Stand Volume Research Articles

Tradeoffs between Stand Volume and Understory Vegetation Diversity in Quercus wutaishanica Forests under Climate Change

Natural forests play a crucial role in providing various ecosystem services, including timber production and biodiversity conservation. However, climate change and anthropogenic factors pose a severe threat to competing forest ecosystem services functions. Therefore, to optimize and sustainably utilize competing forest services, tradeoffs are often necessary. This study was conducted in Northwest China to explore tradeoffs aimed at improving the quality of Quercus wutaishanica Mayr natural forests under climate change conditions, focusing on stand volume, timber production, and understory vegetation diversity conservation. Data from 77 field surveys were used to construct a coupled model for stand growth, stand structure, and site conditions. Changes in understory vegetation species number (UVSN) with crown cover were quantified. These models and relationships can be used as tools to estimate tradeoffs. As stand density increased, single-tree volume decreased, whereas timber volume increased. UVSN increased and then decreased with increasing crown cover and was able to maintain a relative maximum at 0.5–0.65. Under the current climatic conditions, the optimum stand densities corresponding to 30, 40, 50, and 60 years were 1390, 1153, 1042, and 871 trees/ha, respectively, to maintain a high UVSN and adequate stand volume. When mean annual temperature rose, stand densities could be reduced to maintain high-quality timber. Although only two major services were considered, the tradeoffs presented in this study can inform future research to improve the quality of natural forests.

Estimation of Carbon Stock in Chinese Fir Stands Based on Stacking Ensemble Learning and LSTM Models

Abstract Forest carbon sinks, a critical component of the global carbon cycle, constitute nearly half of the total terrestrial carbon pool. This study employed correlation analysis and factor effect analysis to quantify the influences of various factors on the volume of Chinese fir stands. A novel modeling framework was developed using the stacking ensemble learning and LSTM (LongShort-TermMemory) models. This framework incorporated diverse base learners, including XGBoost, Adaboost, KNN, and DT, which are intelligently ensemble via GBDT as a meta learner. The model was further optimized by comparing activation functions and optimizers, with ReLU selected as the activation function and Adam as the optimizer. Model accuracy was evaluated using RMSE, MAE, and R2 metrics, significantly enhancing its learning ability and generalization performance. The findings are as follows: (1) Stand stocking showed strong positive correlations with depression, age group, average diameter at breast height (ADBH), average tree height (ATH), slope, and elevation. Conversely, it exhibited significant negative correlations with origin, stand density, and slope position. In investigating Chinese fir growth on slopes, no significant growth differences were observed between downslopes and midslopes; however, both differed significantly from upper slopes. (2) The stacking ensemble learning method constructed here surpassed all existing single models in terms of estimation and assessment indices, demonstrating superior comprehensive performance. (3) Among the LSTM models, Adam-LSTM performed the best (R2=0.844), followed by Sigmoid-LSTM (R2=0.656), while the RMSprop-LSTM model performed the worst (R2=0.618). Combined with artificial intelligence methods, our optimized carbon stock estimation model can help to improve the ability of forest land management and provide a theoretical basis for the scientific management of forest areas.

Mapping and Estimating Forest Stand Volume using Machine Learning Methods and Multi-Spectral Sentinel 2 Data

Sustainable forest management necessitates the mapping and estimation of forest stand attributes such as density, volume, basal area, and aboveground biomass. This study was conducted to explore the potential of geographic information systems (GIS), remote sensing, machine learning, and field inventories to estimate the forest stand volume of natural and plantation forests within watersheds in the Abra River Basin. The common machine learning regression techniques, which are random forest (RF), k-nearest neighbors (KNN), and support vector machines (SVM), were used to model and predict forest stand volume. The validation of the three machine learning methods showed that the best model to estimate and map forest stand volume is the RF algorithm (R2 = 0.42, RMSE = 0.40 m3/plot, MAE = 0.31 m3/plot). Topographic variables such as the Digital Elevation Model (DEM) and the spectral band Near Infrared (NIR) were the most important variables in predicting forest stand volume. The estimated forest stand volume using the RF model ranged from 33 to 115 m3/ha, with a mean of 59 m3/ha. The results of this study revealed that forest volume can be measured using freely available satellite data and machine learning techniques.

