Search Results (1,140)

Young forests are recognized as important contributors to climate change mitigation due to their high productivity. However, their structural simplicity and transitional nature render them ecologically vulnerable to long-term climatic stress. We explored the long-term responses of young forests to climate change by applying the LANDIS-II forest landscape model coupled with a PnET-based physiological model to simulate 200 years of forest succession and carbon dynamics. Simulations were conducted under three climate scenarios (BAU, RCP45, and RCP85) to evaluate changes in aboveground biomass (AGB), carbon storage, and carbon absorption across elevation gradients. The results revealed that climate change significantly altered successional pathways and carbon capacity, with effects varying with elevation and initial species composition. Predominant species such as Quercus mongolica maintained dominance under the RCP45 and RCP85 scenarios across all elevations, whereas shade-tolerant mid and understory species showed suppressed growth. Sub-alpine species showed prominent declines in AGB, particularly in the RCP85 scenario. These divergent responses increased the spatial heterogeneity of forest productivity and reduced the predictability of forest carbon dynamics over time. Our findings emphasize the uncertainty of predicting forest development and carbon sequestration in young forests under future climatic conditions. They highlight the urgent need to plan forest management strategies incorporating site-specific ecological characteristics, promote successional advancement, and maintain functional stability for effective climate adaptation and mitigation. Full article

Oak species (Quercus spp.) represent one of the most widespread and ecologically important groups of woody plants in the Northern Hemisphere, forming dominant forest ecosystems across temperate, Mediterranean, subtropical, and montane regions. Due to their longevity, high wood density, extensive root systems, and large biomass, oaks play a significant role in terrestrial carbon cycling and long-term carbon storage. However, a comprehensive synthesis of the contribution of oak forests to carbon sequestration remains limited. This review integrates a systematic bibliometric assessment with a qualitative synthesis of the peer-reviewed literature to evaluate the role of oak species and oak-dominated forests in carbon sequestration and climate change mitigation. A total of 656 publications indexed in Scopus and Web of Science were analyzed, revealing increasing research activity after 2008 and a broad geographic distribution of studies, with the highest contributions from the United States, Spain, China, and Germany. The reviewed studies demonstrate that oak ecosystems function as substantial and durable carbon sinks, storing carbon in aboveground biomass, belowground biomass, deadwood, litter, and soil organic carbon pools. Carbon sequestration is influenced by stand age, site conditions, species composition, and management practices. This review highlights oak forests as resilient, multifunctional ecosystems, with a critical role in nature-based climate solutions and sustainable forest management. Full article

Radiation use efficiency (RUE) is closely associated with cotton biomass and yield, yet the synergistic regulation of phenotypic structure and physiological potential remains unclear. A field experiment (2024–2025) in Anyang, China, utilized three independent trials: six sowing dates (from 12 April to 12 May at 6-day intervals, S1–S6), six planting densities (1.5, 3.3, 5.1, 6.9, 8.7, and 10.5 × 10⁴ plants·ha⁻¹, D1–D6), and ten cultivars with distinct architectures (V1–V10). Feature importance and structural relationships were quantified via random forest (RF) and partial least squares structural equation modeling (PLS-SEM). Results indicated that delaying sowing reduced true leaf number (TLN) and plant height (PH), with the April 24 sowing (S3) optimizing leaf area index (LAI, 2.57) and light interception rate (iPAR, 0.61). Increasing density significantly enhanced population-level LAI, above-ground biomass, and RUE, despite a progressive decline in TLN. Among cultivars, CCRI 60 (V6) exhibited superior structural traits (PH: 72.94 cm; iPAR: 0.61), while CCRI 113 (V8) exhibited the highest maximum carboxylation rate (V_cmax, 88.9 μmol·m⁻²·s⁻¹) and RUE (4.88 g·MJ⁻¹). Across the comprehensive dataset (integrating the density, sowing date, and cultivar trials), iPAR exhibited the highest relative importance (42.01%) for RUE variation, while associated structural traits (PH, LAI, TLN) yielded a cumulative relative importance of 41.69%. RUE was strongly associated with biomass accumulation (path coefficient > 0.97), which subsequently optimized yield components. Conversely, within the cultivar-comparison subset, the relative importance of iPAR decreased to 17.95%, while V_cmax rose significantly to 19.20%. PLS-SEM indicated that canopy structure exerted a significant negative association with photosynthetic potential (V_cmax, J_max) within this cultivar subset (path coefficient ≈ −0.51), whereas enhanced physiological potential was positively associated with resource allocation to yield components (path coefficient ≈ 0.57). Consequently, mitigating the inherent trade-off between canopy structure and leaf photosynthetic capacity is critical for further improving RUE and cotton yield under similar production environments. Full article

