Forest Stocks Research Articles

Sample Plots Forestry Parameters Verification and Updating Using Airborne LiDAR Data

The rapid development of LiDAR technology has promoted great changes in forest resource surveys. The airborne LiDAR point cloud can provide the precise height and detailed structure of trees, and can estimate key forest resource indicators such as forest stock volume, diameter at breast height, and forest biomass at a large scale. By establishing relationship models between the forest parameters of sample plots and the calculated parameters of LiDAR, these developments may eventually expand the models to large-scale forest resource surveys of entire areas. In this study, eight sample plots in northeast China are used to verify and update the information using point cloud obtained by the LiDAR scanner riegl-vq-1560i. First, the tree crowns are segmented using the profile-rotating algorithm, and tree positions are registered based on dominant tree heights. Second, considering the correlation between crown shape and tree species, we use DBN classifier to identify species using features of crowns, which are extracted to 1D array. Third, when the tree species is known, parameters such as height, crown width, diameter at breast height, biomass, and stock volume can be extracted from trees, enabling accurate large-scale forest surveys based on LiDAR data. Finally, experiment results demonstrate that the F-score of the eight plots in the tree segmentation exceed 0.95, the accuracy of tree species correction exceeds 90%, and the R2 of tree height, east–west crown width, north–south crown width, diameter at breast height, aboveground biomass, and stock volume are 0.893, 0.757, 0.694, 0.840, 0.896 and 0.891, respectively. The above results indicate that the LiDAR-based survey is practical and can be widely applied in forest resource monitoring.

Estimation of aboveground carbon stock based on mangrove zones in Ijo River Estuary, Ayah Village, Kebumen, Indonesia

Abstract. Ningtyas SRA, Mu’ali AYF, Putri DNAR, Silaningtyas NW, Tsabita FA, Aprianto MK, Sari SP, Fauziyyah MD, Nugraheni RS, Juniati SD, Isa MN, Nofitasari H, Sutarno, Sugiyarto, Yap CK, Budiharta S, Setyawan AD. 2023. Estimation of aboveground carbon stock based on mangrove zones in Ijo River Estuary, Ayah Village, Kebumen, Indonesia. Indo Pac J Ocean Life 7: 148-155. The increasing concentration of greenhouse gases, including carbon dioxide in the atmosphere is the driver of global warming which triggers climate change. One strategy to mitigate climate change is reducing carbon emissions and increasing the carbon stock. Mangrove forest is recognized as the largest carbon sink on the Earth, therefore, the conservation and restoration of mangrove forest are increasingly promoted as an effective way to tackle climate change. While many studies assess the carbon stock of mangrove forests, a context-specific assessment is needed to enrich the existing studies in this field. This study aimed to investigate the aboveground carbon stored in vegetation occurring in three mangrove zones (i.e., seaward, middle and landward zones) in Ijo River Estuary, Ayah Village, Kebumen, Central Java, Indonesia. Purposive sampling on each mangrove zone was conducted with vegetation data at tree and pole stages collected using nested plot methods. Aboveground biomass was calculated using an allometric equation while carbon stock was estimated using the method of the National Standardization Agency. In total, there were 11 mangrove species in the observation plots, consisting of seven true mangrove species and four mangrove associates. The seaward zone had five species and an amount of aboveground biomass (29324.07 MgB/ha), while the middle zone consisted of five species and had biomass of 52776.62 MgB/ha, and the landward zone was composed of five species and had biomass of 6428.74 MgB/ha. The carbon stock in the seaward, middle and landward zones were 43.06, 197.42 and 186.00 MgC/ha, respectively. In total, the Ayah Mangrove Forest contained aboveground biomass of 88529.43 MgB/ha, equal to 1143.31 MgC/ha carbon stock. The findings of this study reiterate the importance of conserving mangrove forests as an effective way to reduce carbon emissions which are the major cause of global warming.

