scholarly journals Effects of Basal Area Factor and Plot Size on Precision and Accuracy of Forest Inventory Estimates

2011 ◽  
Vol 28 (3) ◽  
pp. 152-156 ◽  
Author(s):  
Peter Becker ◽  
Tom Nichols
Keyword(s):  
Forest Inventory ◽  
Basal Area ◽  
Cost Effective ◽  
Sampling Errors ◽  
Plot Size ◽  
Accuracy And Precision ◽  
Coefficients Of Variation ◽  
Fixed Radius ◽  
Tree Number ◽  
Biased Estimates

Abstract We tested the effects of plot size (0.05-0.30 ac) and basal area factor (BAF) (5-30) on the accuracy and precision of per-acre estimates of tree number, basal area, biomass (all for trees ≥4.5 in. dbh), and sawtimber volume (for trees ≥11.6 in. dbh). Field sampling errors, such as missing in-trees, did not affect our tests. Virtual variable- and fixed-radius plots were randomly located within an artificial matrix of 130 real plots in well-stocked upland hardwood forests of sawtimber-sized trees in the Missouri Ozarks. Inventory parameters were essentially independent of plot size and BAF, whereas their coefficients of variation decreased with plot size and increased with BAF. Thus, our results for random plots agreed with sampling theory, unlike a previous study using concentric virtual plots in West Virginia forests. A very concentrated zone of high tree density around some plot centers apparently caused the biased estimates by concentric plots. Compared with the entire composite forest, inventory means were accurately estimated (to within 5%) and size class distributions were well represented for plots ≥0.1 ac or ≤15 BAF. Our procedures provide a basis for selecting an efficient and cost-effective sampling design suited to forest characteristics and the inventory's purpose.

scholarly journals A Method for Evaluating Fixed-Radius Plot Size When Sampling Western Juniper Seedlings and Saplings

2002 ◽  
Vol 17 (4) ◽  
pp. 207-208
Author(s):  
David L. Azuma ◽  
Larry Bednar
Keyword(s):  
Forest Inventory ◽  
Forest Service ◽  
Usda Forest Service ◽  
Forest Inventory And Analysis ◽  
Size Selection ◽  
Western Juniper ◽  
Plot Size ◽  
Fixed Radius ◽  
Selection For

Abstract This note outlines a method for evaluating plot size selection for an inventory of western juniper woodlands in eastern Oregon. The Forest Inventory and Analysis (FIA) program of the USDA Forest Service in Portland, Oregon, used this method to evaluate several plot sizes to measure seedlings and saplings in the 1998 inventory of eastern Oregon. By choosing a 5 m radius plot, the probability of tallying no seedlings or saplings on four subplots is less than 10% for the three sample densities (0.01, 0.02, and 0.03 trees/m2) used. West. J. Appl. For. 17(4):207–208.

Variation in forest inventory field measurements

1994 ◽  
Vol 24 (9) ◽  
pp. 1766-1770 ◽  
Author(s):  
Ronald E. McRoberts ◽  
Jerold T. Hahn ◽  
Glenda J. Hefty ◽  
Jerry R. Van Cleve
Keyword(s):  
Forest Inventory ◽  
Basal Area ◽  
Field Measurements ◽  
Site Index ◽  
Upper Peninsula ◽  
The North ◽  
Basal Area Growth ◽  
Coefficients Of Variation ◽  
Area Growth ◽  
Forest Experiment Station

Field crews from the North Central Forest Experiment Station independently measured two forest inventory plots in Michigan's Upper Peninsula; one plot was measured by eight crews and the other was measured by nine different crews. For 61 trees, the variation in measurements of diameter at breast height (DBH), crown ratio, and site index is described. For DBH, the distribution of field crew mistakes and the distribution of measurements without mistakes are described separately. For crown ratio, the distribution of differences between individual estimates and the most frequently occurring estimate for corresponding trees is described. For site index, the distribution of differences between individual estimates and the mean of plot estimates is described. Coefficients of variation were less than 5% for DBH, approximately 73% for crown ratio, and 13% and 16% for site index for the two plots. The effects of variation in measurements on 20-year predictions of basal area and cumulative basal area growth were estimated for the two plots using STEMS, TWIGS, and Monte Carlo simulations. Coefficients of variation were 2% and 3% for basal area and 7% and 9% for cumulative basal area growth for the two plots. Variation in site index estimates had the greatest effect on variation in the output variables.

scholarly journals Smoothed Versus Unsmoothed LiDAR in a Double-Sample Forest Inventory

