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Aboveground biomass in Prosopis pallida (Humb. and Bonpl. ex Willd.) H. B. K. ecosystems using Landsat 7 ETM+ images RChHN
PADRÓN,EVA; NAVARRO-CERRILLO,RAFAEL M.
The significance of field work in remote sensing studies when applied to large areas has often been underestimated. The combination of specific forest inventories for the estimation of aboveground biomass in large dry tropical forest areas with remote sensor data has scarcely been explored to date. In this work, a systematic, stratified forest inventory involving 100 X 100 m square plots in an area of Peruvian Prosopis pallida dry forest, roughly one million hectares in size in the Piura province (Peru) has been compiled. The inventory encompassed the principal silvicultural variables defining the ecosystem studied, which were used in allometric equations for the different species, genera and plant associations in the area in order to estimate the amount...
Tipo: Journal article Palavras-chave: Prosopis pallida; Landsat 7 ETM+; Vegetation indices; Biomass estimation.
Ano: 2007 URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0716-078X2007000100004
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Aplicación de tecnologías de percepción remota en la estimación de rendimientos en caña de azúcar. Colegio de Postgraduados
Zenteno Cruz, Guillermo Alberto.
Los productores de caña de azúcar buscan constantemente formas de reducir costos, aumentar la productividad y los rendimientos, pero en general, son pocas las herramientas que se están aplicando para tal fin. Por otra parte, es cada vez más importante la predicción temprana del rendimiento de los cultivos que permita una planificación de políticas agrícolas y de seguridad alimentaria en una economía de mercado globalizado. Con el propósito de calibrar una metodología para enfrentar estos problemas, la presente investigación se llevó a cabo en la zona de abasto del Ingenio Emiliano Zapata, en Morelos, México, en una superficie de 10, 664 hectáreas, durante el ciclo agrícola 2009-2010. El propósito del estudio fue determinar una función matemática para...
Palavras-chave: Predicción temprana; Indices de vegetación; Estrés hídrico; Evapotranspiración; Saccharum officinarum; Early prediction; Vegetation indices; Water stress; Evapotranspiración; Maestría; Hidrociencias.
Ano: 2012 URL: http://hdl.handle.net/10521/737
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Mapping climatic risks in the EU agriculture AgEcon
Gallego, Javier; Conte, Costanza G.; Dittmann, Christoph; Stroblmair, Josef; Bielza, Maria.
Replaced with revised version of paper 11/18/08.
Tipo: Conference Paper or Presentation Palavras-chave: Agrometeorological models; Climatic risk; European Union; Vegetation indices; Environmental Economics and Policy; Risk and Uncertainty.
Ano: 2007 URL: http://purl.umn.edu/9260
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Remote Sensing for Site-Specific Crop Management: Evaluating the Potential of Digital Multi-Spectral Imagery for Monitoring Crop Variability and Weeds within Paddocks AgEcon
Drysdale, G.; Metternicht, G..
This paper analyses the potential and limitations of airborne remote sensing systems for detecting crop growth variability and weed infestation within paddocks at specified capture times. The detection of areas of crop growth variability can help farmers become aware of regions within their paddock where they may be experiencing above and below average yields due to changes in soil or management conditions. For instance, the early detection of weed infestation within cereal crops is crucial for lessening their impact on the final yield. Transect sampling within a canola paddock of a broad acre agricultural property in the South West of Western Australia was conducted synchronous with the capture of 1m spatial resolution DMSI. The four individual bands...
Tipo: Conference Paper or Presentation Palavras-chave: LAI; Remote sensing; Crop density; Vegetation indices; Weed mapping.; Crop Production/Industries.
Ano: 2003 URL: http://purl.umn.edu/24341
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Variáveis e modelos para estimativa da produtividade do cafeeiro a partir de índices de vegetação derivados de imagens Landsat. Repositório Alice
BERNARDES, T.; MOREIRA, M. A.; VERONA, J. D.; SHIMABUKURO, Y. E.; LUIZ, A. J. B..
Abstract. Coffee fields present a specific pattern of productivity resulting in high and low production in alternated years. Branches grown the first phenological year will produce coffee beans the second phenological year. In high-production years a plant works mostly to grain-filling to the detriment of new branches which will be responsible for production the following year. In low-production years the plant works rather to grow new branches which will produce beans the subsequent year. This feature can be related to the foliar biomass, which can be estimated through remote sensing derived vegetation indices. Several studies report this feature must be incorporated in modeling coffee yield coupled with agrometeorogical models. In this paper we derived...
Tipo: Artigo em anais de congresso (ALICE) Palavras-chave: Coffee yield; Biophysics variables; Vegetation indices; Radiometric correction; Stepwise regression; Café; Produtividade; Sensoriamento remoto; Coffee; Grain yield; Agricultural management models; Statistical models.
Ano: 2013 URL: http://www.alice.cnptia.embrapa.br/handle/doc/963264
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