Aboveground biomass (AGB) mapping is fundamental to tropical forest carbon monitoring, yet national-scale estimation remains challenging because field plots are sparse and model performance is often sensitive to predictor choice and validation design. This study assessed whether geospatial embeddings improve national AGB mapping in Guyana when combined with environmental and topographic predictors. Predictor selection was undertaken using repeated grouped resampling at the plot-cluster level, and model performance was evaluated across 100 independent train–test repeats. Three final random forest models were compared. The environmental baseline model (Env + SRTM-derived elevation; 8 predictors) achieved a mean R² of 0.179, an RMSE of 148.5 Mg/ha and a relative RMSE of 36.1%. A retained 8-predictor model combining environmental variables with a selected embedding subset (Env + Emb*) improved performance slightly, with a mean R² of 0.189, an RMSE of 147.6 Mg/ha and a relative RMSE of 35.9%. The best performance was obtained with a 22-variable full-stack model combining environmental, topographic and embedding predictors, after all Sentinel-2 predictors had been eliminated during feature selection; this model achieved a mean R² of 0.203, an RMSE of 146.3 Mg/ha and a relative RMSE of 35.5%. Across models, isothermality, a measure of how day-to-night temperature variation compares to annual temperature variation, and precipitation of the coldest quarter were consistently the most influential predictors. Mean ensemble coefficient of variation, representing relative model disagreement, ranged from 0.336 to 0.361. These results indicate that geospatial embeddings provide useful complementary information, but predictive performance remained modest overall, with the best model explaining only about one-fifth of plot-level AGB variance. The resulting maps are therefore best interpreted as broad-scale decision-support products rather than high-precision local estimates of AGB. Full article

Remote sensing contributes to characterizing forest structure across heterogeneous tropical regions, yet structural parameters used to compare Atlantic Forest phytophysiognomies remain limited, especially in fragmented landscapes affected by multiple drivers of forest loss and degradation. This study used Global Ecosystem Dynamics Investigation (GEDI) data to compare the structure of old-growth candidate forest polygons in four Brazilian Atlantic Forest phytophysiognomies: Dense Ombrophilous Forest (DOF), Mixed Ombrophilous Forest (MOF), Seasonal Semideciduous Forest (SSdF), and Seasonal Deciduous Forest (SDF). We analyzed canopy height (H), canopy cover (COVER), foliage height diversity (FHD), plant area index (PAI), and aboveground biomass density (AGBD) from GEDI L2B and L4A footprints acquired between 2019 and 2024. Structural differences among phytophysiognomies were significant for all variables (Kruskal–Wallis, p < 0.001), with small-to-moderate effect sizes (ε² ≈ 0.05–0.15). The strongest pairwise contrasts occurred for SDF–SSdF and SSdF–DOF, whereas MOF showed greater overlap with the other groups. Across variables, AGBD and H were the most consistent discriminators, and polygon-level summaries strengthened among-group separation. These findings show that GEDI-derived polygon-level metrics can support regional comparisons of forest structure among Atlantic Forest phytophysiognomies and help identify the strongest contrasts in fragmented landscapes. Full article

