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Registro Completo |
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
12/06/2015 |
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Data da última atualização: |
09/05/2016 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
LUZ, N. B. da; OLIVEIRA, Y. M. M. de; ROSOT, M. A. D.; GARRASTAZU, M. C.; FRANCISCON, L.; MESQUITA JÚNIOR, H. N. de; FREITAS, J. V. de. |
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Afiliação: |
Naíssa Batista da Luz, ONU/FAO; YEDA MARIA MALHEIROS DE OLIVEIRA, CNPF; MARIA AUGUSTA DOETZER ROSOT, CNPF; MARILICE CORDEIRO GARRASTAZU, CNPF; LUZIANE FRANCISCON, CNPF; Humberto Navarro de Mesquita Júnior, Serviço Florestal Brasileiro; Joberto Veloso de Freitas, Serviço Florestal Brasileiro. |
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Título: |
Classificação híbrida de imagens Landsat-8 e RapidEye para o mapeamento do uso e cobertura da terra nas Unidades Amostrais de Paisagem do Inventário Florestal Nacional do Brasil. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 17., 2015, João Pessoa. Anais... São José dos Campos: INPE, 2015. |
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Páginas: |
p. 7222-7230. |
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Descrição Física: |
Disponível online. |
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Idioma: |
Português |
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Conteúdo: |
In response to the growing demand for reliable information on forest and tree resources as well as for land use/land cover (LULC) maps at larger scales, the Brazilian National Forest Inventory (NFI-BR) is now being conducted. Besides the traditional approaches related to forest assessment, the NFI-BR includes a geospatial component to provide such information at landscape scale. Using a sampling grid of 20 km × 20 km, field registry sample units were established, and 100 km2 landscape sample units (LSU) were located on a 40 km × 40 km grid. LULC maps are being prepared for each LSU using RapidEye and Landsat-8 imagery. Different remote sensing techniques are being tested to characterize LULC in order to identify patterns in different themes using spatial analysis, such as forest fragmentation, state of conservation, production and forest health. The mapping approach uses a hybrid approach, here understood as the combination of automatic unsupervised pixel-by-pixel classification and object based image classification. Attributes from image objects such as spectral characteristics, texture, and context are also involved in process tree classification, as well as ancillary data such as roads, water bodies and digital terrain models. LULC maps are the basis for analyzing landscape-scale forest fragmentation analysis as well as for evaluating compliance of permanent preservation areas under recently approved environmental legislation. |
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Palavras-Chave: |
Ancillary data; Automatic image classification; Brasil; Classificação automática de imagens; Classificação orientada a objetos; Imagem de satélite; Inventário Florestal Nacional; Object-based classification. |
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Thesagro: |
Sensoriamento Remoto. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/142855/1/2015-Marilice-Classificacao-hibrida-de-imagens-Landsat-8-e-RapidEye.pdf
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Marc: |
LEADER 02615nam a2200301 a 4500
001 2017535
005 2016-05-09
008 2015 bl uuuu u00u1 u #d
100 1 $aLUZ, N. B. da
245 $aClassificação híbrida de imagens Landsat-8 e RapidEye para o mapeamento do uso e cobertura da terra nas Unidades Amostrais de Paisagem do Inventário Florestal Nacional do Brasil.$h[electronic resource]
260 $aIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 17., 2015, João Pessoa. Anais... São José dos Campos: INPE$c2015
300 $ap. 7222-7230.$cDisponível online.
520 $aIn response to the growing demand for reliable information on forest and tree resources as well as for land use/land cover (LULC) maps at larger scales, the Brazilian National Forest Inventory (NFI-BR) is now being conducted. Besides the traditional approaches related to forest assessment, the NFI-BR includes a geospatial component to provide such information at landscape scale. Using a sampling grid of 20 km × 20 km, field registry sample units were established, and 100 km2 landscape sample units (LSU) were located on a 40 km × 40 km grid. LULC maps are being prepared for each LSU using RapidEye and Landsat-8 imagery. Different remote sensing techniques are being tested to characterize LULC in order to identify patterns in different themes using spatial analysis, such as forest fragmentation, state of conservation, production and forest health. The mapping approach uses a hybrid approach, here understood as the combination of automatic unsupervised pixel-by-pixel classification and object based image classification. Attributes from image objects such as spectral characteristics, texture, and context are also involved in process tree classification, as well as ancillary data such as roads, water bodies and digital terrain models. LULC maps are the basis for analyzing landscape-scale forest fragmentation analysis as well as for evaluating compliance of permanent preservation areas under recently approved environmental legislation.
650 $aSensoriamento Remoto
653 $aAncillary data
653 $aAutomatic image classification
653 $aBrasil
653 $aClassificação automática de imagens
653 $aClassificação orientada a objetos
653 $aImagem de satélite
653 $aInventário Florestal Nacional
653 $aObject-based classification
700 1 $aOLIVEIRA, Y. M. M. de
700 1 $aROSOT, M. A. D.
700 1 $aGARRASTAZU, M. C.
700 1 $aFRANCISCON, L.
700 1 $aMESQUITA JÚNIOR, H. N. de
700 1 $aFREITAS, J. V. de
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Registro original: |
Embrapa Florestas (CNPF) |
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| Registros recuperados : 1 | |
| 1. |  | LUZ, N. B. da; OLIVEIRA, Y. M. M. de; ROSOT, M. A. D.; GARRASTAZU, M. C.; FRANCISCON, L.; MESQUITA JÚNIOR, H. N. de; FREITAS, J. V. de. Classificação híbrida de imagens Landsat-8 e RapidEye para o mapeamento do uso e cobertura da terra nas Unidades Amostrais de Paisagem do Inventário Florestal Nacional do Brasil. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 17., 2015, João Pessoa. Anais... São José dos Campos: INPE, 2015. p. 7222-7230. Disponível online.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Florestas. |
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