Forest Inventory Methodology And Applications Pdf

forest inventory methodology and applications pdf

File Name: forest inventory methodology and applications .zip
Size: 1366Kb
Published: 19.05.2021

[PDF Download] Forest Inventory: Methodology and Applications (Managing Forest Ecosystems)

The technique of average and total volume assessment is explained in the example of one beech high forest management class. We first observed stands as strata and then homogeneous groups of stands. We concluded that there are numerous possibilities for using stratified sampling in forest inventory.

Compared to a simple sample of the same size, the obtained volume assessment has a greater precision. The effect of the performed stratification is significant. In the given example, to achieve the same degree of precision of the assessments, the size of the stratified sample can be Keywords: stratified sampling, forest inventory, stand, management class 1.

Kangas, A. The main objective of applying different sampling types is to achieve maximum precision and accuracy in the measurement of forest parameters inventory units at minimum costs. Simple sampling is used only at stand level, while stratified sampling is more convenient for larger inventory units compartments, catchments, management classes, forest categories, management units, woodland etc. In general, the stratified sample is applied to large heterogeneous populations that are or can be subsequently divided into a number of homogeneous subpopulations, called strata.

The primary advantage of the stratified sample compared to the simple one is that if the samples are of the same size, the stratified sample will provide a more precise estimate of the parameters that are measured in an inventory unit.

Theoretically speaking, the more homogeneous the elements within each stratum are, regarding the observed characteristic and the more heterogeneous they are between the strata, the greater is the effect of the stratification. Another advantage of stratified sampling over simple sampling is that it provides insight into the estimated parameters both for the whole inventory unit and for its subpopulations - strata.

For instance, it measures parameters of one management class per stands or of one forest category per management classes, etc. Although the simple sample is primarily intended for small homogeneous 1 Prof. It is probably due to unfamiliarity with the advantages of stratified sampling compared to simple sampling and its somewhat more complex statistical procedure of data collection and processing. Therefore, there is a prevailing opinion that it should be always applied.

However, it must be stressed that the application of stratification can sometimes result in an insignificant increase of precision that is of small practical value.

The task of this paper is to present the technique of estimating average and total volume on the example of one management class of beech high forests inventory unit , using simple and stratified sampling, as then to evaluate the effect of the stratification.

The objective of the paper is to provide forestry experts with better understanding of the technique and possibilities of using stratified sampling in forest inventory or in forestry in general. The data required for estimating stand and management class volume tree diameter and height were obtained from sample plots.

Tree volume was calculated by regression equations Kopriv ica, M. The data for this research were collected within the project Method of evaluation of quality and assortment structure of beech high stands in Serbia, carried out by the Institute of Forestry in Belgrade from to , applying specific methodology Kopriv ica, M.

The study management class comprises eleven all-aged beech stands. Altogether sample plots were established in the management class and they approximately accounted for the whole area of the management class The data were then statistically processed.

It is obvious that the error of the estimated average volume per hectare is high, and in practice it can equal the felling quantity planned in a year long management period, assuming that the planned felling intensity in the stand is This example confirms the conclusion that planning at stand level is usually very unreliable due to the lack of precision accuracy of the estimated volume Kopriv ica, M. The range of the total stand volume i. Of course, here it is assumed that the area of the stand was determined accurately.

It is again assumed that the area of the management class was determined accurately. It means that the felling quantity for the following management period can be planned with greater certainty and more reliable management plans can be developed. If we kept the same sampling intensity and increased the area of the management class, the sampling error would decrease and the precision of the volume estimates would further increase.

This would make felling quantity planning more reliable. The main criterion for stratification in the study sample was the size of the average volume per hectare in the stand or in the homogenous group of stands, and the number of strata was determined by the number of stands or the number of homogeneous groups in the management class Preliminary assessment of stratification effects In this case, the management class is first analyzed as a statistical population divided into eleven strata.

The strata are stands that are internally homogeneous regarding the variation of volume per area and more or less heterogeneous between them. Whether this type of stratification can significantly reduce the sampling error should be preliminarily tested by applying the method of simple analysis of variance with unequal number of observations Parde, J.