Mitigating nitrous oxide (N₂O) emissions from cropland soils is a pressing challenge for climate change mitigation. This study evaluated rockwool-based fertigation (RF) in reducing N₂O emissions from tea plantations. A 17-month field experiment was conducted comparing RF with conventional surface fertilization (CK), measuring tea plant biomass, new tea shoots yield, new tea shoots quality indices, soil N₂O fluxes, physicochemical properties, and nitrogen (N)-cycling functional genes across different soil layers. Results showed that RF treatment significantly increased the aboveground pruning biomass of tea plants, suggesting that RF promotes tea plant growth. The RF treatment showed lower N₂O fluxes and cumulative N₂O emissions within 90 days post-fertilization across the tea-growing season compared with CK, demonstrating that RF effectively mitigates N₂O emissions from tea plantation soils. Random forest analysis further revealed that the RF-induced vertical redistribution of nutrients and N-cycling functional genes was the primary driver of N₂O mitigation. Our findings demonstrate that RF is an effective dual-benefit strategy that simultaneously enhances tea plant productivity and mitigates N₂O emissions by reshaping soil biogeochemical processes and their spatial distribution. Full article

This study quantified total biomass stocks in Carrasco (CAR, n = 12), a dense tropical deciduous vegetation type from the Brazilian semi-arid region for which biomass information remains scarce. We also evaluated differences in floristic composition, diversity, structure, and biomass allocation patterns relative to Cerrado sensu stricto (CSS, n = 40). Forest inventories were conducted in southeastern Brazil. Woody biomass was estimated using a regional allometric equation. Roots were sampled in a position adjacent to the plots, and litter was collected at the center of each plot using a frame. Necromass was assessed along a linear transect corresponding to the length of each plot using the line-intersect method. Biomass differences between vegetation types were assessed using generalized linear and mixed-effects models (GLMs and GLMMs). Total biomass reached 45.24 Mg ha⁻¹ in CSS and 59.01 Mg ha⁻¹ in CAR. In CSS, woody biomass predominated (20.47 Mg ha⁻¹; 45%), followed by roots (18.47 Mg ha⁻¹; 41%), litter (5.49 Mg ha⁻¹; 12%), and necromass (0.81 Mg ha⁻¹; 2%). In CAR, roots were the dominant component (32.37 Mg ha⁻¹; 55%), followed by woody biomass (16.57 Mg ha⁻¹; 28%), litter (8.39 Mg ha⁻¹; 14%), and necromass (1.68 Mg ha⁻¹; 3%). CSS and CAR shared only 10% of their species and showed significant differences in total biomass (TB) and belowground biomass (BGB), while aboveground biomass (AGB), aboveground woody biomass (AGWB), litter, and necromass did not differ significantly (α = 0.05). The BGB/AGWB ratio was <1 in CSS and >1 in CAR, resembling global patterns of savanna/shrubland and grassland formations, respectively. Considering the sampling design adopted, despite the higher stem density in CAR, larger individuals in CSS compensated for structural differences, resulting in similar aboveground biomass stocks. Our findings reinforce the floristic and structural distinctiveness of Carrasco and reveal contrasting biomass allocation strategies, with a strong dominance of belowground biomass in CAR. These results demonstrate that aboveground-based assessments can substantially underestimate total biomass in semi-arid transitional vegetation and highlight the need to incorporate non-forest ecosystems into biomass inventories, conservation planning, and climate change mitigation strategies. Full article

