Search Results (144)

Urban green open spaces play a critical role in mitigating greenhouse gas emissions and enhancing biodiversity in rapidly urbanizing tropical megacities. This study aims to characterize vegetation structure and species diversity, as well as quantify above-ground carbon stocks in Taman Bendera Pusaka, South Jakarta, Indonesia. The results are expected to inform evaluations of urban parks’ contribution as nature-based solutions. A complete tree census was conducted between August and September 2025, recording diameter at breast height, height, and species identity for all individuals. Above-ground biomass (AGB) for woody trees was estimated using a widely applied pantropical allometric model developed for humid tropical forests, while separate generalized equations were applied respectively to the palms and bamboos groups to account for their distinct morphological characteristics. The estimated biomass values were subsequently converted into carbon stocks and CO₂ equivalents. Biodiversity was evaluated using Margalef richness, Shannon–Wiener diversity, and Pielou’s evenness indices. A total of 2459 individuals were recorded. Langsat Park had the highest species richness (S = 79) with diversity (H′ = 3.38; E = 0.77), and Leuser (S = 78) with highest diversity (H′ = 3.44; E = 0.79), whereas Ayodya Parks showed the lowest structures and diversity values (S = 33; H′ = 2.18; E = 0.68). Total AGB reached 5873.63 Mg, with a mean carbon density of 444.64 Mg C ha⁻¹ or 1631.82 Mg CO₂ ha⁻¹. Carbon storage was largely driven by a few dominant ornamental palms, particularly Roystonea regia, although native canopy trees contributed to structural stability. These findings show that even relatively small tropical urban parks can serve as significant localized carbon storage while sustaining urban biodiversity, underscoring the value of structurally diverse, native-enriched planting strategies for resilient urban forest management. Full article

Dwarf cereal cultivars were crucial for the Green Revolution. Dwarfed, lodging-resistant varieties remain essential today, as climate change brings more storms and downpours. In barley, the dwarfing gene HvDEP1 has been widely used in breeding. Although its pleiotropic effects on agronomic traits have been examined, previous studies relied on cultivars developed via random mutagenesis, which carry background mutations that may influence phenotypes. Moreover, its impact on root traits remains underexplored. We used CRISPR/Cas9 to generate precise HvDEP1 mutants and introduce dwarfism into the barley cultivar ‘Maythorpe.’ We assessed the effects on above-ground morphology, yield-related traits, root architecture, biomass via ¹³C labelling, and the root metabolome. HvDEP1 mutations significantly reduced plant height, straw, spike, and awn length, as well as thousand-grain weight. An in-frame mutant showed intermediate height, straw, and awn phenotypes. Belowground, in a root experiment restricted to knockout line #12, specific root length and the length of the finest (0–0.25 mm) roots were reduced, while total root length was lower but not significantly so; (p = 0.062). Root metabolomic profiling detected no genotype-associated differences. These results provide new insights into HvDEP1′s role in both shoot and root systems and demonstrate that precise CRISPR/Cas9-mediated editing can rapidly introduce dwarfism while revealing trade-offs in other agronomic traits. Full article

Perfluorotetradecanoic acid (PFTeDA), a long-chain per- and polyfluoroalkyl substance (PFAS), is highly persistent and bioaccumulative, yet its effects on soil bacterial communities remain poorly understood. This study evaluated the impact of PFTeDA on the taxonomic composition, diversity, and functional potential of soil bacteria, and assessed whether biostimulants (Shigeki and Aminoprim) could mitigate these effects and improve the growth of Amaranthus cruentus. A pot experiment was conducted using Eutric Cambisols soil (pH 4.62), and bacterial communities were analyzed by 16S rRNA gene sequencing. PFTeDA significantly altered bacterial community composition, reducing the abundance of Actinomycetota, Chloroflexota, and Gemmatimonadota, while also changing alpha and beta diversity and predicted functional profiles. Biostimulant application partially alleviated these effects by promoting recovery of the soil bacterial communities and bacteria associated with nitrogen cycling and organic matter degradation, indicating partial restoration of soil ecosystem functions. PFTeDA reduced aboveground biomass but increased root biomass of A. cruentus, without affecting leaf greenness (SPAD). Both biostimulants enhanced plant growth and SPAD values, with Aminoprim exerting a stronger effect on shoot biomass and Shigeki stimulating root development. This study demonstrates that PFTeDA affects not only the taxonomic composition of the soil bacterial communities but, above all, its stability and predicted functional potential, representing a key mechanism underlying its impact on the soil–plant system. It also highlights that biostimulation may serve as an effective tool supporting the potential improvement in soils contaminated with persistent organic compounds. Full article