Aspen and Spruce Densities Affect Tree Size, Future Stand Volume, and Aboveground Carbon Following Precommercial Thinning

Data collected over a 30-year period from an experiment replicated across 21 locations in western Canada are used to explore the effects of precommercial thinning of trembling aspen to a range of densities in combination with three initial white spruce densities on tree growth and stand dynamics. Increasing differentiation amongst the 15 treatments was observed with age after thinning for both spruce and aspen responses. Spruce height and diameter declined with increasing aspen density. At age 10 spruce diameter with no aspen was 1.5× that of spruce in unthinned while it was 2.6× that of spruce in unthinned at age 28. Following thinning aspen DBH and crown width declined with increasing density of retained aspen while slenderness and height to crown base of aspen increased. Thinning to 1500 aspen·ha−1 resulted in aspen DBH that was 22% larger relative to the unthinned at age 28. Spruce volume and stemwood biomass at age 90, estimated using the Mixedwood Growth Model (MGM21), declined with increasing initial aspen density, with the complete removal of aspen resulting in nearly double the spruce volume of unthinned plots. However, total stand volume, total stand biomass, and stemwood carbon at age 90 are predicted to be largest in mixed stands with aspen densities of 1500 stems·ha−1 or higher.

Balancing Forest Regulations and Stakeholder Needs in Latvia: Modeling the Long-Term Impacts of Forest Management Strategies on Standing Volume and Carbon Storage

Forest ecosystems are significant carbon pools on a global scale, and also a source of renewable raw materials. Moreover, the European Union (EU) aims to tackle climate change and reach climate neutrality; therefore, forest regulations are designed to promote sustainable forest management practices and ensure the long-term health and productivity of forests. It is important to balance regulatory requirements with the economic, social, and environmental needs of forest stakeholders. This study analyses four theoretical scenarios (business as usual, green deal, intensive forestry, and intensive forestry with afforestation) and prognoses the management impact on standing volume and carbon stock in living trees and harvested wood products (HWPs). Thus, the aim of this study is to evaluate different theoretical forest management scenarios to predict changes in standing volume and carbon stock in living tree biomass and HWPs for the 100 next years. The results suggest that intensive targeted forestry practices may enhance carbon sequestration and were found to be the most suitable strategy for Latvia’s hemiboreal zone, as they balance economic benefits with carbon sequestration and ecosystem services. The obtained results can be valuable for policymakers and forest managers to promote sustainability and balance the diverse needs of society and forest stakeholders.

Stand Structure and Composition and Model for Estimating Stand Volume Potential at the Citragaluh Sustainable Community Forest Management Unit, Subang Regency, West Java

In general, community forest management is still limited to the management of individual farmers so that it affects diversity, especially in the form of stands. This study aimed to explore the stand structure and composition characteristics and develop a model for estimating the potential stand volume at the Citragaluh community forest management unit with 55 observation plots based on a combination of slope and density classes. The data taken includes slope; plant species; plant coordinates; planting pattern and spacing; tree diameter, tree height, and canopy density. The results of plot observations showed that the cropping pattern of Citragaluh Community Forest Management Unit (CFMU) consisted of monoculture (10.9%), mixed stands (20%), agroforestry (29%), dry fields (27.27%), wet fields (9%), bamboo stands (1.8%) and built-up area (1.8%). The trees species found were Jeungjing, Mahogany, Teak, Tisuk, Sobsi, Akasia, and Puspa. Based on the results of stratification, diameter distribution, and stand volume, mixed gardens were the best cropping pattern. This research proves the role of community forest as a transition between plantation forest and natural forest based on the stand form and composition. The stand volume potential estimator model chosen was linear with the equation Y = 0.074X1 + 2.924 X2 - 1.679 where X1 = slope and X2 = Normalized Difference Vegetation Index (NDVI). The values of R2 models are 51.3%. The average potential for the Citragaluh is 119,835 m3/ha, which tends to be higher than other community forest studies.