2005 ◽  
Vol 29 (1) ◽  
pp. 40-47 ◽  
Author(s):  
Robert C. Parker ◽  
A. Lee Mitchel
Keyword(s):  
Forest Inventory ◽  
Phase 1 ◽  
Basal Area ◽  
Product Distribution ◽  
Sample Volume ◽  
Species Group ◽  
Low Density ◽  
Phase 2 ◽  
Sampling Errors ◽  
Hardwood Species

Abstract Light detection and ranging (LiDAR) data at 0.5- and 1-m postings were used in a double-sample forest inventory on Louisiana State University's Lee Experimental Forest, Louisiana. Phase 2 plots were established with differential global positioning system (DGPS). Tree dbh (>4.5in.) and two sample heights were measured on every 10th plot of the Phase 1 sample. Volume was computed for natural and planted pine and mixed hardwood species. LiDAR trees were selected with focal filter procedures from smoothed and unsmoothed LiDAR canopy surfaces. Dbh-height and ground-LiDARheight models were used to predict dbh from LiDAR height and compute Phase 2 estimates of ft2 basal area and ft3 volume. Phase 1 LiDAR estimates were computed by randomly assigning heights to species classes using the probability distribution from ground plots in eachinventory strata. Phase 2 LiDAR estimates were computed by randomly assigning heights to species-product groups using a Monte Carlo simulation for each ground plot. Regression coefficients for Phase 2 estimates of ft2 and ft3 from the smoothed versus unsmoothed surfacesof high- and low-density LiDAR were computed by species group. Regression estimates for combined volume were partitioned by species-product distribution of Phase 2 volume. There was no statistical difference (α = 0.05) between smoothed versus unsmoothed for high- and low-density LiDAR on adjusted mean volume estimates (sampling errors of 9.52 versus 8.46% for high-density and 9.25 versus 7.65% for low-density LiDAR). South. J. Appl. For. 29(1):40–47.

scholarly journals High- Versus Low-Density LiDAR in a Double-Sample Forest Inventory

2004 ◽  
Vol 28 (4) ◽  
pp. 205-210 ◽  
Author(s):  
Robert C. Parker ◽  
Patrick A. Glass
Keyword(s):  
Forest Inventory ◽  
Phase 1 ◽  
Basal Area ◽  
Sample Volume ◽  
Low Density ◽  
Phase 2 ◽  
Sampling Errors ◽  
Height Difference ◽  
Hardwood Species ◽  
Volume Estimates

Abstract Light detection and ranging (LiDAR) data at 0.5- and 1-m postings were used in a double-sample forest inventory on Louisiana State University's Lee Experimental Forest, Louisiana. Phase 2 plots were established with DGPS. Tree dbh (>4.5 in.) and two sample heights (minimum and maximum dbh) were measured on every 10th plot of the phase 1 sample. Volume was computed for natural and planted pine and mixed hardwood species. LiDAR trees were selected with focal filter procedures and heights computed as the height difference between interpolated canopy and DEM surfaces. Dbh-height and ground-LiDAR height models were used to predict dbh from adjusted LiDAR height and compute ground and LiDAR estimates of ft2 basal area and ft3 volume. Phase 1 LiDAR estimates were computed by randomly assigning heights to species classes using the probability distribution from ground plots in each inventory strata. Phase 2 LiDAR estimates were computed by randomly assigning heights to species-product groups using a Monte Carlo simulation for each ground plot. There was no statistical difference between high-versus low-density LiDAR estimates on adjusted mean volume estimates (sampling errors of 8.16 versus 7.60% without height adjustment and 8.98 versus 8.63% with height adjustment). Low-density LiDAR surfaces without height adjustment produced the lowest sampling errors for stratified and nonstratified, double-sample volume estimates. South. J. Appl. For. 28(4):205–210.

scholarly journals Predicting Forest Inventory Attributes Using Airborne Laser Scanning, Aerial Imagery, and Harvester Data

2019 ◽  
Vol 11 (7) ◽  
pp. 797 ◽  
Author(s):  
Atte Saukkola ◽  
Timo Melkas ◽  
Kirsi Riekki ◽  
Sanna Sirparanta ◽  
Jussi Peuhkurinen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Prediction Models ◽  
Basal Area ◽  
Sample Plot ◽  
Weighted Mean ◽  
Individual Tree ◽  
Plot Size ◽  
Head Positions ◽  
Mean Square Errors

