%0 Conference Proceedings
%4 dpi.inpe.br/sbsr@80/2006/11.15.17.21
%2 dpi.inpe.br/sbsr@80/2006/11.15.17.21.06
%@isbn 978-85-17-00031-7
%T Uso de imagens CBERS-2/CCD no mapeamento das áreas irrigadas e estimativa da demanda hídrica bruta no projeto de irrigação Vale do Gorutuba, Janaúba - MG
%D 2007
%A Santos, Ronaldo Medeiros dos,
%A Silva, Eloy de Souza,
%A Brito, Ronai Soares de,
%A Lima, Jorge Enoch Furquim Werneck,
%A Eid, Nabil Joseph,
%A Koide, Sergio,
%@affiliation Universidade de Brasília (UnB). Depto. Engenharia Civil e Ambiental.
%@affiliation Companhia de Desenvolvimento dos Vales do São Francisco e do Parnaíba (CODEVASF)
%@affiliation Universidade Estadual de Montes Claros (Unimontes)
%@affiliation Universidade de Brasília (UnB). Depto. Engenharia Civil e Ambiental. Embrapa Cerrados. CPAC.
%@affiliation Universidade de Brasília (UnB). Depto. Engenharia Civil e Ambiental.
%@affiliation Universidade de Brasília (UnB). Depto. Engenharia Civil e Ambiental.
%@electronicmailaddress ronaldoenge@yahoo.com.br
%@electronicmailaddress eloyszsilva@gmail.com
%@electronicmailaddress ronaisoares@yahoo.com.br
%@electronicmailaddress jorge@cpac.embrapa.br
%@electronicmailaddress njeid@unb.br
%@electronicmailaddress skoide@unb.br
%E Epiphanio, José Carlos Neves,
%E Galvão, Lênio Soares,
%E Fonseca, Leila Maria Garcia,
%B Simpósio Brasileiro de Sensoriamento Remoto, 13 (SBSR).
%C Florianópolis
%8 21-26 abr. 2007
%I Instituto Nacional de Pesquisas Espaciais (INPE)
%J São José dos Campos
%P 1095-1101
%S Anais
%1 Instituto Nacional de Pesquisas Espaciais (INPE)
%K remote sensing, water resources, irrigated areas, agriculture, sensoriamento remoto, recursos hídricos, áreas irrigadas, agricultura.
%X The present work had as objective the mapping of the irrigated areas, by means of remote sensing and the rough estimate of the gross water demand in the Valley of the Gorutuba Project of Irrigation, in Nova Porteirinha - MG. CBERS-2/CCD imagery was used (free access) and taken data of literature, beyond traditional techniques of classification of orbital images. The supervised classification based in the maximum likelihood was used, your outcomes indicated satisfactory performance (overall accuracy 86%, kappa 0.81%) in the classification of the features limited to the irrigation perimeter. The land use was estimated from each mapped class, from which it was gotten the irrigated area and, from values collected in literature, the rough estimate of annual average water demand for the study area. This study was the starting point to future works with the goal to supply data to better management of the water in the region.
%9 CBERS: Avaliação e Aplicações
%@language pt
%3 1095-1101.pdf