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Campo DCValorIdioma
dc.creatorDavid, Hassan Camil-
dc.creatorAraújo, Emanuel José Gomes de-
dc.creatorMorais, Vinícius Augusto-
dc.creatorScolforo, José Roberto Soares-
dc.creatorMarques, Jair Mendes-
dc.creatorPéllico Netto, Sylvio-
dc.creatorMacFarlane, David W.-
dc.date.accessioned2019-01-25T14:51:30Z-
dc.date.available2019-01-25T14:51:30Z-
dc.date.issued2017-11-15-
dc.identifier.citationDAVID. H. C. et al. Carbon stock classification for tropical forests in Brazil: Understanding the effect of stand and climate variables. Forest Ecology and Management, Amsterdam, v. 404, p. 241-250, 15 Nov. 2017.pt_BR
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0378112717308332#!pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/32580-
dc.description.abstractForest ecosystems play an important role in the global carbon cycle and with this there is an increasing need for quantifying carbon at large scales. The aim of this research was to develop a system for classifying tropical forests in Brazil into carbon stock classes, applicable to large areas, emphasizing different sets of stand and climate variables. We used data from forests inventoried in two Brazilian biomes: Atlantic Forest and Savanna. We applied discriminant analysis to generate a classification rule by biome. Three types of variables were used: climatic (mean annual temperature and precipitation, or MAT and MAP), geographical (latitude and longitude), and stand variables (density of trees, mean height or , mean square diameter or dg, and basal area or G). We combined these into three scenarios for analysis: (1) all variables; (2) all variables, except ; (3) all variables, except , dg, and G, to determine their contribution to classifying carbon stocks. We also assessed each set of variables in the presence/absence of MAP and MAT, used simultaneously or not. The best classification rules resulted in 83.9% and 98.5% of correct classifications for Atlantic Forest and Savanna biomes, respectively. Stand variables contributed significantly to successful classification; for the Atlantic Forest biome, dg and G contributed from 36% to 42% and from 2% to 5%, yet for the Savanna biome the gains ranged from 31% to 42% and 6%–9%, respectively. For the climate variables, the simultaneous use of MAT and MAP played an important role in the classification in all cases in the Atlantic Forest biome, contributing up to 9.2% for the classification. In the Savanna biome, we found significant positive gains by the simultaneous use in the absence of , dg, and G, on the other hand, the simultaneous use exerted negative effects when was used. We concluded that climate variables are most helpful when stand variables are not included in the analysis. In terms of carbon stock variation, the Atlantic Forest biome tended to be more sensitive to both MAT and MAP, whereas the Savanna biome had no significant climatic dependence in the classification. The variable exerted a greater effect in the Savanna biome than in the Atlantic Forest, however, basal area and mean square diameter were the most important in both biomes.pt_BR
dc.languageen_USpt_BR
dc.publisherElsevierpt_BR
dc.rightsrestrictAccesspt_BR
dc.sourceForest Ecology and Managementpt_BR
dc.subjectDiscriminant analysispt_BR
dc.subjectStem carbonpt_BR
dc.subjectTemperature and precipitationpt_BR
dc.subjectMean diameter and heightpt_BR
dc.subjectBrazilian biomespt_BR
dc.subjectAnálise discriminantept_BR
dc.subjectCarbono da hastept_BR
dc.subjectTemperatura e precipitaçãopt_BR
dc.subjectDiâmetro e altura médiospt_BR
dc.subjectBiomas brasileirospt_BR
dc.titleCarbon stock classification for tropical forests in Brazil: Understanding the effect of stand and climate variablespt_BR
dc.typeArtigopt_BR
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