The aim of the study was to develop a new method to use tree stem information recorded by harvesters along operative logging in remote sensing-based prediction of forest inventory attributes in mature stands. The reference sample plots were formed from harvester data, using two different tree positions: harvester positions (XYH) in global satellite navigation system and computationally improved harvester head positions (XYHH). Study materials consisted of 158 mature Norway-spruce-dominated stands located in Southern Finland that were clear-cut during 2015–16. Tree attributes were derived from the stem dimensions recorded by the harvester. The forest inventory attributes were compiled for both stands and sample plots generated for stands for four different sample plot sizes (254, 509, 761, and 1018 m2). Prediction models between the harvester-based forest inventory attributes and remote sensing features of sample plots were developed. The stand-level predictions were obtained, and basal-area weighted mean diameter (Dg) and basal-area weighted mean height (Hg) were nearly constant for all model alternatives with relative root-mean-square errors (RMSE) roughly 10–11% and 6–8%, respectively, and minor biases. For basal area (G) and volume (V), using either of the position methods, resulted in roughly similar predictions at best, with approximately 25% relative RMSE and 15% bias. With XYHH positions, the predictions of G and V were nearly independent of the sample plot size within 254–761 m2. Therefore, the harvester-based data can be used as ground truth for remote sensing forest inventory methods. In predicting the forest inventory attributes, it is advisable to utilize harvester head positions (XYHH) and a smallest plot size of 254 m2. Instead, if only harvester positions (XYH) are available, expanding the sample plot size to 761 m2 reaches a similar accuracy to that obtained using XYHH positions, as the larger sample plot moderates the uncertainties when determining the individual tree position.

Contribution to the automation of the calculations involving the forest inventory with the aid of an office computer

1970 ◽  
Vol 20 ◽  
Author(s):  
R. Goossens
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Basal Area ◽  
Annual Increment ◽  
Mixed Stands ◽  
Mean Values ◽  
Standing Trees ◽  
General Program ◽  
Magnetic Card ◽  
The Right

Contribution to the automation of the calculations involving  the forest inventory with the aid of an office computer - In this contribution an attempt was made to perform the  calculations involving the forest inventory by means of an office computer  Olivetti P203.     The general program (flowchart 1), identical for all tree species except  for the values of the different parameters, occupies the tracks A and B of a  magnetic card used with this computer. For each tree species one magnetic  card is required, while some supplementary cards are used for the  subroutines. The first subroutine (flowchart 1) enables us to preserve  temporarily the subtotals between two tree species (mixed stands) and so  called special or stand cards (SC). After the last tree species the totals  per ha are calculated and printed on the former, the average trees occuring  on the line below. Appendix 1 gives an example of a similar form resulting  from calculations involving a sampling in a mixed stand consisting of Oak  (code 11), Red oak (code 12), Japanese larch (code 24) and Beech (code 13).  On this form we find from the left to the right: the diameter class (m), the  number of trees per ha, the basal area (m2/ha), the current annual increment  of the basal area (m2/year/ha), current annual volume increment (m3/year/ha),  the volume (m3/ha) and the money value of the standing trees (Bfr/ha). On the  line before the last, the totals of the quantities mentioned above and of all  the tree species together are to be found. The last line gives a survey of  the average values dg, g, ig, ig, v and w.     Besides this form each stand or plot has a so-called 'stand card SC' on  wich the totals cited above as well as the area of the stand or the plot and  its code are stored. Similar 'stand card' may replace in many cases  completely the classical index cards; moreover they have the advantage that  the data can be entered directly into the computer so that further  calculations, classifications or tabling can be carried out by means of an  appropriate program or subroutine. The subroutine 2 (flowchart 2) illustrates  the use of similar cards for a series of stands or eventually a complete  forest, the real values of the different quantities above are calculated and  tabled (taking into account the area). At the same time the general totals  and the general mean values per ha, as well as the average trees are  calculated and printed. Appendix 2 represents a form resulting from such  calculations by means of subroutine 2.

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.

