Search Results (383)

Ozonized vegetable oils are gaining attention for their antimicrobial and therapeutic potential, yet the lack of standardized ozonation protocols and incomplete characterization of their chemical profiles hinder clinical translation. In this study, we standardized the ozonation process of sunflower oil and investigated the chemical evolution and antimicrobial efficacy of the resulting products. Ozonation proceeded through a classical three-step mechanism involving the formation of primary ozonides, their decomposition into carbonyl compounds and carbonyl oxides, and subsequent recombination into stable secondary ozonides capable of sustained ozone release with reduced toxicity. Time-course analysis at 100, 240, and 480 min revealed key reaction products, including the appearance of azelaic acid after 240 min, progressive depletion of linoleic acid, and the emergence of 2,5-furandione exclusively after 480 min—indicative of advanced oxidative processes. The formation of hydroperoxides and their secondary degradation into ketones, acids, and epoxides was also observed, with implications for both biological activity and sensory properties. Importantly, the ozonized oil demonstrated potent antimicrobial activity against Staphylococcus aureus, Escherichia coli, Salmonella choleraesuis, Pseudomonas aeruginosa, Candida albicans, and Aspergillus brasiliensis. These findings provide a comprehensive chemical and functional characterization of ozonized sunflower oil and support its development as a standardized antimicrobial agent for therapeutic use. Full article

As the world’s most populous and geographically diverse continent, active fire occurrence in Asia exhibits pronounced spatiotemporal heterogeneity, driven by climactic and anthropogenic factors. However, systematic analyses of Asian fire occurrence characteristics are still scarce, the quantitative and spatial relationship between fire dynamics and drivers remain poorly understood. Here, utilizing active fire and land cover products alongside climate and human footprint datasets, we explored the spatiotemporal distribution and dynamics of active fire counts (FC) over 20 years (2003–2022) in Asia, quantifying the effects of climate and human management. Results analyzed over 10 million active fires, with cropland fires predominating (25.6%) and Southeast Asia identified as the hotspot. FC seasonal dynamics were governed by temperature and precipitation, while spring was the primary burning season. A continental inter-annual FC decline (mean slope: −8716 yr⁻¹) was identified, primarily attributed to forest fire reduction. Subsequently, we further clarified the drivers of FC dynamics. Time series decomposition attributed short-term FC fluctuations to extreme climate events (e.g., 2015 El Niño), while long-term trends reflected cumulative human interventions (e.g., cropland management). The trend analysis revealed that woody vegetation fires in the Indochina Peninsula shifted to herbaceous fires, Asian cropland FC primarily increased but were restricted in eastern China and Thailand by strict policies. Spatially, hydrometeorological factors dominated 58.1% of FC variations but exhibited opposite effects between arid and humid regions, followed by human factor, where human activities shifted from fire promotion to suppression through land-use transitions. These driving mechanism insights establish a new framework for adaptive fire management amid escalating environmental change. Full article

This study proposes an Automatic Branch Modeling (ABM) framework that combines AdTree and AdQSM algorithms to reconstruct individual tree models and estimate timber volume from fused Hand-held Laser Scanners (HLS) and Unmanned Aerial Vehicle Laser Scanners (UAV-LS) point cloud data. The research focuses on two 50 × 50 m primary tropical rainforest plots in Hainan Island, China, characterized by dense and vertically stratified vegetation. Key steps include multi-source point cloud registration and noise removal, individual tree segmentation using the Comparative Shortest Path (CSP) algorithm, extraction of diameter at breast height (DBH) and tree height, and 3D reconstruction and volume estimation via cylindrical fitting and convex polyhedron decomposition. Results demonstrate high accuracy in parameter extraction, with DBH estimation achieving R² = 0.89–0.90, RMSE = 2.93–3.95 cm and RMSE% = 13.95–14.75%, while tree height estimation yielded R² = 0.89–0.94, RMSE = 1.26–1.81 m and RMSE% = 9.41–13.2%. Timber volume estimates showed strong agreement with binary volume models (R² = 0.90–0.94, RMSE = 0.10–0.18 m³, RMSE% = 32.33–34.65%), validated by concordance correlation coefficients (CCC) of 0.95–0.97. The fusion of HLS (ground-level trunk details) and UAV-LS (canopy structure) data significantly improved structural completeness, overcoming occlusion challenges in dense forests. This study highlights the efficacy of multi-source LiDAR fusion and 3D modeling for precise forest inventory in complex ecosystems. The ABM framework provides a scalable, non-destructive alternative to traditional methods, supporting carbon stock assessment and sustainable forest management in tropical rainforests. Future work should refine individual tree segmentation and wood-leaf separation to further enhance accuracy in heterogeneous environments. Full article