Urban plant communities are widely regarded as important nature-based solutions for climate mitigation, yet their actual carbon benefits remain uncertain: vegetation growth is accompanied by carbon emissions from construction and long-term maintenance, and existing assessments rarely integrate community succession, interspecific competition, and maintenance-related emissions within a consistent life-cycle framework. To address these limitations, this study developed a dynamic succession-based life-cycle simulation model to project the 50-year carbon source–sink transitions of 150 typical urban plant communities in Tianjin, China. The model updates plant structural attributes—diameter at breast height, crown width, and tree height—iteratively by linking individual plant growth to environmental suitability and neighborhood competition through a Plant Health Index. Simulated structural trajectories were coupled with biomass equations and carbon content coefficients to estimate aboveground carbon sequestration, while construction and maintenance emissions were quantified using life cycle assessment, enabling evaluation of modeled net carbon balance rather than gross carbon sequestration alone. Under the modeled 50-year scenario, most communities were projected to act as carbon sources during the early stage but gradually shifted toward carbon sinks as biomass accumulated; 86.1% of the communities were projected to become net carbon sinks after 50 years (a scenario-based projection under specified growth, maintenance, and emission assumptions). The highest modeled net carbon balance reached 3186.08 kg·C·ha⁻¹, whereas the weakest community remained a slight carbon source at −81.21 kg·C·ha⁻¹. Vertical structural complexity and species richness were the strongest positive predictors of modeled net carbon balance, followed by three-dimensional green quantity and canopy closure. Among maintenance processes, fertilization was the dominant emission source, followed by pesticide application and irrigation; comparative scenario analysis showed that resource-saving maintenance consistently improved projected net carbon balance relative to high-maintenance management. These results suggest that low-carbon planting design should prioritize locally adapted species, multi-layered vertical structures, and adaptive maintenance over simply maximizing planting density or minimizing inputs. The results represent scenario-based projections of aboveground vegetation carbon balance; belowground biomass, soil carbon, litter carbon, dead organic matter, and parameter uncertainty were not fully incorporated, and future studies should address these limitations to improve the robustness and transferability of the proposed framework. Full article

Estimating carbon stocks in semi-urban ecosystems remains challenging due to spatial heterogeneity and the scale limitations of conventional datasets. This study aims to estimate and analyse the spatial and temporal distribution of carbon stocks at Universitas Padjadjaran using high-resolution remote sensing imagery and to support sustainable campus and green campus initiatives. Multi-temporal data from WorldView-2 (2015, 2017), WorldView-3 (2021), and Legion-03 (2025) were used to derive vegetation indices, followed by aboveground biomass (AGB) modelling through regression analysis. Carbon stock was calculated using a standard conversion factor of 0.5. The results show a consistent increase in vegetation density and carbon stock, with average values rising from 20.381 tonnes/ha in 2015 to 29.160 tonnes/ha in 2025. The use of the MSAVI produced an accurate model for predicting AGB (R² = 0.987–0.993). This study introduces a novel integration of high-resolution imagery using MSAVI to improve AGB estimation at the campus scale, providing a more detailed and reliable approach for carbon assessment in heterogeneous semi-urban environments and contributing to the implementation of sustainable, environmentally friendly campus management strategies. Full article

Soybean is a critical economic, oil and industrial raw material crop, yet its production is often hindered by pathogen infection and pesticide residues. This study explored the synergistic effects of Rhizophagus intraradices and mycorrhizal helper bacteria (MHB) on AMF colonization, AMF spore density, total number of bacterial colonies, soybean growth, root rot disease index, and carbendazim residues. Hydroponic and pot experiments were conducted using a completely randomized design (CRD) with five biological replicates per treatment; after 30 days of growth, three replicates were randomly selected for all measurements. Results showed that inoculation with microbial agents, particularly co-inoculation, increased soybean biomass, reduced disease index, and decreased carbendazim residues. In the hydroponic experiment, co-inoculation increased plant height, aboveground fresh weight, and underground dry weight by 64.28%, 78.13%, and 109.09%, respectively, and decreased carbendazim residues by 71.84% relative to the carbendazim-alone group. In the pot experiment, co-inoculation reduced carbendazim residues by 81.25% and root rot disease index by 45.56% compared with the carbendazim-alone group. Correlation analysis showed a strong positive correlation (p < 0.001) between carbendazim degradation in hydroponic and pot systems, indicating stable degradation function across environments. Co-inoculation of R. intraradices and MHB synergistically promotes soybean growth, suppresses root rot, and reduces carbendazim residues, providing a theoretical basis for developing functional microbial inoculants for safe and green soybean production. Full article