Effect of sample size on the estimation of forest inventory attributes using airborne LiDAR data in large-scale subtropical areas

Key messageSample size (number of plots) may significantly affect the accuracy of forest attribute estimations using airborne LiDAR data in large-scale subtropical areas. In general, the accuracy of all models improves with increasing sample size. However, the improvement in estimation accuracy varies across forest attributes and forest types. Overall, a larger sample size is required to estimate the stand volume (VOL), while a smaller sample size is required to estimate the mean diameter at breast height (DBH). Broad-leaved forests require a smaller sample size than Chinese fir forests.ContextSample size is an essential factor affecting the cost of LiDAR-assisted forest resource inventory. Therefore, investigating the minimum sample size required to achieve acceptable accuracy for airborne LiDAR-based forest attribute estimation can help improve cost efficiency and optimize technical schemes.AimsThe aims were to assess the optimal sample size to estimate the VOL, basal area, mean height, and DBH in stands dominated by Cunninghamia lanceolate, Pinus massoniana, Eucalyptus spp., and other broad-leaved species in a large subtropical area using airborne LiDAR data.MethodsStatistical analyses were performed on the differences in LiDAR metrics between different sample sizes and the total number of plots, as well as on the field-measured attributes. The relative root mean square error (rRMSE) and the determination coefficient (R2) of multiplicative power models with different sample sizes were compared. The logistic regression between the coefficient of variation of the rRMSE and the sample size was established, and the minimum sample size was determined using a threshold of less than 10% for the coefficient of variation.ResultsAs the sample sizes increased, we found a decrease in the mean rRMSE and an increase in the mean R2, as well as a decrease in the standard deviation of the LiDAR metrics and field-measured attributes. Sample sizes for Chinese fir, pine, eucalyptus, and broad-leaved forests should be over 110, 80, 85, and 60, respectively, in a practical airborne LiDAR-based forest inventory.ConclusionThe accuracy of all forest attribute estimations improved as the sample size increased across all forest types, which could be attributed to the decreasing variations of both LiDAR metrics and field-measured attributes.

Multiple linear regression models for the estimation of water flows for forest management and planning in Türkiye

While there are many factors, including climatology, geography, topography, vegetation and soil, that affect hydrologic processes, understanding the role of forests seems most essential, due to their manageable nature. In this study, a holistic approach was taken, and possible factors affecting streamflow, including tree, sapling, shrub, herb and soil strata, were measured for 29 small catchments/stream basins located in Turkey. Linear regression models were developed in order to estimate water flow (m³‧ha−1). Several models were suggested for use in practice. These models were based on the data on hand and displayed a sufficient level of explained variance in the dependent variable. Model 5, based on the variablesof catchment area (ha), drainage density, ratio of coniferous stand areas in the catchment (%), tree volume (m³‧ha−1), leaf area index, number of short saplings (number‧ha−1), and topsoil sand rate (%), was recommended for flow estimation, achieving a 0.73 adjR² value for test data. These variables can be obtained as part of a survey and water managers can use them to estimate water flow of the catchment. The generated models can be used in multiple-use planning of forests, e.g. in adjusting the volume of stands to get optimum benefit from wood and water production. One of the most interesting results and one that was opposite to that documented in the general literature, was the positive correlation between tree volume and flow per hectare, which suggests a strategy of growing older tree stands to enable greater water production.

