Please use this identifier to cite or link to this item: https://repositorio.inpa.gov.br/handle/1/14686
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dc.contributor.authorBispo, Polyanna da Conceição-
dc.contributor.authorSantos, João Roberto dos-
dc.contributor.authorMorisson Valeriano, Márcio de-
dc.contributor.authorGraça, Paulo Maurício Lima Alencastro de-
dc.contributor.authorBalzter, Heiko-
dc.contributor.authorFrança, Helena-
dc.contributor.authorBispo, Pitágoras C.-
dc.date.accessioned2020-04-24T17:00:18Z-
dc.date.available2020-04-24T17:00:18Z-
dc.date.issued2016-
dc.identifier.urihttps://repositorio.inpa.gov.br/handle/1/14686-
dc.description.abstractSurveying primary tropical forest over large regions is challenging. Indirect methods of relating terrain information or other external spatial datasets to forest biophysical parameters can provide forest structural maps at large scales but the inherent uncertainties need to be evaluated fully. The goal of the present study was to evaluate relief characteristics, measured through geomorphometric variables, as predictors of forest structural characteristics such as average tree basal area (BA) and height (H) and average percentage canopy openness (CO). Our hypothesis is that geomorphometric variables are good predictors of the structure of primary tropical forest, even in areas, with low altitude variation. The study was performed at the Tapajo's National Forest, located in the Western State of Pará, Brazil. Forty-three plots were sampled. Predictive models for BA, H and CO were parameterized based on geomorphometric variables using multiple linear regression. Validation of the models with nine independent sample plots revealed a Root Mean Square Error (RMSE) of 3.73 m2/ha (20%) for BA, 1.70 m (12%) for H, and 1.78% (21%) for CO. The coefficient of determination between observed and predicted values were r2 = 0.32 for CO, r2 = 0.26 for H and r2 = 0.52 for BA. The models obtained were able to adequately estimate BA and CO. In summary, it can be concluded that relief variables are good predictors of vegetation structure and enable the creation of forest structure maps in primary tropical rainforest with an acceptable uncertainty. © 2016 Bispo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en
dc.language.isoenpt_BR
dc.relation.ispartofVolume 11, Número 4pt_BR
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Brazil*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/br/*
dc.subjectAltitudeen
dc.subjectCanopyen
dc.subjectForest Structureen
dc.subjectPolymorphism, Geneticen
dc.subjectHeighten
dc.subjectModelen
dc.subjectMultiple Linear Regression Analysisen
dc.subjectTropical Rain Foresten
dc.subjectUncertaintyen
dc.subjectValidation Processen
dc.subjectVegetationen
dc.subjectBiological Modelen
dc.subjectRainforesten
dc.subjectTropic Climateen
dc.subjectModels, Biologicalen
dc.subjectRainforesten
dc.subjectTropical Climateen
dc.titlePredictive models of primary tropical forest structure from geomorphometric variables based on SRTM in the Tapajo's region, Brazilian Amazonen
dc.typeArtigopt_BR
dc.identifier.doi10.1371/journal.pone.0152009-
dc.publisher.journalPLoS ONEpt_BR
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