Search Results (219)

Forests play a vital role in the global carbon cycle but face growing anthropogenic pressures, with climate change and forest fragmentation among the most critical. In West Africa, particularly in Ghana, the interaction between increasing aridity and forest fragmentation remains underexplored, despite its significance for forest biomass dynamics and carbon storage processes. This study examined how spatial variation in climatic aridity (Aridity Index, AI) affects above-ground biomass (AGB) in Ghana’s ecological zones, both directly and indirectly through forest fragmentation and biodiversity, using structural equation modeling (SEM) and generalized additive models (GAMs). Results from this study show that AGB declines along the aridity gradient, with humid zones supporting the highest biomass and semi-arid zones the lowest. The SEM analysis revealed that areas with a lower aridity index (drier conditions) had significantly lower AGB, indicating that arid conditions are associated with lower forest biomass. Fragmentation patterns align with this relationship, while biodiversity (as measured by species richness) showed weak associations, likely reflecting both ecological and data limitations. GAMs highlighted nonlinear fragmentation effects: mean patch area (AREA_MN) was the strongest predictor, showing a unimodal relationship with biomass, whereas number of patches (NP), edge density (ED), and landscape shape index (LSI) reduced AGB. Overall, these findings demonstrate that aridity and spatial configuration jointly control biomass, with fragmentation acting as a key mediator of this relationship. Dry and transitional forests emerge as particularly vulnerable, emphasizing the need for management strategies that maintain large, connected forest patches and integrate restoration into climate adaptation policies. Full article

Moso bamboo, widely distributed in subtropical regions of China, plays an important role in forest management and carbon cycle research. However, accurate estimation of tree density and aboveground biomass (AGB) remains challenging due to the unique characteristics of Moso bamboo forests in their growth and stand structure. This research aims to develop a new procedure for bamboo tree density and AGB estimation based on UAV-LiDAR and sample plots from multiple sites through comparative analysis of the incorporation of two groups of variables—regular point cloud metrics (e.g., height, point density) and layered texture metrics—and three modeling methods—multiple linear regression (MLR), mixed-effects modeling (MEM), and hierarchical Bayesian modeling (HBM). The results showed that incorporating layered texture metrics with regular variables substantially improved the estimation accuracy of both tree density and AGB. Among these models, HBM achieved the highest predictive performance, yielding coefficient of determination (R²) values of 0.54 for tree density and 0.59 for AGB, with corresponding relative root mean square errors (rRMSE) of 21.46% and 17.97%. This study presents a novel and effective method for estimating Moso bamboo tree density and AGB using multi-site UAV-LiDAR and sample plots, offering a scientific basis for precise management and carbon stock assessment. Full article

Community-managed forests within agroforestry landscapes are vital for both carbon sequestration and agricultural sustainability. This study assesses the Hariyali Community Forest (HCF) in western Nepal, emphasizing its role in carbon storage within a Sal (Shorea robusta)-dominated lowland forest containing diverse native and medicinal species. Stratified field inventories combined with satellite-derived biomass and land-use/land-cover data were used to quantify carbon stocks and spatial trends. In 2022, the mean aboveground carbon density was 165 tC ha⁻¹, totaling approximately 101,640 tC (~373,017 tCO₂e), which closely matches satellite-based trends and indicates consistent carbon accumulation. Remote sensing from 2015–2022 showed a net tree cover gain of 427 ha compared to a 2000 baseline of 188 ha, evidencing effective community-led regeneration. The 615 ha Sal-dominated landscape also sustains agroforestry, small-scale horticulture, and subsistence crops, integrating livelihoods with conservation. Temporary carbon declines between 2020 and 2022, linked to localized harvesting and management shifts, highlight the need for stronger governance and local capacity. This study, among the first integrated carbon assessments in Nepal’s lowland Sal forests, demonstrates how community forestry advances REDD+ (Reducing Emissions from Deforestation and Forest Degradation, and the role of conservation, sustainable management of forests, and enhancement of forest carbon stocks in developing countries) objectives while enhancing rural resilience. Linking field inventories with satellite-derived biomass and land-cover data situates community forestry within regional environmental change and SDG (Sustainable Development Goals) targets (13, 15, and 1) through measurable ecosystem restoration and livelihood gains. Full article

