Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/922003
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dc.contributor.authorLU, D.pt_BR
dc.contributor.authorCHEN, Q.pt_BR
dc.contributor.authorWANG, G.pt_BR
dc.contributor.authorMORAN, E.pt_BR
dc.contributor.authorBATISTELLA, M.pt_BR
dc.contributor.authorZHANG, M.pt_BR
dc.contributor.authorLAURIN, G. V.pt_BR
dc.contributor.authorSAAH, D.pt_BR
dc.date.accessioned2012-04-11T11:11:11Zpt_BR
dc.date.accessioned2012-04-11T11:11:11Zpt_BR
dc.date.available2012-04-11T11:11:11Zpt_BR
dc.date.available2012-04-11T11:11:11Zpt_BR
dc.date.created2012-04-11pt_BR
dc.date.issued2012pt_BR
dc.identifier.citationInternational Journal of Forestry Research, v. 2012. p. 16, 2012pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/922003pt_BR
dc.descriptionLandsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM?s shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data.pt_BR
dc.language.isoengeng
dc.rightsopenAccesseng
dc.titleAboveground forest biomass estimation with Landsat and LiDAR data and uncertainty analysis of the estimates.pt_BR
dc.typeArtigo de periódicopt_BR
dc.date.updated2013-01-18T11:11:11Zpt_BR
dc.subject.thesagroBiomassapt_BR
dc.format.extent216 p.pt_BR
riaa.ainfo.id922003pt_BR
riaa.ainfo.lastupdate2013-01-18pt_BR
dc.contributor.institutionDENGSHENG LU, INDIANA UNIVERSITY; QI CHEN, ZHEJIANG A&F UNIVERSITY; GUANGXING WANG, SOUTHERN ILLINOIS UNIVERSITY AT CARBONDALE; EMILIO MORAN, INDIANA UNIVERSITY; MATEUS BATISTELLA, CNPM; MAOZHEN ZHANG, ZHEJIANG A&F UNIVERSITY; GAIA VAGLIO LAURIN, UNIVERSITY OF TOR VERGATA; DAVID SAAH, SPATIAL INFORMATICS GROUP.pt_BR
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