Total Tree Biomass Research Articles

Community structure, species diversity and biomass of trees in Tranum Forest Reserve, Raub, Pahang

This study was conducted at the Tranum Forest Reserve, Raub, Pahang to determine the community structure, species diversity and biomass of trees in the area. Four study plots, each measuring 25 m × 20 m, were established at the study site, which covered a total area of 0.2 ha. All trees with diameter at breast height (DBH) of 5 cm and above were measured and recorded. A total of 175 trees was enumerated in the study plots, comprising 106 species and 79 genera from 40 families. The largest family was Lauraceae, represented by 11 species from six genera, followed by Myrtaceae with eight species from one genus. Tree species diversity of the study area was high, as reflected by a Shannon Diversity Index (H’) value of 4.49 (Hmax = 4.66). Margalef Richness Index (R’) and Shannon Evenness Index (E) showed values of 20.33 and 0.84, respectively. The most important family, based on the highest Family Important Value Index (FIVi), was Dipterocarpaceae, with a value 17.31%. Shorea platyclados was the most important species with Important Value Index (IVi) of 17.04%. The tree density of the study area was 875 indidivuals per hectare (ind/ha), with Lauraceae being the densest family at 85 ind/ha. Vitex gamosepala was the densest species at 40 ind/ha. The total basal area of trees was 66.23 m2/ha, with Dipterocarpaceae showed the highest basal area of 31.69 m2/ha and Shorea platyclados had the highest basal area of 31.69 m2/ha at species level. The total biomass of trees was estimated at 1,307.56 t/ha, of which the above ground biomass and the below ground biomass were 1,091.33 t/ha and 216.23 t/ha, respectively. Dipterocarpaceae contributed the highest biomass with an estimate of 743.83 t/ha and Shorea platyclados showed the highest biomass at species level with 743.83 t/ha.

Biomass, carbon stock and carbon dioxide sequestration by trees outside forests: A case study from Puducherry, India

Trees outside forests (ToF) play a vital role in reducing carbon from industrial activities and vehicles by sequestering and storing atmospheric Co2 generated as biomass. However, there is a scarcity of studies quantifying the biomass and carbon stock in the ToFs. To bridge this gap, we conducted a study on the potential of biomass and carbon dioxide sequestration in trees planted in Puducherry. Our findings show that the total above-ground biomass of adult trees in the city was 1926.03 Megagram (Mg), while belowground biomass was 244.47 Mg. The total carbon stored in adult trees was 966.53 Mg, while the volume of sequestered CO2 was 3547.17 Mg in the study area. To increase carbon dioxide sequestration in Puducherry town, we recommend increasing urban green cover and planting more fast-growing native species.

Impact of forest management on structure, composition, biomass and carbon stock in Chir-pine (P. roxburghii) forest, Western Himalaya

This study performs a comparative analysis of tree composition, biomass, and carbon stock across different management regimes namely, Van Panchayat forest (VP), Reserve forest (RF), and Civil & Soyam forest (C&SF) in the state of Uttarakhand, with a focus on P. roxburghii (Chir-pine) forests. Collectively, these management regimes cover approximately 60 % of the forest area in the state. In this assessment, a total five tree species (Pinus roxburghii, Quercus leucotrichophora, Myrica esculenta, Acacia catechu, and Pinus wallichiana) were identified across the management regimes. The Pinus roxburghii emerged as the dominant species across all management regimes, with relative density of 92.79, 94.39, and 92.31 % in Van Panchayat forest, Reserve forest, and Civil & Soyam forest respectively. However, co-dominant species are varying across the sites. The total tree biomass and carbon stock were notably higher in Van Panchayat forest (236.31 & 112.25 t ha−1) compared to Reserve forest (224.85 & 106.80 t ha−1) and Civil & Soyam forest (193.58 & 91.95 t ha−1). These findings suggest that Van Panchayat forests exhibit a superior status in terms of density, biomass, and carbon stock as compared to Reserve and Civil & Soyam forests. Pinus roxburghii emerges as a crucial species, underscoring its ecological significance in the region. These findings underscore the critical role of effective forest management practices in fostering sustainable biomass accumulation and carbon sequestration in the Himalayan Chir-pine forests. Thus, it helps to mitigate the impacts of climate change and biodiversity conservation, and sustainable management of forests.