Leaf litter conservation practices in forests can contribute to increasing CO₂ storage in natural soils as organic matter; however, this process depends on the type of vegetation cover. This study, using different approaches, aimed to assess this process starting from the characteristics of three different types of litters and topsoil (0–5 cm depth) originating from chestnut, beech, and pine in various forest locations within the territory of Edolo (Camonica Valley, Central Italian Alps). Both labile (DOM) and recalcitrant (ROM) organic matter fractions were considered. Microbial degradation activity was strongly influenced by DOM (DOM vs. Respiration mg CO₂ g⁻¹ dry matter: r = 0.96), and NMR spectroscopy showed that aromatic C and polymethylene C in long-chain aliphatic structures (e.g., lipids, cutin) became more evident from litters to topsoils due to a concentration effect. Finally, chemometric elaboration of quantitative and qualitative data identified two principal component (PC) profiles, explaining 88% of the total variance, in which litter and the topsoil samples were spatially separated, indicating that significant changes occurred during the decomposition process. An Evolution Index (EI) calculated highlighted greater changes for chestnut (0.90) followed by pine (0.60) and beech (0.48), in agreement with chemical (degradation rates of 14.21%, 49.11%, and 48% for beech, chestnut, and pine litter, respectively) and spectroscopic data. Beech litter appears to be more efficient at conserving organic carbon. These findings underscore the importance of understanding litter characteristics for forest management, suggesting which species are most effective in promoting soil carbon storage. Full article

This study investigates the composition and transformation of soil organic matter (SOM) across seven sites in Maritime Antarctica, focusing on the impact of bird activity and vegetation cover on SOM dynamics. There is limited knowledge of the stability of Antarctic SOM and the effects of seabird colonies on it. This study aims to address the knowledge gap regarding drivers of soil organic matter transformations in polar ecosystems. Hot water-extractable carbon (HWC) and carbon extracted with phosphoric acid (PHP-C) were chosen as parameters for the labile carbon pool. A stable carbon pool was here characterized as one with alkali-soluble organic compounds opposing microbial decomposition. This carbon pool has long (decades) turnover rates, and therefore is regarded stable. The mentioned carbon pools were used to calculate humification indices. The HWC in studied soils ranged from 1.5 to 4.3% of total carbon, while the PHP-C varied largely and was not correlated with HWC. Soils influenced by current or historical bird colonies (particularly penguins and skuas) exhibited elevated labile carbon fractions, indicating active microbial processing. In contrast, sites without bird influence showed lower biological activity. The stable carbon peaked at 18.9% of total carbon, indicating distinct soil transformation stages. The humification degree (HD) and labile-to-stable carbon (L/S) ratio were used to assess SOM stability, revealing that former bird rookeries had the most stabilized SOM, while recently deglaciated sites were in early stages of organic matter accumulation. Vegetation cover, though secondary to bird impact, was positively correlated with SOM humification, supporting the role of vascular plant-derived organic input in carbon stabilization. The study showed a clear link between bird activity and SOM dynamics, supporting the concept of biological legacies in soil formation in Antarctica. It highlighted the role of vegetation in SOM stabilization, which is crucial for understanding how terrestrial ecosystems may evolve as ice retreats and plant colonization expands. Full article

In arid regions with limited water resources, improving cropland water-use efficiency (WUEc) is crucial for maintaining crop production. This study aims to investigate how changes in meteorological and vegetation factors affect WUEc in drylands and to identify its primary drivers, which are essential for understanding how cropland ecosystems respond to complex environmental changes. Using remote sensing data, we analyzed the spatiotemporal patterns of WUEc in Xinjiang from 2002 to 2022 by applying STL decomposition, Sen’s slope combined with the Mann–Kendall test, and an XGBoost–SHAP model, quantifying its key controlling factors. The results indicate that from 2002 to 2022, WUEc in Xinjiang showed an overall declining trend. Prior to 2007, WUEc increased at 0.05 gC·m⁻¹·m⁻²·a⁻¹, after which it fluctuated downward at −0.01 gC·m⁻¹·m⁻²·a⁻¹. Intra-annual peaks consistently occurred in May and during September–October. Spatially, WUEc exhibited significant heterogeneity, increasing from south to north, with 53.26% of the region showing declines. Temperature (T) and leaf area index (LAI) emerged as the primary meteorological and vegetation drivers, respectively, influencing WUEc change in 45.7% and 17.6% of the area. Both variables were negatively correlated with WUEc, with negative correlations covering 60% of the region for T and 83% for LAI. These findings provide scientific guidance for optimizing crop structure and water-resource management strategies in arid regions. Full article

