Search Results (376)

Despite successful land reclamation efforts, post-mining areas are still prone to secondary effects of mineral extraction. These effects include surface deformations, damage to infrastructure and buildings, and periodic or permanent changes to surface water resources. This study focused on analyzing a former copper mine in southwest Poland in terms of surface water changes, which may be caused by the restoration of groundwater conditions in the region after mine closure. The main objective of the study was to detect areas with statistically significant changes in surface water between 2015 and 2024, as well as to identify the main factors influencing the observed changes. The methodology integrated open remote sensing datasets from Landsat and Sentinel-1 missions for deriving spectral indices—Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Moisture Index (NDMI), as well as Surface Soil Moisture index (SSM); spatial statistics methods, including Emerging Hot Spot analysis; and regression models—Random Forest Regression (RFR) and Geographically Weighted Regression (GWR). The results obtained indicated a general increase in vegetation water content, a reduction in the extent of surface water, and minor soil moisture changes during the analyzed period. The Emerging Hot Spot analysis revealed a number of new hot spots, indicating regions with statistically significant increases in surface water content in the study area. Out of the investigated regression models, global regression (RFR) outperformed local (GWR) models, with R2 ranging between 74.7% and 87.3% for the studied dependent variables. The most important factors in terms of influence were the distance from groundwater wells, surface topography, vegetation conditions and distance from active mining areas, while surface geology conditions and permeability had the least importance in the regression models. Overall, this study offers a comprehensive framework for integrating multi-source data to support the analysis of environmental changes in post-mining regions. Full article

The climate and environment of metropolitan areas have been negatively impacted by swift urbanization and industrialization. Surface Urban Heat Islands (SUHIs) are among the most critical environmental phenomena. This research focused on the spatiotemporal analysis of land use/land cover (LULC) changes in relation to surface urban heat islands and their interconnections from 1992 to 2022. Land Surface Temperature (LST), LULC, and LULC indices, such as the Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI), were generated using Landsat data. Urban hot spots (UHSs) were identified, and the Urban Thermal Field Variance Index (UTFVI) was then used to evaluate the spatiotemporal variation in thermal comfort. The results indicated LST values between a low of 14.24 and a maximum of 46.30. Urban areas and exposed surfaces, such as open or bare soil, exhibit the highest surface radiant temperatures. Conversely, regions characterized by vegetation and water bodies have the lowest. Additionally, this study explored the correlation between LULC, LULC indices, LST, and SUHIs. LST and NDBI show a positive relationship because of urbanization and industrialization (R² = 0.57 for the year 1992, R² = 0.38 for the year 2010, and R² = 0.35 for the year 2022), while LST shows an inverse relationship with NDVI and NDMI. Urban development should account for thermal sensitivity in densely populated regions. This study introduced an innovative spatiotemporal framework for monitoring long-term changes in urban surface environments. Furthermore, this research can assist planners in creating urban green spaces in cities of developing nations to minimize the adverse impacts of urban heat islands and improve thermal comfort. Full article