Research on the Estimation of Chinese Fir Stand Volume Based on UAV-LiDAR Technology

Chinese fir (Cunninghamia lanceolata) is the main fast-growing timber species in China, and studies of its stand volume are important for evaluating the effectiveness of forest management. However, it is difficult to accurately estimate stand volume from the perspective of a single tree due to the mutual concealment among Chinese fir trees. Therefore, in this study, we propose a method for identifying different forms of Chinese fir. The specific idea is to realize the accurate identification of a single Chinese fir tree, two Chinese fir trees, and three Chinese fir trees, and construct their respective stand volume estimation models to obtain an estimate of the forest stand volume. The key results are as follows: (1) the overall accuracy of recognition of different forms of Chinese fir is 79%, and the construction of different forms of Chinese fir units is beneficial for identifying forest trees; (2) a multiunit volume equation for different forms of Chinese fir is constructed; (3) based on predictions obtained with the constructed stand volume model, the difference between the estimated stand volume and the measured stand volume is small, and the average accuracy reaches 89.19%; and (4) compared to traditional volume estimation methods based on individual tree scale, the research method in this study shows a significant improvement (about 9.96%) in overall accuracy. In summary, this method can weaken the influence of erroneous individual tree segmentation on the accuracy of stand volume estimation, and can greatly reduce the working time of single tree segmentation to achieve the fast and accurate estimation of fir plantation stand volume.

EVALUATIONS OF DIFFERENT MODELS FOR PREDICTING MERCHANTABLE VOLUME OF PINUS BRUTIA TEN. IN DUHOK GOVERNORATE

This study was initiated with the main objective of evaluating the prediction power of previously 24 published models in the literature, developed for estimating the merchantable volume of natural stands of Pinus brutia Ten. The estimation was based on measuring the breast diameter (D), tree height (h), and absolute form quotient (F) of 120 pine trees (Pinus brutia Ten.) from 2 natural stands situated to the east of Duhok governorate. Six indicators of Fit test statistics, namely Adjusted coefficient of determination (R2- adj.) standard error of estimate (SEE), mean absolute error (MAE), Durbin-Watson statistic (D-W), p-value, and mean biased error (Bias) were used to test the performance of the applied models. The result from the centroid model was considered a reference method for the evaluation during this study. The results indicated that the square root –y logarithmic-x offered the highest performance followed by the double square root model. The square root –y logarithmic-x (equation 11) attributed more than 90% of the variation in merchantable volume to variations in D, h, and F. Furthermore, the mean absolute error of prediction of this model was 0.0434. According to this study, the mean stem form of Pinus brutia trees is (0.64), which signifies quadratic paraboloid.

Estimating the amount of British Columbia’s “big-treed” old growth: Navigating messy indicators

British Columbia’s (BC) diverse forest ecosystems include highly productive old growth with global importance for carbon storage and biodiversity. Current estimates of the remaining amount of “big-treed” old growth vary 10-fold, creating uncertainty that challenges provincial attempts to shift management policy toward ecological integrity. This uncertainty arises from using different remotely sensed indicators and definitions of tree size. No ideal indicator exists. We attempt to improve clarity by evaluating the reliability of candidate indicators, calibrating selected indicators to improve consistency, and generating multiple estimates of the amount of big-treed old growth using calibrated indicators. To evaluate reliability, we compared inventory estimates of tree size and site productivity with measured tree size in 1,945 ground plots. To assess the amount of big-treed old growth, we determined an equivalent “big” size threshold for each indicator and calculated the area of old growth above the size threshold. Stand volume, tree density, basal area, and diameter estimates performed poorly; we selected tree height and two measures of site productivity for further analysis. Estimated tree height best indicated the current old growth size, followed by inventory-based site index and finally ecosystem-based site index. The calibrated indicators agreed that very little remaining old growth supports large trees (1.5–3.3% for the biggest trees; 6–13% including medium-sized trees that represent the largest growing trees in lower productivity interior ecosystems). Tree height cannot be used to compare the remaining area of big-treed old growth to the area expected naturally, an important input for ecological risk assessment and conservation planning because height data are lost from the inventory after harvest. The two calibrated site productivity indicators agreed that the amount remaining is less than 30% of the expected historical amount, posing a high risk to biodiversity and resilience. We recommend using estimated height to identify the biggest remaining old-growth stands for regional planning and calibrated inventory-based site index for risk assessment until a detailed ecosystem mapping has been verified to represent old-growth variability. To reduce uncertainty, we suggest that planning groups compare several indicators and analysis approaches, adjusted to ensure equivalence, and use precaution to avoid any unknowingly increasing risks.

