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Registro Completo |
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Biblioteca(s): |
Embrapa Pantanal. |
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Data corrente: |
18/01/1999 |
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Data da última atualização: |
30/03/2017 |
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Autoria: |
MAURO, R. de A.; POTT, A. |
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Afiliação: |
EMBRAPA. Centro de Pesquisa Agropecuaria do Pantanal (Corumba, MS). |
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Título: |
Dieta de capivara (Hydrochaeris hydrochaeris) basada en analisis microhistologico de las heces. |
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Ano de publicação: |
1996 |
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Fonte/Imprenta: |
Vida Silvestre Neotropical, v.5, n.2, p.151-153, 1996. |
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Idioma: |
Espanhol |
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Conteúdo: |
La capibara (Hydrochaeris hydrochaeris) es un tipico roedor de humedales de la region Neotropical e es considerada la especie mas grande de su ordem. Se distribuye desde panama hasta el nordeste de Argentina (Ojati 1973). En Brasil, este roedor es mas abundante en el Pantanal matogrossense (aprox. 140.000 km2), region que se subdivide en 10 regiones en funcion de su diferente geomorfologia e vegetacion. |
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Palavras-Chave: |
Diets. |
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Thesagro: |
Capivara; Dieta; Nutrição. |
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Thesaurus Nal: |
Hydrochaeris hydrochaeris; nutrition. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00970naa a2200205 a 4500
001 1793374
005 2017-03-30
008 1996 bl --- 0-- u #d
100 1 $aMAURO, R. de A.
245 $aDieta de capivara (Hydrochaeris hydrochaeris) basada en analisis microhistologico de las heces.
260 $c1996
520 $aLa capibara (Hydrochaeris hydrochaeris) es un tipico roedor de humedales de la region Neotropical e es considerada la especie mas grande de su ordem. Se distribuye desde panama hasta el nordeste de Argentina (Ojati 1973). En Brasil, este roedor es mas abundante en el Pantanal matogrossense (aprox. 140.000 km2), region que se subdivide en 10 regiones en funcion de su diferente geomorfologia e vegetacion.
650 $aHydrochaeris hydrochaeris
650 $anutrition
650 $aCapivara
650 $aDieta
650 $aNutrição
653 $aDiets
700 1 $aPOTT, A.
773 $tVida Silvestre Neotropical$gv.5, n.2, p.151-153, 1996.
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Registro original: |
Embrapa Pantanal (CPAP) |
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Biblioteca |
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Registro |
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Registro Completo
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Biblioteca(s): |
Embrapa Agrossilvipastoril. |
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Data corrente: |
01/03/2017 |
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Data da última atualização: |
23/03/2018 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
RODRIGUES, D. A.; VENDRUSCULO, L. G.; ZOLIN, C. A.; LOPES, T. R. |
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Afiliação: |
DANILO AVANCINI RODRIGUES, UFMT-SINOP; LAURIMAR GONCALVES VENDRUSCULO, CNPTIA; CORNELIO ALBERTO ZOLIN, CPAMT; TARCIO ROCHA LOPES, UFMT-SINOP. |
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Título: |
Evaluating clustering methods on topographic and hidrological features on lidar data at forest environment. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
In: JORNADA CIENTÍFICA DA EMBRAPA AGROSSILVIPASTORIL, 5., 2016, Sinop. Anais. Sinop, MT: Embrapa, 2017. p. 14-18. |
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Idioma: |
Inglês |
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Conteúdo: |
The acquisition of high resolution geographic data through laser technology has recently being expanded due to the development of LiDAR (Light Detection and Ranging) system. This technology?s growth is relying on its great ability to acquire information in large quantity and short time. The geographic data provided from laser scanning is capable of raising information for coast planning, assess flooding risk, power transmission network and telecommunication, forests, agriculture, oil, transportation, urban planning, mining, among others (GIONGO et al., 2010). LiDAR technology follows the same principles as the RADAR system, with the difference of using laser pulses to locate features, instead of radio waves. Not only for its ability to deal with large amounts of information in such a short period of time, LiDAR has the advantage upon the classic passive sensors (aerial photographs and satellite images) of not depending on a source of light, and so its data will never present shadows from clouds or neighboring features (GIONGO et al., 2010). Data from LiDAR sensor is distributed in a point cloud where each point has at least three-dimensional spatial coordinates (latitude, longitude and height) that correspond to a particular point on the Earth?s surface from which the laser pulse was reflected. Once LiDAR data is acquired the next step is use algorithms that separate points (also referred to as returns) on the point cloud that represents the ground and the ones above the ground level, those algorithms can then process series of interpolation that allows the operator to generate Digital Elevation Models (DEMs). In order to add information for the points within the DEM, labeling those returns following a pattern and then grouping them on clusters is useful as one of the steps in exploratory data analysis. Several methodologies were developed to organize a pattern of points in a multidimensional space into clusters based on similarity. Points belonging to the same cluster are given the same label and present a pattern where they are more similar to each other than they are to a pattern belonging to a different