Search Results (1,184)

Investigating vegetation dynamics and their drivers in ecologically vulnerable regions is essential for evaluating ecological restoration outcomes. This study examined the spatiotemporal evolution of the Normalized Difference Vegetation Index (NDVI) and its influencing factors in Fengning county, the Bashang region from 2001 to 2023 using land use transition matrix, trend analysis, and geographical detector methods. Key findings include the following: (1) Land use transition exhibited a clear phased pattern, shifting from cropland-to-grassland conversion (2001–2010) to grassland-to-forest conversion (2010–2023).(2) The annual mean NDVI increased significantly, showing a southeast–northwest spatial gradient consistent with landforms. The long-term trend followed a sequential “degradation–improvement–consolidation” trajectory. (3) Factor detection identified land use type as the primary driver of vegetation spatial heterogeneity (q = 0.297), highlighting the dominant influence of human activities. (4) Interaction detection demonstrated bivariate enhancement for all factor pairs, with the combination of land use type and precipitation yielding the highest explanatory power (q = 0.440). This underscores that vegetation dynamics are predominantly governed by nonlinear interactions between human-driven land use and climate. The research highlights the effectiveness of ecological restoration policies and offers valuable insights for guiding future ecosystem management in ecologically fragile areas under climate change. Full article

The study and understanding of spatial and temporal changes in the landscape is essential for assessing environmental trends and predicting future developments in the area. Changes in land cover and vegetation dynamics are key indicators of the ecological stability of an area. This study analyzes long-term changes in land cover and vegetation dynamics in Jelšava and neighboring municipalities. The selected area has long been classified as one of the areas with poor air quality in Slovakia. The analysis is based on data from the CORINE Land Cover program for the period 1990–2018 and Landsat data from 1990 to 2025. The condition and vitality of vegetation were assessed using the Normalized Difference Vegetation Index (NDVI), while temporal trends were assessed using non-parametric Mann–Kendall and Sen’s slope tests. The results show a decrease in the area of class 31—Forests between 2012 and 2018, accompanied by an increase in the area of class 324—Transitional woodland–shrub. Analysis of the NDVI confirmed a slightly positive trend in vegetation cover development, with statistically significant growth (p < 0.05) recorded on approximately 43% of the territory. The combination of remote sensing data and spatial analysis in a GIS environment has proven to be an effective approach to monitoring ecological dynamics and provides valuable insights for regional environmental management and sustainable land use planning. Full article

Background: Dictyostelium discoideum is widely used in developmental and evolutionary biology due to its ability to transition from a single cell to a multicellular organism in response to starvation. While transcriptome information across its life cycle is widely available, only early-stage data exist at the proteome level. This study characterizes and compares the proteomes of D. discoideum cells at the vegetative, aggregation, mound, culmination and fruiting body stages. Methods: Samples were collected from cells developing synchronously on nitrocellulose filters. Proteins were extracted and digested with trypsin, and peptides were analyzed by liquid chromatography–tandem mass spectrometry. Data were processed using Proteome Discoverer^TM for protein identification and label-free quantification. Results: A total of 4502 proteins were identified, of which 1848 (41%) were present across all stages. Pairwise comparisons between adjacent stages revealed clear transitions, the largest ones occurring between the culmination and fruiting body and between the fruiting body and vegetative stage, involving 29% and 52% of proteins, respectively. Hierarchical clustering assigned proteins to one of nine clusters, each displaying a distinct pattern of abundances across the life cycle. Conclusions: This study presents the first complete developmental proteomic time series for D. discoideum, revealing changes that contribute to multicellularity, cellular differentiation and morphogenesis. Full article

