Search Results (12,938)

Extensive coal mining activities can exert substantial negative impacts on surface ecosystems. Vegetation indices are widely recognized as effective indicators of land ecological conditions and provide valuable insights into long-term ecological changes in mining areas. In this study, the Zhaogu mining area of the Jiaozuo Coalfield was selected as the study site. Using the Google Earth Engine (GEE) platform, the Kernel Normalized Difference Vegetation Index (kNDVI) was constructed to generate a vegetation dataset covering the period from 2010 to 2024. The temporal dynamics and future trends of vegetation coverage were analyzed using Theil–Sen median trend analysis, the Mann–Kendall test, the Hurst index, and residual analysis. Furthermore, the relative contributions of climatic factors and human activities to vegetation changes were quantitatively assessed. The results indicate that: (1) vegetation coverage in the Zhaogu mining area exhibits an overall improving trend, affecting approximately 77.1% of the study area, while slight degradation is mainly concentrated in the southeastern region, accounting for about 15.2%; (2) vegetation dynamics are predominantly characterized by low and relatively low fluctuations, covering approximately 78.5% of the region, whereas areas with high fluctuations are limited and mainly distributed in zones with intensive mining activities; although the current vegetation trend is generally increasing, future projections suggest a potential decline in approximately 55.8% of the area; and (3) vegetation changes in the Zhaogu mining area are jointly influenced by climatic factors and human activities, with climatic factors promoting vegetation growth in approximately 70.6% of the study area, while human activities exert inhibitory effects in about 24.2%, particularly in regions affected by mining operations and urban expansion. Full article

Compared with other forest vegetation, bamboo forests have a stronger carbon sequestration capacity, which plays a vital role in achieving the national goals of carbon peak and carbon neutrality. Global warming has profoundly impacted the adaptive distribution and landscape fragmentation of bamboo forests. This study utilized an optimized MaxEnt model to calculate the current habitat range of Neosinocalamus affinis (Rendle) Keng f. forests across China and project their potential distribution under three future climate scenarios (SSP126, SSP370, SSP585) for the 2050s and 2090s and analyzed the landscape fragmentation of their land use using landscape indices. The results reveal that Neosinocalamus affinis forests are currently primarily distributed in Chongqing Municipality, eastern and southeastern Sichuan Province, and northern Guizhou Province. The key environmental factors influencing their distribution are identified as: mean diurnal temperature range (Bio2), precipitation of warmest quarter (Bio18), and precipitation of wettest quarter (Bio16). Across the three future scenarios, the suitable habitat area for Neosinocalamus affinis forests demonstrates an overall expanding trend. Rising CO₂ concentrations correlate with a reduction in suitable habitat. The habitat centroid shifts southward in the 2050s and northeastward in the 2090s. In the future, the fragmentation degree of highly suitable areas for Neosinocalamus affinis forests will be higher than at present and show an increasing trend, with forest fragmentation significantly intensifying and overall landscape quality further declining. The predictive results of this study provide a scientific basis for the effective conservation and management of Neosinocalamus affinis forests, thereby contributing to the sustainable utilization of bamboo forest resources. Full article

Agroforestry is defined as a multifunctional approach to land management that enhances biodiversity and soil health while mitigating environmental impacts compared to intensive agriculture. The efficacy of maize cultivation in agroforestry systems is significantly influenced by nutrient competition. The factors that influence this phenomenon include the dimensions and configuration of the tree rows, as well as the availability of nutrients. This study examined the effect of nitrogen fertilization, tree line spacing, and seasonal changes on the productivity and the leaf spectral characteristics of the intercropped maize (Zea mays L.) within a willow-based agroforestry system in eastern Hungary. The experiment involved the cultivation of maize with two spacings (narrow and wide field strips) and four nitrogen levels (0, 50, 100, and 150 kg N ha⁻¹) across two growing seasons (2023–2024). The results demonstrated that yield-related parameters, including biomass, cob size and weight, and grain weight, exhibited a strong response to nitrogen level and tree line spacing. The reduction in spacing resulted in a decline in maize productivity. However, a high nitrogen input (150 kg N ha⁻¹) partially mitigated this effect in the first growing season. Vegetation indices demonstrated a high degree of sensitivity to annual variations, particularly with regard to tree competition and weather conditions. Multispectral vegetation indices exhibited a heightened responsiveness to environmental and management factors when compared to indices based on visible light (RGB). The findings of this study demonstrate that a combination of optimized tree spacing and optimized nitrogen management fosters productivity while maintaining agroecological sustainability in temperate agroforestry systems. Full article

