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Registro Completo |
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
26/01/2016 |
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Data da última atualização: |
19/01/2023 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
TRABAQUINI, K.; LUIZ, A. J. B.; EBERHARDT, I. D. R.; SCHULTZ, B.; FORMAGGIO, A. R.; ATZBERGER, C. |
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Afiliação: |
KLEBER TRABAQUINI, INPE; ALFREDO JOSE BARRETO LUIZ, CNPMA; ISAQUE DANIEL ROCHA EBERHARDT, INPE; BRUNO SCHULTZ, INPE; ANTONIO ROBERTO FORMAGGIO, INPE; CLEMENT ATZBERGER, University of Natural Resources and Life Sciences, Viena. |
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Título: |
Metodologia para monitoramento agrícola com emprego de imagens orbitais e amostragem estatística. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 17., 2015, João Pessoa. Anais... São José dos Campos: INPE, 2015. p. 4482-4489. |
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Idioma: |
Português |
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Conteúdo: |
Abstract: Brazil still has not a system based in earth observation images to map and monitoring the aimed crops in large scale. Many programs have been made with Landsat-like and MODIS data to monitoring crops in Brazil, but only the CANASAT has worked in operation level. The clouds and unit products (UPS) size in Brazil, have not permitted the use these data to correct classify maize, sugarcane and soybean. The use of sample frame and visual pixels classification with multitemporal OLI images could be a solution to monitor these three crops. The goal of this study was evaluate the sample frame performance to maize (c1), soybean (c2) and sugarcane (c3) in Paraná (PR) State using OLI images and pixel visual classification. Were used four periods to classify 20.000 random pixels over all the Paraná State: (p1) Nov/Dec, (p2) Jan/Feb, (p3) Mar/Apr and (p4) May/Jun. Each period was compost for 4 OLI images, and 5.000 pixels were classified as c1, c2, c3 and others. IBGE data from 2012 were used to determinate the number of random pixels in each PR mesoregion/stratum. The Stratified Random Sample by Maximum Corrected (SRSMC) showed good performance for tree crops. The coefficient of variation (CV) for each period ranged of 1.42 for soybean in p2 until 16.87 for soybean in p4. The sugarcane CVs have not varied ( and maize CV had the minimum value (2.16) in p4. |
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Palavras-Chave: |
Amostragem estatística; Romote sensing; Statistical sampling. |
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Thesagro: |
Agricultura; Estatística agrícola; Sensoriamento Remoto. |
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Thesaurus Nal: |
Agricultural statistics; agriculture; remote sensing; Sampling. |
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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/137814/1/2015AA003.pdf
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Marc: |
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001 2035210
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100 1 $aTRABAQUINI, K.
245 $aMetodologia para monitoramento agrícola com emprego de imagens orbitais e amostragem estatística.$h[electronic resource]
260 $aIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 17., 2015, João Pessoa. Anais... São José dos Campos: INPE, 2015. p. 4482-4489.$c4489
520 $aAbstract: Brazil still has not a system based in earth observation images to map and monitoring the aimed crops in large scale. Many programs have been made with Landsat-like and MODIS data to monitoring crops in Brazil, but only the CANASAT has worked in operation level. The clouds and unit products (UPS) size in Brazil, have not permitted the use these data to correct classify maize, sugarcane and soybean. The use of sample frame and visual pixels classification with multitemporal OLI images could be a solution to monitor these three crops. The goal of this study was evaluate the sample frame performance to maize (c1), soybean (c2) and sugarcane (c3) in Paraná (PR) State using OLI images and pixel visual classification. Were used four periods to classify 20.000 random pixels over all the Paraná State: (p1) Nov/Dec, (p2) Jan/Feb, (p3) Mar/Apr and (p4) May/Jun. Each period was compost for 4 OLI images, and 5.000 pixels were classified as c1, c2, c3 and others. IBGE data from 2012 were used to determinate the number of random pixels in each PR mesoregion/stratum. The Stratified Random Sample by Maximum Corrected (SRSMC) showed good performance for tree crops. The coefficient of variation (CV) for each period ranged of 1.42 for soybean in p2 until 16.87 for soybean in p4. The sugarcane CVs have not varied ( and maize CV had the minimum value (2.16) in p4.
650 $aAgricultural statistics
650 $aagriculture
650 $aremote sensing
650 $aSampling
650 $aAgricultura
650 $aEstatística agrícola
650 $aSensoriamento Remoto
653 $aAmostragem estatística
653 $aRomote sensing
653 $aStatistical sampling
700 1 $aLUIZ, A. J. B.
700 1 $aEBERHARDT, I. D. R.
700 1 $aSCHULTZ, B.
700 1 $aFORMAGGIO, A. R.
700 1 $aATZBERGER, C.
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Registro original: |
Embrapa Meio Ambiente (CNPMA) |
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| 1. |  | TRABAQUINI, K.; LUIZ, A. J. B.; EBERHARDT, I. D. R.; SCHULTZ, B.; FORMAGGIO, A. R.; ATZBERGER, C. Metodologia para monitoramento agrícola com emprego de imagens orbitais e amostragem estatística. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 17., 2015, João Pessoa. Anais... São José dos Campos: INPE, 2015. p. 4482-4489.| Tipo: Artigo em Anais de Congresso |
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