%0 Conference Proceedings
%4 sid.inpe.br/marte2/2017/10.27.16.32.50
%2 sid.inpe.br/marte2/2017/10.27.16.32.51
%@isbn 978-85-17-00088-1
%F 59781
%T Discriminação da Castanheira (Bertholletia excelsa) com Análise de Mistura Espectral com Múltiplos Membros-finais aplicada a dados multiespectrais WorldView-2
%D 2017
%A Amaral, Cibele Hummel do,
%A Gleriani, José Marinaldo,
%A Xaud, Haron Abrahim Magalhães,
%A Shiratsuchi, Luciano Shozo,
%A Silva, Katia Emidio da,
%@electronicmailaddress chamaral@ufv.br
%E Gherardi, Douglas Francisco Marcolino,
%E Aragão, Luiz Eduardo Oliveira e Cruz de,
%B Simpósio Brasileiro de Sensoriamento Remoto, 18 (SBSR)
%C Santos
%8 28-31 maio 2017
%I Instituto Nacional de Pesquisas Espaciais (INPE)
%J São José dos Campos
%P 7899-7906
%S Anais
%1 Instituto Nacional de Pesquisas Espaciais (INPE)
%X Bertholletia excelsa is one of the most important species of Amazon. Its nut has a particular economic, social and environmental importance for the Brazil northern region, since it is one of the main product of exportation and the unique non-timber product completely extract from native forests by traditional communities. The aim of this research is to test the use of Multiple Endmember Spectral Mixture Analysis on high-spatial-resolution multispectral image from WorldView 2 sensor, in order to map the Bertholletia excelsa fraction, in a per-pixel basis, in Tefé, state of Amazonas, Brazil. The image had its continuum removed, and methods for endmember selection, as EAR-MASA-CoB (EMC) and Iterative Endmember Selection (IES), were tested. Fraction thresholds were also tested. By using field samples and pan-sharpening regions of interest were selected on the Bertholletia excelsa and other vegetation types crowns. They were randomly divided into training and validation samples. The best result was obtained on the continuum removed image, using endmembers selected through EMC, with fraction threshold between 0,01 and 1,01, and RMSE ≤ 0,025. When defined a threshold of 56% for Bertholletia excelsa fraction, 65% of its validation crowns were rightly classified and 9,5% of the validation crowns from other vegetation types were erroneously classified. This result shows the good performance of the visible-near infrared data for mapping and discussing the spatial distribution of the target species.
%9 Floresta e outros tipos de vegetação
%@language pt
%3 59781.pdf