Street livability is widely recognized as a fundamental indicator of urban livability. Despite growing global agendas advocating human-centered, sustainable, and smart cities, the microscale implementation of streetscape interventions remains limited and non-integrated. This gap is particularly evident in developing cities’ contexts where policy-level frameworks fail to translate into tangible street-level transformations. Responding to this challenge, this paper investigates how streetscape components can enhance everyday street livability. The study aims to explore opportunities for improving street livability through the utilization of three core streetscape components: vegetation, street furniture, and lighting. The discourse on street livability identifies vegetation, street furniture, and lighting as the primary drivers of high-quality urban spaces. Scholarly research suggests that these micro-interventions are most effective when viewed through the combined lenses of human-centered design, environmental sustainability, and smart city technology. While the literature indicates that integrating climate-responsive greenery and renewable energy systems can enhance social interaction and safety, it also highlights significant implementation hurdles. Specifically, researchers point to policy limitations, technical feasibility in developing nations, and the socio-economic threat of green gentrification. Despite these complexities, microscale streetscape improvements remain a vital strategy for fostering inclusive and resilient cities. Full article

Early and non-destructive detection of plant-parasitic nematodes is critical for implementing site-specific management in coffee production systems. This study evaluated the potential of unmanned aerial vehicle (UAV) multispectral and thermal imaging, combined with textural analysis, to detect Meloidogyne exigua infestation in Coffea arabica (Topázio variety). Field surveys were conducted in two contrasting seasons (dry and rainy), and nematode incidence was identified and quantified by counting root galls. Vegetation indices (NDVI, GNDVI, NGRDI, NDRE, OSAVI), individual spectral bands, canopy temperature, and Haralick texture features were extracted from UAV-derived imagery and correlated with gall counts. Under the conditions of this experiment, strong correlations were observed between gall number and the red spectral band in both seasons (R > 0.60), while GNDVI (dry season) and NGRDI (rainy season) showed strong negative correlations with gall density. Thermal imaging revealed moderate positive correlations with infestation levels during the dry season, indicating potential for early stress detection when foliar symptoms were absent. Texture metrics from the red and green bands further improved detection capacity, particularly with a 3 × 3 pixel window at 135°. These results demonstrate that UAV-based multispectral and thermal imaging, enhanced by texture analysis, can provide reliable early indicators of nematode infestation in coffee. 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

This study, conducted in the Lifang Dryland Experimental Area in Jinzhong, Shanxi Province, China, aimed to develop a method to accurately predict maize yield by combining UAV multispectral data with meteorological information. A DJI Mavic 3M UAV was used to capture four-band imagery (red, green, red-edge, and near-infrared), from which 16 vegetation indices were calculated, along with daily meteorological data. Among eight machine learning algorithms tested, ensemble models, Random Forest and Gradient Boosting Trees performed best, with R² values of 0.8696 and 0.8163, respectively. SHAP analysis identified MSR and RVI as the most important features. The prediction accuracy varied across growth stages, with the jointing stage showing the highest performance (R² = 0.7161), followed by the flowering stage (R² = 0.6588). The yield exhibited a strip-like spatial distribution, ranging from 6450 to 9600 kg·ha⁻¹, influenced by field management, soil characteristics, and microtopography. K-means clustering revealed high-yield areas in the central-northern region and low-yield areas in the south, supported by a global Moran’s I index of 0.4290, indicating moderate positive spatial autocorrelation. This study demonstrates that integrating UAV multispectral data, meteorological information, and machine learning can achieve accurate yield prediction (with a relative RMSE of about 2.8%) and provides a quantitative analytical framework for spatial management in drought-prone areas. Full article

