Precision of inventory using different edge overlap methods

2013 ◽  
Vol 43 (11) ◽  
pp. 1081-1083 ◽  
Author(s):  
P.W. West
Keyword(s):  
Forest Inventory ◽  
Stocking Density ◽  
Basal Area ◽  
Radiata Pine ◽  
Forest Edge ◽  
Substantial Effect ◽  
Sampling Procedure ◽  
Simulation Studies ◽  
Forest Types ◽  
Plantation Forest

Bias due to the sampling procedure may occur in estimates from forest inventory when sampled trees are close to the forest edge. The “mirage,” “walkthrough,” and “walk through and fro” methods are three practical measurement methods developed to avoid this problem. However, as an increasing proportion of the sample requires use of these methods, the precision of the population estimates made from the sample is likely to decline. Simulation studies were undertaken of forest inventory, using point sampling, to estimate mean stand basal area and stocking density in both an even-aged, monoculture radiata pine (Pinus radiata D. Don) plantation forest and an uneven-aged, multispecies, complex primary rainforest. In both forest types, bias arising from use of any of the three methods appeared to be negligible. As well, precision of estimates from the inventory was reduced only slightly, even when a high proportion of the samples required use of any one of the three methods. None of the methods appeared appreciably superior in this respect to any of the others. It was concluded that use of any of the three methods was unlikely to have any substantial effect on the overall precision of estimates made from forest inventory.

scholarly journals An Approximate Point-based Alternative for the Estimation of Variance under Big BAF Sampling

2020 ◽  
Author(s):  
Thomas B. Lynch ◽  
Jeffrey H Gove ◽  
Timothy G Gregoire ◽  
Mark J Ducey
Keyword(s):  
Forest Inventory ◽  
Variance Estimation ◽  
Basal Area ◽  
Simulation Studies ◽  
Variance Estimator ◽  
Forest Types ◽  
Variance Estimators ◽  
Computer Simulation Studies ◽  
New Formula ◽  
Estimation Of Variance

Abstract BackgroundA new variance estimator is derived and tested for big BAF (Basal Area Factor) sampling which is a forest inventory system that utilizes two BAF sizes, a small BAF for tree counts and a larger BAF on which tree measurements are made usually including \dbh s and heights needed for volume estimation.MethodsThe new estimator is derived using the \Dm\ 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.ResultsSeveral 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.ConclusionsA 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 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 1/n where n is the number of sample points.

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.

Incorporating predictions into an annual forest inventory

1996 ◽  
Vol 26 (9) ◽  
pp. 1709-1713 ◽  
Author(s):  
Paul C. Van Deusen
Keyword(s):  
Forest Inventory ◽  
Variance Components ◽  
Growth Modeling ◽  
Estimation Procedure ◽  
Regression Estimator ◽  
Forest Types ◽  
Model Estimate ◽  
Individual Tree ◽  
Time Period ◽  
The Individual

Growth modeling of forests at the individual tree and stand levels is a highly refined procedure for many forest types. A method to incorporate predictions from such models into a forest inventory system is developed. Variance components from the actual measurements and from the predicted measurements are used to estimate the variance of the combined predicted value. The only assumption required to justify this method is that the model estimate has a bias that does not change from one time period to the next. The estimation procedure proposed here can also incorporate remotely sensed information via a regression estimator.

Stratified forest inventory estimation with mapped plots

2005 ◽  
Vol 35 (10) ◽  
pp. 2382-2386 ◽  
Author(s):  
Paul C Van Deusen
Keyword(s):  
Forest Inventory ◽  
Forest Service ◽  
Forest Types ◽  
Inventory Data ◽  
Expansion Factor ◽  
Usda Forest Service ◽  
Weighted Estimation ◽  
Forest Conditions

Weighted estimation formulas are developed for producing stratified estimates of means and variances where data come from plots that can contain multiple forest conditions. Each plot is mapped to allow the analyst to focus on specific forest types or conditions. The weights required to accommodate mapped plots are somewhat more complicated than the weights for unmapped plots. In particular, these weights depend on the mapped condition of interest. The implication is that a single plot weight or expansion factor will not suffice for all analyses as it does for unmapped plots. The methods are demonstrated using USDA Forest Service inventory data.

scholarly journals Using Tree Detection Algorithms to Predict Stand Sapwood Area, Basal Area and Stocking Density in Eucalyptus regnans Forest

2015 ◽  
Vol 7 (6) ◽  
pp. 7298-7323 ◽  
Author(s):  
Dominik Jaskierniak ◽  
George Kuczera ◽  
Richard Benyon ◽  
Luke Wallace
Keyword(s):  
Stocking Density ◽  
Basal Area ◽  
Sapwood Area ◽  
Eucalyptus Regnans ◽  
Detection Algorithms ◽  
Tree Detection

scholarly journals Zur Auswirkung der Hangneigungskorrektur auf Schätzwerte im Schweizerischen Landesforstinventar (LFI) | Investigation of the effect of the slope correction method as applied in the Swiss National Forest Inventory of estimates

