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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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Registro original: |
Embrapa Agrossilvipastoril (CPAMT) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Florestas. Para informações adicionais entre em contato com cnpf.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
10/05/2023 |
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Data da última atualização: |
14/08/2023 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
DALMOLIN, R. S. D.; PEDRON, F. de A.; CURCIO, G. R. |
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Afiliação: |
RICARDO S. D. DALMOLIN, UNIVERSIDADE FEDERAL DE SANTA MARIA; FABRÍCIO DE ARAÚJO PEDRON, UNIVERSIDADE FEDERAL DE SANTA MARIA; GUSTAVO RIBAS CURCIO, CNPF. |
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Título: |
Soils of the Southern Araucaria Highlands. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
In: SCHAEFER, C. E. G. R. (ed.). The soils of Brazil. Cham: Springer, 2023. cap. 10. |
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Páginas: |
p. 269-297. |
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Descrição Física: |
Ebook |
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Série: |
(World soils book series). |
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ISBN: |
978-3-031-19949-3 |
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DOI: |
https://doi.org/10.1007/978-3-031-19949-3_10 |
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Idioma: |
Inglês |
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Conteúdo: |
The Araucaria Highland Plateaux is known as Mixed Subtropical Rainforest dominated by Araucaria trees, in the highlands plateaus of the southern states of Brazil, such as Paraná, Santa Catarina, and Rio Grande do Sul, with minor areas in isolated highlands in southern São Paulo, Minas Gerais, and Rio de Janeiro States. The climate of Araucaria Plateau is humid temperate mesothermal (subtropical to temperate). The vast majority of soils developed from weathered volcanic rocks of the Serra Geral Formation have a clayey or very clayey texture, resulting from long-term weathering and the intense alteration of riodacites, basalts, and andesitic-basalts. Under subtropical conditions, plagioclases, pyroxenes, amphiboles, biotites, and olivines undergo an almost complete dissolution, leaving little mineral reserves in the coarse fractions of soils, where resistant quartz, magnetite, and ilmenite dominate. The main soil classes are: Latossolos Vermelhos, Latossolos Brunos, Nitossolos Vermelhos or Brunos, Argissolos Bruno-acinzentados, Cambissolos Húmicos or Hísticos, on volcanic rocks, mainly. Argissolos Vermelhos or Vermelho Amarelos also occur on sedimentary or granitic/metamorphic rocks. The chemical weathering of Araucaria Plateau is moderately intense, leading to the formation of kaolinite mixed with hydroxy-Al vermiculite or smectite with little or no illite, due to the absence of muscovite in the parent material. The presence of gibbsite is occasional in some soils, but in low proportions. The predominance of Kaolinite in Araucaria soils is attributed to past colder and wetter climates, favoring organic matter accumulation and aluminum complexation, preventing the formation of gibbsite. Soils from the Araucaria Plateau have unusually high proportions of hydroxy-interlayered Al in 2:1 minerals, representing a marked difference with other deep-weathered tropical soils from elsewhere is Brazil. Most Latossolos (Ferralsols) of southern Brazil show an atypical development of blocky structures and less friable consistency (when wet) compared with Latosolos from elsewhere in the Brazilian tropical regions. This also applies to Nitossolo, with slightly higher clay activity values, as well as higher nutrient reserves. In the Araucaria Plateau, soils below 600 m and well-drained have more hematite than goethite, forming Latossolos Vermelhos or Nitossolos Vermelhos. In the highlands, cool and wetter climates result in greater organic matter contents, high goethite formation and brownification and xanthization process, by the selective dissolution of hematite and precipitation of goethite due to the current humid climate. The Araucaria Plateau possesses large areas with deep, well-developed soils, with high agricultural potential, leading to agribusiness development. This fact, associated with the economic importance of Araucaria as a raw material for the wood and cellulose industry, has contributed to the widespread degradation of the forest and the conversion of areas into annual crops and pastures. It is estimated that only approximately 15% of primitive Araucaria vegetation remains, with an urgent need for conservation measures. MenosThe Araucaria Highland Plateaux is known as Mixed Subtropical Rainforest dominated by Araucaria trees, in the highlands plateaus of the southern states of Brazil, such as Paraná, Santa Catarina, and Rio Grande do Sul, with minor areas in isolated highlands in southern São Paulo, Minas Gerais, and Rio de Janeiro States. The climate of Araucaria Plateau is humid temperate mesothermal (subtropical to temperate). The vast majority of soils developed from weathered volcanic rocks of the Serra Geral Formation have a clayey or very clayey texture, resulting from long-term weathering and the intense alteration of riodacites, basalts, and andesitic-basalts. Under subtropical conditions, plagioclases, pyroxenes, amphiboles, biotites, and olivines undergo an almost complete dissolution, leaving little mineral reserves in the coarse fractions of soils, where resistant quartz, magnetite, and ilmenite dominate. The main soil classes are: Latossolos Vermelhos, Latossolos Brunos, Nitossolos Vermelhos or Brunos, Argissolos Bruno-acinzentados, Cambissolos Húmicos or Hísticos, on volcanic rocks, mainly. Argissolos Vermelhos or Vermelho Amarelos also occur on sedimentary or granitic/metamorphic rocks. The chemical weathering of Araucaria Plateau is moderately intense, leading to the formation of kaolinite mixed with hydroxy-Al vermiculite or smectite with little or no illite, due to the absence of muscovite in the parent material. The presence of gibbsite is occasional in some soils, but in low... Mostrar Tudo |
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Palavras-Chave: |
Acid soil; Highland soil; Humic Latossolo; Neotropical soil; Subtropical landscape; Subtropical soil; Tropical pedology. |
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Thesagro: |
Araucária. |
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Thesaurus NAL: |
Climate change. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 04083naa a2200301 a 4500
001 2153643
005 2023-08-14
008 2023 bl uuuu u00u1 u #d
020 $a978-3-031-19949-3
024 7 $ahttps://doi.org/10.1007/978-3-031-19949-3_10$2DOI
100 1 $aDALMOLIN, R. S. D.