Enhancing productivity and sustainability of ravine lands through horti-silviculture and soil moisture conservation: A pathway to land degradation neutrality

Ravine lands are the worst type of land degradation affecting soil quality and biodiversity. Crop production in such lands is impossible without adopting proper conservation measures. In-situ moisture conservation techniques could play an instrumental role in restoring ravine lands by improving soil moisture. We hypothesized that restoring ravine land through a combination of tree planting, fruit crop cultivation, and in-situ moisture conservation practice would result in significant improvements in productivity, profitability, and soil fertility. An experiment was conducted involving the combination of Malabar Neem (Melia dubia) and Dragon fruit (Hylocereus undatus) in conjunction with in-situ soil moisture conservation measures specifically involving half-moon structures (HM). The experiment was conducted under randomized block design (RBD) comprising eight treatments. These treatments include sole Melia cultivation (MD 3m × 3m), sole cultivation of dragon fruit (DF 3m × 3m), silviculture system (MDF-3m × 3m), horti-silviculture system with larger spacing (MDF-4m × 4m), sole Melia cultivation with in-situ moisture conservation (MDH-3m × 3m), sole Dragon fruit cultivation with in-situ moisture conservation (DFH-3m × 3m), horti-silviculture system of Melia and Dragon fruit with in-situ moisture conservation (MDFH-3m × 3m), and horti-silviculture system with larger spacing and in-situ moisture conservation (MDFH-4m × 4m). Each treatment was replicated thrice to evaluate their impact on productivity, profitability, soil fertility, and carbon sequestration for 8 years (2016–2023). The results revealed that the horti-silviculture system (MDFH-3 × 3 m) exhibited the highest total tree biomass and total carbon sequestration with an increase of 183.2% and 82.8% respectively, compared to sole Melia cultivation without HM and sole Melia with HM. Furthermore, sole Melia with HM augmented soil nutrients (N, P, K, and SOC) by 74.4%, 66.4%, 35.2%, and 78.3%, respectively, compared to control (no planting), with performance at par with MDFH-3 × 3 m. Similarly, sole Melia with HM enhanced SOC stock and SOC sequestration rate by 79.2% and 248% over control. However, it was found at par with MDFH-3 × 3 m. The horti-silviculture system (MDFH-3 × 3 m) consistently produced the highest fruit yield throughout the years surpassing other treatments. This treatment increased the average dragon fruit yield by 115.3% compared to sole dragon fruit without HM. Hence, the adoption of the horti-silviculture system (MDFH-3 × 3 m) could be a promising strategy for achieving enhanced environmental and economic benefits in ravine lands. Therefore, dragon fruit based horti-silviculture system (MDFH-3 × 3 m) could be recommended for restoration of ravine lands, improving land productivity, and mitigating impact of soil erosion particularly in Western India or similar agro-climatic regions of the world.

Influence of climate, soil and vegetation diversity on the leaf area index as measured by canopy hemispherical photography in tropical rainforests across a wide elevational gradient

