Search Results (174)

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

Assimilated carbon allocation to belowground processes may influence soil respiration (Rs). Because Rs includes autotrophic respiration (Ra) and heterotrophic respiration (Rh), different root and microbial responses complicate the separation of these effects. In a temperate deciduous broadleaf forest, we used sap flux density and estimated photosynthesis as indicators of plant activity. Total soil respiration and heterotrophic respiration were measured using automated chambers, and autotrophic respiration was estimated as Rs minus Rh. We examined the overall responses and time lags of respiration components. Ra showed positive relationships with sap flux density and estimated photosynthesis (R² = 0.37 and 0.30, p < 0.05), whereas Rh showed weaker relationships (R² = 0.20 and 0.15, p < 0.05). In lagged cross-correlation analyses using high-resolution data, Rs and Ra showed maximum responses 13 h after plant activity changes, whereas Rh showed no lag response (p > 0.05). These results suggest that associations with plant activity were clearer for Ra than Rh, and that the detected lagged response of soil respiration was more consistent with partitioned Ra than Rh. However, because Ra was estimated as Rs minus Rh, these patterns should be interpreted cautiously. Considering the responses and time lags of respiration components may improve ecosystem carbon cycling predictions. Full article

Driven by rapid global urbanization and expanding urban footprints, air pollution, particularly from industrial emissions and vehicular exhaust, has intensified, with rising concentrations of inhalable particulate matter (PM) posing direct threats to public health. To address this challenge, we conducted field measurements of ambient PM concentrations across diverse urban plant communities and quantitatively compared their capacity to mitigate four key size-fractionated pollutants: total suspended particles (TSPs), PM₁₀, PM_2.5, and PM₁. Our objective was to identify the most effective plant community type for PM abatement in urban settings. Results demonstrate that: (1) evergreen broad-leaved forests exhibit the highest overall PM removal efficiency among all studied communities; (2) removal efficacy declines markedly with decreasing particle size, indicating limited capacity to capture ultrafine particles (e.g., PM₁); and (3) seasonal performance peaks in summer, especially for deciduous broad-leaved forests attributable to maximal leaf area index, enhanced stomatal activity, and favorable meteorological conditions. By rigorously evaluating species composition, canopy structure, and seasonal dynamics, this study provides empirically grounded guidance for evidence-based urban greening strategies aimed at optimizing airborne particulate mitigation worldwide. Full article

Understanding land-use and land-cover (LULC) dynamics within protected areas (PAs) is fundamental for assessing conservation effectiveness and ecosystem resilience under increasing anthropogenic and climatic pressures. This study examines the spatio-temporal evolution of LULC across Bavaria’s protected areas between 2000 and 2023 by integrating categorical land-cover data, satellite-derived vegetation indices, and environmental drivers. Annual LULC changes were first quantified using MODIS MCD12Q1 land-cover classifications to evaluate class persistence, transitions, and area trajectories and were subsequently interpreted alongside 16-day MODIS NDVI and SAVI composites to assess associated vegetation greening and browning trends. Ecological stability was characterized by using class-level persistence indicators, coefficients of variation (CVs), and linear trend slopes. The results reveal a marked greening signal after 2010, coinciding with pronounced land-cover transitions, including a decline in evergreen needleleaf forests (−480.6 km²; −32.2%) and substantial expansion of deciduous broadleaf forests (+390.8 km²; +106.1%) and grasslands (+275.8 km²; +28.4%), while wetlands experienced a severe contraction (−203.4 km²; −73.7%), indicating heightened hydrological sensitivity within protected ecosystems. Correlation analysis further indicates that anthropogenic pressure, quantified using the human footprint index, remains a dominant driver of change in croplands and urban areas, even within legally protected boundaries. Overall, this study demonstrates that vegetation trends, land-cover transitions, climatic exposure, and human pressure jointly shape ecological stability in protected areas, highlighting the value of an integrated indicator-based framework. Full article

Understanding spatial and seasonal variations in leaf stoichiometry is essential for understanding plant nutrient-use strategies and their implications for ecosystem biogeochemical cycling. Although broad-scale stoichiometric patterns have been well documented for terrestrial plants, comparable evidence for vascular epiphytes remains limited. Here, we examined spatial and seasonal variation in leaf stoichiometry of vascular epiphytes by integrating field data from subtropical and tropical forests in southwestern China with a global literature synthesis. At the global scale, leaf nitrogen and phosphorus concentrations (LNC and LPC) of vascular epiphytes were significantly related to climate variables, whereas no clear latitudinal pattern was detected for leaf N:P ratios. At the regional scale, vascular epiphytes in the subtropical montane moist forest exhibited higher LNC and LPC and lower C:N and C:P ratios than those in the tropical seasonal rainforest. At the local scale, LPC of epiphytes was positively correlated with host LPC, whereas LNC showed a weak but statistically significant association with N availability in canopy soils. Seasonally, evergreen epiphytes exhibited higher leaf nutrient concentrations during the rainy season, and deciduous species showed significantly higher stem N, P, and K concentrations during the dry season, indicating contrasting seasonal nutrient-use strategies. Our results demonstrate that leaf stoichiometry of vascular epiphytes is jointly shaped by climate, canopy-level nutrient dynamics, and seasonal regulation, and differs fundamentally from patterns commonly observed in terrestrial plants. These findings highlight the importance of considering canopy-specific processes and fine-scale species turnover when assessing large-scale stoichiometric patterns and forest nutrient cycling. Full article

