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Registro Completo |
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Biblioteca(s): |
Embrapa Clima Temperado. |
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Data corrente: |
03/04/2023 |
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Data da última atualização: |
03/04/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
CROSA, C. F. R.; MARCO, R. DE; BARRETO, C. F.; SOUZA, R. S. de; YAMAMOTO, R. R.; MARTINS, C. R. |
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Afiliação: |
CLAUDIA FARELA RIBEIRO CROSA; RUDINEI DE MARCO; CAROLINE FARIAS BARRETO; RAFAELA SCHMIDT DE SOUZA; ROBSON RYU YAMAMOTO; CARLOS ROBERTO MARTINS, CPACT. |
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Título: |
Budbreak of pecan cultivars subject to artificial chill. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Revista Ceres, Viçosa, v. 70, n. 1, p. 42-50, jan./feb. 2023. |
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Idioma: |
Inglês |
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Conteúdo: |
Chill is a limiting factor in commercial production of temperate fruit due to their dormancy mechanism. Thus, knowledge of chill requirements of cultivars is important to reach successful production. This study aimed at evaluating responses given by different pecan cultivars subject to artificial chill. Pecan branches were collected from twelve 9-yearold cultivars ? Success, Shoshoni, Farley, Elliott, Mohawk, Jackson, Desirable, Barton, Importada, Shawnee, Choctaw and Melhorada ? in two orchards located in Canguçu, Rio Grande do Sul (RS) state, Brazil, in 2017 and 2018. Treatments consisted in exposing branches to 0, 250, 500, 750 and 1,000 chill hours in a cold chamber (4.0 ± 0.5 °C) and then taking them to the germination chamber (25 ± 0.5 °C and photoperiod of 16 hours of light) until the end of the evaluations. Final budbreak rate (FBR) of every cultivar and the number of days required to reach 50% of budbreak (DD50%) were evaluated. Chill required by cultivars to start budbreak varied in both years under evaluation. Both FBR and DD50 were higher in 2017 than in 2018. Due to high variation in FBR and DD50, chill requirements of pecan cultivars could not be clearly determined by the biological method. |
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Thesagro: |
Frio; Noz Peca. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1152922/1/Artigo-Claudia.pdf
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Marc: |
LEADER 01796naa a2200205 a 4500
001 2152922
005 2023-04-03
008 2023 bl uuuu u00u1 u #d
100 1 $aCROSA, C. F. R.
245 $aBudbreak of pecan cultivars subject to artificial chill.$h[electronic resource]
260 $c2023
520 $aChill is a limiting factor in commercial production of temperate fruit due to their dormancy mechanism. Thus, knowledge of chill requirements of cultivars is important to reach successful production. This study aimed at evaluating responses given by different pecan cultivars subject to artificial chill. Pecan branches were collected from twelve 9-yearold cultivars ? Success, Shoshoni, Farley, Elliott, Mohawk, Jackson, Desirable, Barton, Importada, Shawnee, Choctaw and Melhorada ? in two orchards located in Canguçu, Rio Grande do Sul (RS) state, Brazil, in 2017 and 2018. Treatments consisted in exposing branches to 0, 250, 500, 750 and 1,000 chill hours in a cold chamber (4.0 ± 0.5 °C) and then taking them to the germination chamber (25 ± 0.5 °C and photoperiod of 16 hours of light) until the end of the evaluations. Final budbreak rate (FBR) of every cultivar and the number of days required to reach 50% of budbreak (DD50%) were evaluated. Chill required by cultivars to start budbreak varied in both years under evaluation. Both FBR and DD50 were higher in 2017 than in 2018. Due to high variation in FBR and DD50, chill requirements of pecan cultivars could not be clearly determined by the biological method.
650 $aFrio
650 $aNoz Peca
700 1 $aMARCO, R. DE
700 1 $aBARRETO, C. F.
700 1 $aSOUZA, R. S. de
700 1 $aYAMAMOTO, R. R.
700 1 $aMARTINS, C. R.
773 $tRevista Ceres, Viçosa$gv. 70, n. 1, p. 42-50, jan./feb. 2023.
