scholarly journals Using double-sampling techniques to reduce the number of measurement trees during forest inventories

REFORESTA ◽  
2017 ◽  
pp. 31
Author(s):  
Curtis L. VanderSchaaf ◽  
Gordon Holley ◽  
Joshua Adams
Keyword(s):  
Loblolly Pine ◽  
Basal Area ◽  
Strong Relationship ◽  
Sampling Techniques ◽  
Sampling Point ◽  
Individual Tree ◽  
Variable Radius ◽  
Sampling Schemes ◽  
Forest Inventories ◽  
Sample Tree

Variable-radius sampling techniques are commonly used during forest inventories. For each sample tree at a particular sampling point, diameter and height(s) are measured and then weight is estimated using established equations.  Heights can require a fair amount of time to measure in the field.  Separating the weight per acre estimate into two components; average basal area per acre and WBAR (individual tree weight-basal area ratio) across all points, can often lead to more efficient sampling schemes. Variable-radius sampling allows for a quick estimate of basal area per acre at a point since no individual tree measurements are needed.  If there is a strong relationship between weight and basal area, then by knowing basal area you essentially know weight.  Separation into two components is advantageous because in most cases there is more variability among basal area estimates per point then there is in WBAR. Hence, you can spend more resources establishing many points that only estimate basal area – often called “Count” points. “Full” points are those where individual tree measurements are also conducted. There is little published information quantifying the impacts on basal area, weight, etc., estimates among different “Full/Count” sample size ratios at the same site. Inventories were examined to determine this method’s applicability to loblolly pine plantations in southern Arkansas and northern Louisiana. Results show there is more variability among basal area estimates than WBAR and that the amount of trees being “intensively” measured is excessive.  Based on these four plantations, a “Full” point could be installed ranging from every other point to every fifth point depending on site conditions and the desired variable.

Crown efficiency in a loblolly pine (Pinus taeda) spacing experiment

1998 ◽  
Vol 28 (9) ◽  
pp. 1344-1351 ◽  
Author(s):  
Hubert Sterba ◽  
Ralph L Amateis
Keyword(s):  
Pinus Taeda ◽  
Loblolly Pine ◽  
Basal Area ◽  
Tree Height ◽  
Projection Area ◽  
Individual Tree ◽  
Dominant Height ◽  
Crown Projection Area ◽  
Pinus Taeda L ◽  
Crown Closure

Crown efficiency was first defined by Assmann (1961. Waldertragskunde. BLV, München) as individual tree volume increment per unit of crown projection area. He hypothesized that within a given crown class, smaller crowns are more efficient because their ratio between crown surface and horizontal crown projection is higher. Data from a loblolly pine (Pinus taeda L.) spacing experiment were used to test if this hypothesis also holds in young loblolly pine stands and, if so, to determine if it explains the increment differences between spacings in the spacing experiment. Using individual tree height relative to plot dominant height to describe crown class, within-plot regression showed that crown efficiency decreased with crown size for trees below dominant height. This relationship was much less pronounced than indicated from Assmann's examples, although the crown surface to crown projection ratio behaved in the same way as Assmann had hypothesized. Crown efficiency as well as the crown surface to crown projection area ratio decreased with increasing density. Basal area increment per hectare increased until total crown closure approached 130% and then stayed constant. This major impact of total crown coverage brings into question the usefullness of crown efficiency as an indicator for unit area growth.

Removal of Competing Vegetation from Established Loblolly Pine Plantations Increases Growth on Piedmont and Upper Coastal Plain Sites

1996 ◽  
Vol 20 (4) ◽  
pp. 188-193 ◽  
Author(s):  
James C. Fortson ◽  
Barry D. Shiver ◽  
Lois Shackelford
Keyword(s):  
Loblolly Pine ◽  
Coastal Plain ◽  
Basal Area ◽  
Pine Plantations ◽  
Slope Position ◽  
Age Classes ◽  
Individual Tree ◽  
Competing Vegetation ◽  
Positive Treatment ◽  
The Individual

Abstract A series of paired plots was installed in loblolly pine plantations at 42 locations in Georgia's Piedmont and Alabama's Piedmont and Coastal Plain. One plot of each pair had all competing vegetation eliminated. The other plot was left as an uncontrolled check. Locations were stratified over two age classes (5-9 and 12-16 yr old) and three slope positions (top, midslope, and bottom). Analysis of 33 surviving locations 8 yr after treatment revealed a positive treatment effect for both individual tree (dbh and total height) and stand characteristics (basal area per acre, total volume per acre, and merchantable volume per acre). There was no difference in volume response between age classes. Slope position was not significant for the individual tree variables, but was significant for the stand variables, with midslopes responding most positively followed by bottom and then top slope positions. Over all locations, the average treatment response was approximately ½ cord/ac/yr. Economic analyses indicate that the magnitude of the response will be economical for many stumpage prices, particularly on midslope and bottom slope positions, in plantations where access and species composition make herbicide spraying possible. South J. Appl. For. 20(4):188-192.

