Search Results (302)

The spatial variation of forest ecosystem services at regional scales remains poorly understood, and few studies have explicitly analyzed how ecosystem services are distributed across different forest management types. This study assessed the spatial overlap between forest management types and ecosystem service hotspots in the Southeastern United States (SEUS) and the Pacific Northwest (PNW) forests. We used the InVEST suite of tools and GIS to quantify carbon storage and water yield. Carbon storage was estimated, stratified by forest group and age class, and literature-based biomass pool values were applied. Average annual water yield and its temporal changes (2001–2020) were modeled using the annual water yield model, incorporating precipitation, potential evapotranspiration, vegetation type, and soil characteristics. Ecosystem service outputs were classified to identify hotspot zones (top 20%) and to evaluate the synergies and tradeoffs between these services. Hotspots were then overlaid with forest management maps to examine their distribution across management types. We found that only 2% of the SEUS and 11% of the PNW region were simultaneous hotspots for both services. In the SEUS, ecological and preservation forest management types showed higher efficiency in hotspot allocation, while in PNW, production forestry contributed relatively more to hotspot areas. These findings offer valuable insights for decision-makers and forest managers seeking to preserve the multiple benefits that forests provide at regional scales. Full article

Urban expansion fragments once-contiguous forest patches, generating pronounced edge gradients that modulate soil physicochemical properties and biodiversity. We quantified how fragmentation reshaped the soil microbiome continuum and its implications for soil carbon storage in a temperate urban mixed deciduous forest. A total of 18 plots were considered in this study, with six plots for each fragment type. Intact interior forest (F), internal forest path fragment (IF), and external forest path fragment (EF) soils were sampled at 0–15, 15–30, and 30–45 cm depths and profiled through phospholipid-derived fatty acid (PLFA) chemotyping and amino sugar proxies for living microbiome and microbial-derived necromass assessment, respectively. Carbon fractionation was performed through the chemical oxidation method. Diversity indices (Shannon–Wiener, Pielou evenness, Margalef richness, and Simpson dominance) were calculated based on the determined fatty acids derived from the phospholipid fraction. The microbial biomass ranged from 85.1 to 214.6 nmol g⁻¹ dry soil, with the surface layers of F exhibiting the highest values (p < 0.01). Shannon diversity declined systematically from F > IF > EF. The microbial necromass varied from 11.3 to 23.2 g⋅kg⁻¹. Fragmentation intensified the stratification of carbon pools, with organic carbon decreasing by approximately 14% from F to EF. Our results show that EFs possess a declining microbiome continuum that weakens their carbon sequestration capacity in urban forests. Full article

Ecosystem services—the benefits humans derive from nature—are foundational to environmental sustainability and economic well-being, with carbon sequestration and storage standing out as critical regulating services in the fight against climate change. This study presents a comprehensive financial valuation of the carbon sequestration, storage and product substitution ecosystem services provided by the Hungarian forest-based sector. Using a multi-scenario framework, four complementary valuation concepts are assessed: total carbon storage (biomass, soil, and harvested wood products), annual net sequestration, emissions avoided through material and energy substitution, and marketable carbon value under voluntary carbon market (VCM) and EU Carbon Removal Certification Framework (CRCF) mechanisms. Data sources include the National Forestry Database, the Hungarian Greenhouse Gas Inventory, and national estimates on substitution effects and soil carbon stocks. The total carbon stock of Hungarian forests is estimated at 1289 million tons of CO₂ eq, corresponding to a theoretical climate liability value of over EUR 64 billion. Annual sequestration is valued at approximately 380 million EUR/year, while avoided emissions contribute an additional 453 million EUR/year in mitigation benefits. A comparative analysis of two mutually exclusive crediting strategies—improved forest management projects (IFMs) avoiding final harvesting versus long-term carbon storage through the use of harvested wood products—reveals that intensified harvesting for durable wood use offers higher revenue potential (up to 90 million EUR/year) than non-harvesting IFM scenarios. These findings highlight the dual role of forests as both carbon sinks and sources of climate-smart materials and call for policy frameworks that integrate substitution benefits and long-term storage opportunities in support of effective climate and bioeconomy strategies. Full article

