Graduate Thesis Or Dissertation

 

A computer simulation study of the relative efficiency of several forest sampling techniques as influenced by the spatial distribution of trees found in five major forest types of the Pacific Northwest Public Deposited

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  • This study was initiated to determine the relative efficiency of systematic, stratified and simple random sampling for crown area and tree frequency estimation of five of the major forest types found in the Pacific Northwest. Several of the more common methods of estimating spatial distribution coefficients were evaluated. Finally the effect of spatial distribution of trees in these forest types on the relative efficiency of two-dimensional systematic sampling was analyzed. Crown maps of five 48-acre tracts of the following types were made from large scale (1:2500) aerial photographs and photographically enlarged to the scale of 1:624: 1.Typical ponderosa-pine of Eastern Oregon 2.Mixed species of Oregon’s Coastal type 3.Mixed pine stands of Northeastern Oregon 4.Even aged Douglas-fir stands common to the Pacific Northwest 5.Typical old growth Douglas-fir of the Pacific Northwest. The basic data consisted of the location (by grid coordinates) and size of each tree crown as obtained from crown maps. These data were committed to the memory of the CDC 3300 computer and the entire analyses executed through computer simulation techniques as follows: Exhaustive two-dimensional systematic sampling was taken using one quarter acre sampling units, Sample mean variances were computed for the three sampling schemes using analysis of variance principles. Relative efficiency of systematic and stratified sampling was also computed. The same parameters were also estimated by variable plot sampling based on 24 points systematically located at the middle 24 acre of each type. The same principles were used to compute variances as close approximations. Eight of the more common non-randomness measures were compared. To evaluate these methods and obtain the required data for examining the effect of spatial variation on relative efficiency of systematic sampling, four new populations were generated from each forest type. These newly computer generated populations were sampled in the same way as for the original forest types. Spatial distribution coefficients of these populations were also computed. Multiple regression analysis was employed using stepwise computer program to establish relationships between the relative efficiency of systematic sampling and the coefficients of randomization. Logarithmic transformation was used to satisfy equality of variances in establishing regression equations. The results of this study indicated that the relative efficiency of two-dimensional systematic sampling may vary greatly depending on the parameter being estimated. For tree frequency estimation (a discrete variable) of the original forest types the gain in precision of systematic sampling varied from 20 to 167 percent. In estimating crown area (a continuous variable) systematic sampling was less precise than simple random sampling on one forest type, while yielding gains in precision ranging from 10 to 179 percent for the other original forest types. There were no significant differences in precision obtained by the three sampling schemes for estimating both parameters when applied to completely randomly dispersed populations. Systematic sampling was less precise than both stratified and random sampling when applied to uniformly spaced populations. The loss in precision in this case ranged from 15 to about 79 percent. Of the eight non-randomness measures (measures of spatial distribution) the point method proved to be best. Grosenbaugh' s Q-factor which is considered to be the most practical method was rejected as being invalid. All original forest types were found to be clustered. Regression equations of relative efficiency on the coefficient of randomization for crown area estimation of the five forest types were mostly non-linear. Since these predicting models produced low correlations and were obviously different from each other, no attempt was made to establish a generalized model. Relative efficiency of systematic sampling for tree frequency estimation was highly correlated with the coefficient of randomization. Of the five equations established, four were not significantly different from each other, thus they were pooled and a generalized model developed which turned out to be y = 1.402x with simple correlation coefficient of r=0.81 where: y = common log of relative efficiency of systematic sampling x = common log of spatial distribution coefficient measured by the point method. Therefore, when an estimate of spatial distribution of trees in a forest is known, the relative efficiency of systematic plot sampling can be predicted with a high degree of accuracy. Such relationships could also be applied as adjustment factors to systematic sampling variance when being treated as simple random samples.
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