An individual-tree basal area growth model for loblolly pine stands

1996 ◽  
Vol 26 (2) ◽  
pp. 327-331 ◽  
Author(s):  
Paul A. Murphy ◽  
Michael G. Shelton
Keyword(s):  
Loblolly Pine ◽  
Basal Area ◽  
Growth Function ◽  
Logistic Function ◽  
Growth Patterns ◽  
Potential Growth ◽  
Individual Tree ◽  
Basal Area Growth ◽  
Area Growth ◽  
Using Data

Tree basal area growth has been modeled as a combination of a potential growth function and a modifier function, in which the potential function is fitted separately from open-grown tree data or a subset of the data and the modifier function includes stand and site variables. We propose a modification of this by simultaneously fitting both a growth component and a modifier component. The growth component can be any function that approximates tree growth patterns, and the logistic function is chosen as the modifier component. This approach can be adapted to a variety of stand conditions, and its application is demonstrated using data from an uneven-aged loblolly pine (Pinustaeda L.) study located in Arkansas and Louisiana.

scholarly journals Modeling Early Responses of Loblolly Pine Growth to Thinning in the Western Gulf Coastal Plain Region

2020 ◽  
Vol 66 (5) ◽  
pp. 623-633
Author(s):  
Y H Weng ◽  
J Grogan ◽  
D W Coble
Keyword(s):  
Loblolly Pine ◽  
Basal Area ◽  
Tree Height ◽  
Height Growth ◽  
Growth Responses ◽  
Individual Tree ◽  
Large Trees ◽  
The Difference ◽  
Pinus Taeda L ◽  
Stand Basal Area

Abstract Growth response to thinning has long been a research topic of interest in forest science. This study presents the first 3–4 years of response of loblolly pine (Pinus taeda L.) growth to thinning at different intensities. Data were collected from the East Texas Pine Research Project’s region-wide loblolly pine thinning study, which covers a wide variety of stand conditions. Four treatments, light, moderate, and heavy thinning, respectively having 370, 555, and 740 residual trees per hectare after thinning, and an unthinned control, were included. Individual tree diameter at breast height (dbh) and total height were recorded annually for the first 3–4 years after thinning. Results indicate significant differences between treatments in dbh growth in each year after thinning, as well as for all years combined. Each thinning treatment had significantly greater dbh growth than the control in the first growing season with this positive response being more evident in the case of the heavier thinning or at the later years post-thinning. Conversely, the thinning effect on tree height growth was initially negligibly negative, then becoming positive after 2–4 years, with the heavier thinning becoming positive sooner. Tree size class, assigned based on prethinning dbh, had a significant effect on both dbh and height growth responses. Compared to the control, small trees had a greater response both in dbh and in height growth than the medium and large trees over the measurement period. At the stand level, the heavier thinning had significantly less stand basal area per hectare, but the difference in stand basal area per hectare between the thinned and the unthinned plots decreased with years post-thinning. Results from this study can improve our understanding in thinning effects and help forest managers make accurate decisions on silvicultural regimes.

scholarly journals Voronoi polygons quantify bias when sampling the nearest plant

2015 ◽  
Vol 45 (12) ◽  
pp. 1853-1859 ◽  
Author(s):  
Thomas B. Lynch
Keyword(s):  
Basal Area ◽  
Individual Tree ◽  
Sample Mean ◽  
Increment Core ◽  
Population Mean ◽  
Sample Tree ◽  
Polygon Area ◽  
Coefficients Of Variation ◽  
Voronoi Polygons ◽  
Design Bias

