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		<isbn>978-85-17-00088-1</isbn>
		<label>60031</label>
		<citationkey>MarinAlvChaCorMak:2017:UsÍnVe</citationkey>
		<title>Uso de índices de vegetação na avaliação remota da cercosporiose em uma lavoura cafeeira</title>
		<format>Internet</format>
		<year>2017</year>
		<secondarytype>PRE CN</secondarytype>
		<numberoffiles>1</numberoffiles>
		<size>761 KiB</size>
		<author>Marin, Diego Bedin,</author>
		<author>Alves, Marcelo de Carvalho,</author>
		<author>Chaves, Michel Eustáquio Dantas,</author>
		<author>Cortez, Matheus Luiz Jorge,</author>
		<author>Makins, Victor Gabriel Fontes,</author>
		<electronicmailaddress>db.marin@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>2916-2923</pages>
		<booktitle>Anais</booktitle>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<abstract>Brown eye spot, caused by Cercospora coffeicola is one of the oldest diseases related to coffee trees. It is estimated that an infected cultivation can lose up to 30% of its productivity. In this context, remote sensoring techniques can be useful to help the farmer to the management the coffee culture. The objective of this study was to analyze the incidence of brown eye spot on leaves of coffee tree using different vegetation indexes. For this study images from Landsat 5 from 10/06/2005 were used from a coffee farm in Ijaci  MG, from where data was collected in situ observing the signs from the disease in 100 leaves that were collected in each sample point from the third and the fourth pair of leaves on third of the average. The spectral data were obtained from images with Standard Terrain Correction Levels (L1T) and transformed into radiance values, and afterwards converted into reflectance values. Subsequently we determined the vegetation indexes: Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Green Normalized Difference Vegetation Index (GNDVI) and Modified Triangular Vegetation Index (MTVI2). We observed negative correlation between the vegetation indexes and the incidence of disease on the coffee tree leaves. In the places with higher water stress, there was probably a higher susceptibility of the plants to the disease, causing defoliation and reduction in photosynthesis activity. The vegetation index that best correlated with the occurrence of disease was MTV12.</abstract>
		<area>SRE</area>
		<type>Floresta e outros tipos de vegetação</type>
		<language>pt</language>
		<targetfile>60031.pdf</targetfile>
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