Sustainable Management of Land Resources: The Case of China’s Forestry Carbon Sink Mechanism

Compared to more developed countries, the use of land resources is less efficient in China. China’s vast forest land area gives it a rich and underutilized carbon sink. This is an important way for China to achieve the goals of “carbon peaking” and “carbon neutrality”, which is of great significance to China’s sustainable development. In the past 20 years, China has designed a series of policies to serve the development of forestry carbon sinks, namely the forestry carbon sink mechanism (FCSM). However, the questions of which policy is the most important, and what is the socio-economic value it generates, have not been fully investigated. Accordingly, this paper studied 30 provincial-level regions in China from 2005 to 2020 using the difference-in-differences (DID) model. The conclusions show that: (1) the FCSM does increase the socio-economic value of land resources, thus improving the sustainability of land resources; (2) the FCSM helps to increase forest coverage, forest stock volume and the forest coverage rate, which increases the social value of land resources from the greening path; (3) the FCSM helps to increase the gross forestry product, which increases the economic value of land resources through the path of increasing production value.

Combination Strategies of Variables with Various Spatial Resolutions Derived from GF-2 Images for Mapping Forest Stock Volume

Spectral features (SFs) and texture features (TFs) extracted from optical remote sensing images can capture the structural composition and growth information of forests, and combining remote sensing variables with a few ground measurement samples is a common method for mapping forest stock volume (FSV). However, the accuracy of mapping FSV using optical images with a high spatial resolution (one meter or sub-meters) is often lower than medium resolutions (larger than 10 m) using the same types of features and approaches. To overcome the limitations of high spatial resolution images in mapping FSV, down-scaled images with spatial resolution ranging from 1 to 30 m were obtained by GF-2 image to interpret the relationships between spatial resolutions of features and the accuracy of mapping FSV, and combination strategies of variables with various spatial resolutions were proposed to improve the accuracy of mapping FSV. The results show that the spatial resolution of features significantly affects the performance of employed models in estimating FSV, the sensitivity between SFs and FSV gradually increases with the decreasing of spatial resolution, and the optimal spatial resolutions of two types of features (SFs and TFs) are not synchronized in mapping forest FSV. After using combination strategies of variables with various spatial resolutions, the accuracy of mapping FSV is significantly higher than those derived from variable sets with the same spatial resolutions. It is proved that TFs derived from GF-2 images have great potential to improve the accuracy of mapping FSV, and the contribution of features depends on the approaches of extracting and combination strategies.

The Extension and Improvement of the Forest Land Net Present Value Model and Its Application in the Asset Evaluation of Cunninghamia lanceolata Forest Land

In order to solve the problem in that the classical forest land expectation value method ignores the actual forest stock volume when assessing the income at the end of the current production cycle in the forest, and fill the research gap in this area, the technical system of the forest land asset evaluation was enriched. The forest land returns were divided into two parts, i.e., the segmented forest land return price from the growth of the actual forest stand to the end of the growth cycle (Bu1), and the segmented forest land return price for an infinite number of growth cycles after the growth of the actual forest stand to the end of the growth cycle (Bu2). Through structure, the forest land gain price expansion model was obtained, and the stand quality including the average diameter at breast height, average height, stock volume, and outturn of stand as dummy variables were used to construct the growth harvest model related to asset evaluation. Taking Cunninghamia lanceolata forest land as an example, the traditional asset evaluation methods were comparatively analyzed. The residual standard deviation (RSD) of the growth model was less than 10%, the total relative error (TRE) and mean system error (MSE) were within ±10%, the mean prediction errors (MPE) were less than 5%, and the mean percentage standard errors (MPSE) were less than 10%, respectively. Combining the forest land net present value expansion model with the traditional evaluation method, the evaluation value of the forest land assets was subsequently calculated, and accordingly, the forest land asset evaluation prime stand factors were predicated. It was found that the valuation results of the forest land net present value expansion model were consistent with the actual situation. The forest land net present value expansion model can therefore be used for asset evaluation of tree forest land (including natural uneven-aged forest land), bamboo forest land, shrub forest land, and economic forest land, and provide new technical support for forest land asset evaluation.