Peatlands store substantial amounts of carbon (C) in the form of peat, but are increasingly threatened by drought and shrub encroachment under climate warming. However, how peat decomposition and its temperature sensitivity (Q₁₀) vary with depth and plant litter input under these stressors remains poorly understood. We incubated peat from two depths with different degrees of decomposition, either alone or incubated with Sphagnum divinum shoots or Betula ovalifolia leaves, under five temperature levels and two moisture conditions in growth chambers. We found that drought and Betula addition increased CO₂ emissions in both peat layers, while Sphagnum affected only shallow peat. Deep peat alone or with Betula exhibited higher Q₁₀ than pure shallow peat. Drought increased the Q₁₀ of both depths’ peat, but this effect disappeared with fresh litter addition. The CO₂ production rate showed a positive but marginal correlation with microbial biomass carbon, and it displayed a rather similar responsive trend to warming as the microbial metabolism quotient. These results indicate that both deep and dry peat are more sensitive to warming, highlighting the importance of keeping deep peat buried and waterlogged to conserve existing carbon storage. Additionally, they further emphasize the necessity of Sphagnum moss recovery following vascular plant encroachment in restoring carbon sink function in peatlands. Full article

Given that the Sunkoshi River watershed (located in the southern foot of the Himalayas) is sensitive to climate change and its mountain ecosystem provides important services, we aim to evaluate its spatial and temporal variation patterns of vegetation, represented by the Normalized Difference Vegetation Index (NDVI), during 2000–2021 and identify the dominant driving factors of vegetation change. Based on the NDVI dataset (MOD13A1), we used the simple linear trend model, seasonal and trend decomposition using loess (STL) method, and Mann–Kendall test to investigate the spatiotemporal variation features of NDVI during 2000–2021 on multiple scales (annual, seasonal, monthly). We used the partial correlation coefficient (PCC) to quantify the response of the NDVI to land surface temperature (LST), precipitation, humidity, and soil moisture. The results indicate that the annual NDVI in 52.6% of the study area (with elevation of 1–3 km) increased significantly, while 0.9% of the study area (due to urbanization) degraded significantly during 2000–2021. Daytime LST dominates NDVI changes on spring, summer, and winter scales, while precipitation, soil moisture, and nighttime LST are the primary impact factors on annual NDVI changes. After removing the influence of soil moisture, the contributions of climate factors to NDVI change are enhanced. Precipitation shows a 3-month lag effect and a 5-month cumulative effect on the NDVI; both daytime LST and soil moisture have a 4-month lag effect on the NDVI; and humidity exhibits a 2-month cumulative effect on the NDVI. Overall, the study area turned green during 2000–2021. The dominant driving factors of NDVI change may vary on different time scales. The findings will be beneficial for climate change impact assessment on the regional eco-environment, and for integrated watershed management. Full article

This study evaluates the agronomic potential of two types of vermicompost—one produced solely from wine industry residues (WIR) and one incorporating sewage sludge (WIR + SS)—under rainfed and deficit irrigation conditions in Mediterranean vineyards. The vermicompost was obtained through a two-phase process involving initial thermophilic pre-composting, followed by vermicomposting using Eisenia fetida for 90 days. The conditions were optimized to ensure aerobic decomposition and maintain proper moisture levels (70–85%) and temperature control. This resulted in end products that met the legal standards required for agricultural use. However, population dynamics revealed significantly higher worm reproduction and biomass in the WIR treatment, suggesting superior substrate quality. When applied to grapevines, WIR vermicompost increased soil organic matter, nitrogen availability, and overall fertility. Under rainfed conditions, it improved vegetative growth, yield, and must quality, with increases in yeast assimilable nitrogen (YAN), sugar content, and amino acid levels comparable to those achieved using chemical fertilizers, as opposed to the no-fertilizer trial. Foliar analyses at veraison revealed stronger nutrient uptake, particularly of nitrogen and potassium, which was correlated with improved oenological parameters compared to the no-fertilizer trial. In contrast, WIR + SS compost was less favorable due to lower worm activity and elevated trace elements, despite remaining within legal limits. These results support the use of vermicompost derived solely from wine residues as a sustainable alternative to chemical fertilizers, in line with the goals of the circular economy in viticulture. Full article