Precocious-fruiting Amomum villosum Lour. is characterized by early fruit set, rapid yield formation, and shortened economic return cycles, indicating strong cultivation potential. However, the molecular mechanisms underlying its flowering transition remain unclear. To elucidate the flowering mechanism of A. villosum, we used the Illumina NovaSeq X Plus platform to compare gene expression profiles in three tissues (Rhizomes, R; Stems, S; Leaves, L) during the vegetative stage and three tissues (Rhizomes and Inflorescences, R&I; Stems, S; Leaves, L) during the flowering stage of individual plants: VS-R vs. FS-R&I, VS-S vs. FS-S, and VS-L vs. FS-L. We obtained 52.5 Gb clean data and 789 million reads, and identified 2963 novel genes. The 3061 differentially expressed genes (DEGs, FDR ≤ 0.05 and |log2FC| ≥ 1) identified in the three comparison groups included six overlapping genes. The DEGs were enriched primarily in GO terms related to cellular process, metabolic process, binding, catalytic activity, and cellular anatomical entity, as well as multiple terms associated with development and reproduction. KEGG enrichment analysis revealed enrichment primarily in metabolic pathways, including global and overview maps, energy metabolism, and carbohydrate metabolism. Moreover, the most significantly enriched core pathways included metabolic pathways, photosynthesis, and carbon assimilation. Among all alternative splicing (AS) events, skipped exons (SEs) accounted for the largest proportion (59.5%), followed by retained introns (RI, 19.4%), alternative 3′ splice sites (A3SS, 10.7%), alternative 5′ splice sites (A5SS, 6.8%), and mutually exclusive exons (MXE, 3.6%). A preliminary set of 43 key DEGs was predicted, displaying spatiotemporal expression specificity and strong interactions among certain genes. Nine genes were further selected for RT-qPCR validation to confirm the reliability of the RNA-seq results. This study established a foundational framework for elucidating the flowering mechanism of precocious-fruiting A. villosum. Full article

Climate change and rapid urbanization are increasingly disrupting urban water cycles by intensifying runoff and reducing infiltration, particularly in watersheds designated for future development. However, most existing studies have focused on fully urbanized areas, with limited attention given to semi-rural or urban–rural transition watersheds at the planning stage. In this context, the Dawoon watershed in Ulsan, Republic of Korea, represents a critical case, as it is currently undeveloped but designated for large-scale urban expansion. This study evaluates the effectiveness of Low Impact Development (LID) strategies in restoring water-cycle soundness under anticipated urbanization conditions. A hydrological model of the Dawoon watershed was developed using the Storm Water Management Model (SWMM), and multiple land-use-specific LID scenarios were designed to reflect realistic planning-stage applications. Long-term simulations were conducted to assess changes in runoff, infiltration, evapotranspiration, and overall water-cycle performance. The results indicate that urban development substantially increases surface runoff while reducing infiltration and evapotranspiration. The integrated application of LID measures significantly mitigated these impacts, reducing total runoff by approximately 3% and improving the water cycle recovery rate to nearly 99%, restoring hydrological conditions close to the pre-development state. Among the evaluated scenarios, the combined implementation of vegetated swales, infiltration–storage basins, green roofs, and permeable pavements showed the highest effectiveness. These findings highlight the importance of incorporating LID strategies at the early stages of urban planning to enhance climate resilience and prevent long-term water cycle degradation. The proposed framework provides practical guidance for setting water-cycle management targets and selecting effective LID measures in developing or peri-urban watersheds. Full article

Background: University students are often exposed to environments that encourage unhealthy eating, but universities can promote better health and sustainability by making sustainable food options more accessible. Methods: Temporal changes in dietary patterns and environmental footprints of 1684 students at the University of Rijeka, Croatia, over a 16-year period (2009–2025) were retrospectively analyzed using data from 3 cross-sectional studies. Results: A significant transition toward less sustainable diets, increased consumption of animal-based foods, and proinflammatory eating habits was observed (both p < 0.001). Adherence to the Mediterranean and Planetary Health Diet declined over time (p < 0.001), followed by increased prevalence of overweight and obesity. Three dietary patterns were identified: high fruit and vegetable intake, consistently high milk and dairy consumption, and lower-to-moderate intake of all other food groups with temporal variation. Consumption of most food groups increased, leading to higher water and ecological footprints. Only the intake of fruits, vegetables, whole grains, and fish declined, which corresponded with reduced carbon footprints for these and a few other food groups, while the environmental impact of other foods significantly increased (all p < 0.001). Gender, diet quality, and a proinflammatory diet were significant predictors of dietary environmental footprints. Conclusions: The findings underscore the need for systemic changes and addressing barriers at the university level to support sustainable eating behaviors. This study offers valuable insights for policymakers, educators, and researchers, which aim to help students become health-conscious and environmentally responsible consumers. Further research is needed to explore the broader factors influencing dietary choices and the long-term impact of future institutional interventions. Full article

