Prism Cruising Coefficients for Appalachian Hardwoods Biomass

1980 ◽  
Vol 4 (1) ◽  
pp. 25-26
Author(s):  
W. B. Stuart ◽  
R. G. Oderwald ◽  
E. C. Ford
Keyword(s):  
Basal Area ◽  
Weight Estimation ◽  
Winter Condition ◽  
Summer Condition ◽  
Volume Equation ◽  
Appalachian Hardwoods

Abstract Coefficients were developed to convert directly from prism-cruising tally sheets to tons of biomass per acre for Appalachian hardwood stands. It was found that 10 square feet of basal area per acre represent 12 tons per acre in the summer condition and 11 tons per acre in the winter condition. Coefficients for point cruising with diameter obviation are also presented. The D2H volume equation form was sufficient for weight estimation.

scholarly journals A Comparison of the Arithmetic and Geometric Means in Estimating Stump Diameter, Basal Area, and Volume in Appalachian Hardwoods

2007 ◽  
Vol 24 (1) ◽  
pp. 71-73 ◽  
Author(s):  
Harry V. Wiant ◽  
John R. Brooks
Keyword(s):  
Geometric Mean ◽  
Basal Area ◽  
Cross Sectional Area ◽  
Practical Significance ◽  
Sectional Area ◽  
Cross Sectional ◽  
Geometric Means ◽  
The Difference ◽  
Appalachian Hardwoods ◽  
Stump Diameter

Abstract The difference between the use of the arithmetic and geometric means for estimation of average stump diameter, stump cross-sectional area and estimated tree volume was investigated using measurements from 739 stumps from an Appalachian hardwood stand located in central West Virginia. Although average stump diameter, cross-sectional area, and tree volumes were statistically different between estimates based on the arithmetic and geometric mean diameter, these differences were of little practical significance. The difference in average stem diameter, cross-sectional area, tree cubic volume, and board foot volume were 0.05 in, 0.01 ft2, 0.45 ft3, and 2.41 bd ft, respectively.

scholarly journals A Simple Technique for Estimating Board Foot Volume Yields in Appalachian Hardwoods

2006 ◽  
Vol 23 (3) ◽  
pp. 211-214
Author(s):  
John R. Brooks ◽  
Harry V. Wiant
Keyword(s):  
Simple Technique ◽  
Basal Area ◽  
Site Index ◽  
Parameter Estimates ◽  
Separate Model ◽  
Yield Table ◽  
Input Variables ◽  
Table Data ◽  
Stand Yield ◽  
Appalachian Hardwoods

Abstract A simple whole stand yield equation based solely on basal area per acre for sawtimber-sized trees and average dominant height was found to provide reasonably accurate estimates of board foot (International) yield in Appalachian hardwoods. Separate parameter estimates were obtained for mesophytic hardwood and mixed oak remeasurement plot data and the yield tables of Schnur (Schnur, G.L. 1937. Yield, stand and volume tables for even-aged upland oak forests. USDA Tech. Bull. 560). Estimates of board foot yield per acre for the remeasurement plot data were within 10% of observed values for stands over 30 years old and within 5% for stands over 45 years old. A separate model form based on the same two input variables was developed for Schnur’s yield table data for site index classes 50–80. Estimates of board foot yield were within 10% of tabular values for stands over 45 years old, regardless of site index class.

Forest sampling combining fixed- and variable-radius sample plots

2007 ◽  
Vol 37 (8) ◽  
pp. 1460-1471 ◽  
Author(s):  
Kevin C. Packard ◽  
Philip J. Radtke
Keyword(s):  
Basal Area ◽  
Sampling Strategy ◽  
Tree Height ◽  
Model Parameters ◽  
Stem Density ◽  
Variable Radius ◽  
Plot Sampling ◽  
Allowable Error ◽  
Fixed Radius ◽  
Volume Equation

We examine the statistical properties of a forest sampling strategy that combines methods of fixed- and variable-radius plot sampling. Circular fixed-radius plots are established at the same locations as variable-radius plots to take advantage of their known efficiencies for unbiasedly estimating stem density and basal area, respectively. The design eliminates the need for measuring stem diameters, except to check borderline trees on variable-radius plots. Separate controls on allowable error for stem density and basal area estimates are possible. An unbiased estimator of volume (Vol) is derived that uses an existing volume equation having the form Vol = a + bd2h, where d is tree diameter at breast height, h is tree height, and a and b are model parameters. Calculation of volume requires only the measurement of heights for those trees tallied on the variable-radius plots. Properties of the estimator are demonstrated for a mixed-species hardwood forest in the southern Appalachian Mountains of North Carolina, USA.

