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Provedor de dados:  Anais da ABC (AABC)
País:  Brazil
Título:  Sampling procedures for inventory of commercial volume tree species in Amazon Forest
Autores:  NETTO,SYLVIO P.
PELISSARI,ALLAN L.
CYSNEIROS,VINICIUS C.
BONAZZA,MARCELO
SANQUETTA,CARLOS R.
Data:  2017-09-01
Ano:  2017
Palavras-chave:  Adaptive cluster sampling
Spatial species distribution
Volume estimation
Zero-plots
Resumo:  ABSTRACT The spatial distribution of tropical tree species can affect the consistency of the estimators in commercial forest inventories, therefore, appropriate sampling procedures are required to survey species with different spatial patterns in the Amazon Forest. For this, the present study aims to evaluate the conventional sampling procedures and introduce the adaptive cluster sampling for volumetric inventories of Amazonian tree species, considering the hypotheses that the density, the spatial distribution and the zero-plots affect the consistency of the estimators, and that the adaptive cluster sampling allows to obtain more accurate volumetric estimation. We use data from a census carried out in Jamari National Forest, Brazil, where trees with diameters equal to or higher than 40 cm were measured in 1,355 plots. Species with different spatial patterns were selected and sampled with simple random sampling, systematic sampling, linear cluster sampling and adaptive cluster sampling, whereby the accuracy of the volumetric estimation and presence of zero-plots were evaluated. The sampling procedures applied to species were affected by the low density of trees and the large number of zero-plots, wherein the adaptive clusters allowed concentrating the sampling effort in plots with trees and, thus, agglutinating more representative samples to estimate the commercial volume.
Tipo:  Info:eu-repo/semantics/article
Idioma:  Inglês
Identificador:  http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652017000401829
Editor:  Academia Brasileira de Ciências
Relação:  10.1590/0001-3765201720160760
Formato:  text/html
Fonte:  Anais da Academia Brasileira de Ciências v.89 n.3 2017
Direitos:  info:eu-repo/semantics/openAccess
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