Warm-temperate evergreen broad-leaved forests are distinguished from the deciduous broad-leaved forests in that their foliage is retained year-round and their specific leaf area is often higher than that of temperate evergreen coniferous forests. However, the implications of these traits for carbon and water fluxes and their coupling remain poorly understood. In this study, we quantified gross primary production (GPP), evapotranspiration (ET), water-use efficiency (WUE), and intrinsic WUE (IWUE) using seven years of eddy-covariance measurements from a warm-temperate evergreen broad-leaved forest in southern South Korea. We further evaluated the abiotic and biotic drivers of their seasonal variability using structural equation modeling. The forest acted as a carbon sink even during winter, with an annual GPP of 2176 ± 135 g C m⁻² yr⁻¹, ET of 596 ± 59 kg H₂O m⁻² yr⁻¹, a mean daily WUE of 4.15 ± 0.25 g C (kg H₂O)⁻¹, and a mean daily IWUE of 20.3 ± 3.0 g C hPa (kg H₂O)⁻¹. Among WUE components, the main driver of GPP during warm and humid periods was incoming shortwave radiation (RSDN) followed by air temperature (Tair), whereas ET was primarily controlled by Tair. Cold and dry seasons and main annual drivers of GPP and ET were Tair. Although the leaf area index (LAI) was increased by RSDN and Tair, it did not serve as a mediator affecting GPP and ET, contrary to our expectations. Differences in how GPP and ET responded to abiotic factors ultimately governed WUE and IWUE in this forest type, underscoring the importance of site-specific characteristics in evaluating ecosystem water-use efficiency. Full article

Forest type strongly influences soil microbial community composition and associated carbon cycling, yet its influence on microbial functional traits remains poorly understood. In this study, metagenomics sequencing was used to investigate soil microbial communities and carbon metabolism genes across three forest types: deciduous broadleaf (DBF), mixed coniferous–broadleaf (CBMF), and coniferous forest (CF) at two soil depths (0–20 cm and 20–40 cm) on Lushan Mountain in subtropical China. The results showed that CF exhibited higher bacterial diversity and a distinct microbial composition, with an increase in Actinomycetota and Bacteroidota and a decrease in Acidobacteriota and Pseudomonadota. The Calvin cycle was the dominant carbon fixation pathway in all forests, while the relative abundance of secondary pathways (i.e., the 3-hydroxypropionate bi-cycle and reductive citrate cycle) varied significantly with forest type. Key carbon fixation genes (sucD, pckA) were more abundant in CF and CBMF, with higher levels of rpiA/B and ackA in DBF. Functional profiling further indicated that CF soils, especially in the surface layer, were enriched in glycoside hydrolases (GHs) and carbohydrate esterases (CEs), while CBMF showed a greater potential for starch and lignin degradation. Multivariate statistical analyses identified soil available phosphorus (AP) and pH as primary factors shaping microbial community variation, with AP emerging as being the dominant regulator of carbon-related functional gene abundance. Overall, the prevalence of these distinct genetic potentials across forest types underscores how vegetation composition may shape microbial functional traits, thereby influencing the stability and dynamics of the soil carbon pool in forest ecosystem. Full article

Gross Primary Productivity (GPP) plays a vital role in the terrestrial carbon cycle and ecosystem functioning. Understanding its spatio-temporal dynamics and driving mechanisms is critical for predicting ecosystem responses to climate change. China’s GPP has experienced complex responses due to heterogeneous climate, environment, and human activities, yet their impacts and interactions across ecosystems remain unquantified. This study used the Mann–Kendall test and SHapley Additive exPlanations to quantify the contributions and interactions of climate, vegetation, topography, and human factors using GPP data (2001–2020). Nationally, GPP showed a significant upward trend, particularly in deciduous broadleaf forests, croplands, grasslands, and savannas. Leaf area index (LAI) is identified as the primary contributor to GPP variations, while climate factors exhibit nonlinear interactive effects on the modeled GPP. Ecosystem-specific sensitivities were evident: forest GPP is predominantly associated with climate–vegetation coupling. Additionally, in coniferous forests, the interaction between anthropogenic factors and topography shows a notable association with productivity patterns. Grassland GPP is primarily linked to topography, while cropland GPP is mainly related to management practices and environmental conditions. In contrast, the GPP of savannas and shrublands is less influenced by factor interactions. These findings high-light the necessity of ecosystem-specific management and restoration strategies and provide a basis for improving carbon cycle modeling and climate change adaptation planning. Full article