The Caribbean Coastal Marine Productivity (CARICOMP) program was conceptualized in 1985 to monitor coral reefs, seagrass beds, and mangrove forests at multiple sites across the wider Caribbean. Mangrove monitoring was focused on the dominant Caribbean species, red mangrove (Rhizophora mangle). Forest structure and productivity were monitored at 21 sites (18 countries) across different geomorphological settings, from tropical to subtropical mainland and island systems. Here, we provide the key findings from the CARICOMP mangrove data collected, mostly from 1992 to 2013, to assess spatial and temporal variability across the region. Red mangrove above-ground biomass averaged 190 t ha⁻¹ (far higher than previously reported) but ranged widely across sites from 33 to 590 t ha⁻¹, equating to an average above-ground ‘blue carbon’ of 84 t ha⁻¹ (range 15–260 t ha⁻¹). Tree density averaged 3237 trees ha⁻¹, tree basal area averaged 19.7 m² ha⁻¹, tree height averaged 6.1 ± 2.8 m, and seedling density varied from 1.2 to 74 seedlings m⁻² across the sites. Among the environmental factors that influence mangroves, local temperature and rainfall explained 48% of the variability in measured tree structure parameters. Annual litterfall, as a proxy for productivity, measured on average 1.24 ± 0.70 kg m⁻² yr⁻¹, with 60% of the total litterfall composed of leaves. Litterfall varied seasonally by 42%. No relationship was apparent between litterfall and seasonal ocean–atmosphere climate indices (ONI and AMM). With exception of the three most southwesterly CARICOMP sites, hurricanes and tropical storms impacted the mangrove sites repeatedly, resulting in considerable damage. A direct strike by a category-4 hurricane in 1998 in Dominican Republic killed 67% of the red mangrove trees, lowered above-ground biomass by 91%, basal area by 89%, litterfall by 63%, and resulted in the subsequent growth of many tall and thin saplings, totally changing the structure of the forest ecosystem in the first few years after the hurricane. In comparing mangrove systems, major differences may be explained by time elapsed since the last destructive event (hurricane) affecting each site. This highlights the fact that despite an increasing focus on preserving these valuable ecosystems, they are still highly vulnerable to natural hazards and likely to face a poor outcome under ongoing climate change. Full article

High-resolution above-ground biomass (AGB) data play a critical role in advancing low-carbon development strategies across cities. However, research on urban trees’ AGB largely relies on high-accuracy field measurements, which limits the feasibility of conducting cross-regional studies. In contrast, existing remote-sensing-based AGB products provide extensive coverage while lacking the spatial resolution required for precise city-scale analysis. To address the dilemma of achieving both high spatial resolution and broad coverage, this study integrated 149 feature variables derived from multi-source datasets and implemented quality-control procedures to select high-quality samples from two globally representative AGB products (GEDI AGB and CCI AGB). This strategy substantially improved the performance of the random forest model and generated 10 m resolution urban trees’ AGB maps for 51 C40 cities across Eurasia continent. The results indicate that: (1) after applying quality control to the target variables, the mean R² of ten-fold cross validation improved from 0.37 to 0.75, and the MAE decreased substantially from 47.02 Mg/ha to 17.48 Mg/ha; (2) by enhancing the spatial resolution of AGB maps to 10 m, the resulting products exhibit superior spatial detail, better capture local variations, and maintain greater spatial continuity compared with the CCI AGB and GEDI AGB datasets; (3) the mean AGB density across the Eurasian continent was 39.44 Mg/ha, with total urban tree s’ AGB reaching 83.83 × 10⁶ t. Comparison with previous single-city C40 studies shows that our estimated AGB density and total AGB closely align with previously reported values. The above data implies that cities carry an undeniable amount of carbon storage, both in terms of carbon density and total amount. This study provides a robust foundation for accurately assessing the potential of urban carbon sinks and optimizing the path to achieving carbon neutrality. Full article

