A comparison of some estimators in forest sampling Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/f7623f59q

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  • The objectives of this study were to ascertain the relative precision and accuracy of certain estimators on several forest populations and to determine if relative performance could be predicted from knowledge of population characteristics. Performance was tested on three populations of trees drawn from stands in northern Ontario. The first population consisted of 479 spruce and fir trees drawn from an uneven aged second growth spruce-fir stand. The second consisted of 309 maple and birch trees from a mature hardwood stand. The third population was composed of 500 red pine drawn from a forty year old plantation. Measurement data obtained for each tree included breast height diameter and total height. For the spruce-fir and hardwood stands, measurements o height and crown area were also obtained from large scale aerial photography. Estimators for total volume, height and crown area were compared for the test populations. Independent or supplementary variables employed were diameter, height and crown area plus several transformations and combinations of these variables. Four sample sizes, n = 4, 12, 24, and 40 were employed for each of 25 dependent-independent variable combinations considered. Simple expansion, ratio, unbiased ratio, regression and unequal probability estimators and stratified sampling with the simple expansion estimator were compared using Monte Carlo techniques. Relative performance was evaluated using estimates of sampling variances, biases and mean square errors obtained from repeated sampling of the test populations. Results indicated linear and parabolic regression and the Horvitz- Thompson pps estimator were usually among the best three estimators for the two largest sample sizes studied. For the smaller sample sizes, linear egression, the Horvitz- Thompson pps and ratio of means estimators were best. For the estimation of volume using diameter- squared as the supplementary variable, linear regression was the best approach. Parabolic regression using diameter and diameter- squared was equal precise for the larger sample sizes. Major factors affecting the relative performance of estimators were: 1) the form of the dependent-independent variable relationship (linear or curvilinear), 2) the correlation between these variables, 3) the position of the intercept of the population regression line, 4) the variance of the dependent variable given the independent variable and 5) sample size.
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