Производительность и возобновление насаждений притундрового леса в Республике Коми

The paper highlights the evaluation results of the productivity and regeneration of stands in the Pechora forestry of the Komi Republic. At the research sites, the stands are mixed by composition and simple to complex by shape (with one or two stories). They are dominated by sphagnum spruce stands of Va - Vb quality classes. The total standing volume of stands varies from 44 to 161 m3/ha whereas the average volume increment varies from 0.49 to 1.46 m3/ha per year. The dependence of standing volume on the absolute and relative forest density is a closely correlated value being reliable at a high level of significance. Young spruce trees grow at 87 % and young birch trees - at 73 % of sample plots. 1/3 of the sample plots is characterized by the density of large spruce new growth exceeding 1.2 thousand units/ha. In this case, we can recommend natural reforestation by young growth conservation and care for it. At the other areas, the forest density varies from 0.25 to 0.8 thousand units/ha. Here, we also think that natural reforestation is an appropriate forest regeneration method but on condition of soil mineralization and with leaving seed trees. The relationship between the density of spruce or birch regrowth and the total stand stock can be expressed by the parabola equations. The maximum density of large spruce regrowth (1.75 thousand units/ha), calculated by these equations, corresponds to the stand stock of 84 m3/ha and relative stand density of 0.93. The maximum density of large birch regrowth (0.95 thousand units/ha) corresponds to the stand stock of 88 m3/ha and relative stand density of 0.98. Along with decreasing or increasing the stand stock value providing for the maximum regrowth density, the regrowth density values decrease. We explain the identified dependency as follows. In the given conditions, the maximum density of large regrowth that can further become a stand, can be achieved on condition of the optimal density and stock of stands that provide favorable conditions for regeneration and growth of young trees. Any decrease or increase in the density and stock of stands from the values being optimal for regeneration and growth of young trees weakens the protective role of maternal stand or, vice versa, increases its negative impact on the younger generation and, as a result, decreases the regrowth density under the canopy. The above mechanisms should be taken into account when conducting selective cuttings and cleaning cuttings under these conditions.

Investigating of Kriging Geostatistic Method Capability for Forest Stand Volume Zoning (Case Study: Haftkhal Area)

Investigating of Kriging Geostatistic Method Capability for Forest Stand Volume Zoning (Case Study: Haftkhal Area)

The Effect of Bark Stripping by Deer (Cervus elaphus L.) on Biometric Parameters of the Scots Pine (Pinus sylvestris L.)

Bark stripping by red deer (Cervus elaphus L.) is one of the most acute instances of damage wrought by these animals in a forest. Because of its location, bark stripping causes damage to the thickest and most valuable part of the tree trunk, which lowers the quality of the wood due to technical flaws. The research was conducted in 25 sampling sites of pine stands aged 35 years in a mixed, fresh forest habitat in south-western Poland. The study was aimed at assessing the influence of bark damage by deer on biometric parameters of pine stands (Pinus sylvestris L.). The assessment involved the diameter at breast height, height, basal area and volume of the tree stands. The results revealed a strong deer-specific pressure on the analysed pine stands. The share of bark stripped trees ranged from 65% to 96%. The number of bark damage instances (NBDI) on a given tree was found to significantly affect its diameter at breast height. The strip-damaged trees had breast height diameters lower by 23% on average, and their mean height was lower by 8.7% as compared with the trees without damage. The mean reduction in basal area increments (G1.3) of the trees was identified at 39%. In a similar way to the diameter at breast height and the basal area, the losses in tree stand volume growth caused by deer stripping were significant and oscillated at 41%.