Assessing the carbon storage value of kelp forest restoration in the Hauraki Gulf Marine Park, New Zealand: Lessons from no-take Marine Protected Areas

Anthropogenic CO2 emissions are one of the principal driving forces of climate change. Climate change leads to systematic changes of ecosystems that cause significant impacts on species interactions, ecosystem processes, and result in profound socio-economic consequences. While carbon offsetting of terrestrial forests and their monetary value are well accepted, the carbon offsetting potential and value of coastal and marine ecosystems (Blue Carbon, BC) remains excluded from the international carbon accounting framework. In this study, we estimated the carbon offsetting value of kelp forests (Ecklonia radiata) against urchin barrens (dominated by crustose coralline algae) using the standing stock biomass as a metric. We propose that standing stock biomass is a crucial next step in introducing BC to the general carbon offsetting framework. Our approach is informed by long-term ecological research conducted in a marine reserve, which highlights the potential of utilizing trophic cascades to limit the dominance of urchin barrens in shallow coastal habitats. We found that the value of carbon standing stock in current kelp forests with reforestation in the Hauraki Gulf Marine Park (HGMP) is worth up to $NZ 7.9 million, based on 2021 carbon market pricing in New Zealand. This simple step towards realizing the value of marine conservation and its multiple benefits to society highlights the missed opportunities when policy and plans do not align with ecology and economics. Including the ocean's contributions to carbon storage is a critical step in using monetization techniques to overcome climate change challenges. Monetizing the carbon offsetting value of kelp forests contributes to establishing funding mechanisms to attract diverse investments in marine ecosystem restoration while providing ecological benefits in ocean governance.

Estimation of land cover changes and biomass carbon stock in north-eastern hill forests of Bangladesh

Estimation of land cover changes and biomass carbon stock in north-eastern hill forests of Bangladesh

Estimating biomass and soil carbon change at the level of forest stands using repeated forest surveys assisted by airborne laser scanner data

BackgroundUnder the growing pressure to implement mitigation actions, the focus of forest management is shifting from a traditional resource centric view to incorporate more forest ecosystem services objectives such as carbon sequestration. Estimating the above-ground biomass in forests using airborne laser scanning (ALS) is now an operational practice in Northern Europe and is being adopted in many parts of the world. In the boreal forests, however, most of the carbon (85%) is stored in the soil organic (SO) matter. While this very important carbon pool is “invisible” to ALS, it is closely connected and feeds from the growing forest stocks. We propose an integrated methodology to estimate the changes in forest carbon pools at the level of forest stands by combining field measurements and ALS data.ResultsALS-based models of dominant height, mean diameter, and biomass were fitted using the field observations and were used to predict mean tree biophysical properties across the entire study area (50 km2) which was in turn used to estimate the biomass carbon stocks and the litter production that feeds into the soil. For the soil carbon pool estimation, we used the Yasso15 model. The methodology was based on (1) approximating the initial soil carbon stocks using simulations; (2) predicting the annual litter input based on the predicted growing stocks in each cell; (3) predicting the soil carbon dynamics of the annual litter using the Yasso15 soil carbon model. The estimated total carbon change (standard errors in parenthesis) for the entire area was 0.741 (0.14) Mg ha−1 yr−1. The biomass carbon change was 0.405 (0.13) Mg ha−1 yr−1, the litter carbon change (e.g., deadwood and leaves) was 0.346 (0.027) Mg ha−1 yr−1, and the change in SO carbon was − 0.01 (0.003) Mg ha−1 yr−1.ConclusionsOur results show that ALS data can be used indirectly through a chain of models to estimate soil carbon changes in addition to changes in biomass at the primary level of forest management, namely the forest stands. Having control of the errors contributed by each model, the stand-level uncertainty can be estimated under a model-based inferential approach.

