Search Results (169)

Spatial predictions of forest biomass at regional scale in forests are critical to evaluate the effects of management practices across environmental gradients. Although multi-source remote sensing combined with machine learning has been widely applied to estimate forest biomass, these approaches often rely on complex data acquisition and processing workflows that limit their scalability for large-area assessments. To improve the efficiency, this study evaluates the potential of annual multi-sensor satellite embeddings derived from the AlphaEarth Foundations model for forest biomass prediction. Using field inventory data from 89 forest plots at the Yunhe Forestry Station in Zhejiang Province, China, we assessed and compared the performance of four machine learning algorithms: Random Forest (RF), Support Vector Regression (SVR), Multi-Layer Perceptron Neural Networks (MLPNN), and Gaussian Process Regression (GPR). Model evaluation was conducted using repeated 5-fold cross-validation. The results show that SVR achieved the highest predictive accuracy in broad-leaved and mixed forests, whereas RF performed best in coniferous forests. When all forest types were modeled together, predictive performance was consistently limited across algorithms, indicating substantial heterogeneity (e.g., structure, environment, and topography) among forest types. Spatial prediction maps across Yunhe Forestry Station revealed ecologically coherent patterns, with higher biomass values concentrated in intact forests with less human disturbance and lower biomass primarily occurring in fragmented forests and near urban regions. Overall, this study highlights the potential of embedding-based remote sensing for regional forest biomass estimation and suggests its utility for large-scale forest monitoring and management. Full article

Understanding the influence of tree species and their intrinsic traits on biochar yield and carbon retention is essential for optimizing the conversion of biomass to biochar in carbon-negative systems. While it is well-established that pyrolysis temperature and broad feedstock categories significantly affect biochar properties, the extent of species-level variation within woody biomass under standardized pyrolysis conditions remains insufficiently quantified. Here, we synthesized biochar from seven common subtropical tree species at 600 °C under oxygen-limited smoldering conditions and quantified three key indices: biochar yield (Y), carbon recovery efficiency (ηC), and carbon enrichment factor (EC). We further examined the relationships of these indices with feedstock characteristics (initial carbon content, wood density) and functional group identity (conifer vs. broadleaf). Analysis of variance revealed significant interspecific differences in ηC but weaker effects on Y, indicating that species identity primarily governs carbon retention rather than total mass yield. Broadleaf species (Liquidambar formosana, Castanea mollissima) exhibited consistently higher ηC and EC than conifers (Pinus massoniana, P. elliottii), reflecting higher lignin content and wood density that favor aromatic char formation. Principal component and cluster analyses clearly separated coniferous and broadleaf taxa, accounting for over 80% of total variance in carbon-related traits. Regression models showed that feedstock carbon content, biochar carbon content, and wood density together explained 15.5% of the variance in ηC, with feedstock carbon content exerting a significant negative effect, whereas wood density correlated positively with carbon retention. These findings demonstrate that tree species and their functional traits jointly determine carbon fixation efficiency during smoldering. High initial carbon content alone does not guarantee enhanced carbon recovery; instead, wood density and lignin-derived structural stability dominate retention outcomes. Our results underscore the need for trait-based feedstock selection to improve biochar quality and carbon sequestration potential, and provide a mechanistic framework linking species identity, functional traits, and carbon stabilization in biochar production. Full article