In fact, statistical significance of the difference between the sample means taken from different strata stands should be tested. If the difference is statistically random, we should not expect a significant effect of the implemented stratification. From the theoretical aspect, it is about testing the null hypothesis of equality of the mean values of different strata, at a given probability.

The results of the analysis of variance are given in Table 2. Table 2 Source of variation Analysis of variance of the difference between the average stand volumes perhectare Sum of squares Degree of freedom Mean squares F 0 F 0. This supports the hypothesis that significant effect more precise estimate can be expected if we use the stratified sample for the purpose of estimating average and total volume of the management class than if we use the simple sampling.

This decrease in the standard deviation was due to allocation of a portion of the total volume variation to the variation of the stand volume per hectare around the average management class volume per hectare. In the conducted analysis of variance, the stands were observed as treatments in the eld e periment analysis, because each stand has a number of speci c features concerning its habitat, structure, and management systems.

By de nition, a management class should be a homogeneous part of a forest, comprising stands with similar site and stand characteristics, and Applying the same principle, when we carry out the inventory of all high beech forests in a forest management unit or in woodland, its management classes can be singled out as strata. Practically, in this way, we again con rmed that the strati cation of the management class was justi ed.

Hence, both types of stratification show that the effect of stratification is more significant than the effect of simple sampling, but there is no significant difference between the two types of stratification. Therefore, the first type of stratification would be appropriate enough and in this case management class poststratification is not necessary.

Furthermore, the change in the number of strata from eleven to three had no significant effect on the results of the analysis of variance. This is a practical confirmation of the statement that the size of the average volume per hectare is the most easily defined criterion for the formation of strata in the forest inventory Kopr iv ic a, M.

The only difference is in the way certain elements of the inequalities are determined. Otherwise, if there is an error in the management class area calculation, it should be taken into account. Sample plots would be again systematically arranged in a x m grid. Since the size of the sample is now reduced per stands compared to the initial sample size, the error of the average and total volume estimate would be somewhat higher for each individual stand.

The change in the values of individual statistical indicators can be most easily observed in Table 5. Although it has been already shown that the size of the simple sample can be reduced by 66 sample plots or Again, the number of sample plots in the stratified sample has decreased by Compared to the initial size of the simple sample, this sample has increased in size by To put it simply, if we want to reduce the sampling error 1.

It would be interesting to see what the size of the stratified sample would be with proportionate and optimal allocation of sample plots, if it was calculated directly by the formulas Loetsch, F. Similar results can be found in Ny yssonen, A. It can be also seen that the application of optimal allocation in the stratified sample would reduce the size of the simple sample by 77 sample plots from to or by The most common reasons are lack of knowledge about the volume variability in individual strata before the sample planning and different spacing between the sample plots grid density in the strata.

Due to the limited scope of the paper, we cannot analyze the optimal allocation of sample plots with different cost per stratum. That is an issue that needs special attention. The subject of this paper was a management class of beech high forests with eleven stands.

The size of the average and total management class volume was estimated using a simple and stratified systematic sampling. Proportionate sampling was used. If the stratified sample is of the same size as the simple sample, higher precision level is obtained in the estimates of the average and total management class volume.

Application of optimal sample plot allocation in the stratified sample leads to a further reduction in the size of the simple sample and increases the estimate precision. However, due to the problems in planning and implementation of optimal stratification, proportionate systematic allocation should be primarily used in forest inventory. If forest inventory deals with inventory units larger than a stand compartments, catchments, management classes, forest categories, management units, woodland, etc.

In fact, most often, in practice, you only need to perform poststratification of large inventory units and then to process data by stratified sampling formulas. However, when there is the possibility that we know or we can estimate variability of volume, basal area, etc. We have shown the possibility and technique of applying stratified sampling in forest inventory, as well as the advantage of this type compared to simple sampling.