The cotton boll opening rate (BOR) is an important indicator for evaluating the physiological maturation process of cotton and the critical stage of yield formation, and it provides essential guidance for subsequent defoliant application and mechanical harvesting. The investigation of cotton BOR usually relies on manual field surveys, which are time-consuming and destructive, making it difficult to achieve large-scale and efficient monitoring. UAV remote sensing technology has been widely used in crop growth monitoring due to its operational flexibility and high image resolution. However, because of the dense growth of the cotton canopy in UAV remote sensing imagery, the boll opening condition in the lower parts of the canopy cannot be completely observed. In contrast, UAV imagery can effectively monitor cotton leaf chlorophyll content (SPAD) and leaf area index (LAI), both of which undergo continuous changes during the boll opening process. Therefore, this study proposes using SPAD and LAI retrieved from UAV multispectral imagery as physiological intermediary variables to construct an empirical statistical equation and compare it with end-to-end machine learning baselines. Multispectral and ground synchronous data (n = 360) were collected in Baibi Town, Anyang, Henan Province, across four dates (8/28, 9/6, 9/13, 9/24). Twenty-eight commonly used vegetation indices were calculated from multispectral imagery, and Pearson’s correlation analysis was conducted to select indices sensitive to cotton SPAD, LAI, and BOR. Prediction models were constructed using the Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), and Partial Least Squares (PLS) models. The results showed that GBDT achieved the best prediction performance for SPAD (R² = 0.86, RMSE = 1.19), while SVM performed best for LAI (R² = 0.77, RMSE = 0.38). The quadratic polynomial equation constructed using SPAD and LAI achieved R² = 0.807 and RMSE = 0.109 in BOR testing, which was significantly better than the baseline model using vegetation indices to directly regress BOR. The method demonstrated stable performance in spatial mapping of BOR during the boll opening period and showed promising potential for guiding defoliant application and harvest timing. Full article

Fermented products have recently garnered substantial interest in both research and commercial contexts. Although probiotic fermentation is predominantly practiced with dairy, fruits, vegetables, and grains, its application to dual-purpose food-medicine materials like Ganoderma lucidum has been comparatively underexplored. In this study, Ganoderma lucidum fermented juice (GFJ) served as the substrate and was fermented with five probiotic strains. The optimal inoculation ratios—determined by employing a uniform design experiment—were as follows: Bifidobacterium animalis 6.05%, Lacticaseibacillus paracasei 9.52%, Lacticaseibacillus rhamnosus 6.63%, Pediococcus pentosaceus 21.38%, and Pediococcus acidilactici 56.42%. Optimal fermentation parameters established by response surface methodology included 24 h of fermentation at 37 °C, a final cell density of 5 × 10⁶ CFU/mL, and a sugar content of 4.5 °Brix. Experiments with RAW264.7 macrophages revealed that GFJ significantly promoted both phagocytic activity and nitric oxide (NO) secretion, indicating enhanced immune characteristics as a result of fermentation. Untargeted metabolomics profiling of GFJ across different fermentation stages showed upregulation of functional metabolites, including polyphenols, prebiotics, functional oligosaccharides, and Ganoderma triterpenoids (GTs)—notably myricetin-3-O-rhamnoside, luteolin-7-O-glucuronide, raffinose, sesamose, and Ganoderma acids. These increments in metabolic compounds strongly correlate with improved functional properties in GFJ, specifically heightened superoxide dismutase activity and immunomodulatory capacity. These results highlight an effective approach for developing functionally enriched fermented products from medicinal fungi, with promising applications in functional food and nutraceutical industries. 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

Family Allotment Gardens (FAGs) represent key components of urban cooling and air-purification systems. However, research has mainly focused on their social roles and on their contributions to food production. This study quantified the capacity of FAGs in Warsaw (Poland) to provide two key ecosystem services at distances up to 300 m from their boundaries: air-pollution filtration and microclimate regulation. Measurements of particulate matter (PM1, PM2.5, PM10), air temperature and relative humidity were conducted along transects inside and outside three allotment complexes in autumn 2023, a period characterised by increased traffic emissions and elevated particulate levels. The results show a moderate but significant reduction in PM concentrations inside gardens (by about 2 µg/m³; r = 0.22–0.29) and slightly higher humidity (by 2.1%; r = −0.34). The cooling effect was weak (<0.3 °C; r = 0.06), indicating a limited spatial range under autumn conditions, though selected transects exhibited stronger local effects. The results confirm that FAGs can contribute to air purification and local climate regulation, but their effectiveness depends on vegetation structure and urban context. Strengthening their role requires integration with green-infrastructure planning and emission-reduction practices within gardens. FAGs, beyond their recreational and productive value, should be recognised as active components of urban adaptation strategies. Full article