To address grassland ecosystem degradation caused by mining disturbance and its severe threats to regional ecological security in alpine mining areas, this study systematically evaluated the synergistic effects of different application ratios of Effective Microorganisms inoculant and organic fertilizers on artificial grassland ecosystem functions in the Muli alpine mining region of the Qinghai-Tibet Plateau, based on field experiments conducted from 2022 to 2024. The results demonstrated significant improvements in production performance. The Y2E2 treatment (0.60 t·hm⁻² Effective Microorganisms inoculant + 20 t·hm⁻² organic fertilizer) exhibited optimal effects, with aboveground biomass increasing by 75.97% and 68.88% in 2023 and 2024, respectively, compared to the control (p < 0.05), while belowground biomass simultaneously increased by 36.05% and 35.53% (p < 0.05), showing a sustained upward trend. Nutritional quality was markedly enhanced, with the Y2E2 treatment consistently achieving the best performance across both years. Crude protein and ether extract contents increased by 46.18%~46.52% and 62.42%~63.25%, respectively (p < 0.05), while soluble sugar content rose significantly by 19.49%~20.56% (p < 0.05). Concurrently, crude ash and fiber fractions were significantly reduced. Soil physicochemical properties improved substantially, with the Y2E2 treatment in 2024 reducing soil pH and bulk density by 11.10% and 37.20%, respectively (p < 0.05), while increasing soil organic carbon, available nitrogen, and available potassium by 92.94%, 49.25%, and 96.08% (p < 0.05). Soil biological activity was significantly enhanced, as evidenced by increases of 78.33%, 55.69%, 55.87%, and 183.67% in β-glucosidase, dehydrogenase, urease, and acid phosphatase activities, respectively (p < 0.05), alongside rises of 117.64% and 94.78% in microbial biomass carbon and phosphorus (p < 0.05). Mechanistic analysis via structural equation modeling revealed strong positive direct effects of the Effective Microorganisms inoculant–organic fertilizer combination on forage yield (β = 0.27, p < 0.001) and nutritional quality (β = 0.73, p < 0.001). Principal component analysis (cumulative variance explained: 88.90%) further confirmed Y2E2 treatment superior performance in soil improvement, microbial function enhancement, and grassland productivity. In conclusion, the optimal remediation strategy for alpine mining grasslands was identified as the combined application of 0.60 t·hm⁻² Effective Microorganisms inoculant and 20 t·hm⁻² organic fertilizer. This approach drives ecosystem function restoration through a multidimensional synergistic mechanism involving soil physicochemical amelioration–microbial activity stimulation–nutrient supply optimization, providing both theoretical foundations and practical solutions for ecological restoration of degraded grasslands in similar regions. Full article

Understanding how biotic and environmental drivers jointly shape forest carbon dynamics over time is essential for climate-adapted management of subtropical forests. We investigated the long-term interactions between biotic factors, environmental factors, and forest carbon dynamics in the subtropical forests of Jiangxi Province, China, over the period 1989–2019. The High Accuracy Surface Modelling (HASM) multi-source data fusion method integrates ground observation points with area-wide data from remote sensing and existing datasets to simulate the spatial distribution of forest carbon density across the entire study area. In Zixi, forest carbon density increased most rapidly between 1989 and 2009, after which the rate slowed as forest stands matured. Structural Equation Modelling (SEM) disentangled direct and indirect effects of drivers, and identified species richness and community-weighted functional traits as key positive drivers of aboveground carbon density. The influence of environmental factors reversed over the study period. Under ongoing global warming, the combined effects of altitude, temperature, and precipitation shifted from suppressing to reinforcing carbon accumulation in later years, increasingly operating through pathways mediated by functional traits. These findings enhance our understanding of carbon dynamics in subtropical forests and underline the importance of preserving species richness, especially in subtropical mountain forest. This study provides valuable insights for adaptive forest management and climate change mitigation strategies, aiming to improve ecosystem resilience and sustain carbon sequestration efforts in the face of ongoing global warming. Full article