The transition toward Agriculture 5.0 requires intelligent and autonomous monitoring systems capable of providing early, accurate, and scalable crop health assessment. This study presents the design and field evaluation of an artificial intelligence (AI)–based unmanned aerial vehicle (UAV) system for the detection of Botrytis cinerea in vineyards using multispectral imagery and deep learning. The proposed system integrates calibrated multispectral data with vegetation indices and a YOLOv8 object detection model to enable automated, geolocated disease detection. Experimental results obtained under real vineyard conditions show that training the model using the Chlorophyll Absorption Ratio Index (CARI) significantly improves detection performance compared to RGB imagery, achieving a precision of 92.6%, a recall of 89.6%, an F1-score of 91.1%, and a mean Average Precision (mAP@50) of 93.9%. In contrast, the RGB-based configuration yielded an F1-score of 68.1% and an mAP@50 of 68.5%. The system achieved an average inference time below 50 ms per image, supporting near real-time UAV operation. These results demonstrate that physiologically informed spectral feature selection substantially enhances early Botrytis cinerea detection and confirm the suitability of the proposed UAV–AI framework for precision viticulture within the Agriculture 5.0 paradigm. Full article

Understanding the spatiotemporal evolution of soil salinization is essential for elucidating its driving mechanisms and supporting sustainable land and water management in arid regions. In this study, the Alar Reclamation Area in Xinjiang, a typical desert–oasis transition zone, was selected to investigate the drivers of spatiotemporal variation in soil salinization. GRACE gravity satellite observations for the period 2002–2022 were used to estimate groundwater storage (GWS) fluctuations. Contemporaneous Landsat multispectral imagery was employed to derive the normalized difference vegetation index (NDVI) and a salinity index (SI), which were further integrated to construct the salinization detection index (SDI). Pearson correlation analysis, variance inflation factor analysis, and a stepwise regression framework were employed to identify the dominant factors controlling the occurrence and evolution of soil salinization. The results showed that severe salinization was concentrated along the Tarim River and in low-lying downstream zones, while salinity levels in the middle and upper parts of the reclamation area had generally declined or shifted to non-salinized conditions. SDI exhibited a strong negative correlation with NDVI (p ≤ 0.01) and a significant positive correlation with both irrigation quota and GWS (p ≤ 0.01). A pronounced collinearity was observed between GWS and irrigation quota. NDVI and GWS were identified as the principal drivers governing spatial–temporal variations in SDI. The resulting regression model (SDI = 0.946 − 0.959 × NDVI + 0.318 × GWS) established a robust quantitative relationship between SDI, NDVI and GWS, characterized by a high coefficient of determination (R² = 0.998). These statistics indicated the absence of multicollinearity (variance inflation factor, VIF < 5) and autocorrelation (Durbin–Watson ≈ 1.876). These findings provide a theoretical basis for the management of saline–alkali lands in the upper Tarim River region and offer scientific support for regional ecological sustainability. Full article

Examining the long-term spatiotemporal distribution of grassland types and their transitions is crucial for better understanding regional and global changes. Most research in this field has examined the spatial distribution, temporal dynamics of grasslands, and their causes as a unified entity. This study predicted the distribution of nine major grassland types in Xinjiang under three climate change scenarios from 2041 to 2100 based on 1980s grassland maps, field data in 2023, and 28 factors. The total area of the nine grassland types showed a decreasing trend from 2041 to 2100. The lowland meadow (LM), temperate meadow steppe (TMS), temperate steppe desert (TSD), temperate desert steppe (TDS), and mountain meadow (MM) expanded, while significant declines occurred in alpine meadow (AM), alpine steppe (AS), temperate desert (TD), and temperate steppe (TS). Among cumulative contribution rate of the 28 factors examined in this study, NDVI, vegetation type, slope, elevation, soil_symbol, soil_ph, Bio1, Bio5, Bio8, Bio9, Bio10, Bio12, Bio13, Bio15, and Bio18 played important roles in most grassland types. LM, TD, and AS grassland were found to be more sensitive to E (environment), while AM, TDS, and TSD were more influenced by T (temperature). The distributions of MM and TMS are significantly influenced by the combined effects of all three categories of factors. For TS, the impacts of both temperature and environmental factors are substantial. These findings provided a robust foundation for conservation planning and the sustainable management of grassland ecosystems in temperate and alpine regions. Full article

