Ecosystem Carbon Sequestration Research Articles

Climate-smart forestry: an AI-enabled sustainable forest management solution for climate change adaptation and mitigation

AbstractClimate change is the most severe ecological challenge faced by the world today. Forests, the dominant component of terrestrial ecosystems, play a critical role in mitigating climate change due to their powerful carbon sequestration capabilities. Meanwhile, climate change has also become a major factor affecting the sustainable management of forest ecosystems. Climate-Smart Forestry (CSF) is an emerging concept in sustainable forest management. By utilizing advanced technologies, such as information technology and artificial intelligence, CSF aims to develop innovative and proactive forest management methods and decision-making systems to address the challenges of climate change. CSF aims to enhance forest ecosystem resilience (i.e., maintain a condition where, even when the state of the ecosystem changes, the ecosystem functions do not deteriorate) through climate change adaptation, improve the mitigation capabilities of forest ecosystems to climate change, maintain high, stable, and sustainable forest productivity and ecosystem services, and ultimately achieve harmonious development between humans and nature. This concept paper: (1) discusses the emergence and development of CSF, which integrates Ecological Forestry, Carbon Forestry, and Smart Forestry, and proposes the concept of CSF; (2) analyzes the goals of CSF in improving forest ecosystem stability, enhancing forest ecosystem carbon sequestration capacity, and advocating the application and development of new technologies in CSF, including artificial intelligence, robotics, Light Detection and Ranging, and forest digital twin; (3) presents the latest practices of CSF based on prior research on forest structure and function using new generation information technologies at Qingyuan Forest, China. From these practices and reflections, we suggested the development direction of CSF, including the key research topics and technological advancement.

Particle size is an important factor influencing the effects of biochar return to woodland soils: An evaluation from the perspective of sapling growth and soil microbial carbon processes

Biochar can increase ecosystem carbon sequestration by promoting plant growth and stabilizing soil organic carbon (SOC). Biochar produced from forest waste typically varies in particle size and is frequently applied directly for soil enhancement without pulverization. The effects of different biochar particle sizes on sapling growth, woodland soil properties and microbial carbon processes are unclear. This study used field experiments to compare the effects of different biochar particle sizes on sapling growth and microbial metabolic entropy (qCO2). The impacting mechanisms were explored by examining soil physicochemical properties, enzyme activity, and microbial community structure. The application of forest waste (FW) and small particle biochar (SPBC, particle size<2 mm) did not significantly affect sapling growth. Conversely, middle particle biochar (MPBC, particle size 2–10 mm) and large particle biochar (LPBC, particle size>10 mm) reduced sapling biomass by 20.76% and 38.87%, respectively, compared to SPBC. MPBC and LPBC applications resulted in soil nutrient loss (total nitrogen and available phosphorus), inhibiting sapling growth. After 167 days, qCO2 rankings were as follows: FW (30.37 ± 5.18) (P<0.05)> LPBC (20.91 ± 3.62) > CK (16.21 ± 2.71) > MPBC (15.99 ± 3.54) > SPBC (7.8 ± 0.80) (P < 0.05). The rankings of organic carbon retention rates rankings were as follows: SPBC (85.14%) > LPBC (70.35%) > FW (67.31%) > CK (54.53%) > MPBC (51.96%). SPBC increased biochar-soil-microbe interactions, raised the relative proportion of k/r-strategy bacteria, reduced extracellular cellulase activity thus inhibit qCO2. In conclusion, small particle biochar (<2 mm), compared to larger-particle biochar, improves SOC sequestration without negatively affecting sapling growth. Therefore, particle size should be considered as a management indicator for biochar applications in artificial forest practices.

Afforestation Reduces Deep Soil Carbon Sequestration in Semiarid Regions: Lessons From Variations of Soil Water and Carbon Along Afforestation Stages in China's Loess Plateau