Mapping soil organic carbon stocks in Nepal’s forests

Comprehensive forest carbon accounting requires reliable estimation of soil organic carbon (SOC) stocks. Despite being an important carbon pool, limited information is available on SOC stocks in global forests, particularly for forests in mountainous regions, such as the Central Himalayas. The availability of consistently measured new field data enabled us to accurately estimate forest soil organic carbon (SOC) stocks in Nepal, addressing a previously existing knowledge gap. Our method involved modelling plot-based estimates of forest SOC using covariates related to climate, soil, and topographic position. Our quantile random forest model resulted in the high spatial resolution prediction of Nepal’s national forest SOC stock together with prediction uncertainties. Our spatially explicit forest SOC map showed the high SOC levels in high-elevation forests and a significant underrepresentation of these stocks in global-scale assessments. Our results offer an improved baseline on the distribution of total carbon in the forests of the Central Himalayas. The benchmark maps of predicted forest SOC and associated errors, along with our estimate of 494 million tonnes (SE = 16) of total SOC in the topsoil (0–30 cm) of forested areas in Nepal, carry important implications for understanding the spatial variability of forest SOC in mountainous regions with complex terrains.

Assessment of aboveground biomass and carbon stock of subtropical pine forest of Pakistan

Assessment of aboveground biomass and carbon stock of subtropical pine forest of Pakistan

Estimation of Bamboo Forest Aboveground Carbon Using the RGLM Model Based on Object-Based Multiscale Segmentation of SPOT-6 Imagery

Remote sensing is an important tool for the quantitative estimation of forest carbon stock. This study presents a multiscale, object-based method for the estimation of aboveground carbon stock in Moso bamboo forests. The method differs from conventional pixel-based approaches and is more suitable for Chinese forest management inventory. This research indicates that the construction of a SPOT-6 multiscale hierarchy with the 30 scale as the optimal segmentation scale achieves accurate information extraction for Moso bamboo forests. The producer’s and user’s accuracy are 88.89% and 86.96%, respectively. A random generalized linear model (RGLM), constructed using the multiscale hierarchy, can accurately estimate carbon storage of the bamboo forest in the study area, with a fitting and test accuracy (R2) of 0.74 and 0.64, respectively. In contrast, pixel-based methods using the RGLM model have a fitting and prediction accuracy of 0.24 and 0.01, respectively; thus, the object-based RGLM is a major improvement. The multiscale object hierarchy correctly analyzed the multiscale correlation and responses of bamboo forest elements to carbon storage. Objects at the 30 scale responded to the microstructure of the bamboo forest and had the strongest correlation between estimated carbon storage and measured values. Objects at the 60 scale did not directly inherit the forest information, so the response to the measured carbon storage of the bamboo forest was the smallest. Objects at the 90 scale serve as super-objects containing the forest feature information and have a significant correlation with the measured carbon storage. Therefore, in this study, a carbon storage estimation model was constructed based on the multiscale characteristics of the bamboo forest so as to analyze correlations and greatly improve the fitting and prediction accuracy of carbon storage.

Estimation of Aboveground Carbon Stocks in Forests Based on LiDAR and Multispectral Images: A Case Study of Duraer Coniferous Forests