Invasive plant species pose an increasing threat to mangroves globally. This study assessed the impact of Paspalum vaginatum invasion on carbon loss and early recovery following four years of restoration in a mangrove forest with Rhizophora racemosa in Benin. Organic carbon was quantified in the total biomass, including both aboveground and belowground components, as well as in the soil to a depth of −50 cm. In addition, soil gas fluxes of CO₂, CH₄, and N₂O were measured. Three sites were evaluated: a conserved mangrove, a site degraded by P. vaginatum, and the same site post-restoration via hydrological rehabilitation and reforestation. Invasion significantly reduced carbon storage, especially in soil, due to lower biomass, incorporation of low C/N ratio organic residues, and compaction. Restoration recovered 7.8% of the total biomass carbon compared to the conserved mangrove site, although soil organic carbon did not rise significantly in the short term. However, improvements in deep soil C/N ratios (15–30 and 30–50 cm) suggest enhanced soil organic matter recalcitrance linked to R. racemosa reforestation. Soil CO₂ emissions dropped by 60% at the restored site, underscoring restoration’s potential to mitigate early carbon loss. These results highlight the need to control invasive species and suggest that restoration can generate additional social benefits. Full article

Increasing soil carbon storage is an important strategy for achieving sustainable development. Enhancing soil carbon sequestration capacity can effectively reduce the concentration of atmospheric carbon dioxide, which not only contributes to the carbon neutrality goal but also helps maintain ecosystem stability. Based on 146 soil samples collected at plot locations selected across Beijing, we examined relationships between soil organic carbon (SOC) and key characteristics of urban forests, including their spatial structure and species complexity. The results showed that SOC in the topsoil with a depth of 20 cm was highest over forested plots (6.384 g/kg–20.349 g/kg) and lowest in soils without any vegetation cover (5.586 g/kg–6.783 g/kg). The plots with herbaceous/shrub vegetation but no tree cover had SOC values in between (5.586 g/kg–15.162 g/kg). The plot data revealed that SOC was better correlated with the physical structure than the species diversity of Beijing’s urban trees. The correlation coefficients (r) between SOC and five physical structure indicators, including average diameter at breast height (DBH), average tree height, basal area density, and the diversity of DBH and tree height, ranged from 0.32 to 0.52, whereas the r values for four species diversity indicators ranged from 0.10 to 0.25, two of which were not statistically different from 0. Stepwise linear regression analyses revealed that the species diversity indicators were not very sensitive to SOC variations among a large portion of the plots and were about half as effective as the physical structure indicators for explaining the total variance of SOC. These results suggest that urban planning and greenspace management policies could be tailored to maximize the carbon co-benefits of urban land. Specifically, trees should be planted in urban areas wherever possible, preferably as densely as what can be allowed given other urban planning considerations. Protection of large, old trees should be encouraged, as these trees will continue to sequester and store large quantities of carbon in above- and belowground biomass as well as in soil. Such policies will enhance the contribution of urban land, especially urban forests and other greenspaces, to nature-based solutions (NBS) to climate change. Full article