scholarly journals An Evaluation of Big Basal Area Factor Sampling in Appalachian Hardwoods

2006 ◽  
Vol 23 (1) ◽  
pp. 62-65 ◽  
Author(s):  
John R. Brooks
Keyword(s):  
Data Collection ◽  
West Virginia ◽  
Sampling Error ◽  
Basal Area ◽  
Volume Estimation ◽  
Hardwood Forest ◽  
Standard Errors ◽  
Sampling Techniques ◽  
Appalachian Hardwoods

Abstract Big basal area factor (BAF) sampling techniques were investigated in a 70-year-old even-aged hardwood forest in northern West Virginia. Data collection procedures permitted the investigation of several small BAFs when employed with 12 big BAFs ranging from 55 to 300. Mean board foot volume per acre for sawtimber products was investigated along with a comparison of the resultant standard errors. The estimated mean volume per acre was quite stable. The same approximate mean volume per acre was obtained using big BAF values of 55 and 150 but with a 66% reduction in the number of sample trees needed for volume estimation. Sampling error increased with increasing big BAF, especially above values of 150. Sampling error within a single big BAF value was stable across the range of small BAFs sampled.

scholarly journals A stand-level multispecies growth model for Appalachian hardwoods

1989 ◽  
Vol 19 (4) ◽  
pp. 405-412 ◽  
Author(s):  
Ernest H. Bowling ◽  
Harold E. Burkhart ◽  
Thomas E. Burk ◽  
Donald E. Beck
Keyword(s):  
Unit Area ◽  
Basal Area ◽  
Species Group ◽  
Arithmetic Mean ◽  
Growth And Yield ◽  
Diameter Distribution ◽  
Individual Species ◽  
Yield Model ◽  
Recovery Method ◽  
Appalachian Hardwoods

A stand-level growth and yield model was developed to predict future diameter at breast height (dbh) distributions of thinned stands of mixed Appalachian hardwoods. The model allows prediction by species group and dbh class. Stand attributes (basal area per unit area, trees per unit area, minimum stand dbh, and arithmetic mean dbh) were projected through time for the whole stand and for individual species groups. Future dbh distributions were obtained using the three-parameter Weibull probability density function and a variation of the parameter recovery method. The recovery method used employed the first two noncentral moments of dbh (arithmetic mean dbh and quadratic mean dbh2) to generate Weibull parameters. Future dbh distributions were generated for the whole stand and every species group but one; the diameter distribution for the remaining species group was obtained by subtraction.

Regenerating Southern Appalachian Mixed Hardwood Stands with the Shelterwood Method

1983 ◽  
Vol 7 (4) ◽  
pp. 212-217 ◽  
Author(s):  
David L. Loftis
Keyword(s):  
Species Composition ◽  
Basal Area ◽  
Herbicide Application ◽  
Oak Regeneration ◽  
Yellow Poplar ◽  
Southern Appalachian ◽  
Prominent Component ◽  
Appalachian Hardwoods

Abstract Shelterwood cuts, with understory treatment by cutting or herbicide application, can be used to regenerate southern Appalachian hardwoods. Cuts with residual basal area ranging from 25 to 66 square feet per acre and overwood retained from 5 to 13 years established desirable, well-stocked stands. However, species composition was essentially the same as in clearcuts with intolerant species, especially yellow-poplar, dominating. Oak regeneration did not benefit from either higher residual basal area or longer periods of overstory retention, even though oaks were prominent in the overstories of all stands. Only where larger oak advance reproduction was numerous before the initial cut did oaks become a prominent component of the new stand.