The correct estimation of forest aboveground carbon stocks (AGCs) allows for an accurate assessment of the carbon sequestration potential of forest ecosystems, which is important for in-depth studies of the regional ecological environment and global climate change. How to estimate forest AGCs quickly and accurately and realize dynamic monitoring has been a hot topic of research in the forestry field worldwide. LiDAR and remote sensing optical imagery can be used to monitor forest resources, enabling the simultaneous acquisition of forest structural properties and spectral information. A high-density LiDAR-based point cloud cannot only reveal stand-scale forest parameters but can also be used to extract single wood-scale forest parameters. However, there are multiple forest parameter estimation model problems, so it is especially important to choose appropriate variables and models to estimate forest AGCs. In this study, we used a Duraer coniferous forest as the study area and combined LiDAR, multispectral images, and measured data to establish multiple linear regression models and multiple power regression models to estimate forest AGCs. We selected the best model for accuracy evaluation and mapped the spatial distribution of AGC density. We found that (1) the highest accuracy of the multiple multiplicative power regression model was obtained for the estimated AGC (R2 = 0.903, RMSE = 10.91 Pg) based on the LiDAR-estimated DBH; the predicted AGC values were in the range of 4.1–279.12 kg C. (2) The highest accuracy of the multiple multiplicative power regression model was obtained by combining the normalized vegetation index (NDVI) with the predicted AGC based on the DBH estimated by LiDAR (R2 = 0.906, RMSE = 10.87 Pg); the predicted AGC values were in the range of 3.93–449.07 kg C. (3) The LiDAR-predicted AGC values and the combined LiDAR and optical image-predicted AGC values agreed with the field AGCs.

Spatial distribution of aboveground biomass stock in tropical dry forest in Brazil

Spatial distribution of aboveground biomass stock in tropical dry forest in Brazil

Tree Structure Analysis, Biomass and Carbon Stock of Sal Forests in Central Himalayan Region

Present study deals with density, species diversity, basal area, IVI, biomass and carbon stock in Sal forests. The study was conducted in three Sal forest sites around Champawat at Tanakpur. Among the fifteen tree species, total density of trees varied between 290-690 ind. ha-1 in all studied forest. The basal area of trees in forests was 50.92 to 78.84 m2 ha-1. Tree species diversity of Sal forests varied from 1.07-2.11. Biomass and carbon stock of trees ranged from 473.3 to 786.8 tha-1 and 224.8- 373.7 tha-1yr-1 respectively. Shorea robusta was dominated species based on density parameter in all studied forest sites. The tree density of Sal forests was slightly on lower side with comparison to natural forests in and around the region. However, density exhibits positive correlation at (p?0.05) with biomass and carbon. But the difference in age and size of Sal trees showed the variation in biomass and carbon stock among the studied forest site. Present study concludes that Sal forests in and around nearby villages were in degraded conditions due to anthropogenic pressure. Thus, the forests must be conserved through judicious management by providing proper scientific inputs for high biomass and carbon.

How many plots are needed to estimate sapling density and stocking in temperate forests?

Performing a complete silvicultural diagnosis before a silvicultural treatment generally requires assessing the state of regeneration with the help of an inventory by sampling, particularly for stands dominated by sugar maple (Acer saccharum Marsh.) or yellow birch (Betula alleghaniensis Britt.), in which partial cuts are recommended. This inventory may then be compared to the standard or used in a growth model for saplings (trees for which the diameter measured at 1.3 m above the ground (DBH) varies from 1.1 cm to 9.0 cm). Some of these tools are based on sapling density, while others are based on the stocking of the saplings or on the stocking of total regeneration (combining saplings and seedlings with a DBH ≤ 1.0 cm). We assessed the number of plots required to estimate the density and the stocking of saplings with a given margin of error in 28 stands. The results show that more plots are required than usual in practice to inventory sapling density. The stocking is much easier to estimate precisely.

Carbon stock and uptake in the high-elevation tropical montane forests of the threatened Atlantic Forest hotspot: Ecosystem function and effects of elevation variation

