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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: |
10/07/2018 |
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Data da última atualização: |
10/07/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
CANETTI, A.; GARRASTAZU, M. C.; MATTOS, P. P. de; BRAZ, E. M.; PELLICO NETTO, S. |
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Afiliação: |
Aline Canetti, Pós graduanda da UFPR; MARILICE CORDEIRO GARRASTAZU, CNPF; PATRICIA POVOA DE MATTOS, CNPF; EVALDO MUNOZ BRAZ, CNPF; Sylvio Pellico Netto, UFPR. |
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Título: |
Understanding multi-temporal urban forest cover using high resolution images. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Urban Forestry & Urban Greening, v. 29, p. 106-112, Jan. 2018. |
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DOI: |
10.1016/j.ufug.2017.10.020 |
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Idioma: |
Inglês |
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Conteúdo: |
Urban forests offer city residents a better quality of life. They also act as barrier and filter to pollutants resulting from human activities. Mapping the distribution of forests in urban areas is therefore important for good urban planning. Currently, high resolution images are considered useful tools to quantify forested urban areas for large scale urban development. The objective of this work was to evaluate multi-temporal urban forest area changes over different land-use classes, using available high-resolution images obtained from different satellites. The municipality of Araucaria (Paraná State) was chosen as study area because it is a large industrial zone of southern Brazil. The effects on the forests within this municipality caused by the increase in human activities between 2005 and 2012 were determined using high resolution images (5 m). We recorded a reduction of 22.8% in the forests surrounding urban areas of the municipality, as a result of deforestation of 791 ha and plantation of only 251 ha. The utility, commercial and residential zones which are more crowded the areas of highest population density were those which showed the greatest loss of tree cover. Object-based classification accuracy using images from different satellites was sufficient to quantify the evolution of tree-cover over the studied period. |
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Palavras-Chave: |
Image processing; Object-based classification; Processamento de imagem. |
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Thesagro: |
Cobertura Vegetal; Floresta Urbana. |
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Thesaurus NAL: |
Urban forestry. |
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Categoria do assunto: |
K Ciência Florestal e Produtos de Origem Vegetal |
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Marc: |
LEADER 02122naa a2200253 a 4500
001 2093157
005 2018-07-10
008 2018 bl uuuu u00u1 u #d
024 7 $a10.1016/j.ufug.2017.10.020$2DOI
100 1 $aCANETTI, A.
245 $aUnderstanding multi-temporal urban forest cover using high resolution images.$h[electronic resource]
260 $c2018
520 $aUrban forests offer city residents a better quality of life. They also act as barrier and filter to pollutants resulting from human activities. Mapping the distribution of forests in urban areas is therefore important for good urban planning. Currently, high resolution images are considered useful tools to quantify forested urban areas for large scale urban development. The objective of this work was to evaluate multi-temporal urban forest area changes over different land-use classes, using available high-resolution images obtained from different satellites. The municipality of Araucaria (Paraná State) was chosen as study area because it is a large industrial zone of southern Brazil. The effects on the forests within this municipality caused by the increase in human activities between 2005 and 2012 were determined using high resolution images (5 m). We recorded a reduction of 22.8% in the forests surrounding urban areas of the municipality, as a result of deforestation of 791 ha and plantation of only 251 ha. The utility, commercial and residential zones which are more crowded the areas of highest population density were those which showed the greatest loss of tree cover. Object-based classification accuracy using images from different satellites was sufficient to quantify the evolution of tree-cover over the studied period.
650 $aUrban forestry
650 $aCobertura Vegetal
650 $aFloresta Urbana
653 $aImage processing
653 $aObject-based classification
653 $aProcessamento de imagem
700 1 $aGARRASTAZU, M. C.
700 1 $aMATTOS, P. P. de
700 1 $aBRAZ, E. M.
700 1 $aPELLICO NETTO, S.
773 $tUrban Forestry & Urban Greening$gv. 29, p. 106-112, Jan. 2018.
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