Tropical rainforests (TRFs) play an important role in regulating the global climate and conserving biodiversity. Leaf area index (LAI) determines the capacity of TRFs for radiation interception and carbon sequestration via photosynthesis while also influencing the water loss from TRF canopies via transpiration. Elevational gradients in tropical environments provide an approach to investigate the influence of environmental variations on key vegetation properties such as the LAI. In this work we determined the concurrent, interactive influences of the elevational gradients of the environment (i.e. selected climatic and soil properties) and vegetation (tree diversity and structure) in determining the variation of LAI of one-hectare permanent sampling plots (PSPs) within TRFs across a wide elevational gradient (from 117 m to 2132 m above sea level). Eight rounds of canopy hemispherical photography were carried out at three-month intervals over a 24-month period from 2019 to 2022. Canopy LAI was estimated using HemiView image processing software. Long-term mean climatic data from 1990 to 2021 were extracted for the PSPs from monthly global weather database of CRU-TS-4.06-bias corrected with WorldClim-2.1. Key soil properties were measured at 0–15 cm and 15–30 cm depths via soil sampling. Tree diversity was determined by a complete census of all trees with equal or greater than 10 cm in diameter at breast height. Total tree biomass was estimated using allometric equations. Linear mixed model analysis of LAI showed significant (p < 0.05) variance for the random effects of elevation and elevation × time interaction. In a majority of measurement rounds, variance of LAI due to elevation was significant while at a majority of elevations, the fixed effect of measurement round on LAI was significant. Leaf area index showed significant linear reductions with elevation in all rounds, at rates ranging from 0.175 to 0.357 per 1000 m. Linear and multiple regression analyses identified increasing maximum cumulative soil water deficit, CSWDMax (an index of water deficit computed as the difference between evapotranspiration and precipitation), as the major climatic factor responsible for the reduction of LAI with elevation. Decreasing solar irradiance (SR) and increasing day-night temperature difference (DTR) with elevation also contributed to the observed LAI trend. Multiple regression analysis showed soil exchangeable potassium (KEx) and available phosphorus (PAv) influencing LAI positively while electrical conductivity (EC) and total nitrogen (NTot) influencing LAI negatively. Variation of Shannon-Wiener tree diversity index (H) and total tree biomass per hectare (TBM) contributed positively to the observed elevational variation of LAI. Factor analysis extracted two underlying factor constructs which accounted for 86 % of the combined variation in climate, soil and vegetation across the elevational gradient. Plot-wise scores of Factor 1, which had CSWDMax, SR, DTR, soil EC, NTot and H loading strongly and accounting for 70 % of the variation, showed a significant linear relationship with LAI. A cluster analysis incorporating the elevational variation of LAI, climate, soil and vegetation clearly classified the TRFs into four ranges of elevation at a linkage distance of 0.75. We conclude that LAI of TRFs in our study decreases with increasing elevation and identify increasing water deficits with elevation as the major climatic factor controlling this reduction.

Comparative Study of Single-Wood Biomass Model at Plot Level Based on Multi-Source LiDAR

Forests play an important role in promoting carbon cycling and mitigating the urban heat island effect as one of the world’s major carbon storages. Scientifically quantifying tree biomass is the basis for assessing tree carbon storage and other ecosystem functions. In this study, a sample plot of Populus tomentosa plantation in the Olympic Forest Park in Beijing was selected as the research object. Point cloud data from three types of laser scanners, including terrestrial laser scanner (TLS), backpack laser scanner (BLS), and handheld laser scanner (HLS), were used to estimate the biomass of single tree trunks, branches, leaves, and aboveground total biomass based on the Allometric Biomass Model (ABM) and Advanced Quantitative Structure Model (AdQSM). The following conclusions were drawn from the estimation results: (1) For the three types of laser scanner point clouds, the biomass estimation values obtained using the AdQSM model were generally higher than those obtained using the Allometric Biomass Model. However, the estimation values obtained using the two models were similar, especially for tree trunks and total biomass. (2) For total biomass and individual biomass components of single trees, the results obtained from handheld and terrestrial laser scanner point clouds are consistent; however, they show some differences from the results obtained from backpack-mounted point clouds. This study further enriches the methodological system for estimating forest biomass, providing a theoretical basis and reference for more accurate estimates of forest biomass and more sustainable forest management.

Modeling branch attributes and biomass for Catalpa bungei plantations under various fertilization regimes

The development and morphology of branches, a crucial step in producing high-quality large-diameter lumber, may be influenced by fertilization. The response of branch attributes to different fertilization regimes, however, is still poorly understood. The Catalpa bungei plantations, which had been growing for 6 years in northern China, were chosen to study how various fertilization measures affected branch attributes. The two fertilization techniques used were hole fertilization (HF) and water and fertilizer integration (WF), with no fertilization (CK) as a control. The quantity, density, morphology (e.g., diameter, length, and angle), and position (e.g., height and orientation) of branches, and organ biomass of 18 standard trees (total of 516 branches) were investigated. The results demonstrated a considerable increase in tree height, diameter at breast height (DBH), canopy ratio, branch quantity, and organ biomass following the addition of fertilizer. Both the maximum branch diameter and the number of branches rose with fertilization. Following fertilization, the number of branches rose by 16% (HF) and 28% (WF) compared to non-fertilized trees, while the maximum branch diameter increased by 3.5% (HF) and 17.3% (WF), respectively. WF led to an increase in the number of branches and largest branch diameter in comparison to CK and HF. The length, angle, and diameter of branches, however, were not affected significantly by different fertilization treatments. There were roughly equal amounts of branches in four orientations. The mixed-model analysis revealed that the number of branches was positively correlated with branch density and tree height. The branch diameter increased with the increase of branch length and angle. The branch length was negatively correlated with branch height and angle. The branch angle showed a larger angle at the bottom of the canopy. Tree height plus diameter at breast height combined, or just the diameter at breast height indicator alone, can both reliably predict the total biomass of trees. The branch models created in this research may offer some theoretical backing for understanding the crown dynamics of valuable tree species in northern China.

