Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/31571
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dc.contributor.authorLU, D.pt_BR
dc.contributor.authorBATISTELLA, M.pt_BR
dc.contributor.authorMORAN, E.pt_BR
dc.date.accessioned2014-08-26T06:26:10Z-
dc.date.available2014-08-26T06:26:10Z-
dc.date.created2009-03-02pt_BR
dc.date.issued2008pt_BR
dc.identifier.citationPhotogrammetric Engineering & Remote Sensing, v. 74, n. 4, p. 421-430, 2008.pt_BR
dc.identifier.isbn0099-1112pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/31571pt_BR
dc.descriptionTraditional change detection approaches have been proven to be difficult in detecting vegetation changes in the moist tropical regions with multitemporal images. This paper explores the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data for vegetation change detection in the Brazilian Amazon. A principal component analysis was used to integrate TM and HRG panchromatic data. Vegetation change/non-change was detected with the image differencing approach based on the TM and HRG fused image and the corresponding TM and HRG multispectral images into thematic maps with three coarse land-cover classes: forest, non-forest vegetation, and non-vegetation lands. A hybrid approach combining image differencing and post-classification comparison was used to detect vegetation change trajectories. This research indicates promising vegetation chance trajectories. This research indicates promising vegetation change techniques especially for vegetation gain and loss, even if very limited reference data are available.pt_BR
dc.language.isoporpt_BR
dc.rightsopenAccesspt_BR
dc.subjectBrazilian Amazonpt_BR
dc.subjectImage collection and preprocessingpt_BR
dc.subjectVegetation Chance Detectionpt_BR
dc.titleIntegration of landsat TM and SPOT HRG Images for vegetation change detection in the Brazilian Amazon.pt_BR
dc.typeArtigo de periódicopt_BR
dc.date.updated2014-08-26T06:26:10Zpt_BR
riaa.ainfo.id31571pt_BR
riaa.ainfo.lastupdate2014-08-25pt_BR
dc.contributor.institutionDENGSHENG LU, Indiana University; MATEUS BATISTELLA, CNPM; EMÍLIO MORAN, Indiana University.pt_BR
Aparece nas coleções:Artigo em periódico indexado (CNPM)

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