Light Detection and Ranging (LiDAR) provides three-dimensional information that can be used to extract tree parameter measurements such as height (H), canopy volume (CV), canopy diameter (CD), canopy area (CA), and tree stand density. LiDAR data does not directly give diameter at breast height (DBH), an important input into allometric equations to estimate biomass. The main objective of this study is to estimate tree DBH using existing allometric models. Specifically, it compares three global DBH pantropical models to calculate DBH and to estimate the aboveground biomass (AGB) of the Lake Broadwater Forest located in Southeast (SE) Queensland, Australia. LiDAR data collected in mid-2022 was used to test these models, with field validation data collected at the beginning of 2024. The three DBH estimation models—the Jucker model, Gonzalez-Benecke model 1, and Gonzalez-Benecke model 2—all used tree H, and the Jucker and Gonzalez-Benecke model 2 additionally used CD and CA, respectively. Model performance was assessed using five statistical metrics: root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), percentage bias (MBias), and the coefficient of determination (R²). The Jucker model was the best-performing model, followed by Gonzalez-Benecke model 2 and Gonzalez-Benecke model 1. The Jucker model had an RMSE of 8.7 cm, an MAE of −13.54 cm, an MAPE of 7%, an MBias of 13.73 cm, and an R² of 0.9005. The Chave AGB model was used to estimate the AGB at the tree, plot, and per hectare levels using the Jucker model-calculated DBH and the field-measured DBH. AGB was used to estimate total biomass, dry weight, carbon (C), and carbon dioxide (CO₂) sequestered per hectare. The Lake Broadwater Forest was estimated to have an AGB of 161.5 Mg/ha in 2022, a Total C of 65.6 Mg/ha, and a CO₂ sequestered of 240.7 Mg/ha in 2022. These findings highlight the substantial carbon storage potential of the Lake Broadwater Forest, reinforcing the opportunity for landholders to participate in the carbon credit systems, which offer financial benefits and enable contributions to carbon mitigation programs, thereby helping to meet national and global carbon reduction targets. Full article

Forest biomass is closely related to carbon sequestration capacity and can reflect the level of forest management. This study utilizes four machine learning algorithms, namely Multivariate Stepwise Regression (MSR), K-Nearest Neighbors (k-NN), Artificial Neural Network (ANN), and Random Forest (RF), to estimate forest aboveground biomass (AGB) in Chenzhou City, Hunan Province, China. In addition, a canopy height model, constructed from a digital surface model (DSM) derived from Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) and an ICESat-2-corrected SRTM DEM, is incorporated to quantify its impact on the accuracy of AGB estimation. The results indicate the following: (1) The incorporation of multi-source remote sensing data significantly improves the accuracy of AGB estimation, among which the RF model performs the best (R² = 0.69, RMSE = 24.26 t·ha⁻¹) compared with the single-source model. (2) The canopy height model (CHM) obtained from InSAR-LiDAR effectively alleviates the signal saturation effect of optical and SAR data in high-biomass areas (>200 t·ha⁻¹). When FCH is added to the RF model combined with multi-source remote sensing data, the R² of the AGB estimation model is improved to 0.74. (3) In 2018, AGB in Chenzhou City shows clear spatial heterogeneity, with a mean of 51.87 t·ha⁻¹. Biomass increases from the western hilly part (32.15–68.43 t·ha⁻¹) to the eastern mountainous area (89.72–256.41 t·ha⁻¹), peaking in Dongjiang Lake National Forest Park (256.41 t·ha⁻¹). This study proposes a comprehensive feature integration framework that combines red-edge spectral indices for capturing vegetation physiological status, SAR-derived texture metrics for assessing canopy structural heterogeneity, and canopy height metrics to characterize forest three-dimensional structure. This integrated approach enables the robust and accurate monitoring of carbon storage in subtropical forests. Full article

Carbon dioxide (CO₂) is a predominant greenhouse gas significantly contributing to atmospheric heat retention, primarily driven by anthropogenic activities intensifying the greenhouse effect. This study aims to evaluate the diversity of plant species, above-ground biomass (AGB), and carbon stock within a dry dipterocarp forest, which is a vital local natural resource. This study presents a comprehensive evaluation of plant species diversity, AGB, and carbon stock capacity within a dry dipterocarp forest at the Nature Study Center, Mahasarakham University, located in the Kham Riang Subdistrict of Kantharawichai District, Maha Sarakham Province, spanning an area of 20.80 hectares. Ten sample plots, each measuring 40 × 40 m, were established and distributed across the study area. The diameter at breast height (DBH) and the height of the trees were meticulously recorded for all trees within these plots. Advanced statistical techniques were employed to calculate the relative dominance (RD), relative frequency (RF), and Importance Value Index (IVI), alongside a comprehensive assessment of plant species diversity. The AGB was assessed using precise allometric equations, with a focus on analyzing carbon storage within woody biomass. The findings revealed the presence of 52 tree species across 26 families within the forest. The total AGB was measured at 144.510 tons, with carbon stock reaching 67.920 tCO₂. These results offer critical insights into enhancing land management strategies to optimize carbon stock, thereby playing a vital role in mitigating greenhouse gas emissions, a significant factor in climate change dynamics. Full article