Tropical montane forests (TMF) of the threatened Atlantic Forest hotspot play an important role in providing essential ecosystem services associated with hydrological regime and biodiversity conservation. However, important ecological patterns such as those related to the woody carbon biogeochemical cycle are not yet known for these forests, especially those located at high elevations (> 1500 m. a.s.l.). Herein, we used a dataset of 60 plots (2.4 ha) of old-growth TMF sampled along a high-elevation gradient (1500–2100 m a.s.l.) and monitored in two inventories (2011 and 2016) to better understand the patterns of carbon stock and uptake of these high-elevation forests and the related environmental (soil) and elevation controls. We found differences in the carbon stock along different elevation levels (120.36–170.4C.ton.ha−1) and a carbon accumulation trend over the period along the entire gradient. Thus, forest carbon gain (3.82–5.14 ton.ha.year−1) was greater than the carbon loss (2.1–3.4 ton.ha.year−1) and resulted in a positive productivity net. In other words, the TMF acted as a carbon sink, removing carbon from the atmosphere and storing it in woody tissues. Soil variables also exert significant influences on carbon stock and uptake (significative effects of phosphorus on carbon stock and of cation exchange capacity on carbon loss), driving such patterns in isolation or in interaction with elevation. Considering the high conservation degree of the TMF monitored, our results may be indicative of a similar trend in other similar forests, but which have gone through disturbances in the more recent past. These TMF fragments have a wide occurrence in the Atlantic Forest hotspot and may also be acting or will soon act as carbon sinks in improved conservation scenarios. Thus, these forests can play an essential role in conserving ecosystem services in the region and in mitigating climate changes.

Deadwood density, C stocks and their controlling factors in a beech-silver fir mixed virgin European forest

Deadwood is a fundamental structural and functional component of forests, with a crucial role in supporting forest biodiversity, nutrient and carbon cycling. Precise deadwood density estimates and its relation to environmental factors are necessary to evaluate the biomass and carbon stocked in this component. In this study, we estimated dry deadwood density for two different tree species, silver fir (Abies alba) and European beech (Fagus sylvatica) and for three snags and five logs decomposition classes in a virgin mixed beech-fir forest in the Southern Carpathians. The goal was to assess how deadwood density is influenced by different abiotic (moisture, elevation, slope, aspect) and wood-related factors (rottenness, position along the piece, contact with the soil).For snags, the mean dry density showed a reduced variability within decomposition classes (484–326 kg.m−3 for beech and 374–319 kg.m−3 for fir), compared to the logs (486–139 kg.m−3 for beech and 359–161 kg.m−3 for fir). While the mass moisture varied slowly in the first three decay classes (around 60–80 %), it increased sharply in the last two decay classes of logs (>140 % in the fourth class and > 350 % in the last one). The rottenness increased with the decay class in a similar way for both species. The contact of logs with the soil influenced positively the moisture, but the position of the sampling within the piece did not play any significant role in the variability of density. Based on density estimates per decay classes we estimated that the carbon (C) stored in deadwood varied greatly among the 21 plots from 0.36 to 41.16 MgC ha−1, with a mean value of 15.96 ± 2.36 (±SE) MgC ha−1.Our study suggests that volume-based calculations might yield biased quantitative estimates of C stored in deadwood unless a local estimate of dead wood density corrected per species and decomposition class is applied. Moreover, the use of an averaged value of dry density instead of value for each decay class may result in an overestimation of 22% on the estimation of C stock. Thus, our study emphasises the importance of considering decay class-specific values in future inventories of C stocks in other forests and for other species. Furthermore, it could serve as a methodological basis for more specific research designed to uncover the potential influence of different forest management practices on dry deadwood density.

Aboveground live tree carbon stock and change in forests of conterminous United States: influence of stand age

BackgroundSequestration of carbon on forest land is a common and practical component within many climate action plans developed by state or municipal governments. Initial planning often identifies the general magnitude of sequestration expected given the scope of the project. Because age plays a key role in forest carbon dynamics, we summarize both the carbon stock and accumulation rates in live trees by age class and region, allowing managers and policymakers to assess the influence of forest age class structure on forest carbon storage as represented in current inventories. State-level information is provided in supplementary tables.ResultsAverage regional aboveground live tree carbon stocks (represented on a per area basis) range from 11.6 tC/ha in the Great Plains to 130 tC/ha in the Pacific Northwest West (west-side of Cascades) and increase with age in all regions, although in three regions carbon stock declined in the oldest age class. Regional average annual net change in live aboveground tree carbon varies from a low of − 0.18 tC /ha/yr in the Rocky Mountain South region to a high value of 1.74 tC/ha/yr in Pacific Northwest West. In all regions except Rocky Mountain South, accumulation rates are highest in the younger age classes and decline with age, with older age classes in several western regions showing negative rates. In the Southeast and Pacific Northwest West, intermediate age classes exhibit lower rates, likely due to harvesting activity.ConclusionsAboveground live tree carbon stocks increase and rates of average change decrease with age with few exceptions; this pattern holds when examining hardwood and softwood types individually. Because multiple forest management objectives are often considered and tradeoffs need to be assessed, we recommend considering both measures—standing stock and average annual change—of carbon storage. The relative importance of each component depends on management and policy objectives and the time frame related to those objectives. Harvesting and natural disturbance also affect forest carbon stock and change and may need to be considered if developing projections of potential carbon storage. We present forest carbon summaries at a scale and scope to meet information needs of managers and policymakers.