Rooftop farms are urban green infrastructure integrating food production, ecological regulation, and public services, and their management increasingly relies on data-driven approaches. However, open built environments, microclimatic heterogeneity, and limited sensor deployment challenge continuous monitoring and short-term prediction of rooftop plant growth. This study proposes and validates a virtual sensor-driven digital twin system using a rooftop tomato case in Xiamen, China. The system adopts a five-layer architecture comprising data acquisition, transmission, modeling, processing, and application service layers. By coupling a Long Short-Term Memory (LSTM) weather prediction model with the Decision Support System for Agrotechnology Transfer (DSSAT) crop growth model, a predictive virtual sensor module was developed to forecast leaf area index (LAI), aboveground biomass, phenology, and yield for seven days. Results show that the system links environmental data acquisition, LSTM–DSSAT prediction, database storage, and three-dimensional visualization, transforming rooftop plant growth into an updatable, predictable, and visualized digital twin object. The coupled model showed high predictive accuracy, with R² values of 0.9814 for LAI and 0.9966 for aboveground biomass, while supporting phenology and yield prediction. The system supports irrigation optimization, landscape management, and activity planning in sensor-constrained rooftop farms. 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

The accurate estimation of pasture above-ground biomass (AGB) is critical for optimizing stocking rates and ensuring the sustainable use of Mediterranean pastures. This study developed empirical models to estimate fresh (AGB_fresh) and dry above-ground biomass (AGB_dry) using multispectral imagery acquired by Unmanned Aerial Vehicles (UAVs) in a Hedysarum coronarium pasture in Sicily, Italy. Field biomass was destructively sampled simultaneously with UAV surveys in 28 georeferenced plots during pre- and post-grazing phases over the 2023–2024 and 2024–2025 seasons. Data were collected with a DJI Mavic 3 Multispectral (for the 2024 test) and a DJI Matrice 300 + Altum-PT (for the 2025 test) and radiometrically calibrated to surface reflectance. Because two different multispectral sensors were used across years, an inter-sensor harmonization step was applied before vegetation-index calculation. Thirty-three vegetation indices were extracted as mean values within circular buffers of 1 m radius, centered on each sample plot to accommodate GNSS/georeferencing uncertainty. For each vegetation index, linear and exponential models were calibrated using 66% of the dataset and validated on the remaining 33% to predict fresh and dry above-ground biomass, and model performance was assessed using R² and RMSE. On the validation dataset, ARVI2 and EVI2 showed the highest explanatory power for AGB_fresh (R² = 0.89), with ARVI2 providing the lower RMSE (2047 g m⁻²). For AGB_dry, visible-band indices such as NGRDI and GRVI were among the best performers, reaching R² = 0.85 with RMSE = 1371 g m⁻². Visible-band greenness indices were among the most competitive predictors, whereas several conventional NIR-based indices showed only moderate performance. Overall, this UAV-based multispectral approach represents a promising and interpretable tool for biomass estimation in heterogeneous Mediterranean pastures, although further validation across additional seasons and sites is required to strengthen its transferability. Full article

Urban green spaces play a key role in supporting biodiversity, climate regulation, and carbon storage in rapidly expanding cities. Urban and peri-urban parks can differ markedly in tree-community structure, floristic diversity, and carbon-storage capacity. The aim of the study was to compare these attributes between an urban and a peri-urban park. The study compared these attributes between an urban park and a peri-urban park in western Mexico using data collected in 500 m² circular plots. Tree structure was assessed through diameter at breast height, height, crown diameter, basal area, and crown projection area, while floristic composition and diversity were evaluated using richness, Shannon, Simpson, Pielou, and Menhinick indices. Aboveground biomass, belowground biomass, and carbon stocks were estimated using generalized allometric equations. A total of 1675 trees belonging to 19 families, 33 genera, and 49 species were recorded. The peri-urban park showed greater structural development, with significantly higher DBH, height, crown diameter, basal area, biomass, and carbon stocks, whereas the urban park supported greater species richness and higher Shannon diversity. Species composition also differed strongly between parks, and carbon storage was concentrated in a reduced number of dominant taxa in each site. DBH was the structural variable most strongly associated with total carbon per tree. These findings show that floristic diversity and carbon-storage capacity do not necessarily increase in parallel and that urban and peri-urban parks can provide contrasting but complementary ecological functions. Full article

