Data/hora: 13/06/2024 17:04:33

Biblioteca(s): | Embrapa Agropecuária Oeste; Embrapa Florestas.
Data corrente: | 29/03/2001
Data da última atualização: | 08/04/2015
Autoria: | OLIVEIRA, Y. M. M. de.
Afiliação: | Pesquisadora da Embrapa Florestas.
Título: | Investigation of remote sensing for assessing and monitoring the Araucaria Forest Region of Brazil.
Ano de publicação: | 1999
Fonte/Imprenta: | 1999.
Páginas: | 247 f.
Idioma: | Inglês
Notas: | Doctor (Philosophy Thesis) - University of Oxford, Oxford.
Conteúdo: | Southern Brazil has experienced a rapid deterioration of its forest during the last century, mainly due to the impacts of exploitative land use practices. Mixed Ombrophilous forest with Araucaria angustifolia (Bertoloni) O. Kuntze (Veloso and Goes-Filho), is typically dominated by the species Araucaria angustifolia, also known as parana-pine. Although the species is predominant, Araucaria forest also supports a complex, variable and regional ecosystem composed of many species, some of which are endemic to this forest type. The present study focused on an area of the Araucaria forest biome located within the first plateau of Parana Slate. It includes the Irati National Forest (INF), the only protected area with Araucaria forest in Paran Slate. The main goal of the thesis was to determine the relationship between vegetation and satellite remote sensing for assessing and monitoring Araucaria forest and ecosystems associated with the Araucaria forest region. The reflective properties and spectral signatures of the four main forest types in the INF (Araucaria forest, Araucaria angustifolia plantation, Pinus elliottii plantation and Pinus taeda plantation) were analysed and characterised. It was noted that spectral responses of A. angustifolia plantation and P. taeda plantation are similar. Standard Principal Component Analysis (PCA) revealed that the spectral data space could be reduced to three eingenvectors. The discrimination of A. angustifolia was achieved using the second component of the PCA using near infrared and mid infrared wavebands. Linear Mixing Modelling was undertaken to detect and identify A. angustifolia at sub-pixel level. The shade image was sensitive to forestry structure (density) and species differentiation. Use of the Normalised Difference Vegetation Index was successful in the biomass change detection using image differencing process, resulting in a proposed alternative monitoring system to detect forest changes in the Araucaria forest region.
Palavras-Chave: | Brasil; Forest; Monitoramento; Paraná.
Thesagro: | Araucária; Araucária Angustifólia; Espécie Nativa; Floresta; Sensoriamento Remoto.
Thesaurus Nal: | Brazil; monitoring; remote sensing.
Categoria do assunto: | --
URL: | http://ainfo.cnptia.embrapa.br/digital/bitstream/item/121961/1/Yeda.pdf
Registro original: | Embrapa Florestas (CNPF)

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CPAO - (16356-1) - ADP - TS - PP - T 27/99 -  - 00.00039 - DIS
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