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A purchaser always estimates timber volume in a stand before buying it. In contrast, the seller too often has no idea what volume of timber is being sold. This publication explains how you can make your own tree or log volume measurements.

The nonindustrial private forest sector controls 58 percent of the nation’s commercial forest area. Timber output from the NIPF has been a major forest policy issue throughout this century. A long-run regional timber output projection model was developed for the NIPF sector. The model was based upon two key assumptions: (1) forest management intensity and timber output are inversely related to the forest landowner’s alternative rate of return, and (2) for the NIPF sector, the alternative rate of return is inversely related to the size of forest holding. The timber output model was based on a cross-classification of NIPF land by its physical characteristics (e.g., forest type) and economic characteristics (e.g., landowner’s alternative rate of return). The stratification objective was to create strata with a uniform timber output response to changes in the above characteristics. If the total forest resource is thought of in terms of a matrix, each characteristic serves as a stratification dimension, creating a matrix composed of “homogeneous timber output response cells.” The values of each cell can be used as observations in a regression analysis projecting NIPF timber output. For the NIPF, the size of forest holding would be a primary stratification dimension. Due to the relationships described above, stratification by size of forest holding constitutes stratification by the landowner’s alternative rate of return. Also, since the forest resource is physically uniform, the expected rate of return (marginal value growth percent) of the forest will be uniform within a cell. NIPF landowners base their timber output decision on the relationship between the expected and alternative rates of return, resulting in a convenient forest policy analysis tool. The model was applied to a study region, Virginia’s Coastal Plain, with satisfactory results. Ph. D.

Mathematical methods of assessing the spatial autocorrelation associated with individual tree characteristics in forest stands were identified. These measures were used to investigate the spatial autocorrelation of discrete tree characteristics including the species, product, and defect classifications. With the exception of the species classification, none of the discrete tree characteristics examined showed any evidence of significant (α = 0.05) levels of spatial autocorrelation in loblolly pine stands. The significant autocorrelation of the species classification was probably due to past stand history or microsite variability rather than overall stand conditions such as age, density, or percent pine. The relationship between the level of spatial autocorrelation associated with basal area and several descriptive stand characteristics was also examined. No strong relationships were identified but trends were noticed between the autocorrelation measures and measures of stand competition such as basal area and crown competition factor. The measures of spatial association indicate positive autocorrelation between the characteristics of neighboring trees at very low levels of competition with the autocorrelation becoming increasingly negative as competition increases. At extremely high levels of competition, the spatial autocorrelation measures become positive again, reflecting the stagnated condition of the stand. Methods were developed, using the measures of spatial autocorrelation, to assign characteristics to individual trees in computer generated stands. These methods, applicable for discrete or continuous characteristics; assign the characteristics to individual trees depending on the spatial location of the individual tree and the locations and characteristics of its neighbors. Ph. D.

Nine taper equations were tested to predict diameters inside bark along the stem for Pinus oocarpa Schiede trees growing in natural stands of central Honduras. A five parameter submodel predicted as well as an eight parameter model proposed by Kozak, 1988. Taper variation was explored between two geographic regions from which trees with different taper were suspected. Results showed that different site classes, not fully accounted for in the model, might have an effect on the prediction of taper in each region. Also, the effect of crown class and live crown ratio on prediction was evaluated. The model selected exhibited different prediction patterns for dominant and suppressed trees. On the other hand, live crown ratio did not appear to affect prediction. A computer program was written to use the taper equation developed to compute total and merchantable volume to different top diameter limits. Master of Science

A stand-level growth and yield model was developed to predict future diameter distributions of thinned stands of mixed Appalachian hardwoods. The model allows prediction by species groups and diameter classes. Stand attributes ( basal area per acre, trees per acre, minimum stand diameter, and arithmetic mean dbh) were projected through time for the whole stand and for individual species groups. Future diameter distributions were obtained using the three-parameter Weibull probability density function and parameter recovery method. The recovery method used employed the first two non-central moments of dbh (arithmetic mean dbh and quadratic mean dbh squared) to generate Weibull parameters. Future diameter distributions were generated for the whole stand and every species group but one; the diameter distribution of the remaining species group was obtained by subtraction from whole stand values. A system of tree volume equations which allow the user t o obtain total tree volume or merchantable volume to any top height or diameter completes the model. Volumes can be calculated by species group and summed to get whole stand volume. M.S.

A diameter distribution prediction procedure for single species stands was developed based on the bivariate SB distribution model. The approach not only accounted for and described the relationships between initial and future diameters and their distributions, but also assumed future diameter given initial diameter to be a random variable. While this method was the most theoretically correct, comparable procedures based on the definition of growth equations which assumed future diameter given initial diameter to be a constant, sometimes provided somewhat better results. Both approaches performed as well, and in some cases, better than the established methods of diameter distribution prediction such as parameter recovery, percentile prediction, and parameter prediction. The approaches based on the growth equations are intuitively and biologically appealing in that the future distribution is determined from an initial distribution and a specified initial-future diameter relationship. ln most appropriate. While this result simplified some procedures, it also implied that the initial and future diameter distributions differed only in location and scale, not in shape. This is a somewhat unrealistic assumption, however, due to the relatively short growth periods and the alterations in stand structure and growth due to the repeated thinnings, the data did not provide evidence against the linear growth equation assumption. The growth equation procedures not only required the initial and future diameter distributions to be of a particular form, but they also restricted the initial-future diameter relationship to be of a particular form. The individual tree model, which required no distributional assumptions or restrictions on the growth equation, proved to be the better approach to use in terms of predicting future stand tables as it performed better than all of the distribution-based approaches. For the bivariate distribution, the direct fit, parameter recovery, parameter prediction and percentile prediction diameter distribution prediction techniques, implied diameter relationships were defined. Evaluations revealed that these equations were both accurate and precise, indicating that the accurate specification of the initial distribution and the diameter diameter distribution. Ph. D.

Measurements of DBH, upper diameters, merchantable height and total heights were made on 80 standing white oaks (Quercus alba) which were then felled for detailed measurements of the same parameters. The data obtained were used to evaluate the accuracy of standing tree measurements, to develop and compare volume equations that used upper diameter as one of the predictor variables and to examine the contributions of individual predictor variables to total volume prediction bias and precision. Relative bias ranged from 0.85% for DBH measurement errors to 2.88% for total height measurement errors. Relative standard deviation ranged from 1.52% to 10.13% for DBH and total height errors respectively. When both bias and precision ( standard deviation ) were considered jointly, the relative root mean squared error ranged from 1.75% to 10.48% for DBH and total height errors respectively. Upper diameter and merchantable height showed negative bias. A comparison of eight fitted models against the combined variable model revealed a gain in precision and a reduction in bias for models that used upper diameter as a third predictor variable. The improvement as based on the relative root mean squared error ranged from 28.8% to 71.3% for taped measurements. However, models that used upper diameter as a substitute for either DBH or merchantable height performed worse than the combined variable model. An analytic examination of the impact of measurement error on volume prediction bias showed that merchantable height errors accounted for most of the bias and that DBH and upper diameter errors contributed almost equal amounts(in absolute terms) to the volume bias. Master of Science