AbstractAfforestation represents an effective approach for ecosystem restoration and carbon (C) sequestration. Nonetheless, it poses notable challenges concerning water depletion and soil drought in (semi)arid regions. The underlying mechanisms regulating the influence of afforestation on soil carbon‐water dynamics, particularly how deep soil C reacts to afforestation‐induced soil drying, remain largely unclear. This study examined the variations of soil water content (SWC), soil organic carbon (SOC), and soil inorganic carbon (SIC) in 500 cm depth along four afforestation stages: abandoned grasslands, shrublands, and 20‐year and 40‐year Robinia pseudoacacia forests (RP20 and RP40) in the semiarid Loess Plateau, China. The results indicated that afforestation has significantly increased SWC (+26.6%), SOC (+44.5%), and SIC (+6.5%) in the shallow layer (0–100 cm) but caused evident soil drying (−60.8%), decrease in SOC (−37.8%), and slight reduction in SIC (−0.3%) in the deep layer (300–500 cm) when compared with grasslands. The seriously decline in the coupling coordination between soil C and SWC in the middle and deep layers indicates the unsustainability of afforestation especially for RP40. Structural equation model showed that the negative impact of afforestation on deep SOC through soil water depletion (−0.38) outweighed the direct positive impact of increased aboveground biomass (AGB) (+0.33). The negative impacts of decreased SWC and increased pH on deep SIC was close to the positive impacts of AGB. Afforestation has different effects on SOC and SIC across shallow and deep layers, and its negative effects on deep soil C should be fully integrated into future forest ecosystem restoration and management efforts.

Evaluating Ecosystem Services and Carbon Sequestration in Urban Wetlands: A Case Study of the Gapcheon Wetland in Daejeon, South Korea

Evaluating Ecosystem Services and Carbon Sequestration in Urban Wetlands: A Case Study of the Gapcheon Wetland in Daejeon, South Korea

Spatio-Temporal Disparity of Ecosystem Carbon Sequestration Capacity in the Region of Chengde, China

Spatio-Temporal Disparity of Ecosystem Carbon Sequestration Capacity in the Region of Chengde, China

Knowledge‐Based Deep Learning to Predict Vegetation Carbon, Nitrogen and Phosphorus Densities in China’s Shrublands

AbstractAccurate estimations of carbon (C), nitrogen (N), and phosphorus (P) densities in shrublands are pivotal for assessing terrestrial ecosystem carbon sequestration. Combining in‐situ investigations and machine learning facilitates large‐scale patterns mapping, however, which often overlooks underlying ecological regulations. Here we utilize data from 1,122 survey plots across China's shrublands and develop a novel knowledge‐based deep learning framework that integrates a structural equation model (SEM) to elucidate mechanisms and construct an artificial neural network (ANN) based on these causal relationships. Results show that biomass allocation to different organs follows allometric regulations and that N and P concentrations maintain a degree of stoichiometric homeostasis following biological stoichiometry theory. This insight guides the construction of our ANN, which outperforms both SEM and other prevalent machine learning methods. By leveraging ecological theories to inform model construction, our framework not only enhances prediction accuracy and explainability but also provides a methodological blueprint for ecological research.

Examining the Impact of Topography and Vegetation on Existing Forest Canopy Height Products from ICESat-2 ATLAS/GEDI Data

Forest canopy height (FCH) is an important variable for estimating forest biomass and ecosystem carbon sequestration. Spaceborne LiDAR data have been used to create wall-to-wall FCH maps, such as the forest tree height map of China (FCHChina), Global Forest Canopy Height 2020 (GFCH2020), and Global Forest Canopy Height 2019 (GFCH2019). However, these products lack comprehensive assessment. This study used airborne LiDAR data from various topographies (e.g., plain, hill, and mountain) to assess the impacts of different topographical and vegetation characteristics on spaceborne LiDAR-derived FCH products. The results show that GEDI–FCH demonstrates better accuracy in plain and hill regions, while ICESat-2 ATLAS–FCH shows superior accuracy in the mountainous region. The difficulty in accurately capturing photons from sparse tree canopies by ATLAS and the geolocation errors of GEDI has led to partial underestimations of FCH products in plain areas. Spaceborne LiDAR FCH retrievals are more accurate in hilly regions, with a root mean square error (RMSE) of 4.99 m for ATLAS and 3.85 m for GEDI. GEDI–FCH is significantly affected by slope in mountainous regions, with an RMSE of 13.26 m. For wall-to-wall FCH products, the availability of FCH data is limited in plain areas. Optimal accuracy is achieved in hilly regions by FCHChina, GFCH2020, and GFCH2019, with RMSEs of 5.52 m, 5.07 m, and 4.85 m, respectively. In mountainous regions, the accuracy of wall-to-wall FCH products is influenced by factors such as tree canopy coverage, forest cover types, and slope. However, some of these errors may stem from directly using current ATL08 and GEDI L2A FCH products for mountainous FCH estimation. Introducing accurate digital elevation model (DEM) data can improve FCH retrieval from spaceborne LiDAR to some extent. This research improves our understanding of the existing FCH products and provides valuable insights into methods for more effectively extracting accurate FCH from spaceborne LiDAR data. Further research should focus on developing suitable approaches to enhance the FCH retrieval accuracy from spaceborne LiDAR data and integrating multi-source data and modeling algorithms to produce accurate wall-to-wall FCH distribution in a large area.