Mobile laser scanning (MLS) enables rapid, high-resolution measurement of forest structure, yet its reliability for estimating stem volume and aboveground biomass (AGB) in dense plantations and its sensitivity to tree height uncertainty remain insufficiently quantified. This study evaluates handheld MLS for tree-level stem volume and AGB estimation in a mature Sitka spruce (Picea sitchensis (Bong.) Carr.) plantation in Ireland, using destructive sampling (n = 12) as a reference. MLS-derived diameter measurements were used to reconstruct stem profiles, with merchantable volume calculated by frustum integration to a 7 cm top-end diameter. The central objective was to quantify how uncertainty in tree height propagates through MLS-derived stem reconstruction and affects volume and AGB estimates. On average, 68.2% of merchantable stem volume was directly measured before upper-stem reconstruction. Under ideal validation conditions using true felled-stem height, MLS-derived merchantable volume and total AGB were estimated with RMSE values of 5.6% and 10.9%, respectively. Across practical height-input scenarios, error increased moderately, indicating that direct measurement of the lower stem constrained the propagation of height uncertainty. Compared with the nationally applied spruce allometric benchmark, the MLS-based workflow showed lower sensitivity to height-input uncertainty under the conditions evaluated. These findings demonstrate the potential of handheld MLS as a tree-level validation and calibration tool for measurement-based biomass assessment while highlighting the need for broader testing across stand types, species and operational plot-level workflows. Full article

The atmospheric air quality is one of the crucial factors determining people’s health, duration and quality of life. The importance of ammonia (NH₃) and ethylene (C₂H₄) is due to the fact that they are precursors of secondary organic aerosols (SOA) and phytotoxicants, which significantly affect air quality, cause human diseases and damage plants. The Fourier Transform Infrared (FTIR) spectrometry is a powerful tool for long-term monitoring of the atmospheric gas composition, including toxic gases. The paper presents the results of atmospheric FTIR measurements of NH₃ and C₂H₄ at the St. Petersburg State University observational site (59.88° N, 29.83° E, 20 m above sea level) located in a suburb of greater Saint Petersburg. This work demonstrates the applicability of the ground-based atmospheric FTIR spectroscopy to long-term monitoring of air pollution in urbanized areas and in particular to provide information on the NH₃ and C₂H₄ abundance in the atmosphere, including the analysis of their annual cycle, long-term trends, and positive anomalies. It was shown that for NH₃ and C₂H₄, a statistically significant decrease in column-averaged dry-air mole fraction values (X_NH3 and X_C2H4) was observed, amounting to (−2.3 ± 0.2)%/year for the 2009–2025 period and with the rate (−2.2 ± 0.4)%/year for the 2016–2025 period, respectively. Periodically recorded X_NH3 anomalies indicate the presence of intensive emission sources in the region, subjecting ecosystems in adjacent areas to constant exposure to NH₃ concentrations exceeding the critical level. Anomalously high values of X_NH3 and X_C2H4 were recorded simultaneously only once—on 17 October 2017. Using data on HCN total column (as a forest fire indicator) and the results of atmospheric dispersion modeling, it was shown that this pollution event was caused by the influence of biomass burning products emitted from wildfires located approximately 250 km to the north-west from the observational site in the Helsinki area (Finland). Full article

Continuous monitoring of forest biomass is indispensable for establishing transparent carbon budgets and ensuring sustainable forest management toward achieving carbon neutrality. While satellite data has traditionally been used for wide-area biomass estimation, signal saturation in high-biomass regions has posed a significant challenge to accuracy. To address this saturation issue and enhance the precision of carbon budget estimations, this study develops a new methodology for estimating forest above-ground biomass (AGB). First, a training dataset was constructed by integrating airborne LiDAR data from across Japan with various satellite datasets, such as PALSAR-2 and Sentinel-2. Machine learning (XGBoost) was then employed to generate a nationwide canopy height map, achieving a high coefficient of determination ($R^2=0.594$). Subsequently, allometric equations with parameters optimized for specific forest types (evergreen coniferous, evergreen broadleaf, deciduous coniferous, and deciduous broadleaf) were derived from the relationship between estimated canopy height and AGB to create a nationwide AGB map. Validation results indicated that the resulting AGB map demonstrated higher estimation accuracy ($R^2=0.265$) compared to existing global products (ESA CCI Biomass), with significant improvements in mitigating underestimation (saturation) in high-biomass areas. By combining canopy height estimation with forest-type-specific allometry, this approach enables high-precision mapping that reflects the unique characteristics of Japanese forests and is expected to contribute to more reliable carbon budget assessments. Full article

