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		<isbn>978-85-17-00088-1</isbn>
		<label>59806</label>
		<citationkey>GonçalvesDanGruRibVal:2017:MaCoTe</citationkey>
		<title>Mapeamento da cobertura da terra na bacia do rio Araranguá utilizando o classificador por regiões Bhattacharya</title>
		<format>Internet</format>
		<year>2017</year>
		<secondarytype>PRE CN</secondarytype>
		<numberoffiles>1</numberoffiles>
		<size>706 KiB</size>
		<author>Gonçalves, Arthur Ferreira,</author>
		<author>Daniel, Lucas Barros Esteves,</author>
		<author>Gruschinske, Marcelo Cidade,</author>
		<author>Ribas, Rodrigo Pinheiro,</author>
		<author>Valdati, Jairo,</author>
		<electronicmailaddress>lucas_bar23@hotmail.com</electronicmailaddress>
		<editor>Gherardi, Douglas Francisco Marcolino,</editor>
		<editor>Aragão, Luiz Eduardo Oliveira e Cruz de,</editor>
		<e-mailaddress>daniela.seki@inpe.br</e-mailaddress>
		<conferencename>Simpósio Brasileiro de Sensoriamento Remoto, 18 (SBSR)</conferencename>
		<conferencelocation>Santos</conferencelocation>
		<date>28-31 maio 2017</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<pages>6383-6390</pages>
		<booktitle>Anais</booktitle>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<abstract>The Santa Catarina state is the second biggest rice producer in Brazil, and the region of the Araranguá river basin is the most important area for this production in the state. In addition to rice cultivation, the region produces other cereals, such as tobacco, corns and cassava and has a lot of divertificated vegetations. This paper intends to classify the landcover in the basin and to identify the land use areas of temporary crops, vegetations by sizes, urbanized areas and water bodys in the perimeter worked, using the Bhattacharya classification algorithm supervised in the Landsat sattelite 8 with the sensor Operational Land Imager - OLI - image. The methology is based on dividing the scene in segments for a better choice of samples. It has been used the SPRING 5.4.3 software of geographic information system and digital processing of multispectral images. In this process, we will also analyze the main difficulties encountered in the treatment of the image for the realization of the map, for example the choice of the bands that can better evidantiate the features in the region. The results generated by the algorithm proved a  efficient product for in the mapping of medium and large river basins.</abstract>
		<area>SRE</area>
		<type>Landsat OLI</type>
		<language>pt</language>
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