Relationship between seagrass community structure and carbon stocks on the coasts of Karimunjawa Marine National Park, Indonesia

The seagrass ecosystem is considered one of the most effective coastal ecosystems in storing carbon. Carbon stock estimation for a certain ecosystem is highly affected by factors such as species diversity and habitat type. This study aims to investigate the relationship between plant community structure and carbon stocks in the seagrass ecosystem using a case study of six coastal sites in Karimunjawa Marine National Park, Indonesia. In this region, eight seagrass species were recorded, i.e., Enhalus acoroides, Thalassia hemprichii, Cymodocea rotundata, Halodule pinifolia, Halophila ovalis, Halophila minor, Syringodium isoetifolium, and Oceana serrulata. From the six study sites, the highest estimated carbon stock was 426.2 Mg C ha−1 (Site 5; Telaga, dominated by E. acoroides). Meanwhile, the lowest estimated carbon stock was 127.4 Mg C ha−1 (Site 4; Koin, dominated by T. hemprichii). The density of E. acoroides was positively correlated with the total seagrass biomass carbon stocks (r = 0.97; p < 0.01), while its dominance was positively correlated with sediment carbon stocks (r = 0.92; p < 0.05) and total seagrass ecosystem carbon stocks (r = 0.92; p < 0.05). Seagrass ecosystems with different community structures showed different carbon storage capacities. Seagrass ecosystems dominated by large-sized species such as E. acoroides showed higher estimated carbon stocks thus suggesting the importance of considering the variability of community structure in managing seagrass ecosystems for carbon sequestration and storage.

Simulation of Urban Carbon Sequestration Service Flows and the Sustainability of Service Supply and Demand

Simulating ecosystem carbon sequestration service (ECSS) flows is crucial for optimizing the carbon cycle in ecosystems and achieving sustainable balance between the supply and demand of the ECSS. This study integrates least-cost path analysis with Kriging interpolation to simulate the dominant wind direction and corrects the interpolated wind speeds to account for terrain and surface conditions. Carbon emissions are spatially distributed using points of interest and road network data. Ultimately, by traversing the carbon emission rate grids along wind directions, the ECSS flows are simulated. The results reveal that the study area has a small carbon sink area but a high total carbon emission, leading to a situation where the supply of ECSS is insufficient to meet demand. The ECSS flows, based on the simulated wind field, demonstrate high spatial resolution and highlight the service flow corridors with distinct spatial heterogeneity. The study area has a significant carbon surplus, requiring a forest area ten times larger than the study area itself to fully sequester this carbon. These findings provide valuable insights for urban sustainable development and carbon emission reduction strategies.

Nitrogen addition reduces soil phosphorus leaching in a subtropical forest of eastern Tibetan Plateau

Globally, increased atmospheric nitrogen (N) deposition has comprehensively altered phosphorus (P) biogeochemical cycling in terrestrial ecosystems. Phosphorus is the second most common nutrient limiting for ecosystem productivity. Even though, ultimately under enhanced atmospheric N deposition it is likely to be the primary one. However, the response of soil P leaching to the elevated atmospheric N deposition in subalpine forests are still elusive. Here, we examined the effects of >7-year N additions on soil P leaching in a subalpine forest of eastern Tibetan Plateau. We found that independent of soil horizon (organic versus mineral horizons) and N addition rate (control 0, low 8 and high 40 kg N/ha yr−1), dissolved inorganic P (DIP), rather than dissolved organic P (DOP), dominated soil P leaching. The N addition treatments reduced the leaching of total P and DIP throughout the soil profile by 13 % and 14 %, respectively. The enhanced biological immobilization and P adsorption capacity (increased by 15–33 % in organic horizon and 9–35 % in mineral horizon) under the N addition contributed to the decrease in soil P leaching. We conclude that the reduction in soil P leaching under the elevated atmospheric N deposition should boost the enhanced N driven forest productivity and ecosystem carbon sequestration in the subalpine forests.