Floristic composition and species diversity at Gunung Inas forest reserve, Kedah, Peninsular Malaysia

Monoculture plantations such as Musang King durian farms on a big scale become a controversial issue due to its potential devastating impact to the diversity of forest reserves at some areas in Kedah including Gunung Inas Forest Reserve. This study intended to determine the floristic composition and species diversity at Gunung Inas Forest Reserve (GIFR), Kedah. Three plots of 70 m x 25 m covering 0.53 ha were established at GIFR. Each tree with diameter at breast height of 5 cm and above in the study plots were tagged, measured and identified up to species level. Voucher specimens were then taken for further identification purposes. The floristic composition of GIFR consists of 53 individuals represented by 16 species from 12 families. Anacardiaceae was the most speciose family with four species recorded. The most dominant species was Swintonia schwenkii (Anacardiaceae) with 14 tree individuals. S. schwenkii (Anacardiaceae) was considered to have an absolute dominance at the study site with Important Value Index (IVi) of 12.48%, meanwhile Myrtaceae recorded 20.87% of IVi, reflecting this family to be relatively dominant than other families. GIFR recorded moderate Shannon-Weiner Diversity index of H’ = 2.66 (H’max = 2.77) and Margalef’s Richness Index of DMG = 8.7. High Evenness Index of 0.71 portrayed that the tree species are almost equally abundant at the study area. The total tree biomass in the GIFR was estimated at 76.26 t/ha, contributed by 66.04 t/ha of above ground biomass and 10.22 t/ha below ground biomass. Data and information from this study can serve as a guideline for future ecological research and particularly in planning conservation efforts to ensure the biodiversity and sustainability of forests for future generations.

Carbon sequestration and credit potential of gamhar (Gmelina arborea Roxb.) based agroforestry system for zero carbon emission of India

The agroforestry system is the best option to achieve the net zero carbon emissions target for India. Keeping this view, carbon sequestration and credit potential of gamhar based agroforestry system has been assessed. The experiment was carried out in randomized block design in seven different treatments with five replications. Gamhar tree biomass accumulation was higher in gamhar based agroforestry system compared to sole gamhar. Among different tree components, stem contributed a maximum to total gamhar tree biomass followed by roots, leaves and branches. The average contributions of stems, roots, leaves and branches in total tree biomass in two annual cycles (2016–17 and 2017–18) varied between 50 and 60, 19.8 and 20, 19.2 and 20, and 10.7 and 12.7 percent, respectively. In case of crops, above ground, below ground and total biomass was significantly higher in sole intercrops than gamhar based agroforestry system. Total (Tree + interrops + Soil) carbon stock, carbon sequestration, carbon credit and carbon price were significantly affected by treatments, and was maximum in Sole Greengram-Mustard. Net carbon emission was also recorded lowest in Sole Greengram-Mustard for which the values were 811.55% and 725.24% and 760.69% lower than Sole Gamhar in 2016–17, 2017–18 and in pooled data, respectively.

Tree Biomass Modeling Based on the Exploration of Regression and Artificial Neural Networks Approaches

Understanding the dynamics of tree biomass is a significant factor in forest ecosystems, and accurate quantitative knowledge of its development provides support for the optimization of forest management. This work aimed to employ innovative practices in tree biomass modeling, artificial neural network approaches along with the least-squares regression methodology, in order to construct reliable and accurate estimation and prediction models that contribute to solving the emerging problems in the field of sustainable forest management. Based on this aim, different modeling strategies were developed and explored. The nonlinear seemingly unrelated regression (NSUR) methodology, the generalized regression (GRNN), the resilient propagation (RPNN) and the Bayesian regularization (BRNN) artificial neural network algorithms were utilized for the construction of reliable biomass models to attain the most accurate and reliable tree biomass components and total tree biomass estimations. The work showed that GRNN models provided a significantly better performance compared with the other modeling methodologies tested. Considering the non-parametric nature of the GRNN neural network algorithm, the fact that it was designed for nonlinear regression-type problems capable of dealing with small datasets, this modeling approach warrants consideration as an effective alternative to nonlinear regression or to other neural network approaches to the field of tree biomass modeling.