Identifying Spatial Variation of Carbon Stock in a Warm Temperate Forest in Central Japan Using Sentinel-2 and Digital Elevation Model Data

The accurate estimation of carbon stocks in natural and plantation forests is a prerequisite for the realization of carbon peaking and neutrality. In this study, the potential of optical Sentinel-2A data and a digital elevation model (DEM) to estimate the spatial variation of carbon stocks was investigated in a mountainous warm temperate region in central Japan. Four types of image preprocessing techniques and datasets were used: spectral reflectance, DEM-based topography indices, vegetation indices, and spectral band-based textures. A random forest model combined with 103 field plots as well as remote sensing image parameters was applied to predict and map the 2160 ha University of Tokyo Chiba Forest. Structural equation modeling was used to evaluate the factors driving the spatial distribution of forest carbon stocks. Our study shows that the Sentinel-2A data in combination with topography indices, vegetation indices, and shortwave-infrared (SWIR)-band-based textures resulted in the highest estimation accuracy. The spatial distribution of carbon stocks was successfully mapped, and stand-age- and forest-type-level variations were identified. The SWIR-2-band and topography indices were the most important variables for modeling, while the forest stand age and curvature were the most important determinants of the spatial distribution of carbon stock density. These findings will contribute to more accurate mapping of carbon stocks and improved quantification in different forest types and stand ages.

Tree Biomass and Leaf Area Allometric Relations for Betula pendula Roth Based on Samplings in the Western Carpathians.

Biomass allometric relations are necessary for precise estimations of biomass forest stocks, as well as for the quantification of carbon sequestered by forest cover. Therefore, we attempted to create allometric models of total biomass in young silver birch (Betula pendula Roth) trees and their main components, i.e., leaves, branches, stem under bark, bark, and roots. The models were based on data from 180 sample trees with ages up to 15 years originating from natural regeneration at eight sites in the Western Carpathians (Slovakia). Sample trees represented individuals with stem base diameters (diameter D0) from about 4.0 to 113.0 mm and tree heights between 0.4 to 10.7 m. Each tree component was dried to constant mass and weighed. Moreover, subsamples of leaves (15 pieces of each tree) were scanned, dried, and weighed. Thus, we also obtained data for deriving a model expressing total leaf area at the tree level. The allometric models were in the form of regression relations using diameter D0 or tree height as predictors. The models, for instance, showed that while the total tree biomass of birches with a D0 of 50 mm (and a tree height of 4.06 m) was about 1653 g, the total tree biomass of those with a D0 of 100 mm (tree height 6.79 m) reached as much as 8501 g. Modeled total leaf areas for the trees with the above-mentioned dimensions were 2.37 m2 and 8.54 m2, respectively. The results prove that diameter D0 was a better predictor than tree height for both models of tree component biomass and total leaf area. Furthermore, we found that the contribution of individual tree components to total biomass changed with tree size. Specifically, while shares of leaves and roots decreased, those of all other components, especially stems with bark, increased. The derived allometric relations may be implemented for the calculation of biomass stock in birch-dominant or birch-admixed stands in the Western Carpathians or in other European regions, especially where no species- and region-specific models are available.