The acquisition of plant ecological indicators, such as leaf area index and leaf area density values, typically relies on labor-intensive field sampling and measurements, which are often time-consuming and hinder large-scale application. As different plant ecological indicators are closely related to plants’ geometric characteristics, the development of dynamic correlation and prediction methods for relevant indicators has become an important research topic. However, existing 3D plant models are mainly used for visualization purposes, which cannot accurately reflect the plant’s growth process or geometric characteristics. This study presents a workflow for parametric 3D plant modeling and ecological indicator analysis, integrating dynamic plant modeling, indicator calculation, and microclimate simulation. With the established plant model, a method for calculating and analyzing ecological indicators, including the leaf area index, leaf area density, aboveground biomass, and aboveground carbon storage, was then proposed. A method for exporting the model-generated data into ENVI-met v.5.0 to simulate the microclimate environment was also established. Then, by taking Daijia Lake Park as an example, this study utilized site planting construction drawings and field survey data to perform parametric modeling of 21,685 on-site trees from 65 species at three different growth stages using Blender v.4.0 and The Grove plugin v.10. The generated plant model’s accuracy was then verified using the 3D IoU ratio between the models and on-site scanned point cloud data. Plant ecological indicators at various stages were then extracted and exported to ENVI-met for microclimate analysis. The workflow integrates the simulation of plant growth dynamics and their interactions with environmental factors. It can also be used for scenario-based predictions in planting design and serves as a basis for urban green space monitoring and management. Full article

Mangroves are critical blue carbon ecosystems, yet accurately estimating their aboveground carbon (AGC) stocks remains challenging due to structural complexity and spectral saturation in dense canopies. This study aims to develop a scalable AGC estimation framework by integrating high-resolution canopy height (CH) data from UAV-LiDAR with multi-source satellite features from Sentinel-1, Sentinel-2, and ALOS PALSAR-2. Using the Maowei Sea mangrove zone in Guangxi, China, as a case study, we extracted structural, spectral, and textural features and applied Random Forest regression with Recursive Feature Elimination (RFE) to optimize feature combinations. Results show that incorporating UAV-derived CH significantly improves model accuracy (R² = 0.75, RMSE = 14.18 Mg C ha⁻¹), outperforming satellite-only approaches. CH was identified as the most important predictor, effectively mitigating saturation effects in high-biomass stands. The estimated total AGC in the study area was 88,363.73 Mg, with a mean density of 53.01 Mg C ha⁻¹. This study highlights the advantages of cross-scale UAV–satellite data fusion for accurate, regionally scalable AGC mapping, offering a practical tool for blue carbon monitoring and coastal ecosystem management under global change. Full article

Arctic warming is expected to alter permafrost landscapes and shift tundra ecosystems from greenhouse gas sinks to sources. We quantified plant biomass and necromass, carbon stocks, and microbial activity across five Low-Arctic tundra sites in the Yana–Indigirka Lowland (Chokurdakh, NE Siberia) during the 2024 growing season. Above- and below-ground plant biomass was measured by harvest adjacent to 50 × 50 m permanent plots; total C and N were determined by dry combustion on an elemental analyzer. Total organic carbon (TOC) stocks were calculated by horizon from TOC (%), bulk density, and thickness. Microbial basal respiration (BR), substrate-induced respiration (SIR), microbial biomass C (MBC), and the metabolic quotient (qCO₂) were assessed in litter/organic (O), peat (T), and mineral gley horizons. Mean above-ground biomass was 15.8 ± 1.5 t ha⁻¹; total living biomass averaged 43.1 ± 1.6 t ha⁻¹. Below-ground biomass exceeded above-ground by 1.73×. Carbon in above-ground, below-ground, and necromass pools averaged 7.8, 12.2, and 12.5 t C ha⁻¹, respectively. Surface organic horizons dominated ecosystem C storage: litter–peat stocks ranged from 234 to 449 t C ha⁻¹, whereas 0–30 cm mineral layers held 18–50 t C ha⁻¹; total (surface + 0–30 cm) stocks spanned 258–511 t C ha⁻¹ among sites. Key contributors to biomass and C storage were deciduous shrubs (Salix pulchra, Betula nana), bryophytes (notably Aulacomnium palustre), and the graminoids (Eriophorum vaginatum). BR and MBC were highest in O and T horizons (BR up to 21.9 μg C g⁻¹ h⁻¹; MBC up to 70,628 μg C g⁻¹) and declined sharply in mineral soil; qCO₂ decreased from O to mineral horizons, indicating more efficient C use at depth. These in situ data show that Low-Arctic tundra C stocks are concentrated in surface organic layers while microbial communities remain responsive to warming, implying high sensitivity of carbon turnover to thaw and hydrologic change. The dataset supports model parameterization and remote sensing of shrub–tussock tundra carbon dynamics. Full article