Understanding whether vegetation greening corresponds to changes in estimated forest carbon storage is important for evaluating ecological restoration under coupled climate change and human pressures. However, existing studies often rely on vegetation indices and have limited capacity to examine long-term forest carbon storage patterns or distinguish the roles of climatic and anthropogenic factors. This study integrates long-term remote sensing data with a two-way fixed effects model to examine forest ecosystem carbon storage in Shaanxi Province, China, from 1990 to 2023. Forest carbon storage was estimated by combining historical land-use data with static baseline carbon density coefficients derived from the 2012 field inventory, following an IPCC Tier 1-type approach. The carbon pools considered included aboveground biomass, belowground biomass, litter, and soil organic carbon. The results show that NDVI increased significantly, while estimated forest carbon storage increased by 4.27 × 10⁷ t (21.04%), with evident regional heterogeneity. A mismatch was observed between vegetation greenness and estimated forest carbon storage, and NDVI showed weak and unstable associations with carbon storage after controlling for fixed effects. Nighttime light exhibited a significant negative association with carbon storage, whereas climatic factors were generally insignificant. These findings suggest that vegetation indices alone may not reliably represent land-use-based carbon storage estimates. This study provides empirical evidence for understanding forest carbon storage patterns under ecological restoration and highlights the need for dynamic carbon density parameters in future assessments. Full article

Accurately quantifying aboveground biomass (AGB) in tropical forest enrichment plantations remains challenging, particularly in managed regenerating stands where tree crown architecture, size structure, and species composition differ from the datasets used to calibrate classical allometric equations. Here, we assess whether AGB in tropical forest enrichment plantations can be estimated more accurately by combining tree-specific woody volume reconstructed from mobile laser scanning (MLS) with an explicit foliar-biomass component. We combined destructive measurements from 83 trees with high-resolution MLS point clouds to quantify biomass components, calibrate leaf-mass models, and assess the contribution of foliage to total AGB. Stems accounted for most of the biomass (65%), whereas leaves contributed only 3% on average. Among the models tested, Model 3, which included DBH, projected crown area, and wood density, showed the best performance (R² = 54.4%; RMSE = 2.43 kg). The main gain relative to regional (−20.4%) and pantropical (−25.6%) allometric equations came from the use of MLS-derived woody volume combined with species wood density, whereas the inclusion of predicted leaf biomass provided a moderate additional correction to the remaining bias. These results highlight the importance of canopy structure for biomass estimation in enrichment plantations and managed regenerating stands and support the use of LiDAR data as a robust alternative for AGB assessment in this context. Full article

Accurate mapping of aboveground biomass (AGB) in mountainous forests is essential for carbon stock assessment and ecological management, yet remains challenging due to the difficulty of linking local high-precision observations with regionally continuous coverage. To address this issue, we developed a hierarchical framework integrating local reference construction, UAV–GEDI bridging, footprint-level modeling, and regional continuous mapping, applied to the mountainous forests of Simao District, Pu’er City, Yunnan Province, China. Field plot measurements and UAV-borne LiDAR data were first used to construct a local AGB reference product, which was then transferred to the GEDI footprint scale through geolocation correction and footprint-scale quality control, yielding 252 valid bridging samples across three UAV flight zones, with approximately 65% originating from the TYH zone. Among five candidate models evaluated for GEDI footprint-level AGB estimation, the Stacking ensemble model performed best, with a pooled out-of-fold R² of 0.736 and RMSE of 24.15 Mg ha⁻¹, and was subsequently applied to 89,579 GEDI footprints across the study area. For regional continuous mapping, the empirical Bayesian kriging regression prediction (EBKRP) scheme combining Landsat TCW, Sentinel-2 IRECI, and the Sentinel-1 polarization ratio achieved the best external validation performance, with R² of 0.622 and RMSE of 26.05 Mg ha⁻¹ based on 61 independent field plots. These results indicate that the proposed hierarchical framework effectively bridges local high-precision observations and regional continuous AGB mapping in complex mountainous forest environments, offering a systematic methodological reference for GEDI-based forest carbon monitoring. Full article