Landslides pose persistent threats to mountainous regions in Taiwan, particularly in areas such as Nanfeng Village, Nantou County, where steep terrain and concentrated rainfall contribute to chronic slope instability. This study investigates spatiotemporal patterns of vegetation change as a proxy for identifying potential landslide-prone zones, with a focus on the Tung-An tribal settlement in the eastern part of the village. Using high-resolution satellite imagery from SPOT 6/7 (2013–2023) and Pléiades (2019–2023), we derived annual NDVI layers to monitor vegetation dynamics across the landscape. Long-term vegetation trends were evaluated using the Mann–Kendall test, while spatiotemporal clustering was assessed through Emerging Hot Spot Analysis (EHSA) based on the Getis-Ord Gi* statistic within a space-time cube framework. The results revealed statistically significant NDVI increases in many valley-bottom and mid-slope regions, particularly where natural regeneration or reduced disturbance occurred. However, other valley-bottom zones—especially those affected by recurring debris flows—still exhibited declining or persistently low vegetation. In contrast, persistent low or declining NDVI values were observed along steep slopes and debris-flow-prone channels, such as the Nanshan and Mei Creeks. These zones consistently overlapped with known landslide paths and cold spot clusters, confirming their ecological vulnerability and geomorphic risk. This study demonstrates that integrating NDVI trend analysis with spatiotemporal hot spot classification provides a robust, scalable approach for identifying slope hazard areas in data-scarce mountainous regions. The methodology offers practical insights for ecological monitoring, early warning systems, and disaster risk management in Taiwan and other typhoon-affected environments. By highlighting specific locations where vegetation decline aligns with landslide risk, the findings can guide local authorities in prioritizing slope stabilization, habitat conservation, and land-use planning. Such targeted actions support the Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land), by reducing disaster risk, enhancing community resilience, and promoting the long-term sustainability of mountain ecosystems. Full article

The Mediterranean region, identified by scientists as a ‘climate hot spot’, is experiencing warmer and drier conditions, along with an increase in the intensity and frequency of extreme weather events. One such extreme phenomena is droughts. The recent wildfires in this region are a concerning consequence of this phenomenon, causing severe environmental damage and transforming natural landscapes. However, droughts involve a two-way interaction: On the one hand, climate change and various human activities, such as urbanization and deforestation, influence the development and severity of droughts. On the other hand, droughts have a significant impact on various sectors, including ecology, agriculture, and the local economy. This study investigates drought dynamics in four Mediterranean countries, Greece, France, Italy, and Spain, each of which has experienced severe wildfire events in recent years. Using satellite-based Earth observation data, we monitored drought conditions across these regions over a five-year period that includes the dates of major wildfires. To support this analysis, we derived and assessed key indices: the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI). High-resolution satellite imagery processed within the Google Earth Engine (GEE) platform enabled the spatial and temporal analysis of these indicators. Our findings reveal that, in all four study areas, peak drought conditions, as reflected in elevated NDDI values, were observed in the months leading up to wildfire outbreaks. This pattern underscores the potential of satellite-derived indices for identifying regional drought patterns and providing early signals of heightened fire risk. The application of GEE offered significant advantages, as it allows efficient handling of long-term and large-scale datasets and facilitates comprehensive spatial analysis. Our methodological framework contributes to a deeper understanding of regional drought variability and its links to extreme events; thus, it could be a valuable tool for supporting the development of adaptive management strategies. Ultimately, such approaches are vital for enhancing resilience, guiding water resource planning, and implementing early warning systems in fire-prone Mediterranean landscapes. Full article

With the advancement of automation in modern agriculture, the demand for intelligence in the post-picking sorting of fruits and vegetables is increasing. As a significant global agricultural product, the defect detection and sorting of tomato is essential to ensure quality and improve economic value. However, the traditional detection method (manual screening) is inefficient and involves high labor intensity. Therefore, a defect detection model named YOLO-RGDD is proposed based on YOLOv12s to identify five types of tomato surface defects (scars, gaps, white spots, spoilage, and dents). Firstly, the original C3k2 module and A2C2f module of YOLOv12 were replaced with RFEM in the backbone network to enhance feature extraction for small targets without increasing computational complexity. Secondly, the Dysample–Slim-Neck of the YOLO-RGDD was developed to reduce the computational complexity and enhance the detection of minor defects. Finally, dynamic convolution was used to replace the conventional convolution in the detection head in order to reduce the model parameter count. The experimental results show that the average precision, recall, and F1-score of the proposed YOLO-RGDD model for tomato defect detection reach 88.5%, 85.7%, and 87.0%, respectively, surpassing advanced object recognition detection algorithms. Additionally, the computational complexity of the YOLO-RGDD is 16.1 GFLOPs, which is 24.8% lower than that of the original YOLOv12s model (21.4 GFLOPs), facilitating the model’s deployment in automated agricultural production. Full article