Grassland aboveground biomass (AGB) serves as a critical indicator of ecosystem productivity and carbon cycling, playing a pivotal role in ecosystem functioning. The advances in hyperspectral and terrestrial Light Detection and Ranging (LiDAR) data have provided new opportunities for grassland AGB monitoring, but current research remains predominantly focused on data-driven machine learning models. The black-box nature of such approaches resulted in a lack of clear interpretation regarding the coupling relationships between these two data types in grassland AGB estimation. For grassland aboveground fresh biomass, the theoretical estimation can be decomposed into either the product of planar density (PD) and plot area or the product of stereoscopic density (SD) and grassland community volume. Based on this theory, our study developed a semi-mechanistic remote sensing model for grassland AGB estimation by integrating hyperspectral-derived biomass density with extracted structural parameters from terrestrial LiDAR. Initially, we built hyperspectral estimation models for both PD and SD of grassland fresh AGB using PLSR. Subsequently, by integrating the inversion results with grassland quadrat area and community volume measurements, respectively, we achieved quadrat-scale remote sensing estimation of grassland AGB. Finally, we conducted comparative accuracy assessments of both methods across different phenological stages to evaluate their performance differences. Our results demonstrated that SD, which incorporated structural features, could be more precisely estimated (R² = 0.90, nRMSE = 7.92%, Bias% = 0.01%) based on hyperspectral data compared to PD (R² = 0.79, nRMSE = 10.19%, Bias% = −7.25%), with significant differences observed in their respective responsive spectral bands. PD showed greater sensitivity to shortwave infrared regions, while SD exhibited stronger associations with visible, red-edge, and near-infrared bands. Although both methods achieved comparable overall AGB estimation accuracy (PD-based: R² = 0.79, nRMSE = 10.19%, Bias% = −7.25%; SD-based: R² = 0.82, nRMSE = 10.58%, Bias% = 1.86%), the SD-based approach effectively mitigated the underestimation of high biomass values caused by spectral saturation effects and also demonstrated superior and more stable performance across different growth periods (R² > 0.6). This work provided concrete physical meaning to the integration of hyperspectral and LiDAR data for grassland AGB monitoring and further suggested the potential of multi-source remote sensing data fusion in estimating grassland AGB. The findings offered theoretical foundations for developing large-scale grassland AGB monitoring models using airborne and spaceborne remote sensing platforms. Full article

Karst regions, characterized by thin soil layers, severe rocky desertification, and fragile vegetation, hold significant scientific value for achieving China’s “dual-carbon” goals. This study focuses on Zhijin County in Guizhou Province, integrating provincial carbon density data with forest resource inventory data. By constructing a model to adjust aboveground forest carbon density (AGC) estimation parameters and utilizing the InVEST model alongside hotspot analysis, the research systematically examines the spatiotemporal heterogeneity of carbon storage from 2000 to 2020. These findings provide actionable strategies for enhancing carbon sequestration efficiency in ecologically fragile regions, supporting China’s “dual-carbon” policy goals. Key findings include: (1) Carbon storage exhibits a “growth-turning point” two-phase pattern, increasing by 0.46% from 2000 to 2015 but decreasing by 3.31% in 2020 due to construction land expansion. (2) There are significant differences in carbon storage among ecological engineering projects, with the highest carbon storage found in the “Grain-for-Green Program” project area and the lowest in the “National Rocky Desertification Control Program” area. (3) Elevation is the primary controlling factor for carbon storage, with rocky desertification showing notable spatial differentiation. This study provides theoretical support for the precise regulation of ecological programs and the development of high-precision carbon storage models in karst regions. Full article

