| |
|
|
|
Registro Completo |
|
Biblioteca(s): |
Embrapa Agrossilvipastoril. |
|
Data corrente: |
01/03/2017 |
|
Data da última atualização: |
23/03/2018 |
|
Tipo da produção científica: |
Artigo em Anais de Congresso |
|
Autoria: |
RODRIGUES, D. A.; VENDRUSCULO, L. G.; ZOLIN, C. A.; LOPES, T. R. |
|
Afiliação: |
DANILO AVANCINI RODRIGUES, UFMT-SINOP; LAURIMAR GONCALVES VENDRUSCULO, CNPTIA; CORNELIO ALBERTO ZOLIN, CPAMT; TARCIO ROCHA LOPES, UFMT-SINOP. |
|
Título: |
Evaluating clustering methods on topographic and hidrological features on lidar data at forest environment. |
|
Ano de publicação: |
2017 |
|
Fonte/Imprenta: |
In: JORNADA CIENTÍFICA DA EMBRAPA AGROSSILVIPASTORIL, 5., 2016, Sinop. Anais. Sinop, MT: Embrapa, 2017. p. 14-18. |
|
Idioma: |
Inglês |
|
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 |
|
Palavras-Chave: |
Flooding risk; Raising information. |
|
Thesaurus Nal: |
LiDAR. |
|
Categoria do assunto: |
-- |
|
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/174439/1/2016-cpamt-zolin-methods-topographic-lidar-forest-p14.pdf
|
|
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.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Agrossilvipastoril (CPAMT) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
 | Acesso ao texto completo restrito à biblioteca da Embrapa Pecuária Sudeste. Para informações adicionais entre em contato com cppse.biblioteca@embrapa.br. |
Registro Completo
|
Biblioteca(s): |
Embrapa Instrumentação; Embrapa Pecuária Sudeste. |
|
Data corrente: |
01/06/2023 |
|
Data da última atualização: |
01/06/2023 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
A - 2 |
|
Autoria: |
MAJARON, V. F.; SILVA, M. G. DA; PFEIFER, M.; BORTOLETTOSANTOS, R.; VELLOSO, C. C. V.; KLAIC, R.; POLITO, W. L.; RIBEIRO, S. J. L.; BERNARDI, A. C. de C.; FARINAS, C. S.; OLIVEIRA, C. R. de. |
|
Afiliação: |
VINÍCIUS F. MAJARON, Universidade Federal de São Carlos; MARISA G. DA SILVA, Universidade Federal de São Carlos; MARCELA PFEIFER, Universidade Federal de São Carlos; RICARDO BORTOLETTO‐SANTOS, Universidade de Ribeirão Preto; CAMILA C. V. VELLOSO, Universidade Federal de São Carlos; RODRIGO KLAIC, Laboratório Nacional de Nanotecnologia para o Agronegócio (LNNA), Embrapa Instrumentação; WAGNER L. POLITO, Universidade de São Paulo; SIDNEY J. L. RIBEIRO, Universidade Estadual Paulista Júlio de Mesquita Filho; ALBERTO CARLOS DE CAMPOS BERNARDI, CPPSE; CRISTIANE SANCHEZ FARINAS, CNPDIA; CAUE RIBEIRO DE OLIVEIRA, CNPDIA. |
|
Título: |
Interaction of Aspergillus niger in double-coated urea granules reduces greenhouse gas emissions from N fertilization. |
|
Ano de publicação: |
2023 |
|
Fonte/Imprenta: |
Journal of Soil Science and Plant Nutrition, 2023. |
|
Páginas: |
10 p. |
|
DOI: |
https://doi.org/10.1007/s42729-023-01295-3 |
|
Idioma: |
Inglês |
|
Conteúdo: |
Urea is the main nitrogen source applied in agriculture and directly impacts agricultural productivity. However, it presents significant losses that reduce plants? nitrogen use efficiency (NUE) and promote greenhouse gas emissions, such as N2O. The coating technology allows for an increase in the NUE, making the nutrient available gradually and uniformly, and combining with microorganisms? action. This work developed and evaluated a double-coating system based on castor oil?polyurethane and maize starch activated by Aspergillus niger for urea granules. We tested the coated urea granules in Palisade grass (Brachiaria brizantha) and measured losses of N2O and NH3. The results showed that the combination between controlled release and Aspergillus niger action reduced the N2O and NH3 emissions, suggesting a local buffering pH effect. The urea loss reduction significantly impacted plant development, increasing N use efficiency, dry mass production, and N uptake. The results support the suitability of a coating system combining controlled release and microorganisms, aiming to better synchronize the nutrient with the plant and reduce environmental impacts. |
|
Palavras-Chave: |
N2O emission; NH3 volatilization. |
|
Thesagro: |
Aspergillus Niger. |
|
Thesaurus NAL: |
Castor oil; Starch; Urea. |
|
Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
|
Marc: |
LEADER 02163naa a2200337 a 4500
001 2154156
005 2023-06-01
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1007/s42729-023-01295-3$2DOI
100 1 $aMAJARON, V. F.
245 $aInteraction of Aspergillus niger in double-coated urea granules reduces greenhouse gas emissions from N fertilization.$h[electronic resource]
260 $c2023
300 $a10 p.
520 $aUrea is the main nitrogen source applied in agriculture and directly impacts agricultural productivity. However, it presents significant losses that reduce plants? nitrogen use efficiency (NUE) and promote greenhouse gas emissions, such as N2O. The coating technology allows for an increase in the NUE, making the nutrient available gradually and uniformly, and combining with microorganisms? action. This work developed and evaluated a double-coating system based on castor oil?polyurethane and maize starch activated by Aspergillus niger for urea granules. We tested the coated urea granules in Palisade grass (Brachiaria brizantha) and measured losses of N2O and NH3. The results showed that the combination between controlled release and Aspergillus niger action reduced the N2O and NH3 emissions, suggesting a local buffering pH effect. The urea loss reduction significantly impacted plant development, increasing N use efficiency, dry mass production, and N uptake. The results support the suitability of a coating system combining controlled release and microorganisms, aiming to better synchronize the nutrient with the plant and reduce environmental impacts.
650 $aCastor oil
650 $aStarch
650 $aUrea
650 $aAspergillus Niger
653 $aN2O emission
653 $aNH3 volatilization
700 1 $aSILVA, M. G. DA
700 1 $aPFEIFER, M.
700 1 $aBORTOLETTOSANTOS, R.
700 1 $aVELLOSO, C. C. V.
700 1 $aKLAIC, R.
700 1 $aPOLITO, W. L.
700 1 $aRIBEIRO, S. J. L.
700 1 $aBERNARDI, A. C. de C.
700 1 $aFARINAS, C. S.
700 1 $aOLIVEIRA, C. R. de
773 $tJournal of Soil Science and Plant Nutrition, 2023.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Pecuária Sudeste (CPPSE) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
