Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1045916
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dc.contributor.authorCHEN, Q.pt_BR
dc.contributor.authorLU, D.pt_BR
dc.contributor.authorKELLER, M.pt_BR
dc.contributor.authorSANTOS, M. N. DOSpt_BR
dc.contributor.authorBOLFE, E. L.pt_BR
dc.contributor.authorFENG, Y.pt_BR
dc.contributor.authorWANG, C.pt_BR
dc.date.accessioned2016-05-31T11:11:11Zpt_BR
dc.date.available2016-05-31T11:11:11Zpt_BR
dc.date.created2016-05-31pt_BR
dc.date.issued2015pt_BR
dc.identifier.citationRemote Sensing, v. 8, n. 1, p. 1-17, 2015.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1045916pt_BR
dc.descriptionAgroforestry has large potential for carbon (C) sequestration while providing many economical, social, and ecological benefits via its diversified products. Airborne lidar is considered as the most accurate technology for mapping aboveground biomass (AGB) over landscape levels. However, little research in the past has been done to study AGB of agroforestry systems using airborne lidar data. Focusing on an agroforestry system in the Brazilian Amazon, this study first predicted plot-level AGB using fixed-effects regression models that assumed the regression coefficients to be constants. The model prediction errors were then analyzed from the perspectives of tree DBH (diameter at breast height)?height relationships and plot-level wood density, which suggested the need for stratifying agroforestry fields to improve plot-level AGB modeling. We separated teak plantations from other agroforestry types and predicted AGB using mixed-effects models that can incorporate the variation of AGB-height relationship across agroforestry types. We found that, at the plot scale, mixed-effects models led to better model prediction performance (based on leave-one-out cross-validation) than the fixed-effects models, with the coefficient of determination (R2) increasing from 0.38 to 0.64. At the landscape level, the difference between AGB densities from the two types of models was ~10% on average and up to ~30% at the pixel level. This study suggested the importance of stratification based on tree AGB allometry and the utility of mixed-effects models in modeling and mapping AGB of agroforestry systems.pt_BR
dc.language.isoporpt_BR
dc.rightsopenAccesspt_BR
dc.subjectMixed-effects modelspt_BR
dc.titleModeling and Mapping Agroforestry Aboveground Biomass in the Brazilian Amazon Using Airborne Lidar Data.pt_BR
dc.typeArtigo de periódicopt_BR
dc.date.updated2016-05-31T11:11:11Zpt_BR
dc.subject.nalthesaurusAgroforestrypt_BR
dc.subject.nalthesaurusAboveground biomasspt_BR
dc.subject.nalthesaurusLidarpt_BR
dc.subject.nalthesaurusAllometrypt_BR
dc.subject.nalthesaurusWood densitypt_BR
riaa.ainfo.id1045916pt_BR
riaa.ainfo.lastupdate2016-05-31pt_BR
dc.identifier.doi10.3390/rs8010021pt_BR
dc.contributor.institutionQI CHEN, Zhejiang A&F University; DENGSHENG LU, Michigan State University; MICHAEL KELLER, USDA Forest Service/ Pesquisador Visitante CNPM; MAIZA NARA DOS SANTOS, BOLSISTA CNPM; EDSON LUIS BOLFE, CNPM; YUNYUN FENG, Zhejiang A&F University; CHANGWEI WANG, University of Hawaii at Manoa.pt_BR
Aparece nas coleções:Artigo em periódico indexado (CNPM)

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