It has become increasingly important to incorporate carbon metrics in the forest harvest planning process. The Generic Carbon Budget Model (GCBM) is a well-recognized tool to evaluate the potential impact of management decisions on carbon sequestration and storage, supporting sustainable forest management planning. Although GCBM is effective in carbon budgeting and estimating carbon metrics, its computational complexity makes it difficult to integrate into forest planning with multiple scenarios. In this regard, this study proposes using machine algorithms to expedite the output generated by GCBM. XGBoost was implemented to estimate the carbon pool and NEP in managed forests of Quebec. Furthermore, polynomial regression was also implemented to serve as a validation benchmark. Datasets with total sizes of 13.53 million and 7.56 million samples were compiled for NEP and carbon pool forecasting to run the model. The results indicate that XGBoost was able to accurately replicate the performance of the GCBM model for both NEP forecasting (R² = 0.883) and carbon pool estimation (R² = 0.967 for aboveground biomass). Although machine learning approaches are comparatively faster, GCBM still offers better accuracy. Hence, the decision on which method to use, either machine learning or GCBM, should be dictated by the specific objectives and the constraints of the project. 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

Accurately estimating the forest canopy height is essential for quantifying forest biomass and carbon storage. Recently, the ICESat-2 and GEDI spaceborne LiDAR missions have significantly advanced global canopy height mapping. However, due to inherent sensor limitations, their footprint-level estimates often show systematic bias. Tall forests tend to be underestimated, while short forests are often overestimated. To address this issue, we used coincident G-LiHT airborne LiDAR measurements to correct footprint-level canopy heights from both ICESat-2 and GEDI, aiming to improve the canopy height retrieval accuracy across Puerto Rico’s tropical forests. The bias-corrected LiDAR dataset was then combined with multi-source predictors derived from Sentinel-1/2 and the 3DEP DEM. Using these inputs, we trained a canopy height inversion model based on the AutoGluon stacking ensemble method. Accuracy assessments show that, compared to models trained on uncorrected single-source LiDAR data, the new model built on the bias-corrected ICESat-2–GEDI fusion outperformed in both overall accuracy and consistency across canopy height gradients. The final model achieved a correlation coefficient (R) of 0.80, with a root mean square error (RMSE) of 3.72 m and a relative RMSE of 0.22. The proposed approach offers a robust and transferable approach for high-resolution canopy structure mapping and provides valuable support for carbon accounting and tropical forest management. 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

Forest aboveground biomass (AGB) serves as a crucial indicator of productivity and carbon storage capacity. While the impact of stand structure on AGB is well-documented for pure moso bamboo stands, the specific structural factors influencing AGB and the mechanisms driving these effects in mixed moso bamboo forests, characterized by species diversity and structural complexity, require further elucidation. This study analyzed 9453 bamboos and arbor trees within the TianBao MetaPlot, which were tessellated into 108 standard plots in Tianbaoyan National Nature Reserve, Fujian, China. Using a multi-method voting approach, we identified the key structural factors influencing stand AGB and employed Partial Least Squares Path Modeling (PLS-PM) to assess their direct and indirect effects. We found that the stand density, moso bamboo mixing ratio, Shannon’s index, Simpson’s index, mean tree height, openness, and tree size variation coefficient were the key structural factors influencing the stand AGB. The PLS-PM analysis showed that stand density had a negative effect on stand AGB, which can be explicitly decomposed through a direct negative effect and an indirect negative effect. Tree diversity showed a strong positive effect, supporting the niche complementarity theory. The stand mean tree height and stand tree size variation had positive effects on stand AGB, while stand openness had a negative effect. The direct effects of tree diversity, stand mean tree height, and stand openness were stronger than the indirect effects on stand AGB, while the indirect effect of stand density was greater than the aforementioned effects. These results highlight the complex interactions between stand structure and stand AGB in mixed moso bamboo forests. The negative effect of stand density on stand AGB is in contrast with previous findings on arbor forests, wherein a higher stand density often promotes AGB, highlighting the unique structural characteristics of mixed moso bamboo forests. To promote biomass accumulation and enhance carbon sequestration in mixed moso bamboo stands, it is recommended to increase the tree size variability, enhance the tree species diversity, and apply rational thinning of moso bamboo, based on site-specific conditions. Full article

