Evaluation of sampling methods for the estimation of structural indices in forest stands. Kint, V.; Robert, D.; and Noël, L. Ecological Modelling, 180(4):461–476, Vogelzanglaan 78, Brussels, Belgium, 2004.
abstract   bibtex   
The paper is about the accurate (i.e. unbiased and precise) and efficient estimation of structural indices in forest stands. We present SIAFOR, a computer programme for the calculation of four nearest-neighbour indices, which describe the spatial arrangement of tree positions, the distribution pattern of species, and the size differentiation between trees. The study uses SIAFOR as a sampling simulator in eight completely stem-mapped forest stands of varying area and structural complexity. We statistically evaluate two sample types (distance and plot sampling), comparing sampling error, bias and minimum sample size for index estimation. We introduce the concepts of measurement expansion factor (MEF) and design expansion factor (DEF) for the technical evaluation of sample type efficiency (optimal sample type). Results indicate that sampling error can reach high levels and that minimum sample sizes for index estimation often amply exceed the limit of 20% of tree density or 20 trees per species per hectare, that we set as the highest feasible sample size in normal situations. We found clear feasibility limits (in terms of minimal tree densities and reachable accuracy levels) for the estimation of all investigated indices. Generally, equal or higher sample sizes are needed for plot sampling than for distance sampling to reach equal accuracy levels. Nevertheless, plot sampling resulted more efficient for the estimation of tree size differentiation at low to medium accuracy levels. For all other investigated indices distance sampling resulted more efficient than plot sampling. Minimum sample size increases with accuracy and is negatively correlated with tree density. At a given accuracy level minimum sample size is highest for the estimation of relative mingling and lowest for tree size differentiation; furthermore it is generally lower in large stands than in small ones. Because of the consistency of our conclusions in all of the investigated stands, we think they apply in most stands of similar area (between 1 and 10 ha) and species diversity (not more than four species). © 2004 Elsevier B.V. All rights reserved.
@ARTICLE{Kint2004,
  author = {Kint, V. and Robert, D.W. and No{\"e}l, L.},
  title = {Evaluation of sampling methods for the estimation of structural indices
	in forest stands},
  journal = {Ecological Modelling},
  year = {2004},
  volume = {180},
  pages = {461--476},
  number = {4},
  abstract = {The paper is about the accurate (i.e. unbiased and precise) and efficient
	estimation of structural indices in forest stands. We present SIAFOR,
	a computer programme for the calculation of four nearest-neighbour
	indices, which describe the spatial arrangement of tree positions,
	the distribution pattern of species, and the size differentiation
	between trees. The study uses SIAFOR as a sampling simulator in eight
	completely stem-mapped forest stands of varying area and structural
	complexity. We statistically evaluate two sample types (distance
	and plot sampling), comparing sampling error, bias and minimum sample
	size for index estimation. We introduce the concepts of measurement
	expansion factor (MEF) and design expansion factor (DEF) for the
	technical evaluation of sample type efficiency (optimal sample type).
	Results indicate that sampling error can reach high levels and that
	minimum sample sizes for index estimation often amply exceed the
	limit of 20% of tree density or 20 trees per species per hectare,
	that we set as the highest feasible sample size in normal situations.
	We found clear feasibility limits (in terms of minimal tree densities
	and reachable accuracy levels) for the estimation of all investigated
	indices. Generally, equal or higher sample sizes are needed for plot
	sampling than for distance sampling to reach equal accuracy levels.
	Nevertheless, plot sampling resulted more efficient for the estimation
	of tree size differentiation at low to medium accuracy levels. For
	all other investigated indices distance sampling resulted more efficient
	than plot sampling. Minimum sample size increases with accuracy and
	is negatively correlated with tree density. At a given accuracy level
	minimum sample size is highest for the estimation of relative mingling
	and lowest for tree size differentiation; furthermore it is generally
	lower in large stands than in small ones. Because of the consistency
	of our conclusions in all of the investigated stands, we think they
	apply in most stands of similar area (between 1 and 10 ha) and species
	diversity (not more than four species). © 2004 Elsevier B.V. All
	rights reserved.},
  address = {Vogelzanglaan 78, Brussels, Belgium},
  keywords = {Forest inventory, Forest stand structure, Nearest-neighbour indices,
	Sampling simulator, Software},
  owner = {eric},
  subdatabase = {distance},
  timestamp = {2006.11.05}
}
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