The Arctic region is rich in oil and gas resources and has great potential for development. It has become a new hot spot for international development. However, the harsh climatic and geological conditions and fragile ecosystems in the Arctic region put forward stringent technical requirements for oil and gas development. Polar permafrost has an impact on the growth of plant roots and the absorption of water. When drilling activities are carried out, the permafrost layer may be broken, resulting in the erosion of polar soil and disorder of the water balance, thus affecting local vegetation and ecosystems. Moreover, the legal system of polar environmental protection is lacking, and it is necessary to form a perfect risk assessment method to improve the relevant laws and regulations. Therefore, it is very important to study the environmental risk identification technology for polar drilling. For polar drilling, it is necessary to establish a risk source classification and identification method for environmental pollution events. However, at present, it mainly faces the following challenges: poor polar environment, lack of monitoring data, and lack of a legal system for polar environmental protection. By systematically discussing risk identification technology, the application and applicable models of different types of risk evaluation methods are categorized and summarized, the advantages and disadvantages of different types of risk evaluation methods and their application effects are analyzed based on the unique environment of the polar regions, and then the development direction of the future environmental risk identification technology for polar drilling is proposed. In order to accelerate the development of polar drilling environmental risk identification technology, research should be focused on the following three aspects: ① Promoting the multi-dimensional integration of polar drilling environmental pollution index data, to make up for the short board of less relevant data in the polar region. ② Combining the machine modeling algorithm with risk evaluation of polar drilling environmental pollution to improve the scientificity and accuracy of the evaluation results. ③ Establishing a scientific and accurate polar drilling environmental pollution risk identification system to reduce pollution risk. Full article

Sichuan paocai, a microbial food predominantly fermented by lactic acid bacteria and hosting a complex and diverse microbial ecosystem, serves as an ideal habitat for bacteriophages. However, relatively few studies have been conducted on isolating bacteriophages from fermented vegetables and their application in vegetable fermentation. In this study, three staphylococcal bacteriophages, ΦSx-2, ΦSs-1, and ΦSs-2, were isolated and purified from Sichuan paocai using the spot test method. The morphological features of the phages were characterized using transmission electron microscopy (TEM), while key biological properties such as one-step growth kinetics were systematically evaluated, ultimately verifying their taxonomic placement within the Caudoviricetes class. Furthermore, the potential effects of these phages on the microbial community structure and physicochemical properties during paocai fermentation were investigated using high-throughput sequencing and standard physicochemical assays. Microbial community analysis demonstrated that introducing the phages significantly increased the relative abundance of lactic acid bacteria while reducing the prevalence of spoilage bacteria such as Erwinia, Pantoea, and Enterobacter. Physicochemical assessments revealed that adding phages accelerated the acidification process of paocai, effectively reduced nitrite levels, and increased the concentrations of lactic and acetic acids. Additionally, notable differences in color and flavor were observed between the two groups of paocai during the fermentation process. In summary, the inoculation of bacteriophages ΦSx-2, ΦSs-1, and ΦSs-2 optimized the microbial community structure, enhanced the fermentation process, and improved the quality of Sichuan paocai. Full article