Modeling fisheries and carbon sequestration ecosystem services under deep uncertainty in the ocean twilight zone

Mesopelagic fishes are a vital component of the biological carbon pump and are, to date, largely unexploited. In recent years, there has been an increased interest in harvesting the mesopelagic zone to produce fish feed for aquaculture. However, great uncertainties exist in how the mesopelagic zone interacts with the climate and food webs, presenting a dilemma for policy. Here, we investigate the consequences of potential policies relating to mesopelagic harvest quotas with a dynamic social-ecological modeling approach, combining system dynamics and global sensitivity analyses informed by participatory modeling. Our analyses reveal that, in simulations of mesopelagic fishing scenarios, uncertainties about mesopelagic fish population dynamics have the most pronounced influence on potential outcomes. The analysis also shows that prioritizing the development of the fishing industry over environmental protection would lead to a significantly higher social cost of climate change to society. Given the large uncertainties and the potential large impacts on oceanic carbon sequestration, a precautionary approach to developing mesopelagic fisheries is warranted.

Value assessment of carbon sequestration in generalized black soil distribution area of Amur River Basin, China

ABSTRACT In recent years, the understanding and resolution of imbalances in ecosystem carbon sequestration services (ECSS) have increasingly relied on robust data quantification and value-accounting mechanisms. Based on the interpretation of primary data, including surface vegetation, 30 cm soil cover, and hydrological information of the Amur River Basin, a carbon sequestration evaluation framework centered on the black soil belt of the river basin (BSBB) was constructed. The results showed that: (1) The annual average carbon sequestration of the BSBB from 2000 to 2019 is estimated to be 12.41 Gt C, with an expected economic value of approximately 607.61 billion yuan (China Yuan, CNY); (2) Daxinganling (DXAL, 3.26 Gt), Heihe (HH, 2.72 Gt) and Mudanjiang (MDJ, 1.81 Gt) were the three sub-regions with the highest annual carbon sequestration service, respectively, and the overall carbon density center has shifted from the south to the north within a decade; (3) The results from the geographic detector and partial correlation analysis showed that the forest area (q = 0.97, r = 0.4) and non-cultivated black soil area (q = 0.96, r = 0.97) were both clear driving factors and strong driving relationships. An increase in the forest area of the BSBB is conducive to upgrading the ECSS. This study helps promote the implementation of a carbon sink balance strategy on a global scale, provides theoretical support for future black soil resource management based on trade-offs and synergies, and provides a dual perspective for balancing ecological environmental protection with social and economic development.

Degradation-driven vegetation-soil-microbe interactions alter microbial carbon use efficiency in Moso bamboo forests

Microbial carbon utilization efficiency (CUE) is a crucial indicator for evaluating the efficiency of soil carbon sequestration and transformation, which is applied to quantify the proportion of soil carbon extracted by microbes for anabolism (growth) and catabolism (respiration). Previous studies have shown that the degradation of Moso bamboo forests (Phyllostachys edulis) destroyed the aboveground bamboo structure, reduced vegetation carbon storage, and weakened ecosystem carbon sequestration capacity. Interestingly, soil organic carbon stocks are gradually increasing. However, the mechanism by which degradation-induced changes in soil and vegetation characteristics affect microbial CUE and drive soil carbon sequestration remains unclear. Here we selected four stands with the same origin but different degradation years (intensive management, CK; 2 years' degradation, DM1; 6 years' degradation, DM2; and 10 years' degradation, DM3) based on the local management profiles. The principle of space-for-time substitution was used to investigate the changes in microbial CUE along a degradation time and to further identify the controlling biotic and abiotic factors. Our finding showed that microbial CUE increased by 12.27 %, 31.01 %, and 55.95 %, respectively, compared with CK; whereas microbial biomass turnover time decreased from 23.99 ± 1.11 to 17.16 ± 1.20 days. Promoting microbial growth was the main pathway to enhance microbial CUE. Massive inputs of vegetative carbon replenished soil carbon substrate content, and altered microbial communities and life history strategy, which in turn promoted microbial growth and increased microbial CUE. These findings provide theoretical support for the interactions between carbon dynamics and microbial physiology in degraded bamboo forests, and reinforce the importance of vegetation and microbial properties and soil carbon substrates in predicting microbial CUE.