Frozen vegetables are increasingly valued for their nutritional stability and year-round availability. This study provides a comprehensive assessment of twenty commercially available frozen vegetable blends obtained from retail markets in Poland. Analyses included physicochemical parameters, instrumental measurements of texture, color (CIEL*a*b*), and evaluation of technological quality. The pH values ranged from 4.40 to 7.46, total acidity from 0.034 to 0.322 g/100 g, and dry matter content from 5.02 to 42.97%. The observed variability was mainly attributable to vegetable type and remained consistent with values reported for fresh produce, indicating that industrial freezing largely preserves chemical characteristics. Texture differed markedly between vegetable types, with hardness values ranging from 6 to nearly 100 N, while color parameters remained within typical ranges for blanched and frozen vegetables, suggesting effective pigment stability and enzyme inactivation. In contrast, substantial variability was observed in technological quality. Mechanical fragmentation exceeded acceptable limits in 30% of samples, and complete clumping of vegetable pieces (100%) was observed. Additional defects included frostbite and color deviations, and health-condition defects were observed. These results highlight considerable heterogeneity in frozen vegetable blends and emphasize the need for stricter control of raw materials, processing conditions, and cold-chain management to ensure consistent quality and consumer safety. 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

To address the challenge of obtaining high-spatiotemporal-resolution and high-precision temperature grids for agricultural meteorological monitoring, this research focuses on rice heat stress monitoring with the China Meteorological Administration Land Data Assimilation System (CLDAS) and develops a temperature correction model that synergizes physical mechanisms with a data-driven strategy by introducing a GeoAI framework. Ensemble learning methods (XGBoost, LightGBM, and Random Forest) were utilized to process a comprehensive set of predictors, integrating dynamic surface features derived from FY-4 satellite’s high-frequency observation data. The data comprised surface thermal regime metrics, specifically the daily maximum land surface temperature (LST_max) and its diurnal range (LST_{max_min}), along with vegetation indices including the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). Further, topographic attributes derived from a digital elevation model (DEM) were incorporated, such as slope, aspect, the terrain ruggedness index (TRI), and the topographic position index (TPI). The approach uniquely capitalized on the temporal resolution of geostationary data to capture the diurnal land surface dynamics crucial for bias correction. The proposed models not only enhanced temperature data quality but also achieved impressive accuracy. Across China, the root mean square error (RMSE) was reduced to 1.04 °C, mean absolute error (MAE) to 0.53 °C, and accuracy (ACC) to 0.97. Additionally, the most notable improvement was that the RMSE decreased by nearly 50% (from 2.17 °C to 1.11 °C), MAE dropped from 1.48 °C to 0.80 °C, and ACC increased from 0.72 to 0.96 in the southwestern region of China. The corrected rice heat stress data (2020–2023) indicated that significant negative correlations exist between yield loss and various heat stress metrics in the severely affected middle and lower Yangtze River region. The research confirms that embedding geostationary meteorological satellites within a GeoAI framework can effectively enhance the precision of agricultural weather monitoring and related impact assessments. Full article

Vegetation degradation in arid and semi-arid regions is intensifying due to rising temperatures, declining rainfall, soil exposure, and persistent human pressures. Drylands cover over 41% of the global land surface and support nearly two billion people, making their degradation a major environmental and socio-economic concern. However, many remote sensing and GIS-based assessment approaches remain fragmented and difficult to reproduce. This study proposes a Computational Sustainability Framework for vegetation degradation assessment that integrates multi-source satellite data, biophysical indicators, automated geospatial preprocessing, and the Analytical Hierarchy Process (AHP) within a transparent and reproducible workflow. The framework comprises four phases: data preprocessing, indicator extraction and normalization, AHP-based modeling, and spatial classification with qualitative validation. The framework was applied to the Al-Khunfah and Harrat al-Harrah Protected Areas in northern Saudi Arabia using multi-source datasets for the January–April 2023 period, including Sentinel-2, Landsat-8, CHIRPS precipitation, ESA-CCI land cover, FAO soil data, and SRTM DEM. High degradation zones were associated with low NDVI (<0.079), high BSI (>0.276), and elevated LST (>49 °C), whereas low degradation areas were concentrated near wadis and relatively more fertile soils. Overall, the proposed framework provides a scalable and interpretable tool for early-stage vegetation degradation screening in arid environments, supporting the prioritization of areas for ecological investigation and restoration planning. Full article