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Registro original: |
Embrapa Clima Temperado (CPACT) |
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Biblioteca(s): |
Embrapa Territorial. |
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Data corrente: |
17/05/2012 |
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Data da última atualização: |
16/09/2014 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
LI, G.; LU, D.; DUTRA, L.; BATISTELLA, M. |
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Afiliação: |
GUIYING LI, INDIANA UNIVERSITY; DENGSHENG LU, INDIANA UNIVERSITY; LUCIANO DUTRA, INPE; MATEUS BATISTELLA, CNPM. |
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Título: |
A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
ISPRS Journal of Photogrammetry and Remote Sensing, v. 70, p. 26-38, 2012. |
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Páginas: |
p. 26-38. |
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Idioma: |
Inglês |
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Conteúdo: |
This paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected textural images and correlation coefficients between them were then used to determine the best combination of texture images for land-cover classification. Classification results based on different scenarios with maximum likelihood classifier were compared. Based on the identified best scenarios, different classification algorithms ? maximum likelihood classifier, classification tree analysis, Fuzzy ARTMAP (a neural-network method), k-nearest neighbor, object-based classification, and support vector machine were compared for examining which algorithm was suitable for land-cover classification in the tropical moist region. This research indicates that the combination of radiometric images and their textures provided considerably better classification accuracies than individual datasets. The L-band data provided much better landcover classification than C-band data but neither L-band nor C-band was suitable for fine land-cover classification system, no matter which classification algorithm was used. L-band data provided reasonably good classification accuracies for coarse land-cover classification system such as forest, succession, agropasture, water, wetland, and urban with an overall classification accuracy of 72.2%, but C-band data provided only 54.7%. Compared to the maximum likelihood classifier, both classification tree analysis and Fuzzy ARTMAP provided better performances, object-based classification and support vector machine had similar performances, and k-nearest neighbor performed poorly. More research should address the use of multitemporal radar data and the integration of radar and optical sensor data for improving land-cover classification. MenosThis paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected textural images and correlation coefficients between them were then used to determine the best combination of texture images for land-cover classification. Classification results based on different scenarios with maximum likelihood classifier were compared. Based on the identified best scenarios, different classification algorithms ? maximum likelihood classifier, classification tree analysis, Fuzzy ARTMAP (a neural-network method), k-nearest neighbor, object-based classification, and support vector machine were compared for examining which algorithm was suitable for land-cover classification in the tropical moist region. This research indicates that the combination of radiometric images and their textures provided considerably better classification accuracies than individual datasets. The L-band data provided much better landcover classification than C-band data but neither L-band nor C-band was suitable for fine land-cover classification system, no matter which classification algorithm was used. L-band data provided reasonably good classification accuracies for coarse land-cover cla... Mostrar Tudo |
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Palavras-Chave: |
ALOS PALSAR; Amazon; Land-cover classification; RADARSAT. |
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Thesaurus NAL: |
texture. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/59517/1/MateusISPRS.pdf
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Marc: |
LEADER 02748naa a2200229 a 4500
001 1924819
005 2014-09-16
008 2012 bl uuuu u00u1 u #d
100 1 $aLI, G.
245 $aA comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region.
260 $c2012
300 $ap. 26-38.
520 $aThis paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected textural images and correlation coefficients between them were then used to determine the best combination of texture images for land-cover classification. Classification results based on different scenarios with maximum likelihood classifier were compared. Based on the identified best scenarios, different classification algorithms ? maximum likelihood classifier, classification tree analysis, Fuzzy ARTMAP (a neural-network method), k-nearest neighbor, object-based classification, and support vector machine were compared for examining which algorithm was suitable for land-cover classification in the tropical moist region. This research indicates that the combination of radiometric images and their textures provided considerably better classification accuracies than individual datasets. The L-band data provided much better landcover classification than C-band data but neither L-band nor C-band was suitable for fine land-cover classification system, no matter which classification algorithm was used. L-band data provided reasonably good classification accuracies for coarse land-cover classification system such as forest, succession, agropasture, water, wetland, and urban with an overall classification accuracy of 72.2%, but C-band data provided only 54.7%. Compared to the maximum likelihood classifier, both classification tree analysis and Fuzzy ARTMAP provided better performances, object-based classification and support vector machine had similar performances, and k-nearest neighbor performed poorly. More research should address the use of multitemporal radar data and the integration of radar and optical sensor data for improving land-cover classification.
650 $atexture
653 $aALOS PALSAR
653 $aAmazon
653 $aLand-cover classification
653 $aRADARSAT
700 1 $aLU, D.
700 1 $aDUTRA, L.
700 1 $aBATISTELLA, M.
773 $tISPRS Journal of Photogrammetry and Remote Sensing$gv. 70, p. 26-38, 2012.
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Embrapa Territorial (CNPM) |
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