Carbon Stock Estimation From Vegetation Biomass Using Spot-7 Imagery

Vegetation absorbs carbon dioxide (CO2 ) emissions during photosynthesis. Covering more areas with trees will increase the CO2 absorption capacity more substantially than other vegetation like bushes, grasses, or rice fields. Trees convert the CO2 captured during photosynthesis into organic carbon to be stored in biomass. Woody trees account for approximately 60% of the total aboveground tree biomass, and trunks, where food reserves produced in photosynthesis are stored, have relatively large biomass compared to other parts of the tree. The biomass of a vegetation stand determines the optimization of air pollutant absorption in urban areas. Yogyakarta City is the center for tourism, education, and cultural activities in Indonesia, which is vulnerable to land-use conversion, a factor of the shrinking green space. This study aimed to estimate carbon stock from vegetation biomass in Yogyakarta City using the remote sensing product SPOT-7 imagery. To calculate the vegetation biomass, the diameter at breast height (DBH) of stands was measured in the field. Then, statistical analyses were performed to determine the correlation and regression between the actual or observed biomass and the Normalized Difference Vegetation Index (NDVI) value derived from the SPOT-7 image. The regression model used was y = 1.4277x – 0.0849. The total biomass produced in Yogyakarta City was estimated at 1,399,487.1 tonnes, which contained 643,764.1 tonnes of carbon stock.

Biomass estimation models for Acacia saligna trees in restored landscapes

Acacia saligna, originating from Australia, is a naturalized multipurpose tree species widely grown to restore degraded lands of Africa. The contribution of A. saligna in biomass restoration can be quantified using a precise estimation of tree biomass carbon. This study developed species-specific allometric models and evaluated the spatial variation of tree biomass across restored areas in exclosures and open grazing landscapes. These models could play a considerable role in the monitoring of carbon dynamics across A. saligna planation dominated areas. We harvested, excavated, and weighed twenty-one sample trees representing different size classes to develop allometric models for the estimation of aboveground (AGB), belowground (BGB) and total tree (TB) biomass. The average dry-to-fresh mass ratio and the root-to-shoot ratio was 0.47 (±0.13) and 0.28 (±0.14), respectively. Tree biomass significantly correlated with diameter at breast height (r = 0.93; P < 0.001), diameter at stump height (r = 0.88, P < 0.001) and tree height (r = 0.56, P < 0.05). Our best biomass estimation models explained 86%, 82% and 87% of variations in AGB, BGB, and TB, respectively. Models using DSH and DSH & H explained 70%–78% of the variation in AGB, BGB, and TB. Estimated C-stock showed a significant relationship with stem density (R 2 = 0.91, P < 0.01). Estimated TB varied between 1.5–18 Mg ha−1 on grazed land and exclosures. Estimated C-stocks in the exclosure exceeded the estimated C-stock in the open grazing land by ∼60%. This implies that with proper management practices and enrichment planting A. saligna significantly contributes to increasing carbon accumulation on degraded landscapes, playing a key role in climate change mitigation efforts while improving land productivity.

Modelling tree biomass using direct and additive methods with point cloud deep learning in a temperate mixed forest

Airborne laser scanning (ALS) data has been widely used for total aboveground tree biomass (AGB) modelling, however, there is less research focusing on estimating specific tree biomass components (wood, branches, bark, and foliage). Knowledge about these biomass components is essential for carbon accounting, understanding forest nutrient cycling, and other applications. In this study, we compare additive AGB estimation (sum of estimated components) with direct AGB estimation using deep neural network (DNN) and random forest (RF) models. We utilise two point cloud DNNs: point-based Dynamic Graph Convolutional Neural Network (DGCNN) and Octree-based Convolutional Neural Network (OCNN). DNN and RF models were trained using a dataset comprised of 2336 sample plots from a mixed temperate forest in New Brunswick, Canada. Results indicate that additive AGB models perform similarly to direct models in terms of coefficient of determination (R2) and root-mean square error (RMSE), and reduced the mean absolute percentage error (MAPE) by 22% on average. Compared to RF, the DNNs provided a small improvement in performance, with OCNN explaining 5% more variation in the data (R2 = 0.76) and reducing MAPE by 20% on average. Overall, this study showcases the effectiveness of additive tree AGB models and highlights the potential of DNNs for enhanced AGB estimation. To further improve DNN performance, we recommend using larger training datasets, implementing hyperparameter optimization, and incorporating additional data such as multispectral imagery.