The wildland–urban interface (WUI) has been a global phenomenon, yet parameter threshold determination remains a persistent challenge in this field. In China, a significant research gap exists in the development of WUI mapping methodology. This study proposes a novel mapping approach that delineates the WUI by integrating both vegetation and building environment perspectives. GaoFen 1 Panchromatic Multi-spectral Sensor (GF1-PMS) imagery was leveraged as the data source. Building location was extracted using object-oriented and hierarchical classification techniques, and the pixel dichotomy method was employed to estimate fractional vegetation coverage (FVC). Building location and FVC were used as input for the WUI mapping. In this methodology, the threshold of FVC was determined by incorporating the remote sensing characteristics of the WUI types, whereas the buffer range of vegetation was refined through sensitivity analysis. The proposed method demonstrated high applicability in Anning City, achieving an overall accuracy of 88.56%. The total WUI area amounted to 49,578.05 ha, accounting for 38.08% of Anning City’s entire area. Spatially, the intermix WUI was predominantly distributed in the Taiping sub-district of Anning City, while the interface WUI was mainly concentrated in the Bajie sub-district of Anning City. MODIS fire spots from 2003 to 2022 were primarily clustered in the Qinglong sub-district, Wenquan sub-district, and Caopu sub-district of Anning City. Our findings indicated a spatial overlap between the WUI and fire-prone areas in Anning City. This study presents an effective methodology for threshold determination and WUI mapping, making up for the scarcity of mapping methodologies in China. Moreover, our approach offers valuable insights for a wise decision in fire risk. Full article

Tomato is a widely cultivated vegetable crop worldwide. It is susceptible to various phytopathogens, including fungi, bacteria, viruses, and nematodes. In 2024, an unknown leaf spot disease outbreak, characterized by distinct brown necrotic lesions on leaves, was observed in tomato plants in Yunnan Province, China. Through rigorous pathogen isolation and the fulfillment of Koch’s postulates, it was proved that the fungal isolate could infect tomato leaves and cause typical symptoms. The pathogen isolated from tomato leaves was identified as Nigrospora oryzae based on its morphology and using a multilocus sequence analysis method with the internal transcribed spacer gene (ITS1), beta-tubulin gene (TUB2), and translation elongation factor 1-alpha gene (TEF1-α). This represents the first documented case of N. oryzae infecting tomatoes in the world. Given the damage caused by N. oryzae to tomato plants, we explored biocontrol methods. Through a dual-culture assay on PDA plates, Bacillus velezensis B31 demonstrated significant biocontrol potential, exhibiting strong antagonistic activity toward N. oryzae. In addition, we developed a polyethylene glycol (PEG)-mediated transformation system that successfully introduced pYF11-GFP into the protoplasts of N. oryzae. This achievement provides a foundation for future genetic manipulation studies of N. oryzae. Full article

Black spot is a major postharvest disease of cherry tomatoes, caused by Alternaria alternata. This causes economic losses and storage challenges, so researchers are exploring alternative methods. The biological control of fruits and vegetables using antagonistic bacteria and yeasts is currently a research hotspot. Initially, the biological control impact of Bacillus velezensis T3 on cherry tomato black spot was investigated. Disease defense, scavenging reactive oxygen species, and antioxidant-related enzymes were determined during different storage periods. The relative gene expressions of these enzymes were also confirmed using RT-qPCR. The results showed that B. velezensis T3 reduced the incidence of black spot disease in cherry tomatoes. The growth of A. alternata was suppressed by B. velezensis T3 cell-free filtrate both in vitro and in vivo. In addition, B. velezensis T3 induced the activities of disease resistance-related enzymes such as polyphenol oxidase (PPO), phenylalanine ammonia-lyase (PAL), β-1,3-glucanase (GLU), and chitinase (CHI), and the activities of the ROS-related enzymes superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), and ascorbate peroxidase (APX), and reduced the rate of O₂⁻ production and H₂O₂, and MDA content of cherry tomatoes. This approach offers a promising alternative for extending shelf life, though further studies are needed to fully characterize its effects on fruit quality. Full article

