Forests of the USSR

1985 ◽  
Vol 61 (5) ◽  
pp. 366-373 ◽  
Author(s):  
J. Holowacz
Keyword(s):  
Forest Resources ◽  
European Part ◽  
Forest Land ◽  
Ural Mountains ◽  
Annual Increment ◽  
Paper Mills ◽  
Growing Stock ◽  
Close Proximity ◽  
Stock Volume ◽  
Mean Annual Increment

The USSR has the world's richest endowment of forest resources. Its total growing stock volume accounts for over 22% and that of conifers for over 53% of the world's totals. Relative to Canada, the USSR is 2.2 times larger in area, has roughly 3 times the forested land and its growing stock is 3.7 times larger. The volume of Soviet growing stock in larch alone is slightly larger than Canada's total. Forests occupy over 36% of the USSR and about 55% of this is considered accessible. Over 60% of the growing stock volume is mature and overmature. Most of the forest resources are east of the Ural Mountains. The Uralo-European part, which supports 74% of the population, accounts for 25.5% of the forested area and 27.3% of the growing stock. Fifty-one percent of the total allowable cut in the accessible and potentially accessible forests is utilized, but only 39% of the total wood volume on all the forested land. Only 35% of the total mean annual increment is utilized. Currently, the USSR regenerates annually an area larger than it clearcuts. Establishment of highly efficient spruce monocultures in close proximity to the existing pulp and paper mills in the Uralo-European part of the country is strongly emphasized. Key words: Accessibility, actual cut, allowable cut, forest land, forest regeneration forest resourses inventory, geographical distribution, growing stock, mean annual increment, species, tenure, USSR.

scholarly journals Results of 20 years old Douglas-fir provenance experiment established on the northern slopes of Rila Mountain in Bulgaria

2014 ◽  
Vol 60 (No. 9) ◽  
pp. 394-399 ◽  
Author(s):  
E.B. Popov
Keyword(s):  
Douglas Fir ◽  
Average Height ◽  
Provenance Trial ◽  
Annual Increment ◽  
Growing Stock ◽  
Breast Height ◽  
Dominant Height ◽  
Seed Zone ◽  
Stock Volume ◽  
Mean Annual Increment

The purpose of this study is to facilitate the detection of provenances relevant to the reforestation practice in Bulgaria. Studies were carried out in a Douglas-fir provenance trial plantation established at an altitude of 800 m a.s.l. on the northern slopes of Rila Mountain. To establish the productivity, assess the stands and rank provenances biometric procedures, methods of descriptive statistics and ANOVA were used. The diameter at breast height, average height, dominant height, total growing stock volume, assortment timber structure and survival of trees were analyzed in the stands of 55 Douglas-fir provenances. The provenances were characterized in terms of productivity of forest stands at 20 years of age and their ability to produce high quality timber was evaluated. Provenances: Newhalem No. 4 (Seed zone 402), Darrington No. 7 (Seed zone 403), Parkdale No. 19 (Seed zone 661) and Idanha No. 29 (Seed zone 452) have been recommended. Among the recommended provenances are those with growing stock volume with branches from 296 to 354 m<sup>3</sup>&middot;ha<sup>&ndash;1</sup>, mean annual increment from 14.8 to 17.7 m<sup>3</sup>&middot;ha<sup>&ndash;1</sup> and dominant heights of up to 17.9 m. &nbsp;

scholarly journals Limits to CO2-Neutrality of Burning Wood. (Review)

2016 ◽  
Vol 53 (4) ◽  
pp. 48-55
Author(s):  
J. Abolins ◽  
J. Gravitis
Keyword(s):  
Ecological Footprint ◽  
Biomass Accumulation ◽  
Richards Equation ◽  
Forest Land ◽  
Land Area ◽  
Annual Increment ◽  
Excess Amount ◽  
The Mean ◽  
Mean Annual Increment ◽  
Fire Wood

Abstract Consumption of wood as a source of energy is discussed with respect to efficiency and restraints to ensure sustainability of the environment on the grounds of a simple analytical model describing dynamics of biomass accumulation in forest stands – a particular case of the well-known empirical Richards’ equation. Amounts of wood harvested under conditions of maximum productivity of forest land are presented in units normalised with respect to the maximum of the mean annual increment and used to determine the limits of CO2-neutrality. The ecological “footprint” defined by the area of growing stands necessary to absorb the excess amount of CO2 annually released from burning biomass is shown to be equal to the land area of a plantation providing sustainable supply of fire-wood.

