scholarly journals To the methods for actualization of main stands parameters of hardwood tree species of Ukraine

2020 ◽  
Vol 11 (3) ◽  
Author(s):  
О. P. Bala
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Site Index ◽  
Coefficient Of Determination ◽  
Average Height ◽  
Mixed Stands ◽  
Continuous Forest ◽  
Growth Prognosis ◽  
Hardwood Tree ◽  
Continuous Forest Inventory

Continuous forest inventory, as one of the methods of forest management of the country, in contrast to the basic inventory, has a number of advantages, which primarily provide the opportunity to obtain the most complete and reliable information about the current state of the forest, as it provides annual updates of main stands parameters of forest found on Ukraine. Hardwood tree species (oak, ash, beech, hornbeam, etc.) occupy a special place among all that grow in Ukraine and occupy almost 44 % of the forest area covered with forest vegetation. According to the latest state forest inventory as of 01.01.2011, hardwood tree species are dominated by oak stands - 62.6 % of the area of all hardwood tree species, forest beech - 20.2 %, hornbeam - 3.2 % and ash ordinary, forming mainly mixed stands with oak. A systematic approach to the effective solution of the problem of continuous forest inventory requires the development of objective methods and mathematical models for updating the main stands parameters of forests. In Ukraine, for actualization main stands parameters, two methods have been developed to forecast their growth. The first is based on modeling the percentage of current increment by average height and wood stock, the second - on the developed dynamic site index curves and yield tables for modal stands. The aim of the work is to improve the methodological approaches to modeling the growth prognosis of the main stands parameters by the second method. To achieve these goals used the method of nonlinear regression using IBM SPSS Statistics. As a result of the conducted researches it was offered to model a new unified ratio of the stands parameter a year ahead to the same stands parameter now multiplied by the age of the stand to model the growth prognosis for all stands parameters. This made it possible during the simulation to describe the changes in growth by the main stands parameters with almost absolute accuracy (the coefficient of determination of the obtained models is 1.0). The equation obtained for growth prognosis has the same form for stands of all tree species, of different origin, composition and site index classes.

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.

On the error of estimates from successive forest inventories with SPR

1987 ◽  
Vol 17 (5) ◽  
pp. 442-447
Author(s):  
Tiberius Cunia
Keyword(s):  
Forest Inventory ◽  
Random Sampling ◽  
Regression Coefficients ◽  
Partial Replacement ◽  
Forest Inventories ◽  
Continuous Forest ◽  
Regression Functions ◽  
The Given ◽  
Linear Regressions ◽  
Continuous Forest Inventory

The approach used by Cunia to combine the error from sample plots with the error from volume or biomass tables when Continuous Forest Inventory (CFI) estimates of current values and growth are calculated is extended to the CFI systems using Sampling with Partial Replacement (SPR). The formulae are derived for the case of SPR on two measurement occasions when (i) volume or biomass tables are constructed from linear regressions for which an estimate of the covariance matrix of the regression coefficients is known, and (ii) the sample plots or points are selected by random sampling independently of the given volume or biomass regression functions.

Inventory Design and the Ten-Plots-per-Stand Syndrome

1984 ◽  
Vol 1 (4) ◽  
pp. 76-79
Author(s):  
Alan R. Ek ◽  
Dietmar W. Rose ◽  
Hans M. Gregersen
Keyword(s):  
Decision Making ◽  
Forest Inventory ◽  
Continuous Forest ◽  
Lake States ◽  
Planning Problems ◽  
Continuous Forest Inventory ◽  
Current Practices

Abstract Common Lake States practices of stand description and inventory, and continuous forest inventory (CFI) are criticized. The origin of current practices is discussed and suggestions are made for refinement of practices in light of emerging forest sampling and managment decision-making techniques. Emphasis is placed on developing data of varying precision levels that meet the requirements for specific forest managment and planning problems. North. J. Appl. For. 1:76-79, Dec. 1984.

Comparing Continuous Forest Inventory Programs Across the Northeast

2020 ◽  
Author(s):  
Matthias Nevins ◽  
James Duncan ◽  
Alexandra Kosiba
Keyword(s):  
Forest Inventory ◽  
Continuous Forest ◽  
Continuous Forest Inventory

On the error of continuous forest inventory estimates

1987 ◽  
Vol 17 (5) ◽  
pp. 436-441 ◽  
Author(s):  
Tiberius Cunia
Keyword(s):  
Forest Inventory ◽  
Total Error ◽  
Inventory Systems ◽  
Average Volume ◽  
Common Procedure ◽  
Error Components ◽  
Continuous Forest ◽  
The Common ◽  
Volume Estimates ◽  
Continuous Forest Inventory

Although not explicitly recognized, the error of the volume estimates in Continuous Forest Inventory Systems has two main error components, one due to sample plots and another due to volume tables. The common procedure by which the volume estimates are calculated takes into account only the first component; the second component is simply ignored. An approach is shown that introduces the error of the volume tables into the total error of the CFI estimates of average volume per tree, average volume per acre, and growth components such as net change from one to the next occasion, mortality or ingrowth volume.

scholarly journals Integration of statistical forest reflectance model and Sentinel-2 MSI images into a continuous forest inventory system

2020 ◽  
Vol 26 (2) ◽  
Author(s):  
Andres Kuusk ◽  
Mait Lang
Keyword(s):  
Forest Inventory ◽  
Total Error ◽  
Inventory Data ◽  
Spectral Signatures ◽  
Forest Inventory Data ◽  
Spectral Bands ◽  
Continuous Forest ◽  
Study Sites ◽  
Continuous Forest Inventory ◽  
Sentinel 2

Spectral signatures of forest stands in Sentinel-2 MSI spectral bands are simulated with the statistical forest reflectance model SFRM, and compared to the spectral signatures measured in spectral images at ten study sites in Estonia. As an overall measure of the agreement between simulated and measured spectral signatures is used the total error which is calculated as the sum of relative errors over spectral bands B2 to B11 of Sentinel-2. The distribution of the total error has strongly positive skewness at all study sites and all types of forests (broadleaf, pine and spruce forests). The right tile of the distribution is low. The stands of high value of the total error far right in the tail of the distribution may have some errors in their inventory data, or the inventory data are outdated. Pertinent stands should have priority in their in situ checking process. The model SFRM is a simple and reliable tool for the validity checking of forest inventory data, using routinely collected forest inventory data and operational satellite information of moderate spatial resolution. The model is simple and computationally efficient. Preparing input data for the model is a simple query in the forest inventory database. The suggested procedure can be incorporated into automated systems of continuous forest inventory.

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