Forest ecosystems act as major carbon sinks, highlighting the need for the accurate estimation of aboveground biomass (AGB). The Global Ecosystem Dynamic Investigation (GEDI), a full-waveform spaceborne LiDAR system developed by NASA, provides detailed global observations of three-dimensional forest structures, playing a critical role in quantifying biomass and carbon storage. However, its performance has not yet been assessed in the Mediterranean forest ecosystems of Southern Italy. Therefore, the objectives of this study were to (i) evaluate the utility of the GEDI L4A gridded aboveground biomass density (AGBD) product in the Apulia region by comparing it with the Apulia AGBD map, and (ii) develop GEDI-derived AGBD models using multiple GEDI metrics. The results indicated that the GEDI L4A gridded product significantly underestimated AGBD, showing large discrepancies from the reference data (RMSE = 40.756 Mg/ha, bias = −30.075 Mg/ha). In contrast, GEDI-derived AGBD models using random forest (RF), geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR) demonstrated improved accuracy. Among them, the MGWR model emerged as the optimal choice for AGBD estimation, achieving the lowest RMSE (14.059 Mg/ha), near-zero bias (0.032 Mg/ha), and the highest R² (0.714). Additionally, the MGWR model consistently outperformed other models across four different plant functional types. These findings underscore the importance of local calibration for GEDI data and demonstrate the capability of the MGWR model to capture scale-dependent relationships in heterogeneous landscapes. Overall, this research highlights the potential of the GEDI to estimate AGBD in the Apulia region and its contribution to enhanced forest management strategies. Full article

Accurate assessment of terrestrial ecosystem carbon storage is essential for understanding the global carbon cycle and informing climate change mitigation strategies. However, traditional estimation models face significant challenges in complex mountainous regions due to difficulties in data acquisition and high ecosystem heterogeneity. This study focuses on the Nanling Mountains in Guangdong Province, China, utilizing the Google Earth Engine (GEE) platform to integrate multi-source remote sensing data (Sentinel-1/2, ALOS, GEDI, MODIS), topographic/climatic variables, and field-collected samples. We employed machine learning models to achieve high-precision prediction and high-resolution mapping of ecosystem carbon storage while also analyzing spatial differentiation patterns. The results indicate that the Random Forest algorithm outperformed Gradient Boosting Decision Tree and Classification and Regression Tree (CART) algorithms by suppressing overfitting through dual randomization. The integration of multi-source data significantly enhanced model performance, achieving a coefficient of determination (R²) of 0.87 for aboveground biomass (AGB) and 0.65 for soil organic carbon (SOC). Integrating precipitation, temperature, and topographic variables improved SOC prediction accuracy by 96.77% compared to using optical data alone. The total carbon storage reached 404 million tons, with forest ecosystems contributing 96.7% of the total and soil carbon pools accounting for 60%. High carbon density zones (>160 Mg C/ha) were mainly concentrated in mid-elevation gentle slopes (300–700 m). The proposed integrated “optical-radar-topography-climate” framework offers a scalable and transferable solution for monitoring carbon storage in complex terrains and provides robust scientific support for carbon sequestration planning in subtropical mountain ecosystems. Full article

Accurate above-ground biomass (AGB) quantification is confounded by signal saturation and data fusion challenges, particularly in structurally complex ecosystems like bamboo forests. To address these gaps, this study developed a two-stage framework to map the AGB of Dendrocalamus giganteus in a subtropical mountain environment. This study first employed Empirical Bayesian Kriging Regression Prediction (EBKRP) to spatialize sparse GEDI and ICESat-2 LiDAR metrics using Sentinel-2 and topographic covariates. Subsequently, a stacked ensemble model, integrating four machine learning algorithms, predicted AGB from the full suite of continuous variables. The stacking model achieved high predictive accuracy (R² = 0.84, RMSE = 11.07 Mg ha⁻¹) and substantially mitigated the common bias of underestimating high AGB, improving the predicted observed regression slope from a base model average of 0.63 to 0.81. Furthermore, SHAP analysis provided mechanistic insights, identifying the canopy photon rate as the dominant predictor and quantifying the ecological thresholds governing AGB distribution. The mean AGB density was 71.8 ± 21.9 Mg ha⁻¹, with its spatial pattern influenced by elevation and human settlements. This research provides a robust framework for synergizing multi-source remote sensing data to improve AGB estimation, offering a refined methodological pathway for large-scale carbon stock assessments. Full article