Understory vegetation (shrubs and herbs) mediates belowground biogeochemical processes in forests through litter inputs, root exudation, and microenvironmental regulation; however, the magnitude of these regulatory effects remains poorly quantified. Here, we conducted a 10-year small-scale understory vegetation manipulation experiment in a coniferous–broadleaf mixed forest in central China, aiming to systematically assess the impacts of understory vegetation on soil carbon (C), nitrogen (N), and phosphorus (P) dynamics. Two experimental treatments were established: (1) the “None” treatment (removal of both understory vegetation and litter) and (2) the “Understory” treatment (litter removal while retaining understory vegetation). Results indicated that compared with the “None” treatment, the “Understory” treatment did not significantly alter the concentrations or stocks of soil organic C (SOC) and total N (p > 0.05), suggesting a weak responsiveness of SOC and total N to understory vegetation presence. In contrast, understory vegetation exerted a significant positive effect on soil P fractions: total P concentration and stock increased by 3.97% and 2.68%, organic P by 6.65% and 5.32%, and available P by 46.38% and 43.96%, respectively (p < 0.05). These results demonstrate that understory vegetation exerts a more pronounced regulatory effect on soil P dynamics than on C and N dynamics. In conclusion, understory vegetation plays a pivotal role in promoting soil P sequestration and improving P availability in coniferous–broadleaf mixed forest ecosystems. We recommend retaining understory vegetation in forest management practices to sustain soil P availability and mitigate widespread P limitation in such ecosystems. Full article

Understory vegetation is a critical component of forest ecosystems. Its removal can substantially alter litter decomposition processes, with cascading effects on carbon (C) and nutrient cycling in terrestrial ecosystems. However, the global response patterns of litter decomposition to understory removal and underlying controlling factors remain unclear. We conducted a meta-analysis of 330 observations from 29 peer-reviewed field litterbag studies to assess the effects of understory removal on litter decomposition. We evaluated the changes in decomposition rate, mass loss, and nutrient dynamics to quantify the impacts of understory removal on litter decomposition. We assessed the associated shifts in soil microbial communities, measured using phospholipid fatty acids (PLFAs), to examine how microbial responses mediate decomposition during understory removal. We examined whether canopy type moderated these responses and explored the key predictors of decomposition for understory removal. Understory removal significantly reduced litter decomposition rate and mass loss by an average of 29.6% and 14.8%, respectively, while increasing lignin remaining by 30.1%. Soil microbial biomass also declined, with total, fungal, and actinomycete PLFAs decreasing by 12.0%, 30.8%, and 27.5%, respectively. Across canopy types, understory removal decreased litter mass loss in both broadleaved and coniferous forests. However, the remaining N and P increased significantly in broadleaved forests but changed only marginally in coniferous forests. Random forest analysis showed that initial litter quality and variations in fungal biomass were the primary predictors of decomposition responses. Understory vegetation removal significantly suppresses litter decomposition by reducing fungal biomass, and interacting with litter quality constraints and canopy type strongly moderates these effects. This highlights the essential role of understory vegetation in sustaining nutrient cycling and microbial functioning in forest ecosystems and underscores its critical role in guiding sustainable forest management. Full article

Accurate estimation of individual tree aboveground biomass (AGB) is essential for understanding forest carbon dynamics, optimizing resource management, and addressing climate change. Conventional methods rely on destructive sampling, whereas unmanned aerial vehicle (UAV) remote sensing provides a non-destructive alternative. In this study, spectral indices, textural features, and canopy height attributes were extracted from high-resolution UAV optical imagery and Light Detection And Ranging (LiDAR) point clouds. We developed an improved YOLOv8 model (NB-YOLOv8), incorporating Neural Architecture Manipulation (NAM) attention and a Bidirectional Feature Pyramid Network (BiFPN), for individual tree detection. Combined with a random forest algorithm, this hybrid framework enabled accurate biomass estimation of Chinese fir, Chinese pine, and larch plantations. NB-YOLOv8 achieved superior detection performance, with 92.3% precision and 90.6% recall, outperforming the original YOLOv8 by 4.8% and 4.2%, and the watershed algorithm by 12.4% and 11.7%, respectively. The integrated model produced reliable tree-level AGB predictions (R² = 0.65–0.76). SHapley Additive exPlanation (SHAP) analysis further revealed that local feature contributions often diverged from global rankings, underscoring the importance of interpretable modeling. These results demonstrate the effectiveness of combining deep learning and machine learning for tree-level AGB estimation, and highlight the potential of multi-source UAV remote sensing to support large-scale, fine-resolution forest carbon monitoring and management. Full article