245 $aSoils of the Southern Araucaria Highlands.$h[electronic resource]
260 $c2023
300 $ap. 269-297.$cEbook
490 $a(World soils book series).
520 $aThe Araucaria Highland Plateaux is known as Mixed Subtropical Rainforest dominated by Araucaria trees, in the highlands plateaus of the southern states of Brazil, such as Paraná, Santa Catarina, and Rio Grande do Sul, with minor areas in isolated highlands in southern São Paulo, Minas Gerais, and Rio de Janeiro States. The climate of Araucaria Plateau is humid temperate mesothermal (subtropical to temperate). The vast majority of soils developed from weathered volcanic rocks of the Serra Geral Formation have a clayey or very clayey texture, resulting from long-term weathering and the intense alteration of riodacites, basalts, and andesitic-basalts. Under subtropical conditions, plagioclases, pyroxenes, amphiboles, biotites, and olivines undergo an almost complete dissolution, leaving little mineral reserves in the coarse fractions of soils, where resistant quartz, magnetite, and ilmenite dominate. The main soil classes are: Latossolos Vermelhos, Latossolos Brunos, Nitossolos Vermelhos or Brunos, Argissolos Bruno-acinzentados, Cambissolos Húmicos or Hísticos, on volcanic rocks, mainly. Argissolos Vermelhos or Vermelho Amarelos also occur on sedimentary or granitic/metamorphic rocks. The chemical weathering of Araucaria Plateau is moderately intense, leading to the formation of kaolinite mixed with hydroxy-Al vermiculite or smectite with little or no illite, due to the absence of muscovite in the parent material. The presence of gibbsite is occasional in some soils, but in low proportions. The predominance of Kaolinite in Araucaria soils is attributed to past colder and wetter climates, favoring organic matter accumulation and aluminum complexation, preventing the formation of gibbsite. Soils from the Araucaria Plateau have unusually high proportions of hydroxy-interlayered Al in 2:1 minerals, representing a marked difference with other deep-weathered tropical soils from elsewhere is Brazil. Most Latossolos (Ferralsols) of southern Brazil show an atypical development of blocky structures and less friable consistency (when wet) compared with Latosolos from elsewhere in the Brazilian tropical regions. This also applies to Nitossolo, with slightly higher clay activity values, as well as higher nutrient reserves. In the Araucaria Plateau, soils below 600 m and well-drained have more hematite than goethite, forming Latossolos Vermelhos or Nitossolos Vermelhos. In the highlands, cool and wetter climates result in greater organic matter contents, high goethite formation and brownification and xanthization process, by the selective dissolution of hematite and precipitation of goethite due to the current humid climate. The Araucaria Plateau possesses large areas with deep, well-developed soils, with high agricultural potential, leading to agribusiness development. This fact, associated with the economic importance of Araucaria as a raw material for the wood and cellulose industry, has contributed to the widespread degradation of the forest and the conversion of areas into annual crops and pastures. It is estimated that only approximately 15% of primitive Araucaria vegetation remains, with an urgent need for conservation measures.
650 $aClimate change
650 $aAraucária
653 $aAcid soil
653 $aHighland soil
653 $aHumic Latossolo
653 $aNeotropical soil
653 $aSubtropical landscape
653 $aSubtropical soil
653 $aTropical pedology
700 1 $aPEDRON, F. de A.
700 1 $aCURCIO, G. R.
773 $tIn: SCHAEFER, C. E. G. R. (ed.). The soils of Brazil. Cham: Springer, 2023. cap. 10.
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