The Need for Intensive Forest Management to Reduce Southern Pine Beetle Activity in East Texas

1978 ◽  
Vol 2 (1) ◽  
pp. 19-22 ◽  
Author(s):  
R. L. Hedden
Keyword(s):  
Forest Management ◽  
Southern Pine Beetle ◽  
East Texas ◽  
Forest Land ◽  
Southern Pine ◽  
Growing Stock ◽  
Intensive Forest Management ◽  
Pine Beetle ◽  
Commercial Forest ◽  
Stock Volume

Abstract Since 1955 commercial forest land in east Texas has declined 6 percent while growing stock volume per acre has almost doubled. At the same time, the southern pine beetle population has increased tenfold. It is postulated that increasing numbers of high-density pine stands susceptible to southern pine beetle attack have become available for infestation. This trend will continue for at least 20 to 30 years. At present, the only method available to land managers to reduce the risk of timber loss to the southern pine beetle is to practice intensive forest management.

scholarly journals Volume Growth Trends in a Douglas-Fir Levels-of-Growing-Stock Study

2006 ◽  
Vol 21 (2) ◽  
pp. 79-86 ◽  
Author(s):  
Robert O. Curtis
Keyword(s):  
Volume Growth ◽  
Douglas Fir ◽  
Growth And Yield ◽  
Annual Increment ◽  
Growing Stock ◽  
Average Maximum ◽  
Volume Production ◽  
Mean Annual Increment ◽  
Single Set ◽  
Related Mortality

Abstract Mean curves of increment and yield in gross total cubic volume and net merchantable cubic volume were derived from seven installations of the regional cooperative Levels-of-Growing-Stock Study (LOGS) in Douglas-fir. The technique used reduces the seven curves for each treatment for each variable of interest to a single set of readily interpretable mean curves. To a top height of 100 ft and corresponding average age of 45 years, volume growth and yield are strongly related to stocking level, being highest at the highest stocking levels. At that point, current annual increment is still far greater than mean annual increment. Thinning has accelerated diameter growth of the largest 40 trees per acre as well as of the stand average. Maximum volume production would be obtained at stand relative densities approaching the zone of competition-related mortality, although in practice considerations of feasibility of frequent entries and wildlife and amenity considerations would make somewhat lower average levels necessary.

Modelling growing stock volume of forest stands with various ALS area-based approaches

2021 ◽  
Author(s):  
Karolina Parkitna ◽  
Grzegorz Krok ◽  
Stanisław Miścicki ◽  
Krzysztof Ukalski ◽  
Marek Lisańczuk ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Mean Squared Error ◽  
Prediction Method ◽  
Forest Stand ◽  
Boosted Regression Trees ◽  
Forest Stands ◽  
Individual Tree ◽  
Growing Stock ◽  
Modelling Techniques ◽  
Stock Volume

Abstract Airborne laser scanning (ALS) is one of the most innovative remote sensing tools with a recognized important utility for characterizing forest stands. Currently, the most common ALS-based method applied in the estimation of forest stand characteristics is the area-based approach (ABA). The aim of this study was to analyse how three ABA methods affect growing stock volume (GSV) estimates at the sample plot and forest stand levels. We examined (1) an ABA with point cloud metrics, (2) an ABA with canopy height model (CHM) metrics and (3) an ABA with aggregated individual tree CHM-based metrics. What is more, three different modelling techniques: multiple linear regression, boosted regression trees and random forest, were applied to all ABA methods, which yielded a total of nine combinations to report. An important element of this work is also the empirical verification of the methods for estimating the GSV error for individual forest stand. All nine combinations of the ABA methods and different modelling techniques yielded very similar predictions of GSV for both sample plots and forest stands. The root mean squared error (RMSE) of estimated GSV ranged from 75 to 85 m3 ha−1 (RMSE% = 20.5–23.4 per cent) and from 57 to 64 m3 ha−1 (RMSE% = 16.4–18.3 per cent) for plots and stands, respectively. As a result of the research, it can be concluded that GSV modelling with the use of different ALS processing approaches and statistical methods leads to very similar results. Therefore, the choice of a GSV prediction method may be more determined by the availability of data and competences than by the requirement to use a particular method.