The challenges faced by the metallurgical industry implicate that actions aimed at reducing negative impacts on the environment are becoming extremely important. This is justified both in the search for economically competitive methods of producing basic construction materials, consistent with the circular economy policy, and in improving the efficiency of metal production technology. An essential aspect of biomass use is the introduction of an energy source that naturally reduces the energy supplied to the reactor, thereby reducing the carbon footprint of the metal produced. In this case, the research undertaken aims to determine the possibility of using a bioreductant that will allow for the reduction or elimination of the fossil raw material, which is coal, thus reducing the costs associated with ETS and ETS II (European Union Emissions Trading System). This paper presents the results of research on the reduction process of oxide metal-bearing raw material, the chemical composition of which is similar to slags from the copper industry. The effects of slag reduction time on the degrees of copper and lead removal were examined. The process was carried out at 1300 °C, with the constant addition of a reducing agent, in the form of crushed pine cones. After processing for 1 h, the copper content in the waste slag was 1.30 wt%, whereas extending the process to 5 h reduced the copper content to 0.15 wt%. For lead, at the exact reduction times, the element’s contents in the slag after processing were 1.92 wt% and 0.79 wt%, respectively. The results of the studied process showed that, in the first stage of the slag reduction process, intensive reduction of copper and lead oxides occurs. Research was also conducted to characterize the biomaterial during the high-temperature process. Results show high degrees of removal for basic metals at the following levels: 99% for Cu and 72% for Pb. The waste slag is characterized by low metal content, which allows for safe storage or use in other sectors of the economy. This type of biomaterial is, therefore, recommended for research in large-scale laboratories or on a semi-industrial scale, particularly in relation to the gas phase formed and its possible impacts on the structural elements of industrial installations. It should be noted that there is a lack of data in the literature on the use of forest biomass in the form of pine cones as an alternative to coke as a reducing agent for use in pyrometallurgical processes. Full article

The utilization of photography imagery captured using cameras mounted on unmanned aerial vehicles (UAVs) for aboveground biomass (AGB) inventory has seen rapid growth in recent years. Existing research has predominantly focused on utilizing spectral and textural features for biomass inversion. However, estimating the AGB of trees remains a great challenge using stereoscopic imagery without the help of a digital terrain model (DTM). This study introduces five DTM-independent crown metrics using a digital surface model (DSM) and a canopy height model (CHM) derived from UAV stereoscopic imagery. The accuracy of the five metrics was evaluated against field measurements. The results indicate that the relationship between the crown cross-sectional area (CCSA) and AGB is stronger than that between tree height (TH) and AGB, with R² = 0.62 and RMSE = 69.22 (kg/tree) for Larix gmelinii and R² = 0.93 and RMSE = 142.06 (kg/tree) for Pinus sylvestris. Moreover, these DTM-independent crown metrics could be used to estimate the AGB of forests in the Greater and Lesser Khingan Mountain, with R² = 0.77 and RMSE = 77.10 (kg/tree) for coniferous trees and R² = 0.78 and RMSE = 72.46 (kg/tree) for all other trees. The results of this study demonstrate that UAV stereoscopic imagery can capture forest canopy information, and DTM-independent crown metrics can be used for AGB inversion where information on terrain under forest is unavailable. Full article

