Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/32580
metadata.artigo.dc.title: Carbon stock classification for tropical forests in Brazil: Understanding the effect of stand and climate variables
metadata.artigo.dc.creator: David, Hassan Camil
Araújo, Emanuel José Gomes de
Morais, Vinícius Augusto
Scolforo, José Roberto Soares
Marques, Jair Mendes
Péllico Netto, Sylvio
MacFarlane, David W.
metadata.artigo.dc.subject: Discriminant analysis
Stem carbon
Temperature and precipitation
Mean diameter and height
Brazilian biomes
Análise discriminante
Carbono da haste
Temperatura e precipitação
Diâmetro e altura médios
Biomas brasileiros
metadata.artigo.dc.publisher: Elsevier
metadata.artigo.dc.date.issued: 15-Nov-2017
metadata.artigo.dc.identifier.citation: DAVID. 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.
metadata.artigo.dc.description.abstract: Forest 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.
metadata.artigo.dc.identifier.uri: https://www.sciencedirect.com/science/article/pii/S0378112717308332#!
http://repositorio.ufla.br/jspui/handle/1/32580
metadata.artigo.dc.language: en_US
Appears in Collections:DCF - Artigos publicados em periódicos

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.