Effects of climate change on forest plantation productivity in Chile.

Forest plantations in Chile occupy more than 2.2millionha and are responsible for 2.1% of the GDP of the country's economy. The ability to accurately predictions of plantations productivity under current and future climate has an impact can enhance on forest management and industrial wood production. The use of process-based models to predict forest growth has been instrumental in improving the understanding and quantifying the effects of climate variability, climate change, and the impact of atmospheric CO2 concentration and management practices on forest growth. This study uses the 3-PG model to predict future forest productivity Eucalyptus globulus and Pinus radiata. The study integrates climate data from global circulation models used in CMIP5 for scenarios RCP26 and RCP85, digital soil maps for physical and chemical variables. Temporal and spatial tree growth inventories were used to compare with the 3-PG predictions. The results indicated that forest productivity is predicted to potentially increase stand volume (SV) over the next 50 years by 26% and 24% for the RCP26 scenario and between 73% and 62% for the RCP85 scenario for E. globulus and P. radiata, respectively. The predicted increases can be explained by a combination of higher level of atmospheric CO2 , air temperatures closer to optimum than current, and increases in tree water use efficiency. If the effect of CO2 is not considered, the predicted differences of SV for 2070 are 16% and 14% for the RCP26 scenario and 22% and 14% for RCP85 for the two species. While shifts in climate and increasing CO2 are likely to benefit promote higher productivity, other factors such as lack insufficient availability of soil nutrients, events such as increasing frequency and duration of droughts, longer periods of extreme temperatures, competing vegetation, and occurrence of new pests and diseases may compromise these potential gains.

Rotation age extension synergistically increases ecosystem carbon storage and timber production of Chinese fir plantations in southern China.

Afforestation is an effective method to increase carbon (C) sinks and address climate change. It is crucial to understand how the stand growth affects C sequestration capacity, especially when the trade-offs with timber production from plantations have not been fully examined. We used a chronosequence approach to estimate C storage in Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) plantations (including the trees, understory, litter, and soils) at seven stand ages (3, 8-11, 16, 21, 25, 29, and 32 years). Ecosystem C storage increased nonlinearly from 76.4 to 282.2tha-1 with stand age and was fitted with a logistic model that had a maximum C storage and age of 271.9tha-1 and 33 years, respectively, to reach 95% of the maximum stored C. The C increment was mainly contributed by an increase in tree biomass, which ranged from 2.8 to 177.7tha-1 and comprised 4-64% of the total ecosystem C. Live root C (sum of the stump, coarse, and fine root C) showed a logistic increase from 2.0 to 26.3tha-1 with stand age and constituted 2.5-9.3% of ecosystem C. Understory plants and litter represented a small pool (<2% of ecosystem C). The C storage in shrubs and litter slightly increased, while that in herbs decreased as the stands aged. Soil C storage was an important and relatively stable pool, ranging from 69.6 to 130.1tha-1. Stand volume was also best fitted with a logistic model with a maximum value of 552.6m3ha-1. Additionally, the time needed to reach 95% of the maximum volume was 25 years. Hence, extending the rotation age to over 30 years for Chinese fir plantations could potentially maximize the synergistic benefits of C storage to mitigate climate change and obtain timber products for economic profit.