Understory vegetation plays a pivotal role in enhancing forest biodiversity, and its restoration is crucial for sustainable forest development, energy flow, and nutrient cycling. However, the dynamics of the biomass, diversity, and species composition of understory vegetation in plantations in south China, along with their key drivers, remain poorly understood. This study investigated four mature plantation types (Pinus massoniana, Pinus caribaea, Cunninghamia lanceolata, and mixed Chinese fir–broadleaf forests) in south China through plot surveys, environmental factor measurements, and structural equation modeling (SEM) to explore the diversity, biomass allocation patterns, and driving mechanisms of understory vegetation. The results demonstrated the following. (1) The introduced Caribbean pine forests exhibited higher shrub biomass than native Masson pine forests, which was driven by their high canopy openness favoring light-demanding species (e.g., Melicope pteleifolia, IV = 33.93%), but their low mingling degree limited herb diversity. (2) Masson pine forests showed superior shrub diversity due to their random spatial distribution and higher soil total potassium (TK) content. (3) Mixed Chinese fir–broadleaf forests achieved 24.50–66.06% higher herb biomass compared to coniferous monocultures, supported by high mingling degree, random spatial configuration, and phosphorus-potassium-enriched soil, with concurrently improved herb diversity. SEM revealed that stand structure (DBH, density, mingling degree) directly drove shrub diversity by regulating light availability, while herb biomass was primarily governed by soil total phosphorus (TP) and pH. Canopy-induced light suppression negatively affected herb diversity. We recommend optimizing stand density and canopy structure through thinning and pruning to enhance light heterogeneity alongside supplementing slow-release P fertilizers in P-deficient stands. This study provides theoretical support for the multi-objective management of south China plantations, emphasizing the synergistic necessity of stand structure optimization and soil amendment. Full article

Forest ecosystems are vital for climate mitigation, yet predicting their carbon (C) sequestration remains challenging, especially in urban-proximal regions. This study investigates the C storage dynamics across five major forest types in the Chang-Zhu-Tan Green Heart, a critical ecological buffer zone in China’s Yangtze River Mid-Reach urban agglomeration. We integrated field measurements with structural equation and random forest modeling to analyze vertical C distribution and its drivers. The results revealed that over 90% of vegetation C was stored in the tree layer, with soil C highest in evergreen broad-leaved forests (41.26 Mg C/ha). Biological factors (i.e., tree volume and biomass) primarily drove vegetation C (52–73% of variance), while non-biological factors (soil properties and micronutrients) predominantly regulated soil C. We identified distinct age-related trajectories: J-shaped accumulation in broad-leaved forests versus S-shaped patterns in coniferous and mixed forests. These findings provide a mechanistic framework for forest-type-specific management strategies to enhance C sequestration in urban-agglomeration buffer zones. Full article

The health of ecosystems, their functionality and the fulfilment of ecosystem functions are all dependent on biodiversity and productivity. The ongoing transformation of forests is intensifying the need for conservation. At the same time, the herbaceous layer has not yet been studied enough by researchers. The aim of the study is to ascertain the impact of the composition and age of the stand of primary and secondary forests on the biomass and species diversity of the herbaceous layer in the most prevalent forest type of the Western Macroscline of the Southern Urals: moss spruce forests. The methodological basis was chosen to be genetic forest typology and generally accepted methods of studying forest vegetation. We studied primary dark coniferous forests, as well as secondary birch and aspen forests of different compositions and ages. Positive correlations with the age of the stand were found to be most pronounced for Oxalis acetosella L. and Lycopodium clavatum L., while negative correlations were found to be most pronounced for Deschampsia caespitosa (L.) P. Beauv., Brachypodium pinnatum (L.) Beauv., and Dactylis glomerata L. The positive correlations with the proportion of birch and aspen in the stand composition are most pronounced for Dactylis glomerata L., Geum rivale L., Aegopodium podagraria L., Aconitum septentrionale Koelle, and Prunella vulgaris L. The research results clearly demonstrate the length of time that changes in species composition and productivity of the herbaceous layer of mountain forests take place over. This must be considered when planning forest management and nature conservation in mountain forests in the Urals. On the one hand, our study is certainly regional, but on the other, similar forests, forest degradation, regenerative succession and the plant species studied are widespread in the boreal zone. Therefore, the research results will be of interest to many researchers whose work relates to forest resources, biodiversity conservation and forest succession. To expand the scope of the research, further studies are planned in other types of forest in the Ural Mountains. Full article

