mapping of soil classes in Rio de Janeiro State, Brazil$bdata, modelling and prediction.$h[electronic resource]
260 $c2008
520 $aA soil database for Rio de Janeiro State was collated in Access, for a project on quantifying the magnitude, spatial distribution and organic carbon in the soils of Rio de Janeiro State (Projeto Carbono_RJ). The main activities were the search, selection, analysis and review of the data for each soil profile already described in the study area, the georeferencing of each soil profile (when spatial coordinates were not available) and the input of new soil profiles into a new interface. The Rio de Janeiro soil dataset now contains 731 soil profiles, 2744 soil horizons, and 48 soil attributes usually described at the soil survey process. From this soil dataset, only 431 soil profiles that were adequately geo-located have been used in this application. The dataset contains limited data for bulk density and hydraulic soil properties, among others. From this dataset, quantitative modelling and digital soil mapping have been completed experimentally at 90 m resolution, using soil data and predictor variables, such as satellite images, lithology, a prior soil map and a DEM and its derivates. This dataset, which is one of the more complete soil datasets in Brazil, is being used as a testbed for learning and teaching DSM, using a variety of methods based on the scorpan model (Embrapa, 2006). In the first instance, the soil dataset was used to predict soil classes at the Order level of the Brazilian Soil Classification System ? SiBCS (Embrapa, 2006). Five models were built and their results were compared and mapped.
650 $aSoil map
650 $aSolo
653 $aBrasil
653 $aMapeamento digital
653 $aRio de Janeiro
700 1 $aSANTOS, H. G. dos
700 1 $aDART, R. de O.
700 1 $aPARES, J. G.
773 $tIn: HARTEMINK, A. E.; McBRATNEY, A.; MENDONÇA-SANTOS, M. de L. (ed.). Digital soil mapping with limited data. Dordrecht: Springer, 2008. cap. 34, p. 381-396.
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