Responses of Soil Organic Carbon Fractions and Stability to Forest Conversion in the Nanling Nature Reserve, China

Studying the impact of typical vegetation types in forest conversion zones on soil organic carbon (SOC) structure and stability is crucial for developing terrestrial ecosystem carbon sequestration strategies. In this study, we selected three typical forest stands in the Nanling National Nature Reserve: a primary evergreen broad-leaved forest (BL), a secondary mixed coniferous and broad-leaved forest (ML), and a Chinese fir plantation (CL). Soil samples were collected to examine the SOC fractions and carbon pool management index (CPMI) in three forest stands. The influence of soil property factors on SOC fractions was also analyzed. The results showed that the transformation process from a BL to an ML or a CL changed the structure and stability of organic carbon by reducing the labile SOC fractions and increasing the recalcitrant carbon fraction in the soil. The higher lability index (LI) and CPMI of soils in the BL indicated better carbon accumulation and activity, making this treatment more advantageous for management strategies aimed at promoting natural forest renewal and ecological restoration. Correlation and RDA analysis revealed that the availability of soil P was a key factor limiting the variation in organic C fractions in the acidic soils of tropical forests in South China.

VCPNET: A new dataset to benchmark vegetation carbon phenology metrics

Shifts in plant phenology associated with climate change have received unprecedented attention from land managers, policymakers, and the scientific community, with important implications for the structure and function of the biosphere. Eddy covariance (EC) has been used to measure carbon exchange between the land surface and atmosphere, which provides an opportunity to describe the seasonality of ecosystem processes. Using BASE flux products provided by the AmeriFlux network and a non-linear modeling approach, this study developed a vegetation carbon phenology (VCP) dataset, which we termed VCPNET (version 1.0). VCPNET was developed with fine spatial and temporal scales for a total of 87 EC sites (2820 site-years of data) across North America, including nine vegetation types. We extracted phenology metrics by site to provide long-term VCP metrics across these ecosystems. Analyses revealed significant differences in photosynthetic capacity and respiration rate-derived VCP metrics across the landscape, as well as differences associated with whether daily cumulative or half-hourly maximum measurements were used. Daily carbon fluxes may be more consistent in simulating phenology behavior than 30-min maximum fluxes across all vegetation types, except in sites classified as Grassland. Overall vegetation types, the seasonality of photosynthetic capacity may exert a larger control on the net carbon uptake of the ecosystem compared to the respiration rate. Thus, model-estimated photosynthesis-activated seasons may better represent true phenology, particularly the development of above- and below-ground plant biomass in tree-covered areas, which is crucial in forecasting ecosystem carbon sequestration. In addition, a nonlinear model can robustly capture day-to-day variation in carbon flux; however, precise simulation of phenological patterns with multiple peaks, typically observed in disturbed ecosystems, must be addressed during model optimization. The cross-ecosystem phenology metrics provided by VCPNET could be utilized for regional-global analysis and calibration of satellite-based vegetation indices, which accurately scale land surface phenology. Our findings provide a prospective tool for a deeper understanding of ecosystem function, allowing a direction and foundation for optimizing phenology models and algorithms and ultimately contributing to developing new ecological theories and practices in response to climate change.

Effects of Fertilizer Application Intensity on Carbon Accumulation and Greenhouse Gas Emissions in Moso Bamboo Forest-Polygonatum cyrtonema Hua Agroforestry Systems.

Agroforestry management has immense potential in enhancing forest carbon sequestration and mitigating climate change. Yet the impact and response mechanism of compound fertilization rates on carbon sinks in agroforestry systems remain ambiguous. This study aims to elucidate the impact of different compound fertilizer rates on soil greenhouse gas (GHG) emissions, vegetation and soil organic carbon (SOC) sinks, and to illustrate the differences in agroforestry systems' carbon sinks through a one-year positioning test across 12 plots, applying different compound fertilizer application rates (0 (CK), 400 (A1), 800 (A2), and 1600 (A3) kg ha-1). The study demonstrated that, after fertilization, the total GHG emissions of A1 decreased by 4.41%, whereas A2 and A3 increased their total GHG emissions by 17.13% and 72.23%, respectively. The vegetation carbon sequestration of A1, A2, and A3 increased by 18.04%, 26.75%, and 28.65%, respectively, and the soil organic carbon sequestration rose by 32.57%, 42.27% and 43.29%, respectively. To sum up, in contrast with CK, the ecosystem carbon sequestration climbed by 54.41%, 51.67%, and 0.90%, respectively. Our study suggests that rational fertilization can improve the carbon sink of the ecosystem and effectively ameliorate climate change.

Carbon-phosphorus cycle models overestimate CO2 enrichment response in a mature Eucalyptus forest.