The manuscript explores the valorization of forest logging residues, collected during forest management operations between summer 2023 and spring 2025 in mixed deciduous and coniferous forests, as a raw material for producing valuable bioactive products. These products offer a sustainable alternative to synthetic pesticides and fertilizers. Seven batches of biomass, comprising understory trees and branches from deciduous (mainly aspen, birch, and grey alder) and coniferous (mainly Scots pine) species, were collected during different seasons, crushed, and extracted using an ethanol–water solution. The yield of hydrophilic extracts containing proanthocyanidins (PACs) ranged from 18 to 25% per dry biomass. The highest PACs concentration (42% of extract dry mass) was found in small branches with a high bark content. The extracts and PACs at concentrations of 6.25–12.50 mg mL⁻¹ showed fungicidal activity against several pathogenic fungi, including Botrytis cinerea Pers., Mycosphaerella sp. Johanson, Heterobasidion annosum (Fr.) Bref., and Heterobasidion parviporum Niemelä & Korhonen. Residual biomass after extraction, enriched with sea buckthorn berry pomace and a siliceous complex, was characterized and evaluated for its impact on the growth of Scots pine seedlings and selected agricultural crops. Results from forest and agricultural field trials in 2023–2025 confirmed a positive effect of the fertilizer on crop yield and quality at a low application rate (40 kg ha⁻¹ per crop). Fertilizer increased the yield of radish, dill, potatoes, and wheat by up to 44% (highest for potatoes and dill) compared to the reference, confirming its agronomic value. Full article

Ectomycorrhizal symbiosis is a cornerstone of ecosystem health, facilitating nutrient uptake, stress tolerance, and biodiversity maintenance in trees. Optimizing Pinus armandii–Tuber indicum mycorrhizal synthesis enhances the ecological stability of coniferous forests while supporting high-value truffle cultivation. This study conducted a pot experiment to compare the effects of three root nutrient regulations—Aolu 318S (containing N-P₂O₅-K₂O in a ratio of 15-9-11 (w/w%)), Aolu 328S (11-11-18), and Youguduo (19-19-19)—on the mycorrhizal synthesis of P. armandii–T. indicum. The results showed that root nutrient supplementation significantly improved the seedling crown, plant height, ground diameter, biomass dry weight, and mycorrhizal infection rate of both the control and mycorrhizal seedlings, with the slow-release fertilizers Aolu 318S and 328S outperforming the quick-release fertilizer Youguduo. The suitable substrate composition in this experiment was as follows: pH 6.53–6.86, organic matter content 43.25–43.49 g/kg, alkali-hydrolyzable nitrogen 89.25–90.3 mg/kg, available phosphorus 83.69–87.32 mg/kg, available potassium 361.5–364.65 mg/kg, exchangeable magnesium 1.17–1.57 mg/kg, and available iron 33.06–37.3 mg/kg. It is recommended to mix the Aolu 318S and 328S solid fertilizers evenly into the substrate, with a recommended dosage of 2 g per plant. These results shed light on the pivotal role of a precise N-P-K ratio regulation in fostering sustainable ectomycorrhizal symbiosis, offering a novel paradigm for integrating nutrient management with mycorrhizal biotechnology to enhance forest restoration efficiency in arid ecosystems. Full article

Little information is available about the ecosystem carbon (C) storage among coniferous and broadleaved plantations with similar stand ages in North China. The aim of the present research was to estimate the C storages of the components of plants, litter, and soil in two coniferous plantations (Pinus tabulaeformis and Larix principis-rupprechtii) and two broadleaved plantations (Betula platyphylla and Populus davidiana) on Yanshan Mountain, North China. Allometric equations of diameter at breast height (DBH) and height (H) were used to quantify the biomass of the tree organs. The C storage of trees, herbs, litter, and soil were estimated based on the measured C contents. The C storage varied from 24.0 to 51.9 Mg ha⁻¹, 0.3 to 0.7 Mg ha⁻¹, and 1.9 to 4.0 Mg ha⁻¹ in the tree, herbs, and litter layers, respectively. The ecosystem C storages were as follows: B. platyphylla (164.1 Mg ha⁻¹) > P. davidiana (150.4 Mg ha⁻¹) > L. principis-rupprechtii (122.3 Mg ha⁻¹) > P. tabulaeformis (106.7 Mg ha⁻¹), 65.7%–75.6% of which was stored in the soil layer. Broadleaf plantations stored higher C than coniferous plantations in this study. These results indicate that ecosystem C storage varied among various plantation types, and broadleaf plantations had considerable ecosystem C sequestration potential with even-aged plantation stands. Full article