Urban tunnel portals constitute critical safety zones for autonomous vehicles, where abrupt luminance transitions, shortened sight distances, and densely distributed structural and traffic elements pose considerable challenges to perception reliability. Existing driving scenario datasets are rarely tailored to tunnel environments and have not quantitatively evaluated how specific infrastructure components influence perception latency in autonomous systems. This study develops a requirement-driven framework for the identification and sensitivity ranking of information perception elements within urban tunnel portals. Based on expert evaluations and a combined function–safety scoring system, nine key elements—including road surfaces, tunnel portals, lane markings, and vehicles—were identified as perception-critical. A “mandatory–optional” combination rule was then applied to generate 48 logical scene types, and 376 images after brightness (30–220 px), blur (Laplacian variance ≥ 100), and occlusion filtering (≤0.5% pixel error) were obtained after luminance and occlusion screening. A ResNet50–PSPNet convolutional neural network was trained to perform pixel-level segmentation, with inference rate adopted as a quantitative proxy for perceptual sensitivity. Field experiments across ten urban tunnels in China indicate that the model consistently recognized road surfaces, lane markings, cars, and motorcycles with the shortest inference times (<6.5 ms), whereas portal structures and vegetation required longer recognition times (>7.5 ms). This sensitivity ranking is statistically stable under clear, daytime conditions (p < 0.01). The findings provide engineering insights for optimizing tunnel lighting design, signage placement, and V2X configuration, and offers a pilot dataset to support perception-oriented design and evaluation of urban tunnel portals in semi-enclosed environments. Unlike generic segmentation datasets, this study quantifies element-specific CNN latency at tunnel portals for the first time. Full article

Forest cover dynamics strongly influence ecological integrity and resource sustainability, particularly in ecotonal landscapes, where vegetation is highly sensitive to climate variability, long-term climate change, and anthropogenic disturbances. This study examined Forest Land (FL), representing all areas of dense, canopy-forming woody vegetation with forest-like structure, aggregated from SANLC classes, in relation to eight other land cover classes across three periods: 1990–2014, 2014–2022, and 1990–2022. The study used South African National Land Cover datasets and the TerrSet–LiberaGIS Land Change Modeller to quantify changes in magnitude, direction, and source–sink relationships. Analyses included post-classification comparison to determine spatial changes, transition matrices to identify land-cover conversions, and asymmetric gain–loss metrics to reveal sources and sinks of forest change. The result shows that between 1990 and 2014, forests remained marginal and fragmented in the eastern central part of the study area, while shrubland increased from 40.4% to 60.2% at the expense of grasslands, cultivated land, bare land, wetlands, and forest land. From 2014 to 2022, FL regeneration was pronouncedly increased from 2% to 6%, especially along riparian corridors and reservoir margins, coinciding with shrubland decline (99.3%) and grassland recovery (261.2%). Over the entire 1990–2022 period, FL increased from 2.4% to 6% expanding into bare land, cultivated land, grassland, shrubland, and wetlands. Asymmetric analysis indicated that forests acted as a sink during the first period but as a source of ecological resilience in the second and final. These findings demonstrate strong vegetation feedback to hydrological and anthropogenic drivers. Overall, the findings underscore the potential for forest recovery to enhance biodiversity, ecosystem services, carbon storage, and hydrological regulation, while identifying priority areas for riparian conservation and integrated catchment management. Full article

Quantitative assessment of ecosystem health (EH) effectively provides a scientific reference for regional landscape ecological development and socio-ecological system coordination. This study combined the VORSH framework and the XGBoost-SHAP model to assess EH and its spatiotemporal driving factors in the Pinglu Canal Economic Zone. The results show that the comprehensive ecosystem health index (EHI) generally remained at a moderate level during this period, exhibiting a pattern of initial decline followed by recovery, resulting in an overall improving trend. The period from 2005 to 2010 was identified as a critical transitional phase, during which EH began to recover and gradually improve. The Pinglu Canal Economic Zone exhibits distinct spatial heterogeneity in EH. Areas with poor and unhealthy grades are primarily distributed around urban peripheries, plain regions, and near certain water bodies. In contrast, healthy and relatively healthy areas are predominantly located in the densely vegetated mountainous regions of the southwest, north, and east. Between 2000 and 2020, the EH status demonstrated a significant overall upward trend, with most areas experiencing slight improvement and only a few regions exhibiting significant degradation. Topography and temperature were the primary factors driving the spatiotemporal variations in EH, while the influence of human activities continued to intensify with ongoing socioeconomic development. Full article