New aboveground biomass equations by components for small black spruce in peatland ecosystems of Western Canada

Black spruce is the most common tree species in Canada, dominating treed peatlands where they are usually stunted. We used 495 destructively sampled trees from 56 plots to develop allometric models of aboveground biomass by components (stem, branches, and needles) for small (&lt;5 m tall) black spruce from peatlands in the Taiga Plains and Boreal Plains Ecozones of Western Canada, for which there were no specific models available of biomass by components. We used leave-one-plot-out cross-validation to assess transferability and compare our models with existing national and ecozone-specific equations. Our models predicted total tree biomass with better accuracy and less biased estimates than the national model (relative RMSE: 30% versus 35% national; relative bias: +1% versus –12% national). Similar results were obtained in other external datasets. Existing ecozone equations performed worse than either our models or the national ones. When we applied the models at the plot level to predict aboveground biomass density (Mg·ha−1), our models outperformed the national model again (relative RMSE: 15.9% versus 18.6% national, relative bias: +3.5% versus –13.6% national). These results indicate that at least for peatlands of Western Canada, our models provide better aboveground biomass estimates for small black spruce trees than existing models.

The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe.

Trees are an integral part in European landscapes, but only forest resources are systematically assessed by national inventories. The contribution of urban and agricultural trees to national-level carbon stocks remains largely unknown. Here we produced canopy cover, height and above-ground biomass maps from 3-meter resolution nanosatellite imagery across Europe. Our biomass estimates have a systematic bias of 7.6% (overestimation; R = 0.98) compared to national inventories of 30 countries, and our dataset is sufficiently highly resolved spatially to support the inclusion of tree biomass outside forests, which we quantify to 0.8 petagrams. Although this represents only 2% of the total tree biomass, large variations between countries are found (10% for UK) and trees in urban areas contribute substantially to national carbon stocks (8% for the Netherlands). The agreement with national inventory data, the scalability, and spatial details across landscapes, including trees outside forests, make our approach attractive for operational implementation to support national carbon stock inventory schemes.

Two-decade changes in habitat and abundance of Japanese macaques in primary and logged forests in Yakushima: Interim report

Continuous, rather than discrete, data on animal population are ideal for identifying the causal relationships driving population fluctuations. In this study, we reveal the effect of forest regeneration after logging on the habitat, food availability, and abundance of Japanese macaques over two decades, spanning the period from 2000 until 2019, based on a continuous population census conducted every year. Three vegetation types, covering an area of 7.5 km2, make up our study sites: primary forests, forests logged from 1984 until 1995 and then naturally regenerated (natural regeneration), and forests logged from 1975 until 1983 and then planted with cedars (plantation). In natural regeneration, both individual and total tree biomass increased, in particular naturally grown cedars. In plantation, the number of trees decreased and the total biomass did not change, mainly due to thinning (partial removal of planted cedars to harvest timber) in 2015. A state space model suggests that the relative fruit production in natural regeneration to that in the primary forest tended to be low after 2012, a condition brought about by cleaning cutting to remove non-cedar broad-leaved trees and shrubs in natural regeneration. An analysis of aerial photos detected an increase in naturally grown cedar patches in natural regeneration between 2009 and 2014. We also observed a population decrease of Japanese macaques in natural regeneration but not in primary and plantation forests. The state space model indicates that this decrease occurred in the first half of the study period, which was earlier than the replacement of cedar patches in natural regeneration. The high time resolution of our data refutes the simple scenario that macaques decreased their number in natural regeneration due to the reduced food availability there. The less frequent use of natural regeneration began as a stochastic event; i.e., the disappearance of multiple individuals from a group ranging there, and then the subsequent changes in the habitat reinforced this tendency.

