Evaluation of sampling methods for the estimation of structural indices in forest stands. Kint, V., Robert, D., & 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}
}
Downloads: 0
{"_id":"QcQARf5g2W9kw3LYi","bibbaseid":"kint-robert-nol-evaluationofsamplingmethodsfortheestimationofstructuralindicesinforeststands-2004","authorIDs":[],"author_short":["Kint, V.","Robert, D.","Noël, L."],"bibdata":{"bibtype":"article","type":"article","author":[{"propositions":[],"lastnames":["Kint"],"firstnames":["V."],"suffixes":[]},{"propositions":[],"lastnames":["Robert"],"firstnames":["D.W."],"suffixes":[]},{"propositions":[],"lastnames":["Noël"],"firstnames":["L."],"suffixes":[]}],"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","bibtex":"@ARTICLE{Kint2004,\r\n author = {Kint, V. and Robert, D.W. and No{\\\"e}l, L.},\r\n title = {Evaluation of sampling methods for the estimation of structural indices\r\n\tin forest stands},\r\n journal = {Ecological Modelling},\r\n year = {2004},\r\n volume = {180},\r\n pages = {461--476},\r\n number = {4},\r\n abstract = {The paper is about the accurate (i.e. unbiased and precise) and efficient\r\n\testimation of structural indices in forest stands. We present SIAFOR,\r\n\ta computer programme for the calculation of four nearest-neighbour\r\n\tindices, which describe the spatial arrangement of tree positions,\r\n\tthe distribution pattern of species, and the size differentiation\r\n\tbetween trees. The study uses SIAFOR as a sampling simulator in eight\r\n\tcompletely stem-mapped forest stands of varying area and structural\r\n\tcomplexity. We statistically evaluate two sample types (distance\r\n\tand plot sampling), comparing sampling error, bias and minimum sample\r\n\tsize for index estimation. We introduce the concepts of measurement\r\n\texpansion factor (MEF) and design expansion factor (DEF) for the\r\n\ttechnical evaluation of sample type efficiency (optimal sample type).\r\n\tResults indicate that sampling error can reach high levels and that\r\n\tminimum sample sizes for index estimation often amply exceed the\r\n\tlimit of 20% of tree density or 20 trees per species per hectare,\r\n\tthat we set as the highest feasible sample size in normal situations.\r\n\tWe found clear feasibility limits (in terms of minimal tree densities\r\n\tand reachable accuracy levels) for the estimation of all investigated\r\n\tindices. Generally, equal or higher sample sizes are needed for plot\r\n\tsampling than for distance sampling to reach equal accuracy levels.\r\n\tNevertheless, plot sampling resulted more efficient for the estimation\r\n\tof tree size differentiation at low to medium accuracy levels. For\r\n\tall other investigated indices distance sampling resulted more efficient\r\n\tthan plot sampling. Minimum sample size increases with accuracy and\r\n\tis negatively correlated with tree density. At a given accuracy level\r\n\tminimum sample size is highest for the estimation of relative mingling\r\n\tand lowest for tree size differentiation; furthermore it is generally\r\n\tlower in large stands than in small ones. Because of the consistency\r\n\tof our conclusions in all of the investigated stands, we think they\r\n\tapply in most stands of similar area (between 1 and 10 ha) and species\r\n\tdiversity (not more than four species). © 2004 Elsevier B.V. All\r\n\trights reserved.},\r\n address = {Vogelzanglaan 78, Brussels, Belgium},\r\n keywords = {Forest inventory, Forest stand structure, Nearest-neighbour indices,\r\n\tSampling simulator, Software},\r\n owner = {eric},\r\n subdatabase = {distance},\r\n timestamp = {2006.11.05}\r\n}\r\n\r\n","author_short":["Kint, V.","Robert, D.","Noël, L."],"key":"Kint2004","id":"Kint2004","bibbaseid":"kint-robert-nol-evaluationofsamplingmethodsfortheestimationofstructuralindicesinforeststands-2004","role":"author","urls":{},"keyword":["Forest inventory","Forest stand structure","Nearest-neighbour indices","Sampling simulator","Software"],"downloads":0,"html":""},"bibtype":"article","biburl":"http://distancelive.xyz/MainBibFile.bib","creationDate":"2020-06-16T14:23:34.679Z","downloads":0,"keywords":["forest inventory","forest stand structure","nearest-neighbour indices","sampling simulator","software"],"search_terms":["evaluation","sampling","methods","estimation","structural","indices","forest","stands","kint","robert","noël"],"title":"Evaluation of sampling methods for the estimation of structural indices in forest stands","year":2004,"dataSources":["RjvoQBP8rG4o3b4Wi"]}