Evaluation of &lt;I&gt;n&lt;/I&gt;-Tree Distance Sampling for Inventory of Headwater Riparian Forests of Western Oregon

Basiri R., Moradi M., Kiani B., Maasumi Babaarabi M. (2018): Evaluation of distance methods for estimating population density in Populus euphratica Olivier natural stands (case study: Maroon riparian forests, Iran). J. For. Sci., 64: 230-244.The aim of this study was to determine the performance of distance methods in terms of accuracy, precision, bias, consumed time and sampling efficiency in the Maroon riparian forests, Iran. 40 estimators were used to evaluate the density of Populus euphratica Olivier trees in pure and mixed stands. Fifty quadrates (30 × 30 m) were established in each stand. To evaluate the accuracy, precision, bias, consumed time and efficiency of sampling techniques, relative root mean square error -RRMSE (%), coefficient of variation -CV (%), relative bias -RBIAS (%), t × RBIAS 2 , t × E 2 , where t is study time and E (%) is sampling error at a confidence level of 95%, and efficiency ratio between method j and k (Ef jk ) were used. A compound of three basic distance estimators sampling method and n-tree were the best in both stands according to all criteria for density estimation. Moreover, variable area transect by Parker (g = 3) and quadrat method were the best methods for density estimation only in pure stand, while the angle order-point-centred quarter method was superior in mixed stand. Regarding to the results, we recommend the use of compound of three basic distances (BDAV3) and basic distance-nearest neighbour (BDNN2) for density estimation of P. euphratica stands in riparian forests.

Section: Sampling Methods (Efficiency)supporting

confidence: 82%

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confidence: 99%

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Evaluation of distance methods for estimating population density in Populus euphratica Olivier natural stands (case study: Maroon riparian forests, Iran)

Basiri¹,

Moradi²,

Kiani³

et al. 2018

“…Some of these methods require measuring distances to a large number of additional trees (Kleinn, Vilčko 2006b), and others need intensive stochastic simulations of spatial patterns that emulate the distribution of observed point-to-trees and tree-to-tree distances (Nothdurft et al 2010). Also, many of these estimators are difficult to comprehend and implement without advanced statistical training (Haxtema et al 2012). Others again are impractical for field work (Magnussen et al 2011).…”

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confidence: 99%

Adaptive k-tree sample plot for the estimation of stem density: An empirical approach

Hormoz¹

2018

Sohrabi H. (2018): Adaptive k-tree sample plot for the estimation of stem density: An empirical approach. J. For. Sci., 64: 17-24.Available budgets for the inventory of non-commercial woodlands are small. Therefore, there has been increased interest in using distance methods, such as k-tree sampling, which are faster than fixed plot sampling. In low-density woodlands, large search areas for k nearest trees contradict any practical advantage over sampling with fixed area plots. Here, a modification of a k-tree sample plot with an empirical approach to estimating the number of trees per unit area in low-density woodlands is presented. The standard and modified k-tree sample plots have been tested in one actual and three simulated forests with different spatial patterns. The modified method was superior to other combinations of methods in terms of relative bias and relative efficiency. Considering statistical and practical aspects of sampling for tree density, the modified method is more promising than is the standard one.

“…In difficult terrain and in populations with a high and variable local density of elements, the FCS can be faster and more expedient than sampling with fixed area plots (Cox 1976;McNeill et al 1977;Chen, Tsai 1980;Patil et al 1982;Roser et al 1984;Delince 1986;Jianrang 1988;Lessard et al 1994;Barbour, Gerritsen 1996;Lynch, Rusydi 1999;Doberstein et al 2000;Lessard et al 2002;Picard et al 2005;Kleinn, Vilčko 2006b;Haxtema et al 2012). …”

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confidence: 99%

A fixed count sampling estimator of stem density based on a survival function

Magnussen¹

2015

ABSTRACT:In fixed count sampling (FCS) a fixed number (k) of observations is made at n randomly selected sample locations. For estimation of stem density, the distance from a random sample location to the k nearest trees was measured. It is known that practical FCS estimators of stem density are biased. With the objective of reducing bias in FCS estimators of stem density, a new estimator derived from a survival function with distance acting as time was presented. To allow for spatial heterogeneity in stem density, the survival function includes shared frailty. Encouraging results with k = 6 in terms of bias, root mean squared error (RMSE), and coverage of nominal 95% confidence intervals were obtained in an extensive testing with simulated random sampling from 54 actual and four simulated spatial point patterns of tree locations. Sample sizes were 9, 15, and 30, with 1200 replications per setting. The performance across sites of the new FCS estimator was variable but almost paralleled that of a design-based estimator with fixed area plots. Users of the new FCS estimator can expect an absolute relative bias and a root mean squared error that are 1% greater than for sampling with fixed area plots holding an average of k trees. The chance of a smaller RMSE with the proposed estimator was estimated at 0.44.