Blue–green spaces serve as the core carriers of urban ecosystems, and their conservation and optimization have emerged as pivotal issues in territorial spatial planning and ecological governance. Taking Guilin, a national innovation demonstration zone for China’s Sustainable Development Agenda, as the study area, a deep learning-based DBDTAF-Net classification model is constructed using 2020 Sentinel-2 remote sensing imagery and AW3D30 Digital Surface Model (DSM) data. The model achieves a mean Intersection-over-Union (mIoU) of 86.05% on the test set and an IoU of 94.67% for rocky desertification areas. Based on the classification results, 21 derived indicators (including landscape patterns of BGSs) and six meteorological and topographic factors, alongside three core ecosystem service indicators—Aboveground Biomass (AGB), Net Primary Productivity (NPP), and soil conservation—are extracted to characterize their spatial patterns. The XGBoost-SHAP framework is employed to quantify the driving effects and threshold responses of BGS patterns on ecosystem services. The results indicate that (1) BGSs in Guilin display a spatial pattern of “green-dominated, blue-supplemented, generally contiguous yet locally fragmented,” and all three ecosystem services exhibit significant spatial clustering. (2) Landscape pattern factors of green spaces constitute the dominant influencing factors, with contribution rates ranging from 22.3% to 28.6%. Specifically, green space_COHESION demonstrates a stable linear positive effect. A green space ratio below 45% suppresses AGB, whereas exceeding 45% shifts to a positive effect and represents an efficient enhancement interval for NPP while exerting a continuously positive influence on soil conservation. A cultivated land proportion below 30% leads to a strongly increasing inhibitory effect on AGB and soil conservation, whereas its inhibition on NPP weakens beyond 20%. A construction land proportion exceeding 10% significantly suppresses NPP, and the inhibitory effect stabilizes above 20%. Green space patch density below 0.8 shows a pronounced negative effect, which diminishes above 0.8. Blue space factors exert relatively weak effects. (3) The ecosystem service supply capacity varies across functional zones in Guilin, with the ecological barrier zone performing the best, the modern agricultural zone performing moderately, and the six central urban districts of the Shanshui Metropolis Area exhibiting the lowest levels. This study provides a technical framework for high-precision extraction of urban BGSs and quantitative analysis of factors influencing ecosystem services, offers decision support for ecological conservation and restoration in Guilin, and furthermore proposes insights for the coordinated development of rational land resource utilization and ecosystem service enhancement in other karst cities. Full article

Monitoring boreal above-ground biomass (AGB) change requires approaches that are both measurement-based and spatially explicit. We integrated permanent sample plots from Quebec and Ontario with Landsat-7 spectral trajectories (1999–2023) to quantify non-fire-related AGB change after excluding wildfire-affected intervals and to evaluate whether annualized AGB change can be predicted from spectral change at the plot-interval scale. Tree height was estimated using a multilayer perceptron model (R² = 0.83) and combined with species-specific allometry to derive plot-level AGB and interval ΔAGB. These estimates were aggregated to ecodistricts using effective sample sizes and confidence intervals. Across well-sampled ecodistricts, mean annualized ΔAGB ranged from −0.82 to +3.54 t ha⁻¹ yr⁻¹, with lower or negative changes mainly occurring in eastern regions. Spectral indices derived from NIR–SWIR bands showed relatively stronger associations with ΔAGB than greenness-based indices, consistent with the sensitivity of moisture- and disturbance-related metrics to canopy stress, including defoliation. An XGBoost ensemble correctly predicted the direction of change in 77% of intervals. These results provide a measurement-constrained and scalable framework for monitoring non-fire-related biomass change and supporting greenhouse-gas reporting across boreal forest landscapes. Full article