Ecoregional Variation of Crimean Pine (Pinus nigrasubspeciespallasiana[Lamb.] Holmboe) Stand Growth

AbstractInvestigation and numerical determination of the effects of ecologically different regions on forest growth are important issues to be considered for sustainable forest management. In this study, differences in growth patterns of Crimean pine (Pinus nigra subsp. pallasiana [Lamb.] Holmboe) stands among the ecoregions located in the Kastamonu region of northwestern Turkey were investigated. For this purpose, stand-level growth models for dominant height, number of trees, basal area, and volume were fitted using the data from 274 sample plots and 293 stem analysis in even-aged, pure, and productive Crimean pine stands. The nonlinear extra sum of squares method and graphical examinations revealed that growth characteristics of these Crimean pine stands were affected by ecoregional differences. Ecoregion-based growth models, which are more flexible and have the ability to reflect the ecoregional differences in growth patterns, were found to be superior to regional models. Applying general models without any ecoregional classification resulted in biases of 5.3%, 23.1%, 5.5%, and 6.2% in the estimates of dominant height, number of trees, basal area, and volume of Crimean pine stands, respectively. The results showed that ecological differences must be considered in studies on forest growth modeling and forest management.

A Comparison of Models of Stand Volume in Spruce-Fir Mixed Forest in Northeast China

Based on a multiple linear regression model, random forest algorithm and generalized additive model, a stand volume model was constructed to provide a theoretical basis for sustainable management. A total of 224 fixed plots in the Jingouling forest farm, Wangqing County, Jilin Province, were used as data sources. Specifically, 157 plots were used as training data, and 77 plots were used as test data. The effects of stand structure variables, topography variables, cutting variables, diversity variables and climate variables on stand volume were analyzed. The random forest algorithm explained 95.51% of the stand volume, and the generalized additive model explained 95.45% of the stand volume. Stand structure variables and topography variables had more influence on the stand volume of spruce-fir than other variables. Among the diversity variables, the evenness index, Shannon index and Simpson index had a relatively greater impact on the stand volume. The cutting times and the intensity of the first cutting had a direct relationship with stand volume. The influence of climate variables on the stand volume was relatively small in the study area.

Regression Models for Estimating Aboveground Biomass and Stand Volume Using Landsat-Based Indices in Post-Mining Area

This paper describes the use of remotely sensed data to measure vegetation variables such as basal area, biomass and stand volume. The objective of this research was developed regression models to estimate basal area (BA), aboveground biomass (AGB), and stand volume (SV) using Landsat-based vegetation indices. The examined vegetation indices were SAVI, MSAVI, EVI, NBR, NBR2 and NDMI. Regression models were developed based on least-squared method using several forms of equation, i.e., linear, exponential, power, logarithm and polynomial. Among those models, it was recognized that the best fit of model was obtained from the exponential model, log (y) = ax + b for estimating BA, AGB &amp; SV. The MSAVI had been identified as the most accurate independent variable to estimates basal area with R² of 0.70 and average verification values of 16.39% (4%32.66%); while the EVI become the best independent variable for estimating aboveground biomass (AGB) with R2 of 0.72 and average of verification values of 18,10% (9%-28.01%); and the NDMI was recognized to be the best independent variable to estimate stand volume with R2 of 0.69 and average of verification values of 24.37% (-15%-38.11%).

Uticaj orografskih faktora i sklopa sastojine na drvnu zalihu u čistim raznodobnim šumama bukve

European beech is one of the most important and most common tree species in Europe, which have extremely wide amplitude in sense of horizontal and vertical distribution. In Serbia and Bosnia and Herzegovina European beech (Fagus sylvatica L.) represents one of the most important tree species from an economic and ecological point of view, because it's constituting significant areas of highly productive pure and mixed forests. The main goal of this research is to determine the influence of orography (slope, aspect, elevation, and spatial distribution-climate zone) and one stand characteristic (canopy) on stand volume in pure uneven-aged beech stands. The existence of the aforementioned influence was analyzed using multi-factor ANOVA (Type II sums of squares). The research was conducted on 20 localities (grouped in 3 climatic zones). One in the eastern part of Serbia on 8 localities (Moesian province), second in the eastern part of Republika Srpska on 7 localities (Illyrian-Moesian province), and third in the northwestern part of Republika Srpska on 5 localities (Illyrian province). The obtained results showed that only slope (p≤0.1) and canopy (p≤0.001) have a statistically significant impact on stand volume in pure beech stands.