Plant phenology is a sensitive indicator of ecosystem responses to climate change, yet its dynamics and drivers in subtropical montane forests remain poorly understood. Based on the continuous phenological monitoring of 12 dominant tree species from 2008 to 2022 in a mid-subtropical evergreen broad-leaved forest on Ailao Mountains, China, this study analyzed phenological shifts and their climatic drivers. The results show that, (1) unlike the widely reported trends in northern mid-to-high latitudes, spring phenophases (budburst and leaf-out) did not exhibit significant advancing trends, while autumn phenophases (leaf coloration and fall) remained stable; (2) water availability played a dominant role in regulating spring phenology, with both budburst and leaf-out showing significant negative correlations with winter-spring precipitation, and responses varied significantly across hydrological year types; and (3) the life form strongly influenced phenological strategies, with evergreen species exhibiting earlier spring phenology than deciduous species. This study highlights that in seasonally humid subtropical montane forests, water availability exerts a stronger control on phenology than temperature. Our findings underscore the necessity of incorporating precipitation variability and functional trait differences into assessments of forest phenology and ecosystem functioning under future climate change, providing a scientific basis for the conservation and adaptive management of subtropical forests. Full article

Broad-scale assessments often track forest productivity, yet they rarely quantify how soil conditions determine whether these gains persist as long-lived carbon and generate measurable economic value. This study focused on Northeast China, where forests include boreal coniferous stands dominated by Dahurian larch, temperate conifer–broadleaf mixed forests with Korean pine, and temperate deciduous broadleaf forests dominated by Mongolian oak. We combined GLASS net primary productivity and ESA CCI Land Cover to delineate forest pixels, used 2000 to 2005 as the baseline, and converted productivity anomalies into pixel level carbon economic value using a consistent pricing rule. Forest NPP increased significantly during 2000 to 2018 (slope = 1.57, p = 0.019), and carbon economic value also increased over time during 2006 to 2018 (slope = 2.24, p = 0.002), with the highest values in core mountain forests and lower values in the western forest–grassland transition zone. Correlation analysis, explainable random forests, and variance partitioning characterized spatial and temporal dynamics from 2000 to 2018 and identified environmental controls. Carbon value increased over time and showed marked spatial heterogeneity that mirrored productivity patterns in core mountain forests. Climate was the dominant predictor of value, while higher soil pH and clay content were negatively associated with value. The random forest model explained about 70% of the variance in carbon value (R² = 0.695), and variance partitioning indicated substantial unique and joint contributions from climate and soil alongside secondary topographic effects. The automatable framework enables periodic updates with new satellite composites, supports ecological compensation zoning, and informs soil-oriented interventions that enhance the monetized value of forest carbon sinks in data-limited regions. Full article

Leaf area index (LAI) is essential for understanding vegetation dynamics, ecosystem processes, and land–atmosphere interactions. Various measurement methods exist, but gap-fraction-based indirect methods are preferred due to their reduced labor and field survey time in comparison to direct methods. However, gap-fraction-based field observations, often referred to as plant area index (PAI), frequently overestimate LAI because they include woody components. To mitigate this issue, the woody-to-total-area ratio (α) can be utilized to exclude these woody components from PAI, yielding more accurate LAI estimates (hereafter referred to as LAI_adjusted). In this study, we demonstrate a novel method to estimate α using Sentinel-2-based normalized difference vegetation index (NDVI) and time-series PAI measurements. The α estimates effectively reduce the influence of woody components in PAI within deciduous broadleaf forests (DBF). Moreover, LAI_adjusted shows good agreement with the Sentinel-2 LAI, which represents effective LAI derived from the PROSAIL model. Notably, the spatial distribution of α effectively captured the expected seasonal dynamics across various forest types. In DBF, α values increased during winter due to leaf fall when compared to the growing season, while seasonal variations were relatively small in evergreen needleleaf forest (ENF). We further confirmed that our method demonstrates greater robustness with NDVI than with other vegetation indices that are more susceptible to topographic variation. Ultimately, this framework presents a promising pathway to mitigate biases in most gap-fraction-based PAI measurements, thereby enhancing the accuracy of vegetation structural assessments and supporting broader ecological and climate-related applications. Full article

