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		<isbn>978-85-17-00031-7</isbn>
		<citationkey>WagnerWebFonDucKle:2007:EsÁrSo</citationkey>
		<title>Estimativa de área de soja no Rio Grande do Sul utilizando imagens NDVI/MODIS</title>
		<format>CD-ROM, On-line.</format>
		<year>2007</year>
		<secondarytype>PRE CN</secondarytype>
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		<author>Wagner, Ana Paula Luz,</author>
		<author>Weber, Eliseu José,</author>
		<author>Fontana, Denise Cybis,</author>
		<author>Ducati, Jorge Ricardo,</author>
		<author>Klering, Eliana Veleda,</author>
		<affiliation>Universidade Federal do Rio Grande do Sul (UFRGS). Centro Estadual de Pesquisas em Sensoriamento Remoto e Meteorologia (CEPSRM).</affiliation>
		<affiliation>Universidade Federal do Rio Grande do Sul (UFRGS). Centro Estadual de Pesquisas em Sensoriamento Remoto e Meteorologia (CEPSRM).</affiliation>
		<affiliation>Universidade Federal do Rio Grande do Sul (UFRGS). Centro Estadual de Pesquisas em Sensoriamento Remoto e Meteorologia (CEPSRM). Faculdade de Agronomia. Departamento de Agrometeorologia e Plantas Forrageiras.</affiliation>
		<affiliation>Universidade Federal do Rio Grande do Sul (UFRGS). Centro Estadual de Pesquisas em Sensoriamento Remoto e Meteorologia (CEPSRM).</affiliation>
		<affiliation>Universidade Federal do Rio Grande do Sul (UFRGS). Centro Estadual de Pesquisas em Sensoriamento Remoto e Meteorologia (CEPSRM).</affiliation>
		<electronicmailaddress>anaplw@yahoo.com.br</electronicmailaddress>
		<electronicmailaddress>eweber@portoweb.com.br</electronicmailaddress>
		<electronicmailaddress>dfontana@ufrgs.br</electronicmailaddress>
		<electronicmailaddress>ducati@if.ufrgs.br</electronicmailaddress>
		<electronicmailaddress>elianaklering@yahoo.com.br</electronicmailaddress>
		<editor>Epiphanio, José Carlos Neves,</editor>
		<editor>Galvão, Lênio Soares,</editor>
		<editor>Fonseca, Leila Maria Garcia,</editor>
		<e-mailaddress>ANAPLW@YAHOO.COM.BR</e-mailaddress>
		<conferencename>Simpósio Brasileiro de Sensoriamento Remoto, 13 (SBSR).</conferencename>
		<conferencelocation>Florianópolis</conferencelocation>
		<date>21-26 abr. 2007</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<pages>457-464</pages>
		<booktitle>Anais</booktitle>
		<tertiarytype>Artigo</tertiarytype>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<keywords>agricultural estimates, NDVI/MODIS, digital classification, statisticians of spectral distribution, estimativa de área, NDVI/MODIS, classificação digital, estatísticas de distribuição espectral.</keywords>
		<abstract>The Brazilian agricultural estimates still are strongly based on subjective surveys. In the last years several researches have been developed shown that the remote sensing products can be an important source of objective products. The aim of this study was to evaluate the application of NDVI MODIS image as a ease and fast estimator of the soybean crop area in the Rio Grande do Sul state. The applied methodology uses the temporal dynamic of the soybean crop through the evaluating of the NDVI behavior in some characteristic periods of the crop cycle in the main producer region of the State. The soybean map area produced by a Landsat image was used as a crop mask for extracting the average and standard deviation statistics. This information was used to define NDVI bands corresponding to the implantation and full development periods of the crop. The cross-matches of these binary images were used to map soybean areas. The results had shown the possibility of using this method to map soybean areas in Rio Grande do Sul. The agreement between the numbers of soybean pixels classified in the MODIS image related to the soybean areas identified in the Landsat images was 83.98%, with 0.48 of Kappa. The omission and commission error was near to 42% each. The difference between the crop area in the MODIS and Landsat images was 2.38%. When the comparison was made using MODIS and IBGE data, the difference gone up to 18.78%. The results were very good in soybean regions with high heterogeneity caused by the size, technology and crop calendar, excusing any other type of edition on the classified image. This methodology allows one to produce good indication of the soybean area using a simple, economic and fast form.</abstract>
		<area>SRE</area>
		<subject>Agricultura</subject>
		<session>Agricultura</session>
		<type>Agricultura</type>
		<language>pt</language>
		<targetfile>457-464.pdf</targetfile>
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