scholarly journals Growing Stock Volume Retrieval from Single and Multi-Frequency Radar Backscatter

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 944
Author(s):  
Mihai A. Tanase ◽  
Ignacio Borlaf-Mena ◽  
Maurizio Santoro ◽  
Cristina Aponte ◽  
Gheorghe Marin ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Temporal Frequency ◽  
National Forest Inventory ◽  
Easy Access ◽  
Estimation Errors ◽  
Growing Stock ◽  
L Band ◽  
Stock Volume ◽  
Global Datasets ◽  
Relative Root

While products generated at global levels provide easy access to information on forest growing stock volume (GSV), their use at regional to national levels is limited by temporal frequency, spatial resolution, or unknown local errors that may be overcome through locally calibrated products. This study assessed the need, and utility, of developing locally calibrated GSV products for the Romanian forests. To this end, we used national forest inventory (NFI) permanent sampling plots with largely concurrent SAR datasets acquired at C- and L-bands to train and validate a machine learning algorithm. Different configurations of independent variables were evaluated to assess potential synergies between C- and L-band. The results show that GSV estimation errors at C- and L-band were rather similar, relative root mean squared errors (RelRMSE) around 55% for forests averaging over 450 m3 ha−1, while synergies between the two wavelengths were limited. Locally calibrated models improved GSV estimation by 14% when compared to values obtained from global datasets. However, even the locally calibrated models showed particularly large errors over low GSV intervals. Aggregating the results over larger areas considerably reduced (down to 25%) the relative estimation errors.

scholarly journals Estimates of Forest Growing Stock Volume for Sweden, Central Siberia, and Québec Using Envisat Advanced Synthetic Aperture Radar Backscatter Data

2013 ◽  
Vol 5 (9) ◽  
pp. 4503-4532 ◽  
Author(s):  
Maurizio Santoro ◽  
Oliver Cartus ◽  
Johan Fransson ◽  
Anatoly Shvidenko ◽  
Ian McCallum ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Radar Backscatter ◽  
Central Siberia ◽  
Growing Stock ◽  
Stock Volume ◽  
Aperture Radar

scholarly journals Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

2021 ◽  
Vol 13 (21) ◽  
pp. 4483
Author(s):  
W. Gareth Rees ◽  
Jack Tomaney ◽  
Olga Tutubalina ◽  
Vasily Zharko ◽  
Sergey Bartalev
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Boreal Forest ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Fundamental Parameter ◽  
Sensing Data ◽  
Growing Stock ◽  
Stock Volume ◽  
Sentinel 2

Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data.

scholarly journals THE VOLUME OF WOOD FOREST RESOURCES IN THE EUROPEAN UNION COUNTRIES

2018 ◽  
Author(s):  
Anna TURCZAK
Keyword(s):  
European Union ◽  
Forest Cover ◽  
Well Being ◽  
Causal Analysis ◽  
Forest Resources ◽  
Forest Area ◽  
The European Union ◽  
Growing Stock ◽  
European Union Countries ◽  
Two Factors

The contributions of forests to the well-being of humankind are extraordinarily vast and far-reaching. They are an important element in mitigating climate change. The aim of the paper is to determine the influence of particular factors on the diversity of the European Union countries in terms of the amount of wood forest resources compared with the country size. Two factors affecting the variable have been analysed in the paper: 1) the growing stock per 1 hectare of forest area and 2) the quotient of the forest area and the land area without inland water. Those two independent variables are directly proportional to the dependent variable, thus the higher the growing stock density and the higher the forest cover, the bigger the amount of wood forest resources of the analysed country. The causal analysis allowed to answer the question how the two factors affect the variable considered in the twenty eight countries, namely, what the direction and the strength of their influence are. The logarithmic method was used to carry out the causal analysis. The average results obtained for the entire European Union were compared with those received for each country separately and, on this basis, final conclusions were drawn. Data for 2005, 2010 and 2015 have been used for all needed calculations.

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