The importance of phosphorus (P) in regulating ecosystem responses to climate change has fostered P-cycle implementation in land surface models, but their CO2 effects predictions have not been evaluated against measurements. Here, we perform a data-driven model evaluation where simulations of eight widely used P-enabled models were confronted with observations from a long-term free-air CO2 enrichment experiment in a mature, P-limited Eucalyptus forest. We show that most models predicted the correct sign and magnitude of the CO2 effect on ecosystem carbon (C) sequestration, but they generally overestimated the effects on plant C uptake and growth. We identify leaf-to-canopy scaling of photosynthesis, plant tissue stoichiometry, plant belowground C allocation, and the subsequent consequences for plant-microbial interaction as key areas in which models of ecosystem C-P interaction can be improved. Together, this data-model intercomparison reveals data-driven insights into the performance and functionality of P-enabled models and adds to the existing evidence that the global CO2-driven carbon sink is overestimated by models.

Vegetation factors and atmospheric dryness regulate the dynamics of ecosystem water use efficiency in a temperate semiarid shrubland

Ecosystem water use efficiency (WUE), a key indicator of the coupling between carbon and water cycle, has been widely used to quantify ecosystem responses to climate change. However, large uncertainties remain regarding the dynamics and driving factors of WUE in temperate semiarid shrublands. Using eddy-covariance measurements, we investigated the role of vegetation and hydrometeorological factors in affecting the temporal dynamics of WUE (GPP/ET, where GPP and ET represent gross primary production and evapotranspiration, respectively) over a semiarid shrubland of northern China during 2012–2016. Daily WUE was lower (<1.5 g C kg−1 H2O) in early-spring and late-autumn, and higher (2.0–7.0 g C kg−1 H2O) in summer months (June–August). Annual (January–December) WUE (1.43–1.78 g C kg−1 H2O) was higher than most values previously reported for semiarid and arid shrubland ecosystems. Vegetation factors such as surface conductance (gs) and normalized difference vegetation index (NDVI) played a primary role in promoting monthly WUE during the warm and moist growing-season (April–October), while high vapor pressure deficit (VPD) and low root-zone soil water content (SWC) were key hydrometeorological factors that imposed constraints on daily WUE. Using both a machine-learning model and a data-binning algorithm, we showed that NDVI and VPD were the most important factors regulating WUE dynamics. Ongoing climate warming and increasingly frequent extreme events (e.g., droughts and heatwaves) are expected to induce high VPD, which in turn could reduce ecosystem WUE and undermine ecosystem carbon sequestration in semiarid shrublands. In addition, increasing vegetation cover in the studied area owing to continued restoration efforts is expected to enhance ecosystem WUE, and may result in stronger vegetation controls on WUE dynamics.

Spatial distribution patterns and drivers of above- and below- biomass in Chinese terrestrial ecosystems

Unraveling the dynamics of the global carbon cycle and assessing the sustainability of terrestrial ecosystems are critically dependent on a comprehensive understanding of vegetation biomass. This exploration delves into the pivotal role of biomass within vegetation communities, emphasizing its impact on ecosystem health, productivity, and community structure development. These insights are invaluable for advancing ecological science and conservation efforts. The synthesis of aboveground (AGB) and belowground (BGB) biomass data from 4485 and 3442 locations across China, respectively, collates a wide range of published sources. Integrating this extensive dataset with environmental parameters and applying advanced machine learning techniques facilitated an in-depth analysis of AGB and BGB spatial patterns within China. Techniques such as variance decomposition analysis and piecewise structural equation modeling were employed to dissect the factors contributing to the spatial variability of vegetation biomass. Significant spatial heterogeneity in biomass distribution was uncovered, with vegetation biomass in the northwest markedly lower than in the southern and northeastern regions. It was observed that AGB consistently surpassed BGB. Climatic conditions, soil characteristics, and soil nutrients were found to significantly explain 53 % and 48 % of the total variance in AGB and BGB, respectively. Specifically, solar radiation and soil total nitrogen were identified as critical factors influencing variations in AGB and BGB. The findings offer profound contributions to the understanding of the global carbon balance and the evaluation of terrestrial ecosystems sustainability. Moreover, they provide essential insights into the ecosystems' response mechanisms to global changes, serving as a fundamental reference for future studies on terrestrial ecosystem carbon cycling and carbon sequestration potentials.

Valuation of Madhupur Sal Forest Ecosystem Services and Carbon Sequestration Potency in Bangladesh: an Avenue for Mitigating Climate Change Impact

Valuation of Madhupur Sal Forest Ecosystem Services and Carbon Sequestration Potency in Bangladesh: an Avenue for Mitigating Climate Change Impact