Surface mining activities cause severe disruption to ecosystems, resulting in the substantial destruction of surface vegetation, the loss of soil organic carbon stocks, and a decrease in the ecosystem’s ability to sequester carbon. The ecological restoration of mining areas has been found to significantly enhance the carbon storage capacity of ecosystems. This study evaluated ecological restoration strategies in Chongqing’s Tongluo Mountain mining area by integrating GF-6 satellite multispectral data (2 m panchromatic/8 m multispectral resolution) with ground surveys across 45 quadrats to develop a quadratic regression model based on vegetation indices and the field-measured biomass. The methodology quantified carbon storage variations among engineered restoration (ER), natural recovery (NR), and unmanaged sites (CWR) while identifying optimal vegetation configurations for karst ecosystems. The methodology combined the high-spatial-resolution satellite imagery for large-scale vegetation mapping with field-measured biomass calibration to enhance the quantitative accuracy, enabling an efficient carbon storage assessment across heterogeneous landscapes. This hybrid approach overcame the limitations of traditional plot-based methods by providing spatially explicit, cost-effective monitoring solutions for mining ecosystems. The results demonstrate that engineered restoration significantly enhances carbon sequestration, with the aboveground vegetation biomass reaching 5.07 ± 1.05 tC/ha, a value 21% higher than in natural recovery areas (4.18 ± 0.23 tC/ha) and 189% greater than at unmanaged sites (1.75 ± 1.03 tC/ha). In areas subjected to engineered restoration, both the vegetation and soil carbon storage showed an upward trend, with soil carbon sequestration being the primary form, contributing to 81% of the total carbon storage, and with engineered restoration areas exceeding natural recovery and unmanaged zones by 17.6% and 106%, respectively, in terms of their soil carbon density (40.41 ± 9.99 tC/ha). Significant variations in the carbon sequestration capacity were observed across vegetation types. Bamboo forests exhibited the highest carbon density (25.8 tC/ha), followed by tree forests (2.54 ± 0.53 tC/ha), while grasslands showed the lowest values (0.88 ± 0.52 tC/ha). For future restoration initiatives, it is advisable to select suitable vegetation types based on the local dominant species for a comprehensive approach. Full article

Quantifying the spatio-temporal dynamics of forest biomass in both natural and planted forests over large areas has proven challenging. Using a remote sensing data-based method, this study presents a novel approach to separate the biomass gains and losses of planted forests and natural forests and to quantify their independent contributions to total forest biomass changes. Annual forest biomass data were calculated using 1 km spatial resolution maps of planted and natural forests in China for 2005–2020. Planted forest biomass increased substantially from 1.81 Pg C in 2005 to 3.11 Pg C in 2020 at a rate of 0.086 Pg C yr⁻¹. In contrast, natural forests remained relatively stable at 6.44 Pg C over the same period. Driven largely by extensive afforestation efforts, planted forests accounted for 100% of the increase in China’s forest biomass. Notably, 86.2% of the planted forest biomass and 70.3% of the natural forest biomass were located in southern China, which has a warmer climate. The area’s expansion of newly planted forests (i.e., young forests) contributed all of the total increase in biomass carbon storage (1.30 Pg C) in the planted forest category from 2005 to 2020. Forests planted before 2005 with mid-to-old tree age, together with natural forests, played a minor role in the total increase in forest biomass in China during this period. This is likely due to forest harvesting and natural disasters in these forests offsetting the growth of natural forests and mid-to-old-age planted forests over the 2005 to 2020 interval. This study highlights the complex and distinct biomass dynamics of planted and natural forests in China, which are subject to both human management and natural disturbances. Full article