Everything that relates to stratified sampling in forest inventory can be applied to forestry in general. When it comes to forest inventory, a good criterion for the stratification of the measured forest inventory unit is the size of the average volume per hectare. However, it is a well-known fact that systematic sampling is the only practically applicable solution for forest inventory. It has been empirically proven that if the sample size is the same, systematic samples produce better estimates than random samples, although the whole sampling theory is based on the probability calculations with a random allocation of population units into a sample.

The possibility and technique of estimating the proportion of a specific property of a population unit inventory unit using a simple and stratified sampling have not been considered.

In general, when estimating the proportion, there is no significant difference related to the arithmetic mean of the population, since proportion is actually a specific type of arithmetic mean. Dordrecht, The Netherlands. Koprivica, M. International scientific conference. Sustainable use of forest ecosystems - the challenge of the 21 st century.

Donji Milanovac, Serbia. Proceedings, vol , Institute of Forestry, Belgrade, pp 5 17, Belgrade. Institute of Forestry, Belgrade. Posebno izdanje Faculty of Forestry and Institute of Forestry. Special edition 12 , Sarajevo. Nyyssonen, A. Acta Forestalia Fenica, Parde, J. Editions de lecole natonale des eaux et forets-nancy.

Imprimerie Louis - Jean.

About the NFI

Forest inventory is the systematic collection of data and forest information for assessment or analysis. An estimate of the value and possible uses of timber is an important part of the broader information required to sustain ecosystems. From the data collected one can calculate the number of trees per acre, the basal area , the volume of trees in an area, and the value of the timber. Inventories can be done for other reasons than just calculating the value. A forest can be cruised to visually assess timber and determine potential fire hazards and the risk of fire.

This updated and expanded second edition adds the most recent advances in participatory planning approaches and methods, giving special emphasis to decision support tools usable under uncertainty. The new edition places emphasis on the selection o Airborne laser scanning ALS has emerged as one of the most promising remote sensing technologies to provide data for research and operational applications in a wide range of disciplines related to management of forest ecosystems. This book provi From the reviews: "This book achieving its aim to serve as an academic textbook and as a practical manual It gives an interesting overview over the complexities, problems and impressive achievements of forest inventory in Finland and is a welcome mental fitness trainer for inquisitive, integrating, coordinating and logical thinking, which I rather enjoyed. Bruenig, International Forestry Review, Vol.

Forest Inventory (E-Book, PDF)

Search this site. Forest Inventory: Methodology and Applications by Annika Kangas Synopsis: This book has been developed as a forest inventory textbook for students and could also serve as a handbook for practical foresters. We have set out to keep the mathematics in the book at a fairly non-technical level, and therefore, although we deal with many issues that include highly sophisticated methodology, we try to present first and foremost the ideas behind them.

A constructive review of the State Forest Inventory in the Russian Federation

The National Forest Inventory NFI is a rolling programme designed to provide accurate information about the size, distribution, composition and condition of our forests and woodlands and also about the changes taking place in the woodlands through time. It is essential for developing and monitoring the policies and guidance that support the sustainable management of woodland. We have carried out woodland surveys and compiled forest inventories at 10—15 year intervals since Because forests and woodlands are dynamic and can change a great deal over the years, it is important that information is kept up to date.

Jump to navigation. Reinheckel, Susann. Umfang: S.

Metrics details. Following the stipulations of the Forest Act of , the first SFI sample plots in this vast territory were established in The 34 Russian forest regions were the basic geographical units for all statistical estimates and served as a first-level stratification, while a second level was based on old inventory data and remotely sensed data. Each sample plot consists of three nested concentric circular subplots with radii of

Тебе надо лечиться от паранойи. В трубке повисло молчание. - Мидж… - Джабба попробовал извиниться.  - Позволь мне объяснить.

Potential for wider application of 3P sampling in forest inventory

1 COMMENTS

Brunella J.

REPLY

The k-nearest neighbors kNN method has become popular for forest inventory mapping applications and is widely used to produce pixel-level estimates of continuous forest variables such as biomass, basal area, and volume Franco-Lopez et al.

LEAVE A COMMENT