Data from the 2001 timber product output study for Georgia was explored to determine new methods for stratifying mills and finding suitable sampling estimators. Estimators for roundwood receipts totals comprised several types: simple random sample, ratio, stratified sample, and combined ratio. Two stratification methods were examined: the Dalenius–Hodges (DH) square root of the frequency method and a cluster analysis method. Three candidate sizes for the number of groups were selected from the cluster analysis and subsequently used in the DH stratification as well. Relative efficiency improved when the number of groups increased and when using a ratio estimator, particularly a combined ratio. The two stratification methods performed similarly. Neither the DH method nor the cluster analysis method performed better than the other. Six bound sizes (1%, 5%, 10%, 15%, 20%, and 25%) were considered for deriving samples sizes for the total volume of roundwood receipts. The minimum achievable bound size was found to be 10% of the total receipts volume for the DH method using a two-group stratification. This was true for both the stratified and combined ratio estimators. In addition, for the stratified and combined ratio estimators, only the DH method stratifications were able to reach a 15% bound on the total (six of the 12 stratified estimators). These results demonstrate that the utilized classification methods are compatible with stratified totals estimators and can provide users with the opportunity to develop viable sampling procedures as opposed to complete mill censuses.

Formulas are derived for determining the total number of sample points and the number of volume points or a point, double sample with a ratio of means estimator to replace a point sample and achieve the same variance. A minimum ratio of the cost of measuring volume to the cost of measuring basal area at a point is determined for which the point, double sample will be less costly, in terms of time required to measure points, than the point sample.

Data from the 2001 and 2003 timber product output (TPO) studies for Georgia were explored to determine new methods for handling missing data and finding suitable sampling estimators. Mean roundwood volume receipts per mill for the year 2003 were calculated using the methods developed by Rubin (1987). Mean receipts per mill ranged from 4.4 to 14.2 million ft. The mean value of 9.3 million ft did not statistically differ from the NONMISS, SINGLE1, and SINGLE2 references means (p=.68, .75, and .76 respectively). Fourteen estimators were investigated to investigate sampling approaches, with estimators being of several means types (simple random sample, ratio, stratified sample, and combined ratio) as well as employing two methods for stratification (Dalenius-Hodges (DH) square root of the Frequency method and a cluster analysis method. Relative efficiency (RE) improved when the number of groups increased and when employing a ratio estimator, particularly a combined ratio. Neither the DH method nor the cluster analysis method performed better than the other. Six bound sizes (1, 5, 10, 15, 20, and 25 percent) were considered for deriving samples sizes for the total volume of roundwood. The minimum achievable bound size was found to be 10 percent of the total receipts volume for the DH-method using a two group stratification. This was true for both the stratified and combined ratio estimators. In addition, for the stratified and combined ratio estimators, only the DH method stratifications were able to reach a 10 percent bound on the total (6 of the 12 stratified estimators). The remaining six stratified estimators were able to achieve a 20 percent bound of the total. Finally, nonlinear repeated measures models were developed to spatially allocate mill receipts to surrounding counties in the event of obtaining only a mill’s total receipt volume. A Gompertz model with a power spatial covariance was found to be the best performing when using road distances from the mills to either county center type (geographic or forest mass). These models utilized the cumulative frequency of mill receipts as the response variable, with cumulative frequencies based on distance from the mill to the county.

This study assessed a lidar-based, object-oriented (segmentation) approach to forest volume and aboveground biomass modeling. The study area in the Piedmont physiographic region of Virginia is composed of temperate coniferous, deciduous, and mixed stands. Segmentation objects, hierarchical in terms of area and ranging from 0.035 to 5.632 ha/object, were created using a lidar-derived canopy height model. Horizontal point (basal area) samples were used to calculate volume and aboveground biomass. Per-object lidar point (per return height and intensity) distributional parameters were extracted from small-footprint lidar. Adjusted R 2 and Mallow's Cp metrics were used to select models for the range of segmentation results. Selected variables included intensity-based and structurally related first through fifth return height parameters. Object-based modeling (adjusted R 2 = 0.58–0.79; various object sizes) resulted in distinct improvements over stand-based attempts (adjusted R 2 = 0.40–0.73; majority adjusted R 2 < 0.50). Adjusted R 2 and RMSE values for deciduous volume (0.59; 51.15 m3/ha) and biomass (0.58; 37.41 Mg/ha) were better than those found for another, plot-based study in the study area. Coniferous R 2 values for volume (0.66) and biomass (0.59) were lower than previous studies, which was attributed to variability within the relatively narrow volume range (6.94–50.93 m3/ha).

Effective multi-product inventory requires methods that can estimate the percent of total tree merchantable volume in any one piece regardless of length or position. The usual equations for predicting percent volume by piece are confined to specific log lengths and length units. A simple method of predicting percent board foot volume in any piece of a bole or log in any length units is presented. It is widely applicable but not as accurate as other more specific methods. South. J. Appl. For. 27(2): 149–152.

An approximation to the variances of the regression and mean of ratios estimators for double sampling is presented that can be used to determine how many basal area points are needed to augment an existing inventory to achieve a desired precision. The approximation may also be used to determine the effective regular sample size to achieve equal precision to a double sample.