2001 ◽  
Vol 152 (6) ◽  
pp. 215-225 ◽  
Author(s):  
Michael Köhl ◽  
Peter Brassel
Keyword(s):  
Forest Inventory ◽  
Basal Area ◽  
Correction Method ◽  
National Forest Inventory ◽  
National Forest ◽  
Average Deviation ◽  
Forest Inventories ◽  
Slope Correction ◽  
Number Of Stems ◽  
Elliptical Area

For forest inventories on slopes, it is necessary to correct the test areas, because the circular areas, when projected, become elliptical. Based on 93 samples from the Swiss National Forest Inventory (FNI), it was determined whether the simplified method, which increases the radius to match that of the elliptical area, leads to a distortion of the results. An average deviation of 2% was found between the FNI estimated values and the actual values for the basal area and the number of stems. For estimations of smaller units, greater distortions of the results are expected.

scholarly journals Deriving Stand Structural Complexity from Airborne Laser Scanning Data—What Does It Tell Us about a Forest?

2020 ◽  
Vol 12 (11) ◽  
pp. 1854
Author(s):  
Dominik Seidel ◽  
Peter Annighöfer ◽  
Martin Ehbrecht ◽  
Paul Magdon ◽  
Stephan Wöllauer ◽  
...  
Keyword(s):  
Forest Structure ◽  
Laser Scanning ◽  
Structural Complexity ◽  
Basal Area ◽  
Airborne Laser Scanning ◽  
Stand Age ◽  
Box Dimension ◽  
Forest Types ◽  
3D Point Clouds ◽  
Airborne Laser

The three-dimensional forest structure is an important driver of several ecosystem functions and services. Recent advancements in laser scanning technologies have set the path to measuring structural complexity directly from 3D point clouds. Here, we show that the box-dimension (Db) from fractal analysis, a measure of structural complexity, can be obtained from airborne laser scanning data. Based on 66 plots across different forest types in Germany, each 1 ha in size, we tested the performance of the Db by evaluating it against conventional ground-based measures of forest structure and commonly used stand characteristics. We found that the Db was related (0.34 < R < 0.51) to stand age, management intensity, microclimatic stability, and several measures characterizing the overall stand structural complexity. For the basal area, we could not find a significant relationship, indicating that structural complexity is not tied to the basal area of a forest. We also showed that Db derived from airborne data holds the potential to distinguish forest types, management types, and the developmental phases of forests. We conclude that the box-dimension is a promising measure to describe the structural complexity of forests in an ecologically meaningful way.

scholarly journals Regional Variability of the Romanian Main Tree Species Growth Using National Forest Inventory Increment Cores

Forests ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 409
Author(s):  
Gheorghe Marin ◽  
Vlad C. Strimbu ◽  
Ioan V. Abrudan ◽  
Bogdan M. Strimbu
Keyword(s):  
Mixed Models ◽  
Forest Inventory ◽  
Basal Area ◽  
National Forest Inventory ◽  
Forest Growth ◽  
National Forest ◽  
Sessile Oak ◽  
Basal Area Growth ◽  
Temporal Grouping ◽  
Area Growth

In many countries, National Forest Inventory (NFI) data is used to assess the variability of forest growth across the country. The identification of areas with similar growths provides the foundation for development of regional models. The objective of the present study is to identify areas with similar diameter and basal area growth using increment cores acquired by the NFI for the three main Romanian species: Norway spruce (Picea abies L. Karst), European beech (Fagus sylvatica L.), and Sessile oak (Quercus petraea (Matt.) Liebl.). We used 6536 increment cores with ages less than 100 years, a total of 427,635 rings. The country was divided in 21 non-overlapping ecoregions based on geomorphology, soil, geology and spatial contiguousness. Mixed models and multivariate analyses were used to assess the differences in annual dimeter at breast height and basal area growth among ecoregions. Irrespective of the species, the mixed models analysis revealed significant differences in growth between the ecoregions. However, some ecoregions were similar in terms of growth and could be aggregated. Multivariate analysis reinforced the difference between ecoregions and showed no temporal grouping for spruce and beech. Sessile oak growth was separated not only by ecoregions, but also by time, with some ecoregions being more prone to draught. Our study showed that countries of median size, such as Romania, could exhibit significant spatial differences in forest growth. Therefore, countrywide growth models incorporate too much variability to be considered operationally feasible. Furthermore, it is difficult to justify the current growth and yield models as a legal binding planning tool.

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