Please use this identifier to cite or link to this item: http://hdl.handle.net/10174/9123

Title: Estimativa da biomassa para as espécies florestais quercus rotundifolia e quercus suber com base em imagens de satélite de alta resolução espacial
Authors: Sousa, Adélia
Marques da Silva, José
Gonçalves, Ana Cristina
Mesquita, Paulo
Editors: Epiphanio, José
Galvão, Lênio
Keywords: biomassa
imagens de satélite
quercus rotundifolia
Issue Date: 13-Apr-2013
Publisher: Instituto Nacional de Pesquisas Espaciais
Citation: Sousa, A.M.O., Marques da Silva, J.R., Gonçalves, A.C. e Mesquita, P.A. (2013) Estimativa da biomassa para as espécies florestais quercus rotundifolia e quercus suber com base em imagens de satélite de alta resolução espacial, XVI Simpósio Brasileiro de Sensoriamento Remoto, Foz do Iguaçú, Paraná – Brasil, 13 - 18 Abril, 2013.
Abstract: The forest biomass has had a growing importance in the global economy, by applications in bioenergy, development of bioproducts and issues related to reducing emissions of greenhouse gases. Forest biomass is a global strategic reserve that must be inventoried and monitored. Current techniques for inventory and monitoring of biomass, through the realization of forest inventory, are usually time consuming and expensive. Considering these facts, it is urgent to develop reliable techniques, low costs, to obtain this kind of information. Considering this problem, in this study we applied new techniques for processing of high spatial resolution satellite images. We used the method of multi-resolution segmentation and object-oriented classification to obtain the area of Tree Canopy Horizontal Projection to the forest species under study. With field measurements was also obtained area of Tree Canopy Horizontal Projection and was calculated the forest biomass. The relation between the results of these two methods allowed developing inventory technique to Quercus rotundifolia and Quercus suber forest species considering the Cumulative Canopy Horizontal Projection, measured by high resolution satellite imagery, with a prediction error lower than 5 %. This study was performed considering pure plots, where there is only one forest specie, and mixed plots with both forest species.
URI: http://hdl.handle.net/10174/9123
Type: article
Appears in Collections:ICAAM - Artigos em Livros de Actas/Proceedings
ERU - Artigos em Livros de Actas/Proceedings

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