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		<isbn>978-85-17-00088-1</isbn>
		<label>60174</label>
		<citationkey>SeixasFiorPoleStra:2017:VaMoEs</citationkey>
		<title>Validação de modelos espectrais para a predição de conteúdo relativo de água (CRA) em folhas de Eucalyptus spp</title>
		<format>Internet</format>
		<year>2017</year>
		<secondarytype>PRE CN</secondarytype>
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		<size>670 KiB</size>
		<author>Seixas, Hugo Tameirão,</author>
		<author>Fiorio, Peterson Ricardo,</author>
		<author>Polez, Bruna Mariani,</author>
		<author>Strabeli, Taila Fernanda,</author>
		<electronicmailaddress>hugo.seixas@usp.br</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>6741-6748</pages>
		<booktitle>Anais</booktitle>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<abstract>There will be an increasing demand for wood products in the next years, being necessary to develop new technologies to improve the efficiency of the forestry production. The water status of the plant is an important factor in the productivity, and can have great impacts on the culture. Remote sensing can be considered as a useful tool to measure water content of leaves, being applicable over a variety of scales. The objective of this study was to evaluate the efficiency of three spectral models over their capacity of predicting relative water content of Eucalyptus spp. leaves. The water content data was obtained through gravimetric analysis of fresh, saturated and dry leaves, and by hyperspectral measures in laboratory. This methodology found the average values of relative water content was similar between the observed data and the estimated data from the stepwise model, however, it showed a big difference when compared to the single band and spectrum regions models. However, none of the data generated by the models presented significant correlation with the observed RWC values, which the stepwise model showed the highest coefficient of determination (R2=0.012) and the single band and spectrum regions the lowest (R2=0.004) and (R2=0.002) respectively. The results indicate that these models couldnt predict relative water content values to individual leaves, but the average RWC obtained by the stepwise model can be considered similar to the observed RWC.</abstract>
		<area>SRE</area>
		<type>Radiometria e sensores</type>
		<language>pt</language>
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