Understanding microclimatic changes driven by urbanization is critical in the context of global warming and climate change. This study investigates the land surface temperature (LST), the normalized difference vegetation index (NDVI), and changes in land use types for 10 major cities across seven continents between 2001 and 2021. Utilizing MODIS satellite data processed on the Google Earth Engine (GEE) platform, the analysis focused on yearly median values to examine variations in LST during the day and night, as well as temperature dynamics across different land types, including vegetation and bare land. The global mean LST trend from 2001 to 2021, derived from Terra MODIS MOD11A2 data, was found to be 0.025 °C/year. The analysis of daytime and nighttime (nocturnal) land surface temperature (LST) trends across the ten cities examined in this study reveals notable variations, with most cities exhibiting an increasing trend in LST within urban mosaics. Airports exhibited a mean daytime land surface temperature (LST) that was 2.5 °C higher than surrounding areas, while industrial zones demonstrated an even greater temperature disparity, with an average increase of 2.81 °C. In contrast, cold spots characterized by dense vegetation showed a notable cooling effect, with LST differences reaching −3.7 °C. Similarly, proximity to water bodies contributed to temperature mitigation, as areas near significant water sources recorded lower daytime LST differences, averaging −4.09 °C. A strong negative correlation was found between NDVI and LST, underscoring the cooling effect of vegetation through evapotranspiration and shading. This study provides a comprehensive global perspective on the commonalities of urban temperature dynamics in cities across diverse geographical regions and climates, contributing to a deeper understanding of how urbanization and land use changes influence surface temperatures and climate change. Full article

Urban green spaces are important components of city spaces that are vulnerable to degradation in soil–water–climate processes. This vulnerability is exacerbated by current climate change and park usage density. This study examines the dynamics of soil water repellency in the topsoils of selected urban parks in Berlin, aiming to assess the relationships between weather conditions, soil water content, and soil water repellency. This study is based on monthly sampled soils from spots originating from three selected parks—Fischtal Park, Stadtpark Steglitz, and Rudolph-Wilde Park—between September 2022 and October 2023; two of the parks are exclusively rainwater fed, and one is irrigated during summer months. For each sample soil, water repellency persistence and severity were analyzed. Time series analysis was conducted including soil water content. In addition, the total organic carbon content (TOC) and sample texture were analyzed. The results show that the rainfall amount, number of dry days, and maximum temperature during different time intervals prior to the sampling date predominantly control the variation in the soil water repellency via the soil water content. Soil water repellency variations observed appear more event-related than monthly or seasonal, as rainfall is evenly distributed through the years without a distinct dry or wet season in Berlin. The non-repellency of the soil samples was usually observed when the associated water content was increased, which is linked to high cumulative rainfall and short dry periods. Low rainfall amounts and long dry periods in summer result in the re-establishment of the soil water repellency, possibly affecting increased runoff generation and soil erosion risk. Spatially, the repellency properties were observed at locations under healthy vegetation cover, while soils located on the upper slope locations and on the pathways lacked repellency characteristics. Full article

As an important component of terrestrial ecosystems, mountain vegetation serves as an indicator of climate change. Due to the sensitivity of the Tibetan Plateau Mountains (TPM) to climate change and their ecological fragility, their vegetation dynamics (greenness and greening) have become a hot spot issue in global environmental change. Topography is a relatively stable environmental factor that shapes vegetation by creating localized microenvironments. However, existing research primarily focuses on the effects of climate change and human activities on vegetation dynamics. Therefore, a more comprehensive understanding of the dependence of vegetation dynamics on topography is needed. To elucidate the relationship between topography and the spatial heterogeneity of vegetation dynamics, we conducted this study using the recently released high-precision Sensor-Independent Leaf Area Index product. Through long-term trend analyses and joint comparisons of multiple topographic variables, this study elucidates key patterns: (1) North-facing slopes exhibit higher vegetation greenness and stronger greening trends than south-facing slopes, whereas east- and west-facing slopes show comparable greenness but stronger greening on west-facing slopes. (2) Vegetation greenness and greening increase with slope steepness. (3) With increasing elevation, greenness decreases progressively, while greening follows a unimodal pattern—initially increasing, then decreasing, and nearing zero at high altitudes. These findings underscore the pivotal role of topography in regulating vegetation responses to climate change. This study provides new insights into the interplay between topography and vegetation dynamics, advancing our understanding of ecological processes on the TPM and informing strategies for ecosystem management under global warming. Full article