This work focused on the development of eco-friendly bio-composites based on polylactic acid (PLA) and sugarcane bagasse (SCB) as a natural fiber from Moroccan vegetable waste. First, the fiber surface was treated with an alkaline solution to remove non-cellulosic components. Then, the composite materials with various amounts of treated sugarcane bagasse (TSCB) were fabricated using two routes, melt processing and solvent casting. The primary objective was to achieve high fiber dispersion/distribution and homogeneous bio-composites. The dispersion properties were analyzed using scanning electron microscopy (SEM). Subsequently, the thermal, mechanical, and melt shear rheological properties of the obtained PLA-based bio-composites were investigated. Through a comparative approach between the dispersion state of fillers with extrusion/injection molding and solvent casting method, the work aimed to identify the most suitable processing route for producing PLA-based composites with optimal dispersion, improved thermal stability, and mechanical reinforcement. The results support the potential of TSCB fibers as an effective bio-based additive for PLA filament production, paving the way for the development of eco-friendly and high-performance materials designed for 3D printing applications. Since the solvent-based route did not allow further improvement and presents clear limitations for large-scale or industrial implementation, the transition toward 3D printing became a natural progression in this work. Material extrusion offers several decisive advantages, notably the ability to preserve the original morphology of the fibers due to the moderate thermo-mechanical stresses involved, and the possibility of manufacturing complex geometries that cannot be obtained through conventional injection molding. Although some printing defects may occur during layer deposition, the mechanical properties obtained through 3D printing remain promising and demonstrate the relevance of this approach. Full article

Vegetation phenology significantly influences urban microclimate and thermal comfort in cold regions, yet its quantitative impact—specifically the potential of deciduous trees to enhance winter solar access—remains underexplored. This study investigates how seasonal vegetation changes affect thermal conditions in an urban plaza. Field measurements were conducted at Beiyang Plaza, Tianjin University, during the autumn–winter transition. High-precision Sky View Factors (SVF) were extracted from panoramic images using a deep learning-based semantic segmentation model (PSPNet), validated against field observations. The Universal Thermal Climate Index (UTCI) was calculated to assess thermal stress. Results indicate that the leaf-off phase significantly increases SVF, shifting the radiative balance. Areas experiencing phenological changes exhibited a marked improvement in UTCI, effectively alleviating cold stress by maximizing solar gain. Advanced statistical models (ARIMAX and GAM) confirmed that, after controlling for background climatic variations, the positive effect of vegetation phenology on thermal comfort is statistically significant. These findings challenge the traditional focus on summer shading, highlighting the “winter-warming” potential of deciduous trees and providing quantitative evidence for climate-responsive urban design. Full article

Grasshoppers and their allies (Orthoptera) are numerous and diverse insects globally, while being significant components of biodiversity and nutrient cycling. They are variously responsive to environmental change but are paradoxical, as some species are major pests while others are threatened or even extinct. Most orthopteran species are somewhere in between, with their assemblage composition shifting in response to changing conditions and according to the response traits of the constituent species. With global concern over the impact of conventional agriculture on biodiversity, there is currently a strong transition to agroecology. The agroecological approach is two-fold: to set aside land and to better manage the overall landscape. Both approaches aim to boost the numbers and diversity of most orthopterans, while reducing the impact of the pest species using biologically based pesticides instead of chemical pesticides as part of an integrated pest management program. Set-aside land is present at various spatial scales for conservation action, involving a diversity of practical approaches. Management depends on understanding orthopteran responses to change, and harnessing the positive responses using, for example, improved grazing, fire management, and vegetation diversification for maximizing habitat heterogeneity. These initiatives also recognize the additional interactive effect of climate change and extreme weather events. Importantly, improvement of orthopteran abundance and diversity is an integral component of overall biodiversity conservation. New technologies, both aerial and genomic, are advancing the field of orthopteran biology and ecology greatly. We review advances being made in the field that hold the most promise and suggest ways forward based on three themes: appreciating orthopteran value, recognizing the adverse drivers of orthopteran abundance and diversity, and better design and management of agroecosystems. Full article