Climate change can have a direct impact on the decomposition of organic matter, as well as indirect effects on peatland vegetation (including carnivorous plants) and the microbial communities associated with this environment. The activity of microbes varies depending on the type of peatland they inhabit. Because some microorganisms are highly sensitive, they can be used as indicators of climate change. However, there is still little knowledge of how changes in the temperature of the environment can affect the microbiome of carnivorous plants. The study was conducted to test the following hypotheses: (1) The effect of rising water temperature on the qualitative and quantitative structure of the microbiome of carnivorous peatland plants depends on the type of peatland; (2) habitats with a higher trophic status stimulate the development of microbial communities in the water, but are an unfavourable habitat for the development of the microbiome of plant traps. Irrespective of the type of peatland, the species richness of microorganisms was much higher in the water than in the traps. As the temperature increased, there was an increase in the abundance of bacteria, heterotrophic flagellates, and testate amoebae, which was much more pronounced in the peat bog than in the carbonate fen. Full article

Land desertification represents a significant and sensitive global ecological issue. In the Inner Mongolia region of China, soil desertification and salinization are widespread, resulting from the combined effects of extreme drought conditions and human activities. Using Gaofen 5B AHSI imagery as our data source, we collected spectral data for seven distinct land cover types: lush vegetation, yellow sand, white sand, saline soil, saline shell, saline soil with saline vegetation, and sandy soil. We applied Particle Swarm Optimization (PSO) to fine-tune the Wavelet Packet (WP) decomposition levels, thresholds, and wavelet basis function, ensuring optimal spectral decomposition and reconstruction. Subsequently, PSO was deployed to optimize key hyperparameters of the Random Forest algorithm and compare its performance with the ResNet-Transformer model. Our results indicate that PSO effectively automates the search for optimal WP decomposition parameters, preserving essential spectral information while efficiently reducing high-frequency spectral noise. The Genetic Algorithm (GA) was also found to be effective in extracting feature bands relevant to land desertification, which enhances the classification accuracy of the model. Among all the models, integrating wavelet packet denoising, genetic algorithm feature selection, the first-order differential (FD), and the hybrid architecture of the ResNet-Transformer, the WP-GA-FD-ResNet-Transformer model achieved the highest accuracy in extracting soil sandification and salinization, with Kappa coefficients and validation set accuracies of 0.9746 and 97.82%, respectively. This study contributes to the field by advancing hyperspectral desertification monitoring techniques and suggests that the approach could be valuable for broader ecological conservation and land management efforts. Full article

Over the past few decades, occurrences of extreme climatic events have escalated significantly, with severe repercussions for global ecosystems and socio-economics. northern China (NC), characterized by its complex topography and diverse climatic conditions, represents a typical ecologically vulnerable region where vegetation is highly sensitive to climate change. Therefore, monitoring vegetation dynamics and analyzing the influence of extreme climatic events on vegetation are crucial for ecological conservation efforts in NC. Based on extreme climate indicators and the Normalized Difference Vegetation Index (NDVI), this study employed trend analysis, Ensemble Empirical Mode Decomposition, all subsets regression analysis, and random forest to quantitatively investigate the spatiotemporal variations in historical and projected future NDVI trends in NC, as well as their responses to extreme climatic conditions. The results indicate that from 1982 to 2018, the NDVI in NC generally exhibited a recovery trend, with an average growth rate of 0.003/a and a short-term variation cycle of approximately 3 years. Vegetation restoration across most areas was primarily driven by short-term high temperatures and long-term precipitation patterns. Future projections under different emission scenarios (SSP245 and SSP585) suggest that extreme climate change will continue to follow historical trends. However, increased radiative forcing is expected to constrain both the rate of vegetation growth and its spatial expansion. These findings provide a scientific basis for mitigating the impacts of climate anomalies and improving ecological quality in NC. Full article