Forest restoration sites have a critical role in the maintenance and improvement of forest ecosystem health and resilience, as well as increasing carbon storage capacity. However, previous studies on forest restoration sites have primarily focused on monitoring vegetation changes and investigating changes in carbon storage (e.g., aboveground biomass). Research on identifying the controlling drivers of aboveground biomass (AGB) between/among forest types according to stand age within restoration sites remains limited. Our study analyzed data from a total of 149 plots in forest restoration sites in South Korea, comprising 57 coniferous forest plots (38.3%) and 92 broadleaved forest plots (61.7%). This study employed a piecewise structural equation model to determine the main biotic (i.e., stand structural diversity, species diversity, functional diversity, and tree identity) and abiotic drivers (i.e., topographic, climate factors driver, stand age, and soil properties) influencing AGB in each forest type. The results revealed that stand structural diversity was the most critical driver of AGB across all forest types, highlighting the importance of structural complexity in early stage restoration. Specifically, in coniferous forests, stand structural diversity (DBH STD) and tree identity (CWM WD) were more influential, whereas in broadleaved forests, SR and climatic conditions played a greater role. Therefore, our findings provide empirical evidence for understanding AGB dynamics in early stage forest restoration sites and may help inform the development of management strategies for each forest type and early restoration planning in similar ecosystems. Full article

As the largest terrestrial carbon pool, forest ecosystems play a pivotal role in climate change mitigation through greenhouse gas regulation. This study estimated the carbon sequestration potential of arbor forests at the county-level scale in Mentougou District, Beijing, based on subcompartment vector data from forest surveys and employed the Intergovernmental Panel on Climate Change (IPCC) carbon stock–biomass difference methodology. Additionally, using 2020 as the baseline year, the research projected carbon sink potential and carbon sequestration–oxygen release values for 2030 and 2060 by applying the carbon stock change methodology and the carbon sequestration–oxygen release value methodology. The results showed that there is a total carbon stock of 2.198 million tonnes (Mt) C in Mentougou, with an average storage density of 33.4 t C/ha. Natural broadleaf forests constituted the dominant carbon pool (79.2%), followed by planted coniferous stands (11.9%), collectively accounting for 91.1% of the regional arboreal carbon storage. In the future, the district’s arboreal carbon stock is projected to reach 3.17 Mt C in 2030 and 4.82 Mt C in 2060, with cumulative sequestration reaching 0.97 Mt C and 2.63 Mt C, respectively. It is evident that the carbon storage dynamics in Mentougou were governed by three principal determinants: (1) natural broadleaf forests dominate carbon storage (1.559 Mt C) in Mentougou, exceeding planted coniferous stands by 6.7-fold; (2) carbon storage decreases progressively with younger age classes, while carbon density increases steadily with stand maturity; (3) mid-elevation slopes (600–1200 m) concentrate 48% of regional stocks, with shaded slopes being optimal carbon sinks, and slope position gradients reveal topography-driven carbon accumulation patterns, confirming scale-dependent material transport effects. The value of carbon fixation and oxygen release of existing arbor forests in Mentougou District was CNY 6.12 billion, and this is predicted to reach CNY 8.84 billion by 2030, with a further anticipated increase to CNY 13.45 billion by 2060. Our analysis provides empirical evidence and quantitative support for forestry carbon sink initiatives at the regional scale and thus promotes the achievement of dual-carbon goals proposed by the Chinese government. Full article