Timely and accurate assessment of rice productivity, encompassing grain yield, aboveground biomass (AGB), and harvest index (HI), is essential for harvest planning, supply chain coordination, and food security. This study evaluates the feasibility of predicting all three productivity components using satellite and weather time series data while examining trade-offs between forecast accuracy and operational lead time. Five machine learning models (CatBoost, Gaussian Process Regression (GPR), Random Forest, Ridge regression, and TabPFN) were compared across six in-season prediction windows (December to May) using Sentinel-2 vegetation indices (Normalized Difference Vegetation Index (NDVI), Chlorophyll Index Red Edge 2 (CIRE2), Land Surface Water Index (LSWI)), weather variables (minimum and maximum temperature and radiation), and agronomic records from 256 commercial and experimental rice fields in southern New South Wales, Australia, over four growing seasons (2022–2025) using leave-one-year-out cross-validation. Rolling in-season forecasts were evaluated across December–May; March was selected for further analysis as a practical window that balances accuracy and timeliness for decision-making, with minimal additional error reduction in later months closer to harvest. TabPFN had the lowest RMSE for yield prediction (RMSE = 1.85 t ha⁻¹, $r=0.72$), Ridge had the lowest RMSE for AGB (RMSE = 3.05 t ha⁻¹, $r=0.77$), while tree-based models yielded the lowest RMSE for derived HI (RMSE ≈ 0.07). HI prediction showed weak regional relationships, with direct prediction yielding $\left|r\right|\approx 0.24$ and derived HI (predicted yield divided by predicted AGB) showing $r\approx 0$. Although strong correlations ($r>0.9$) between HI and vegetation indices were observed within individual site-seasons, consistent with other studies, these relationships were highly variable across site-seasons, reflecting the difficulty of inferring HI from canopy reflectance when biotic and/or abiotic stresses decouple AGB accumulation from grain filling. Both direct and derived HI approaches yielded comparable errors, indicating that satellite and weather data lack information content for regional-scale HI prediction. These findings support satellite-based yield and AGB forecasting for operational use. Full article

Plant functional traits help us understand forest carbon storage. We quantified eight functional traits that reflect plant life history strategies: leaf area (LA), specific leaf area (SLA), leaf dry matter content (LDMC), leaf carbon (LC), nitrogen (LN), phosphorus (LP), leaf carbon–nitrogen ratio (LCNR), and wood density (WD). But their role across successional stages is still unclear. We set up sixteen permanent plots in Pu’er, Yunnan, China. Each plot was 60 m × 60 m. The plots covered four successional stages. Stage one was early-successional Simao pine forests. Stage two was mid-successional mixed forests. Stage three was mid-to-late-successional mature mixed forests. Stage four was late-successional mature broad-leaved forests. We measured aboveground carbon storage (CS). We measured carbon growth rates (CAR). We also measured plant traits, soil nutrients, and topography. Carbon storage increased step by step during succession. It became stable in the late stage. Carbon accumulation rate stayed similar across all stages. A key trait axis (LPC2) directly increased carbon storage. LPC2 represents the trade-off between nitrogen use efficiency and leaf construction costs. Environmental factors only affected carbon storage indirectly. They influenced traits first. These results support the metabolic trade-off hypothesis. They also support the leaf economics spectrum theory. Early-successional traits help forests gain biomass quickly. Late-successional traits help forests store carbon for a long time. We suggest protecting mature forests. We also suggest using pioneer species in restoration. This dual strategy can enhance carbon sequestration in subtropical production forests. Full article