The design bias in the sample mean obtained from sampling the trees nearest to points randomly and uniformly distributed over a forested area can be exactly quantified in terms of the Voronoi polygons (V polygons) surrounding each tree in the forest of interest. For this sampling method, the V polygon for a prospective sample tree is its inclusion zone. The sides of such polygons are perpendicular to a line joining adjacent trees and equidistant from these trees. For any individual tree attribute Y, the design bias in such a sample mean for estimating the population mean of Y will be equal to the covariance between Y and V-polygon area V divided by the mean V-polygon area. The bias as a percent of the population mean of Y is the product of the correlation coefficient between Y and V and the coefficients of variation for Y and V multiplied by 100. This implies that attempts to estimate the means of commonly measured individual tree variables such as DBH, basal area, and crown diameter or the area from sampling trees nearest to randomly located points will likely be positively biased, and the magnitude of that bias will depend on the strength of the linear relationship to the V-polygon area, as well as the variability among the V-polygon areas and the variable of interest. It is not obvious whether increment core data will be positively or negatively biased, because this depends on the characteristics of the forest of interest. The main conclusion of the study is that the bias formula derived for unweighted estimation from sampling the tree nearest to a point indicates that bias in the range of 5%–10% or greater can occur in many forest populations.

scholarly journals Composite Estimation for Forest Inventories Using Multiple Value Groups and Strata

1996 ◽  
Vol 20 (2) ◽  
pp. 103-109
Author(s):  
Michael S. Williams ◽  
Ken A. Cormier
Keyword(s):  
Covariance Matrix ◽  
Timber Production ◽  
Individual Tree ◽  
Forest Inventories ◽  
Sample Tree ◽  
Worked Example ◽  
Composite Estimation ◽  
Individual Value ◽  
Multiple Value ◽  
Tree Characteristics

Abstract Due to economic and political pressures placed on timber production, information from timber inventories needs to be broken down into information on particular value groups, which are groupings based on individual tree characteristics. Thus estimates of totals and variances need to be computed for individual value groups, all value groups combined, and any collection of value groups. A method for reporting these estimates using a variance-covariance matrix is given. This is followed by two examples of the estimation of totals and variances for value groups applied to a population comprising two strata. Poisson (3P) and sample-tree sampling are used in the first and second strata, respectively. These examples are included to highlight important estimation considerations. A worked example based on an artificial population is also given. The population for the worked example is divided into two strata, with three value groups in each stratum. 3P and sample-tree sampling are used in the first and second strata. South. J. Appl. For. 20(2):103-109.

Evaluation of distance-independent competition indices in predicting tree survival and diameter growth

2019 ◽  
Vol 49 (5) ◽  
pp. 440-446 ◽  
Author(s):  
Shuaichao Sun ◽  
Quang V. Cao ◽  
Tianjian Cao
Keyword(s):  
Loblolly Pine ◽  
Relative Position ◽  
Basal Area ◽  
Diameter Ratio ◽  
Cumulative Distribution ◽  
Permanent Plots ◽  
Diameter Growth ◽  
Individual Tree ◽  
Tree Survival ◽  
Competition Indices

Competition indices play a significant role in modeling individual-tree growth and survival. In this study, six distance-independent competition indices were evaluated using 200 permanent plots of loblolly pine (Pinus taeda L.). The competition indices were classified into three families: (1) size ratios, which include diameter ratio and basal area ratio; (2) relative position indices, which include basal area of larger trees (BAL) and tree relative position based on the cumulative distribution function (CDF); and (3) partitioned stand density index and relative density. Results indicated that different families of competition indices were suitable for different tree survival or diameter growth prediction tasks. The diameter ratio was superior for predicting tree survival, whereas the relative position indices (BAL and CDF) performed best for predicting tree diameter growth, with CDF receiving the highest rank.

An antithetic variate to facilitate upper-stem height measurements for critical height sampling with importance sampling

2013 ◽  
Vol 43 (12) ◽  
pp. 1151-1161 ◽  
Author(s):  
Thomas B. Lynch ◽  
Jeffrey H. Gove
Keyword(s):  
Importance Sampling ◽  
Basal Area ◽  
Cross Sectional Area ◽  
Sample Point ◽  
Sectional Area ◽  
Critical Height ◽  
Cross Sectional ◽  
Individual Tree ◽  
Sample Tree ◽  
The Cross