To support low-cost, non-destructive crop growth monitoring, this study systematically compared different vegetation indices, voxel sizes, and camera angles using a point cloud voxelization approach combined with a vegetation index weighted canopy volume index (CVM_VI) to assess aboveground biomass (AGB) in winter oilseed rape (Brassica napus L.). Field experiments were conducted from 2021 to 2024 at the Yangma Experimental Base of the Chengdu Academy of Agricultural and Forestry Sciences. Red, green, blue (RGB) imagery of oilseed rape was acquired using an unmanned aerial vehicle (UAV) during the following five key growth stages: seedling, bolting, flowering, podding, and maturity. Collected images were processed to generate point clouds, which were subsequently voxelized at four resolutions (0.03, 0.05, 0.07, and 0.1 m). CVM_VI was constructed by integrating vegetation indices (VIs) derived from the RGB data and the voxelized canopy structural information. Regression models were established between the CVM_VI values and field-measured AGB to estimate biomass. Model performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), and relative error (RE). There were strong correlations (r > 0.80) between the estimated and measured AGB across all voxelization treatments throughout the growth period. Among the 20 VIs tested, regression methods based on the blue green ratio index (BGI), color intensity index, blue red ratio index, vegetative index, and green red ratio index consistently showed superior estimation performance across three consecutive years, demonstrating their good applicability for estimating AGB in oilseed rape under varying agronomic conditions (different varieties, densities, and sowing dates). The cubic regression model CVM_BGI performed best under a 45° UAV camera angle, with the highest R² and lowest RMSE and RE (2021–2022: R² = 0.864, RMSE = 2414.18 kg/ha, RE = 14.8%; 2022–2023: R² = 0.754, RMSE = 2550.53 kg/ha, RE = 14.9%; 2023–2024: R² = 0.863, RMSE = 1953.61 kg/ha, RE = 22.9%). Since the estimation performance showed negligible differences among voxel sizes, and the 0.1–m voxel offered the smallest data volume and shortest analysis time, the CVM_BGI model with a 0.1–m voxel was selected as the preferred approach, providing a practical balance between estimation performance and processing demand. These findings highlight the application potential of point cloud voxelization technology for crop biomass estimation. This study proposes a novel, non-destructive, and efficient framework for estimating field crop AGB using low-cost UAV RGB imagery, facilitating the wider adoption of UAV technology in practical agricultural production. Full article

Cadmium is an element that is unnecessary for the functioning of plant and animal organisms, and its widespread presence in the environment poses a serious threat to human and animal health. Therefore, effective methods are being sought to remediate soils contaminated with this element, including through the enrichment of degraded soils with organic matter. To this end, the effectiveness of selected organic sorbents, including starch, fermented bark, compost and humic acids, in mitigating the transfer of cadmium and other heavy metals from soil to plants was assessed. Model studies compared the effects of 15 and 30 mg of cadmium (Cd) per kg of soil with an uncontaminated control sample. The sorbents were applied on a carbon basis at a rate of 3 g C per kg of soil. The test plant was Zea mays. Cadmium was found to significantly impair plant growth, causing reductions of 21%, 85%, and 77% in leaf greenness, aboveground biomass and root biomass, respectively. Excess cadmium increased the translocation of lead, chromium, copper, nickel, zinc, iron, and manganese from the roots to the aboveground parts of the plant, while simultaneously limiting their uptake. All of the organic sorbents tested reduced the negative impact of cadmium on leaf greenness, except starch. Compost and HumiAgra significantly improved the condition of Zea mays plants weakened by cadmium exposure. Cadmium contamination increased soil acidification. pH was positively correlated with maize yield and the SPAD leaf greenness index and negatively correlated with the cadmium translocation index and cadmium content in the aboveground parts of maize. Compost and humic acids are among the most effective and practically feasible approaches for reducing cadmium bioavailability in soil and its accumulation in Zea mays, and are therefore recommended for the remediation of cadmium-contaminated soils. Full article

Tree and shrub planting is a widely used strategy to restore degraded semi-arid grasslands. Although nutrient resorption is a key adaptation to nutrient-limited environments, its dynamics at decadal scales remain poorly understood. In this study, we measured species-averaged nitrogen resorption efficiency (NRE) at both community and functional group levels, together with soil nutrients, across 20- and 40-year shrub-planted sites and a 40-year tree-planted site in Inner Mongolia, China. At the community level, green and senesced leaf nitrogen (N) concentrations, NRE, and aboveground biomass did not differ significantly among sites. However, clear differences emerged at the functional group level: Poaceae exhibited higher NRE than forbs and lower senesced leaf N than both forbs and Fabaceae. As restoration progressed, Poaceae replaced forbs as the dominant group, coinciding with increased soil nutrient availability. Notably, NRE in Poaceae declined with increasing soil nutrients, suggesting a shift toward greater reliance on direct soil nutrient uptake. This shift, combined with the production of low-nitrogen litter by dominant Poaceae species, may ultimately slow soil nutrient accumulation. Our findings highlight the importance of functional group dynamics in regulating long-term nutrient resorption and cycling and suggest that managing Poaceae dominance could enhance long-term soil nutrient enrichment and biodiversity in restored semi-arid grasslands. Full article