NDLI: Integração de dados de sensoriamento remoto multi resoluções para a representação da cobertura da terra utilizando campos contínuos de vegetação e classificação por árvores de decisão

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Integração de dados de sensoriamento remoto multi resoluções para a representação da cobertura da terra utilizando campos contínuos de vegetação e classificação por árvores de decisão

Content Provider	Semantic Scholar
Author	Latorre, Marcelo Lopes Júnior, Osmar Abílio De Carvalho Santos, João Roberto Dos Shimabukuro, Yosio Edemir
Copyright Year	2007
Abstract	This paper aims to develop a methodology of multisensor integration for an Amazon monitoring system. The proposed system employs the Vegetation Continuous Fields (VCF) method that uses the decision tree algorithm. The algorithm uses a set of independent variables, in this case MODIS multi-temporal metrics, to recursively split a dependent variable, in this case training data from class memberships, into subsets, which maximize the reduction of squares of sum of the residuals. The training data are obtained by high-resolution imagery classification (Landsat/TM, ETM+ and CBERS 2/CCD). In this study, an automated algorithm was developed from IDL language in the ENVI software and the statistical procedure of the S-PLUS software. The study area is Mato Grosso State with an extensive area of Amazon forest. The scenes are classified in three classes: forest, non-forest, and water. Comparisons of the final product with regional land cover maps derived from PRODES revel general agreement. Therefore, the results of this study suggest that the methodology is appropriate for land cover determination in the Amazon forest.
Starting Page	63
Ending Page	74
Page Count	12
File Format	PDF HTM / HTML
DOI	10.1590/S0102-261X2007000100006
Alternate Webpage(s)	http://mtc-m16b.sid.inpe.br/col/sid.inpe.br/mtc-m17@80/2008/01.07.17.24/doc/latorre_integracao.pdf
Alternate Webpage(s)	http://www.scielo.br/pdf/rbg/v25n1/a06v25n1.pdf
Alternate Webpage(s)	https://doi.org/10.1590/S0102-261X2007000100006
Volume Number	25
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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