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		<isbn>978-85-17-00088-1</isbn>
		<label>59524</label>
		<citationkey>CamaraSouKoeRibVal:2017:MaCoTe</citationkey>
		<title>Mapeamento da cobertura da terra na Bacia do Araranguá utilizando imagens do sensor OLI do satélite Landsat 8</title>
		<format>Internet</format>
		<year>2017</year>
		<secondarytype>PRE CN</secondarytype>
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		<size>970 KiB</size>
		<author>Camara, Annika Kauder,</author>
		<author>Souto, Marcus Allan Mastropasqua,</author>
		<author>Koerich, Mariana Pereira,</author>
		<author>Ribas, Rodrigo Pinheiro,</author>
		<author>Valdati, Jairo,</author>
		<electronicmailaddress>mariana.koerich@gmail.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>6575-6581</pages>
		<booktitle>Anais</booktitle>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<abstract>This study was developed to be an evaluative part of the Geography Bachelor Degree of the UDESC (University of Santa Catarina) aiming to map the residual vegetation of the drainage-basin or Ararangua River in the South of Santa Catarina State in Brazil. The conception of the study was based on the knowledge of two subjects, Remote Sensing and Biogeography, and on a field trip to the area. The images used to analyze and map the basin, the Landsat 8, where processed by the Sprint 5.4.2 Software. The classification was divided in 6 major themes according to the image response analyzed. The results where compared to the perception collected on the field trip and the study made from the vegetation based on the IBGE vegetation manual from 2012.Among the challenges faced, the main ones were data capture through supervised classification, due to the special low resolution of the sensor used. Despite the difficulty, an excellent result was obtained that was important to analyze the terrestrial surface studied. Therefore, with the use of adopted techniques of remote sensing with results close to reality. Thus, the methodology was favorable for the construction of the thematic map of the land  cover of the study area according to knowledge raised in site.</abstract>
		<area>SRE</area>
		<type>Landsat OLI</type>
		<language>pt</language>
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