Critical height sampling (CHS) estimates cubic volume per unit area by multiplying the sum of critical heights measured on trees tallied in a horizontal point sample (HPS) by the HPS basal area factor. One of the barriers to practical application of CHS is the fact that trees near the field location of the point-sampling sample point have critical heights that occur quite high on the stem, making them difficult to view from the sample point. To surmount this difficulty, use of the “antithetic variate” associated with the critical height together with importance sampling from the cylindrical shells integral is proposed. This antithetic variate will be u = (1 − b/B), where b is the cross-sectional area at “borderline” condition and B is the tree’s basal area. The cross-sectional area at borderline condition b can be determined with knowledge of the HPS gauge angle by measuring the distance to the sample tree. When the antithetic variate u is used in importance sampling, the upper-stem measurement will be low on tree stems close to the sample point and high on tree stems distant from the sample point, enhancing visibility and ease of measurement from the sample point. Computer simulations compared HPS, CHS, CHS with importance sampling (ICHS), ICHS and an antithetic variate (AICHS), and CHS with paired antithetic varariates (PAICHS) and found that HPS, ICHS, AICHS, and PAICHS were very nearly equally precise and were more precise than CHS. These results are favorable to AICHS, since it should require less time than either PAICHS or ICHS and is not subject to individual-tree volume equation bias.

Using disaggregation to link individual-tree and whole-stand growth models

2006 ◽  
Vol 36 (4) ◽  
pp. 953-960 ◽  
Author(s):  
Jianhua Qin ◽  
Quang V Cao
Keyword(s):  
Loblolly Pine ◽  
Growth Models ◽  
Basal Area ◽  
Tree Model ◽  
Individual Tree ◽  
Stand Growth ◽  
Individual Tree Model ◽  
Stand Attributes ◽  
Pinus Taeda L ◽  
The Individual

Data from 200 plots randomly selected from the Southwide Pine Seed Source Study of loblolly pine (Pinus taeda L.) were used to fit whole-stand and individual-tree equations. Another 100 plots, also randomly selected, were used for validation. Outputs from the individual-tree model were then adjusted to match observed stand attributes (number of trees, basal area, and volume per hectare) by four disaggregation methods: proportional yield, proportional growth, constrained least squares, and coefficient adjustment. The first three are existing methods, and the fourth is new. The four methods produced similar results, and the coefficient adjustment was then selected as the method to disaggregate predicted stand growth among trees in the tree list. Results showed that, compared to the unadjusted individual tree model, the adjusted tree model performed much better in predicting stand attributes, while providing comparable predictions of tree diameter, height, and survival probability. The proposed approach showed promise in the ongoing effort to link growth models having different resolutions.

scholarly journals An approximate point-based alternative for the estimation of variance under big BAF sampling

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Thomas B. Lynch ◽  
Jeffrey H. Gove ◽  
Timothy G. Gregoire ◽  
Mark J. Ducey
Keyword(s):  
Forest Inventory ◽  
Variance Estimation ◽  
Basal Area ◽  
Volume Estimation ◽  
Delta Method ◽  
Sampling Point ◽  
Variance Estimator ◽  
Computer Simulation Studies ◽  
New Formula ◽  
Estimation Of Variance

Abstract Background A new variance estimator is derived and tested for big BAF (Basal Area Factor) sampling which is a forest inventory system that utilizes Bitterlich sampling (point sampling) with two BAF sizes, a small BAF for tree counts and a larger BAF on which tree measurements are made usually including DBHs and heights needed for volume estimation. Methods The new estimator is derived using the Delta method from an existing formulation of the big BAF estimator as consisting of three sample means. The new formula is compared to existing big BAF estimators including a popular estimator based on Bruce’s formula. Results Several computer simulation studies were conducted comparing the new variance estimator to all known variance estimators for big BAF currently in the forest inventory literature. In simulations the new estimator performed well and comparably to existing variance formulas. Conclusions A possible advantage of the new estimator is that it does not require the assumption of negligible correlation between basal area counts on the small BAF factor and volume-basal area ratios based on the large BAF factor selection trees, an assumption required by all previous big BAF variance estimation formulas. Although this correlation was negligible on the simulation stands used in this study, it is conceivable that the correlation could be significant in some forest types, such as those in which the DBH-height relationship can be affected substantially by density perhaps through competition. We derived a formula that can be used to estimate the covariance between estimates of mean basal area and the ratio of estimates of mean volume and mean basal area. We also mathematically derived expressions for bias in the big BAF estimator that can be used to show the bias approaches zero in large samples on the order of $\frac {1}{n}$ 1 n where n is the number of sample points.

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