Carbon storage capacity of Castanopsis hystrix plantations at different stand–ages in South China

The establishment of forest plantations is an excellent silvicultural alternative to improve atmospheric carbon (C) sequestration for climate change mitigation. In recent years, the importance of forest growth and C dynamics at different stand ages have drawn huge attention for investigation. To evaluate the impacts of different stand ages on the C storage capacity of Castanopsis hystrix plantations ecosystems, we measured biomass and C content in trees, understory, litter, and soil components from field measurements at different stand ages, i.e., 6–, 10–, 15–, 25–, and 34–year–old stands. Results showed that the C stocks in trees, understory vegetation, litter, soil and the whole ecosystem increased steadily with stand age. C. hystrix C storage capacity in the 6–, 10–, 15–, 25–, and 34–year–old stands were 29.17, 56.18, 85.60, 132.19 and 157.79 Mg ha−1, respectively, while the total ecosystem C storage capacity was 68.11, 106.11, 136.13, 187.34 and 226.14 Mg ha−1, respectively. Meanwhile, the C sequestration rate in the 6–10, 10–15, 15–25 and 25–35–year–old stands were 675.28, 588.35, 465.97 and 284.39 g m2 year−1, respectively. Similarly, total biomass of trees, understory, and litter increased with stand age. Our study indicated that C. hystrix plantations are still developing in the area, since live biomass and soil C continue to accumulate despite reduced C sequestration rates. The information provided here highlight the C stock and C sequestration rates of C. hystrix at different stand ages, providing baseline information on the C dynamics of young and older stand of C. hystrix in this region; Hence, such knowledge could be useful for designing more realistic policies to mitigate climate change, such as programs to maintain continuous forest growth.

Stand-Level Biomass and Leaf Trait Models for Young Naturally Regenerated Forests of European Hornbeam

European hornbeam (Carpinus betulus L.) is a tree species widely distributed in Europe and the Asian part of the Near East. However, since European hornbeam is not very attractive for commercial purposes, scientific interest in this species has been rather sparse. Our study focused on dense young (up to 10 years old) European hornbeam stands originating from natural regeneration from seeds in Slovakia because in future the importance of this species may increase due to the climate change. We combined previously constructed tree-level biomass models, data on basic leaf traits, i.e., weight and area, and measurements from thirty plots located at ten different sites across Slovakia to construct stand-level allometric relations of the biomass stock in tree components, i.e., leaves, branches, bark, stem under bark and roots, to mean stand diameter at stem base, i.e., at the ground level. Moreover, we calculated and modelled leaf characteristics, namely the specific leaf area (SLA), leaf area ratio (LAR) and leaf area index (LAI), at a stand level. The total tree biomass stock including all tree components ranged between 0.75 and 13.63 kg per m2, out of which the biomass of stem with bark was from 0.31 to 8.46 kg per m2. The biomass models showed that the contribution of roots (omitting those with a diameter under 2 mm) decreased with the increasing mean stand diameter at stem base, whereas the opposite pattern was observed for branches and stem biomass. Further, we found that the mean stand diameter at stem base was a good predictor of both LAR and LAI. The results indicated the high photosynthetic efficiency of European hornbeam leaves per one-sided surface leaf area. Moreover, the growth efficiency (GE), expressed as the biomass increment of woody parts per leaf area unit, of young European hornbeam trees was high. The models proved a close positive linear correlation between LAI and stand biomass stock that may be used for estimating the biomass in young stands from LAI that can be measured using non-destructive terrestrial or aerial methods. The results further indicated that young stands may sequester a non-negligible quantity of carbon; therefore, they should not be omitted from local or country-wide estimates of carbon stocks in forest vegetation.

Estimation of the biomass and carbon in Cupressus lusitanica Mill. trees in Costa Rica

[Introduction]: Forest plantations are an important carbon sinks and reservoirs while providing other important environmental goods and services. [Objective]: In this research, we developed models to estimate the biomass and carbon content of Cupressus lusitanica Mill trees and its components or fractions, in forest plantations in Costa Rica. [Methodology]: Through the destructive sampling of 43 trees, a sample of each component was obtained to determine dry matter and carbon content. The models were built through linear regression analysis and ordinary least squares, using the normal diameter as the independent variable. Models were selected through the weighted sum of the calculated statistics and the graphical analysis of the residuals. [Results]: The coefficient of determination (R2) was greater than 83.8 % and the estimation error or bias was less than 7.2 %. The leaf and root fractions were more difficult to model, given their lower fit and higher error. The stem represents 61.7 % of total tree biomass, the branches 17.1 % and the roots 9.1 %. The aerial biomass expansion factor was 1.54 (1.3 and 1.24 for branches and foliage) and 1.12 for roots. [Conclusions]: Allometric models accurately predict biomass and carbon content, are easy to use, and become useful low-cost tools to quantify the ecologic and greenhouse gas emission mitigation functions of these forests.

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.