Estimating Biomass Yields by Source Components for Appalachian Hardwood Thinnings

1988 ◽  
Vol 5 (1) ◽  
pp. 38-40
Author(s):  
John E. Baumgras
Keyword(s):  
Basal Area ◽  
Sample Plot ◽  
Biomass Yields ◽  
Appalachian Hardwoods ◽  
Whole Tree

Abstract Equations are presented for estimating biomass yields from thinning stands of Appalachian hardwoods. These equations were developed from sample plot data collected in overstocked poletimber and small-sawtimber stands aged 50 to 70 years. Yields in green ton(s) per acre can be estimated for three source components: roundwood and topwood of trees ≥5.0 in. dbh. and whole trees 1.0 to 4.9 in. dbh. Yield estimates are a function of the square feet of basal area to be removed per acre from specified tree dbh classes. Application of these equations provides information needed to evaluate harvesting and utilization alternatives that include whole-tree chipping options. North. J. Appl. For. 5:38-40, March 1988.

scholarly journals Diameter-Limit Harvesting: Effects of Residual Trees on Regeneration Dynamics in Appalachian Hardwoods

2009 ◽  
Vol 26 (2) ◽  
pp. 52-60 ◽  
Author(s):  
Travis Deluca ◽  
Mary Ann Fajvan ◽  
Gary Miller
Keyword(s):  
Positive Relationship ◽  
Canopy Cover ◽  
Basal Area ◽  
Black Cherry ◽  
Red Maple ◽  
Residual Tree ◽  
Negative Relationships ◽  
Appalachian Hardwoods ◽  
Stand Conditions

Abstract Ten-years after diameter-limit harvesting in an Appalachian hardwood stand, the height, dbh, and basal area of sapling regeneration was inversely related to the degree of “overtopping” of residual trees. Black cherry and red maple were the most abundant saplings with 416.5 ± 25.7 and 152.9 ± 16.8 stems per acre, respectively. Models of black cherry height and diameter showed significant negative relationships (P < 0.05) with residual tree basal area. In addition, height, diameter, and basal area of dominant and codominant black cherry and black birch saplings were inversely related to residual tree basal area (P < 0.05), as was the basal area of red maple saplings. Alternatively, red maple sapling diameter had a significant positive relationship (P < 0.05) with residual basal area, and height was not significantly affected. Findings suggest that overall stand conditions were most favorable for the development of shade-tolerant red maple, with shade-intolerant species developing well in open areas. However, the long-term development of black cherry may be jeopardized by side shade and canopy cover. Removal of residual trees and subsequent cleaning operations are recommended to increase growth rates of shade-intolerant sapling regeneration.

Minimum Subsamples of Tree Heights for Accurate Estimation of Loblolly Pine Plot Volume

1993 ◽  
Vol 17 (3) ◽  
pp. 124-129 ◽  
Author(s):  
Damon R. Houghton ◽  
Timothy G. Gregoire
Keyword(s):  
Random Sample ◽  
Loblolly Pine ◽  
Basal Area ◽  
Accurate Estimation ◽  
Estimation Technique ◽  
Estimation Techniques ◽  
Accuracy Level ◽  
Subsample Size ◽  
Volume Equation ◽  
Purposive Sample

Abstract This study determined the minimum number of tree heights necessary to obtain an estimate of merchantable plot volume of loblolly pine that, with a probability of 0.95, is within ± 5% and ± 10% of the volume observed when all tree heights on the plot are measured. The size of the height subsample needed to achieve this objective was determined for all combinations of three estimation techniques and four sample designs. The estimation techniques examined were: A. Summation of tree volumes estimated by a merchantable volume equation using tree dbh coupled with a measured or predicted height. Subsample data were used to fit a plot height-diameter regression of the form ln(H)= α + β/D, where H is tree height and D is dbh, to predict the heights of trees on the plot but not in the subsample, B. Substituting average D and H values into a merchantable volume equation and multiplying the result by the number of trees in the plot or stratum, and C. Computing the volume-basal area ratio of the subsample and multiplying the result by the total basal area in the stratum or plot. The sampling designs examined were a simple random sample, a stratified random sample, a stratified systematic sample, and a purposive sample. Results indicated that for the ± 5% accuracy level, estimation technique A with a stratified random sample required the smallest subsample size, 12 tree heights. For the 10% accuracy level, estimation technique A with a purposive sample required the smallest subsample size, only 4 tree heights. Recommended subsample sizes are given for all combinations of estimation techniques and sample designs. South. J. Appl. For. 17(3):124-129.

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