cluster (JAIN et al., 1999). One example to apply this technology on forestry activities is the application of silvicultural treatment to improve the forest?s productivity, where the decision is taken considering characteristics from the site and sites with similar characteristics may have the same silvicultural system. The variety of techniques for grouping data elements has produced a rich and often confusing assortment of clustering methods. Furthermore, there is a lack of studies grouping topologic and hydrologic variables at forested environments. The goal of this survey is to evaluate k-means and CLARA clustering techniques on a LiDAR-derived DEM from southern Amazonia, in the municipality of Cotriguaçu, Mato Grosso, Brazil. MenosThe acquisition of high resolution geographic data through laser technology has recently being expanded due to the development of LiDAR (Light Detection and Ranging) system. This technology?s growth is relying on its great ability to acquire information in large quantity and short time. The geographic data provided from laser scanning is capable of raising information for coast planning, assess flooding risk, power transmission network and telecommunication, forests, agriculture, oil, transportation, urban planning, mining, among others (GIONGO et al., 2010). LiDAR technology follows the same principles as the RADAR system, with the difference of using laser pulses to locate features, instead of radio waves. Not only for its ability to deal with large amounts of information in such a short period of time, LiDAR has the advantage upon the classic passive sensors (aerial photographs and satellite images) of not depending on a source of light, and so its data will never present shadows from clouds or neighboring features (GIONGO et al., 2010). Data from LiDAR sensor is distributed in a point cloud where each point has at least three-dimensional spatial coordinates (latitude, longitude and height) that correspond to a particular point on the Earth?s surface from which the laser pulse was reflected. Once LiDAR data is acquired the next step is use algorithms that separate points (also referred to as returns) on the point cloud that represents the ground and the ones above the gro... Mostrar Tudo |
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Palavras-Chave: |
Flooding risk; Raising information. |
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Thesaurus NAL: |
LiDAR. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/174439/1/2016-cpamt-zolin-methods-topographic-lidar-forest-p14.pdf
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Marc: |
LEADER 03523nam a2200181 a 4500
001 2065633
005 2018-03-23
008 2017 bl uuuu u00u1 u #d
100 1 $aRODRIGUES, D. A.
245 $aEvaluating clustering methods on topographic and hidrological features on lidar data at forest environment.$h[electronic resource]
260 $aIn: JORNADA CIENTÍFICA DA EMBRAPA AGROSSILVIPASTORIL, 5., 2016, Sinop. Anais. Sinop, MT: Embrapa, 2017. p. 14-18.$c2017
520 $aThe acquisition of high resolution geographic data through laser technology has recently being expanded due to the development of LiDAR (Light Detection and Ranging) system. This technology?s growth is relying on its great ability to acquire information in large quantity and short time. The geographic data provided from laser scanning is capable of raising information for coast planning, assess flooding risk, power transmission network and telecommunication, forests, agriculture, oil, transportation, urban planning, mining, among others (GIONGO et al., 2010). LiDAR technology follows the same principles as the RADAR system, with the difference of using laser pulses to locate features, instead of radio waves. Not only for its ability to deal with large amounts of information in such a short period of time, LiDAR has the advantage upon the classic passive sensors (aerial photographs and satellite images) of not depending on a source of light, and so its data will never present shadows from clouds or neighboring features (GIONGO et al., 2010). Data from LiDAR sensor is distributed in a point cloud where each point has at least three-dimensional spatial coordinates (latitude, longitude and height) that correspond to a particular point on the Earth?s surface from which the laser pulse was reflected. Once LiDAR data is acquired the next step is use algorithms that separate points (also referred to as returns) on the point cloud that represents the ground and the ones above the ground level, those algorithms can then process series of interpolation that allows the operator to generate Digital Elevation Models (DEMs). In order to add information for the points within the DEM, labeling those returns following a pattern and then grouping them on clusters is useful as one of the steps in exploratory data analysis. Several methodologies were developed to organize a pattern of points in a multidimensional space into clusters based on similarity. Points belonging to the same cluster are given the same label and present a pattern where they are more similar to each other than they are to a pattern belonging to a different cluster (JAIN et al., 1999). One example to apply this technology on forestry activities is the application of silvicultural treatment to improve the forest?s productivity, where the decision is taken considering characteristics from the site and sites with similar characteristics may have the same silvicultural system. The variety of techniques for grouping data elements has produced a rich and often confusing assortment of clustering methods. Furthermore, there is a lack of studies grouping topologic and hydrologic variables at forested environments. The goal of this survey is to evaluate k-means and CLARA clustering techniques on a LiDAR-derived DEM from southern Amazonia, in the municipality of Cotriguaçu, Mato Grosso, Brazil.
650 $aLiDAR
653 $aFlooding risk
653 $aRaising information
700 1 $aVENDRUSCULO, L. G.
700 1 $aZOLIN, C. A.
700 1 $aLOPES, T. R.
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Embrapa Agrossilvipastoril (CPAMT) |
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