As climate change accelerates, environmental factors are expected to fluctuate as well. To gain insight into soil respiration (Rs) dynamics, it is essential to conduct long-term measurements of Rs alongside environmental variations. To this end, we examined Rs associated with environmental variables from 2018 to 2024 at a site located on Mt. Jeombong, which is situated in a temperate deciduous broadleaf forest. The interannual variation in Rs was not explained by soil temperature but was primarily associated with rainfall regimes. The mean Rs for April–November was substantially different during the study period and was strongly correlated with cumulative rainfall at all measurement points (R² = 0.68–0.94). These variations were largely attributed to changes in autotrophic respiration (Ra). Furthermore, Rs differed significantly between nearby measurement points (p < 0.05), despite their proximity within a 100 m by 100 m plot, apparently reflecting point-level differences in responses of Rs to environmental drivers that were likely modulated by uneven litter accumulation. Overall, at our site located in temperate deciduous forests, Rs primarily fluctuates as a result of rainfall variation, and Rs variations are strongly influenced by the heterogeneity in the litter deposition. Full article

Forest fires significantly impact the global climate through carbon emissions, yet the multi-scale coupling mechanisms among meteorological factors, fire behavior, and emissions remain uncertain. Focusing on tropical Asia, this study integrated satellite-based fire behavior products, meteorological datasets, and emission factors, and employed machine learning together with structural equation modeling (SEM) to explore the mediating role of fire behavior in the meteorological regulation of carbon emissions. The results revealed significant differences among vegetation types in both carbon emission intensity and sensitivity to meteorological drivers. For example, average gas emissions (GEs) and particle emissions (PEs) in mixed forests (MF, 323.68 g/m²/year for GE and 0.73 g/m²/year for PE) were approximately 172% and 151% higher, respectively, than those in evergreen broadleaf forests (EBF, 118.92 g/m²/year for GE and 0.29 g/m²/year for PE), which exhibited the lowest emission intensity. Mixed forests and deciduous broadleaf forests exhibited stronger meteorological regulation effects, whereas evergreen broadleaf forests were comparatively stable. Temperature and vapor pressure deficit emerged as the core drivers of fire behavior and carbon emissions, exerting indirect control through fire behavior. Overall, the findings highlight fire behavior as a critical link between meteorological conditions and carbon emissions, with ecosystem-specific differences determining the responsiveness of carbon emissions to meteorological drivers. These insights provide theoretical support for improving the accuracy of wildfire emission simulations in climate models and for developing vegetation-specific fire management and climate adaptation strategies. Full article

Vegetation indices (VIs) have been extensively employed as proxies for gross primary productivity (GPP). However, it is unclear how the spatial resolution effects the performance of VIs in GPP estimation in different biomes when matching the flux tower footprint. Here, we examined the relationship with tower GPP between Landsat-footprint VIs and MODIS-footprint VIs. We first calculated Landsat-footprint VIs (e.g., Normalized Difference Vegetation Index (NDVI), enhanced vegetation index (EVI), two-band EVI (EVI2), near-infrared reflectance of vegetation (NIRv) and kernel Normalized Difference Vegetation Index (kNDVI)) averaged over all the pixels within the footprint and MODIS-footprint VIs with 3 × 3 km or 1 × 1 km around the tower, respectively. We then examined the relationship between Landsat- and MODIS-footprint VIs and tower GPP at monthly scale over 76 FLUXNET sites across ten vegetation types worldwide. The results showed that Landsat-footprint VIs had stronger performance than MODIS-footprint VIs for GPP estimation in all ecosystems, with high improvement on croplands, wetlands, and grasslands and moderate improvements on mixed forest, evergreen needleleaf forest, and deciduous broadleaf forest. Moreover, NIRv showed a stronger correlation with tower-based GPP than NDVI, EVI, EVI2, and kNDVI in ten ecosystems both at 30 m and 500 spatial resolutions. Our findings highlighted the critical role of VIs with high spatial resolution and footprint-aware matching in GPP estimation. Full article

Although rainfall is a major determinant of soil moisture content (SMC), various factors affect SMC. The effects of these environmental factors contribute to spatial heterogeneity in SMC, which influences diverse ecological processes. To better understand the dynamics in SMC, litter and slope gradient should be considered. To this end, we analyzed the impacts of litter and slope gradient on SMC from 2020 to 2021 on Mt. Jeombong, located in a temperate deciduous broadleaf forest. We classified the study period into foliage (with a developed canopy) and non-foliage (after leaf fall) seasons. Our results indicated that SMC was affected by slope gradient and litter layer. Rainfall absorption occurred more at gentle slope, leading to higher SMC. Additionally, rainfall absorption was interpreted as being intercepted by the litter layer. Consequently, the correlation coefficient between SMC increment and rainfall was lower in the non-foliage season (R² = 0.37–0.56) than in the foliage season (R² = 0.72–0.84). With temporal progression, however, SMC response to rainfall increased where the litter was thickly accumulated, suggesting that litter interception was gradually diminished by decomposition. In this study, spatial heterogeneity in the litter layer and slope gradient substantially influenced the supply of soil moisture from rainfall. Full article