Evaluating how dams modify hydrological regimes and their long-term impacts on riverine ecosystems is critical. This study evaluated trends and change points in Fractional Vegetation Cover (FVC) of braided riverbanks downstream of the Xiaolangdi Dam (1990–2020) using time-series decomposition and structural breakpoint analysis. Distinct temporal periods corresponding to different dam construction and operational phases were identified. Partial correlation analysis and linear mixed-effects modeling were employed to elucidate the spatiotemporal linkages between FVC and key driving factors. The results identified 1997 and 2004 as significant change points in FVC, corresponding to the dam’s construction and initial interception in 1997, and its subsequent comprehensive water and sediment regulation from 2004 onwards, respectively. Although dam construction may have initially posed short-term challenges to downstream vegetation, the post-operational phase witnessed a notable increase in significant vegetation growth compared to the pre-dam period, primarily attributed to the altered hydrological conditions. Notably, the dam operation’s contribution to the total FVC increase was 56% in the near-dam Xiaolangdi–Jiahetan reach. The analysis revealed distinct differences in vegetation responses to these hydrological alterations between the upstream Xiaolangdi–Jiahetan and downstream Jiahetan–Gaocun river sections, with the latter demonstrating greater ecological sensitivity to the dam-induced hydrological changes. Full article

Monoculture plantation systems face increasing challenges in sustaining ecosystem multifunctionality (EMF) under intensive management and climate change, with long-term functional trajectories remaining poorly understood. Although biodiversity–EMF relationships are well-documented in natural forests, the drivers of multifunctionality in managed plantations, particularly age-dependent dynamics, require further investigation. This study examines how stand development influences EMF in Castanopsis hystrix L. plantations, a dominant subtropical timber species in China, by assessing six ecosystem functions (carbon stocks, wood production, nutrient cycling, decomposition, symbiosis, and water regulation) of six forest ages (6, 10, 15, 25, 30, and 34 years). The results demonstrate substantial age-dependent functional enhancement, with carbon stocks and wood production increasing by 467% and 2016% in mature stand (34 year) relative to younger stand (6 year). Nutrient cycling and water regulation showed intermediate gains (6% and 23%). Structural equation modeling identified plant diversity and microbial community composition as direct primary drivers. Tree biomass profiles emerged as the strongest biological predictors of EMF (p < 0.01), exceeding abiotic factors. These findings highlight that C. hystrix plantations can achieve high multifunctionality through stand maturation facilitated by synergistic interactions between plants and microbes. Conservation of understory vegetation and soil biodiversity represents a critical strategy for sustaining EMF, providing a science-based framework for climate-resilient plantation management in subtropical regions. Full article

Tropical forests harbor a significant portion of global biodiversity but are increasingly degraded by human activity. Assessing restoration efforts requires the systematic monitoring of tropical ecosystem status and recovery. Satellite-borne synthetic aperture radar (SAR) supports monitoring changes in vegetation structure and is of particular utility in tropical regions where clouds obscure optical satellite observations. To characterize tropical forest recovery in the Lowland Chocó Biodiversity Hotspot of Ecuador, we apply over a decade of dual-polarized (HH + HV) L-band SAR datasets from the Japanese Space Agency’s (JAXA) PALSAR and PALSAR-2 sensors. We assess the complementarity of the dual-polarized imagery with less frequently available fully-polarimetric imagery, particularly in the context of their respective temporal and informational trade-offs. We examine the radar image texture associated with the dual-pol radar vegetation index (DpRVI) to assess the associated determination of forest and nonforest areas in a topographically complex region, and we examine the equivalent performance of texture measures derived from the Freeman–Durden polarimetric radar decomposition classification scheme applied to the fully polarimetric data. The results demonstrate that employing a dual-polarimetric decomposition classification scheme and subsequently deriving the associated gray-level co-occurrence matrix mean from the DpRVI substantially improved the classification accuracy (from 88.2% to 97.2%). Through this workflow, we develop a new metric, the Radar Forest Regeneration Index (RFRI), and apply it to describe a chronosequence of a tropical forest recovering from naturally regenerating pasture and cacao plots. Our findings from the Lowland Chocó region are particularly relevant to the upcoming NASA-ISRO NISAR mission, which will enable the comprehensive characterization of vegetation structural parameters and significantly enhance the monitoring of biodiversity conservation efforts in tropical forest ecosystems. Full article