The dynamics of shallow water areas of inland lakes is closely related to the regional ecology and economy. However, it is still a challenge to extract the natural shallow water area for inland lakes using satellite images due to their rapid changes and various human demands. Therefore, we developed a new remote sensing-based method applied in Hongze Lake (one of the largest freshwater lakes in China) to first delineate the lake from the SWIR1 band of Landsat OLI imagery using cold spots in the LISA method, and then distinguish deep and shallow water areas from the G band of Landsat OLI images using hot spots with LISA after masking the lake out, and finally extracting the natural shallow water area by masking aquatic farms out from shallow water areas using farm ridge classification from NDWI images and aggregating points of farm ridges. The results show that (1) the method of this study is efficient in extracting the natural shallow water area with limited effects from aquatic vegetation; (2) water inflow (upstream water supply and precipitation) and the area of aquatic farms, the two dominant factors for the temporal changes in natural shallow water area, contributed 38.3% (positively) and 42.2% (negatively) to the decrease in the natural shallow water area during 2013–2022 in Hongze Lake; (3) the natural shallow water area of Hongze Lake decreased significantly every April as paddy rice farms withdrew a large amount of irrigation water from Hongze Lake. Our research provides a new approach to extract the natural shallow water areas of inland lakes from satellite images and demonstrates that the upstream water supply, precipitation, and agriculture demands are the three main reasons for seasonal and temporal variations in natural shallow water areas for inland lakes. Full article

Sabkha (inland and coastal—saline beds or saline lands) are widespread in Saudi Arabia and are distinguished by their hypersaline nature. These hypersaline habitats are commonly covered by halophytic vegetation. Moreover, Arbuscular mycorrhizal fungi (AMF) are an essential component of these habitats and exhibit a unique adaptation and contribute significantly to ecosystem variability, diversity, and function. Additionally, AMF from saline habitats are an essential component for the successful rehabilitation of salinity-affected areas. Despite their importance, little is known about the distribution and abundance of AMF along inland and coastal sabkhat of Saudi Arabia. Therefore, the main objective of this study was to investigate the abundance and diversity of AMF in the coastal and inland sabkhat of Saudi Arabia. Five soil samples, each from five randomly selected spots (considering the presence of dominant and co-dominant halophytic species), were collected from every location and were used to assess the AMF abundance and diversity. The study indicated that the highest number of AMF spores was recorded from Jouf, averaging ≈ 346 spores 100 g⁻¹ dry soil, and the lowest from Uqair, averaging ≈ 96 spores 100 g⁻¹ dry soil. A total of 25 AMF species were identified, belonging to eight identified genera viz., Acaulospora, Diversispora, Gigaspora, Scutellospora, Claroideoglomus, Funneliformis, Glomus, and Rhizophagus and five families. Of the total identified species, 52% belonged to the family Glomeraceae. Moreover, the highest number of species was isolated from the sabkha in Qasab. Additionally, Glomeraceae was abundant in all the studied locations with the highest relative abundance in Uqair (48.34%). AMF species Claroideoglomus etunicatum, Funneliformis mosseae, Glomus ambisporum, and Rhizophagus intraradices were the most frequently isolated species from all the Sabkha locations with isolation frequency (IF) ≥ 60%, and Claroideoglomus etunicatum (Ivi ≥ 50%) was the dominant species in all the studied locations. Furthermore, data on the Shannon–Wiener diversity index showed that the highest AMF species diversity was in Qaseem and Qasab habitats. The highest Pielou’s evenness index was recorded in Jouf. Moreover, the soil parameters that positively affected the diversity of identified species included Clay%, Silt%, HCO₃¹⁻, OM, MC, N, and P, while some soil parameters such as EC, Na⁺, SO₄²⁻, and Sand% had a significant negative correlation with the isolated AMF species. This study revealed that AMF can adapt and survive the harshest environments, such as hypersaline sabkhas, and thus can prove to be a vital component in the potential restoration of salinity-inflicted/degraded ecosystems. Full article