Biomass estimates derived from sector subsampling of 360° spherical images

2021 ◽  
Author(s):  
Xiao Dai ◽  
Mark J Ducey ◽  
Haozhou Wang ◽  
Ting-Ru Yang ◽  
Yung-Han Hsu ◽  
...  
Keyword(s):  
Basal Area ◽  
Sampling Errors ◽  
Eastern Canada ◽  
Individual Tree ◽  
Inventory Costs ◽  
Standard Error Estimation ◽  
The Mean ◽  
Biomass Ratio ◽  
Spherical Images ◽  
Additional Level

Abstract Efficient subsampling designs reduce forest inventory costs by focusing sampling efforts on more variable forest attributes. Sector subsampling is an efficient and accurate alternative to big basal area factor (big BAF) sampling to estimate the mean basal area to biomass ratio. In this study, we apply sector subsampling of spherical images to estimate aboveground biomass and compare our image-based estimates with field data collected from three early spacing trials on western Newfoundland Island in eastern Canada. The results show that sector subsampling of spherical images produced increased sampling errors of 0.3–3.4 per cent with only about 60 trees measured across 30 spherical images compared with about 4000 trees measured in the field. Photo-derived basal area was underestimated because of occluded trees; however, we implemented an additional level of subsampling, collecting field-based basal area counts, to correct for bias due to occluded trees. We applied Bruce’s formula for standard error estimation to our three-level hierarchical subsampling scheme and showed that Bruce’s formula is generalizable to any dimension of hierarchical subsampling. Spherical images are easily and quickly captured in the field using a consumer-grade 360° camera and sector subsampling, including all individual tree measurements, were obtained using a custom-developed python software package. The system is an efficient and accurate photo-based alternative to field-based big BAF subsampling.

scholarly journals Largest-Crown-Width Prediction Models for 53 Species in the Western United States

2004 ◽  
Vol 19 (4) ◽  
pp. 245-251 ◽  
Author(s):  
William A. Bechtold
Keyword(s):  
United States ◽  
Prediction Models ◽  
Basal Area ◽  
Stem Diameter ◽  
Western United States ◽  
Quadratic Term ◽  
Crown Width ◽  
Coefficients Of Variation ◽  
Bioclimatic Index ◽  
Minor Improvement

Abstract The mean crown diameters of stand-grown trees 5.0-in. dbh and larger were modeled as a function of stem diameter, live-crown ratio, stand-level basal area, latitude, longitude, elevation, and Hopkins bioclimatic index for 53 tree species in the western United States. Stem diameter was statistically significant in all models, and a quadratic term for stem diameter was required for some species. Crown ratio and/or Hopkins index also improved the models for most species. A term for stand-level basal area was not generally needed but did yield some minor improvement for a few species. Coefficients of variation from the regression solutions ranged from 17 to 33%, and model R2 ranged from 0.15 to 0.85. Simpler models, based solely on stem diameter, are also presented. West. J. Appl. For. 19(4):245–251.

scholarly journals Development and Validation of UV Spectrophotometric Method for the Determination of Cefuroxime Axetil in Bulk and Pharmaceutical Formulation

2013 ◽  
Vol 6 (1) ◽  
pp. 133-141 ◽  
Author(s):  
S. Binte Amir ◽  
M. A. Hossain ◽  
M. A. Mazid
Keyword(s):  
Spectrophotometric Method ◽  
Cost Effective ◽  
Cefuroxime Axetil ◽  
Pharmaceutical Formulation ◽  
Uv Spectrum ◽  
Accuracy And Precision ◽  
Relative Standard ◽  
Label Claim ◽  
Development And Validation

The present study was undertaken to develop and validate a simple, sensitive, accurate, precise and reproducible UV spectrophotometric method for cefuroxime axetil using methanol as solvent. In this method the simple UV spectrum of cefuroxime axetil in methanol was obtained which exhibits absorption maxima (?max) at 278 nm. The quantitative determination of the drug was carried out at 278 nm and Beer’s law was obeyed in the range of (0.80-3.60) µg/ml. The proposed method was applied to pharmaceutical formulation and percent amount of drug estimated (95.6% and 96%) was found in good agreement with the label claim. The developed method was successfully validated with respect to linearity, specificity, accuracy and precision. The method was shown linear in the mentioned concentrations having line equation y = 0.05x + 0.048 with correlation coefficient of 0.995. The recovery values for cefuroxime axetil ranged from 99.85-100.05. The relative standard deviation of six replicates of assay was less than 2%. The percent relative standard deviations of inter-day precision ranged between 1.45-1.92% and intra-day precision of cefuroxime axetil was 0.96-1.51%. Hence, proposed method was precise, accurate and cost effective.  Keywords: UV-Vis spectrophotometer; Method validation; Cefuroxime axetil; Recovery studies.  © 2013 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.   doi: http://dx.doi.org/10.3329/jsr.v6i1.14879 J. Sci. Res. 6 (1), 133-141 (2013)  

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