A system of equations for tree and stand volume was derived using dimensional analysis techniques. The equations are analytically compatible and numerically consistent. Two parameters define the entire system, which can be estimated by fitting the tree taper and volume equations or by fitting the stand level equations. Data from a thinning study in loblolly pine (Pinus taeda L.) plantations established on cutover site-prepared lands were used to test the utility of the system. The equations are general and can be applied to other tree species in other locales.

Three ground datasets were used to simulate the canopy height characteristics of tropical forests in Costa Rica for the purposes of forest biomass estimation. The canopy height models (CHMs) were used in conjunction with airborne laser data. Gross biomass estimation errors on the order of 50-90% arose in two of the three analyses. The characteristics of the datasets and the biomass estimation procedure are reviewed to identify sources of error. In one dataset, the width of the fixed-area ground plots were small enough (5 m) that significant portions of the overstory canopy above the plots were not accounted for in the ground samples. The use of mapped stand data from thin ground plots resulted in inaccurate CHMs, which in turn lead to gross overestimates of forest biomass (about 90% larger than the ground reference value). CHMs generated using mensuration data collected on thin, fixed-area plots may significantly underestimate the average canopy height and crown closure actually found on that plot. This underestimation problem is directly related to plot width. Below a critical threshold, the thinner the plot, the greater the underestimation bias. In the second dataset, it is believed that lower-than-normal rainfalls at the beginning and end of the wet season may have produced a forest canopy with reduced leaf area. The airborne laser pulses penetrated further into the canopy, resulting in airborne laser estimates of forest biomass which grossly underestimated reference values by about 50%. Changing canopy conditions (e.g. leaf loss due to drought, insect defoliation, storm damage) can affect the accuracy of a CHM. Sources of error are reported in order to forewarn those researchers who produce and utilize canopy height models.

quent updates,spectral change detection, and maps of forest area include the AVHRR calibration-center technique and various Landsat Thematic Mapper classification algorithms. Should a switch from p r o v e n technology be advised, our general recommendation is to conduct several pilot studies that would focus on developing or refining tools and methodologies to allow objective, repeatable, and accurate forest area estimation using multispectral earth resource satellite data.

Airborne laser profiling data were used to estimate the basal area, volume, and biomass of primary tropical forests. A procedure was developed and tested to divorce the laser and ground data collection efforts using three distinct data sets acquired in and over the tropical forests of Costa Rica. Fixed-area ground plot data were used to simulate the height characteristics of the tropical forest canopy and to simulate laser measurements of that canopy. On two of the three study sites, the airborne laser estimates of basal area, volume, and biomass grossly misrepresented ground estimates of same. On the third study site, where the widest ground plots were utilized, airborne and ground estimates agreed within 24%. Basal area, volume, and biomass prediction inaccuracies in the first two study areas were tied directly to disagreements between simulated laser estimates and the corresponding airborne measurements of average canopy height, height variability, and canopy density. A number of sampling issues were investigated; the following results were noted in the analyses of the three study areas. 1) Of the four ground segment lengths considered (25 m, 50 m, 75 m, and 100 m), the 25 m segment length introduced a level of variability which may severely degrade prediction accuracy in these Costa Rican primary tropical forests. This effect was more pronounced as plot width decreased. A minimum segment length was on the order of 50 m. 2) The decision to transform or not to transform the dependent variable (e.g., biomass) was by far the most important factor of those considered in this experiment. The natural log transformation of the dependent variable increased prediction error, and error increased dramatically at the shorter segment lengths. The most accurate models were multiple linear models with forced zero intercept and an untransformed dependent variable. 3) General linear models were developed to predict basal area, volume, and biomass using airborne laser height measurements. Useful laser measurements include average canopy height, all pulses (h̄a), average canopy height, canopy hits (h̄c) and the coefficients of variation of these terms (ca and cc). Coefficients of determination range from 0.4 to 0.6. Based on this research, airborne laser and ground sampling procedures are proposed for use for reconnaissance level surveys of inaccessible forested regions.

A method is proposed for adjusting small-sample-based stock and stand table entries from point, double sampling with a ratio of means estimator. The proposed adjustment uses the characteristics of the ratio of means estimator, ensures that totals from the stock and stand tables match the large sample estimates, and preserves the volume–basal area ratio by DBH class before and after adjustment. Bias and variance estimates for the adjusted stock table entries are presented.

Abstract Tract acreage estimates based solely on strip line lengths, number of plots, and field maps drawn on grid paper may be unreliable. The errors may be so large that total volume estimates are wrong even if volume per acre is estimated accurately. Methods of evaluating the potential errorare described. South. J. Appl. For. 17(2):100-103.

Abstract Stratification with proportional allocation can reduce the number of plots needed or improve accuracy in a cruise. The greatest gains from stratification occur when the stratum volumes per acre are very different, and the stratum sizes are equal. A formula for calculating the gain fromstratification with proportional allocation is given. The gain from two strata with three levels of relative stratum size is shown. An example is calculated for three strata. Criteria for number of strata and stratum selection are discussed. South. J. Appl. For.:17(2):96-99.

Journal Article Discussion Paper Response Get access Richard G. Oderwald, Richard G. Oderwald Search for other works by this author on: Oxford Academic Google Scholar William A. Duerr William A. Duerr Search for other works by this author on: Oxford Academic Google Scholar Forest Science, Volume 36, Issue 1, March 1990, Page 185, https://doi.org/10.1093/forestscience/36.1.185 Published: 01 March 1990