Search Results (32,231)

As we navigate the fast-paced era of urban expansion, the integration of machine learning (ML) and remote sensing (RS) has become a cornerstone in environmental management. This research, focusing on Silchar City, a non-attainment city under the National Clean Air Program (NCAP), leverages these advanced technologies to understand the urban microclimate and its implications on the health, resilience, and sustainability of the built environment. The rise in land surface temperature (LST) and changes in land use and land cover (LULC) have been identified as key contributors to thermal dynamics, particularly focusing on the development of urban heat islands (UHIs). The Urban Thermal Field Variance Index (UTFVI) can assess the influence of UHIs, which is considered a parameter for ecological quality assessment. This research examines the interlinkages among urban expansion, LST, and thermal dynamics in Silchar City due to a substantial rise in air temperature, poor air quality, and particulate matter PM_2.5. Using Landsat satellite imagery, LULC maps were derived for 2000, 2010, and 2020 by applying a supervised classification approach. LST was calculated by converting thermal band spectral radiance into brightness temperature. We utilized Cellular Automata (CA) and Artificial Neural Networks (ANNs) to project potential scenarios up to the year 2040. Over the two-decade period from 2000 to 2020, we observed a 21% expansion in built-up areas, primarily at the expense of vegetation and agricultural lands. This land transformation contributed to increased LST, with over 10% of the area exceeding 25 °C in 2020 compared with just 1% in 2000. The CA model predicts built-up areas will grow by an additional 26% by 2040, causing LST to rise by 4 °C. The UTFVI analysis reveals declining thermal comfort, with the worst affected zone projected to expand by 7 km². The increase in PM_2.5 and aerosol optical depth over the past two decades further indicates deteriorating air quality. This study underscores the potential of ML and RS in environmental management, providing valuable insights into urban expansion, thermal dynamics, and air quality that can guide policy formulation for sustainable urban planning. Full article

Accurately evaluating drought and its effects on the natural environment is difficult in regions with limited climate monitoring stations, particularly in the hyper-arid region of the Sultanate of Oman. Rising global temperatures and increasing incidences of insufficient precipitation have turned drought into a major natural disaster worldwide. In Oman, drought constitutes a major threat to food security. In this study, drought indices (DIs), such as temperature condition index (TCI), vegetation condition index (VCI), and vegetation health index (VHI), which integrate data on drought streamflow, were applied using moderate resolution imaging spectroradiometer (MODIS) data and the Google Earth Engine (GEE) platform to monitor agricultural drought and assess the drought risks using the drought hazard index (DHI) during the period of 2001–2023. This approach allowed us to explore the spatial and temporal complexities of drought patterns in the Najd region. As a result, the detailed analysis of the TCI values exhibited temporal variations over the study period, with notable minimum values observed in specific years (2001, 2005, 2009, 2010, 2014, 2015, 2016, 2017, 2019, 2020, and 2021), and there was a discernible trend of increasing temperatures from 2014 to 2023 compared to earlier years. According to the VCI index, several years, including 2001, 2003, 2006, 2008, 2009, 2013, 2015, 2016, 2017, 2018, 2020, 2021, 2022, and 2023, were characterized by mild drought conditions. Except for 2005 and 2007, all studied years were classified as moderate drought years based on the VHI index. The Pearson correlation coefficient analysis (PCA) was utilized to observe the correlation between DIs, and a high positive correlation between VHI and VCI (0.829, p < 0.01) was found. Based on DHI index spatial analysis, the northern regions of the study area faced the most severe drought hazards, with severity gradually diminishing towards the south and east, and approximately 44% of the total area fell under moderate drought risk, while the remaining 56% was classified as facing very severe drought risk. This study emphasizes the importance of continued monitoring, proactive measures, and effective adaptation strategies to address the heightened risk of drought and its impacts on local ecosystems and communities. Full article

Sake, which is produced mainly from japonica rice (Oryza sativa subsp. japonica), is one of the most important alcohol products in Japan. In this study, we aimed to investigate a hypothesis that the early root endophytic bacterial communities in Japanese sake rice cultivars would be distinct from those in table rice cultivars, comparing four sake rice cultivars and two table rice cultivars. Rice roots in the vegetative stage were collected 0, 3, and 6 weeks after transplanting, and 16S rRNA gene amplicon sequencing revealed significant differences in bacterial community composition diversity between the sake and table rice cultivars. The root endophytic bacterial communities at the transplanting differed significantly between the rice cultivars, indicating differences in each seed-derived endophytic community. After an overall dominance of Pantoea and Methylobacterium-Methylorubrum at the transplanting, the endophytic community was gradually replaced by soil-derived bacteria that varied by the rice cultivars. Notably, PERMANOVA results showed that the rice endophytic bacterial community composition differed significantly between the sake and table rice cultivars (p < 0.001). These results highlight the distinct root endophytic bacterial composition in the sake rice cultivars compared to those in the table rice cultivars, supporting our hypothesis. Full article

High-resolution and accurate precipitation data are essential for hydrological, meteorological, and ecological research at the watershed scale. However, in regions with complex terrain and significant rainfall variability, the limited number of rain gauge stations (RGS) is insufficient, and the spatial resolution of existing satellite precipitation data is too low to capture detailed precipitation patterns at the watershed scale. To address this issue, the downscaling of satellite precipitation products has become an effective method to obtain high-resolution precipitation data. This study proposes a monthly downscaling method based on a random forest model, aiming to improve the resolution of precipitation data in cloudy and rainy regions at mid-to-low latitudes. We combined the Google Earth Engine (GEE) platform with a local Python environment, introducing cloud cover characteristics into traditional downscaling variables (latitude, longitude, topography, and vegetation index). The TRMM data were downscaled from 25 km to 1 km, generating high-resolution monthly precipitation data for the Dongting Lake Basin from 2001 to 2019. Furthermore, we analyzed the spatiotemporal variation characteristics of precipitation in the study area. The results show the following: (1) In cloudy and rainy regions, our method improves resolution and detail while maintaining the accuracy of precipitation data; (2) The response of monthly precipitation to environmental variables varies, with cloud cover characteristics contributing more to the downscaling model than vegetation characteristics, helping to overcome the lag effect of vegetation characteristics; and (3) Over the past 20 years, there have been significant seasonal trends in precipitation changes in the study area, with a decreasing trend in winter and spring (January–May) and an increasing trend in summer and autumn (June–December). These results indicate that the proposed method is suitable for downscaling monthly precipitation data in cloudy and rainy regions of the Dongting Lake Basin. Full article

The article focuses on the ongoing challenge of eliminating vegetative and spore-forming bacteria from food products that exhibit resistance to the traditional preservation methods. In response to this need, the authors highlight an innovative approach based on the synergistic utilization of high-hydrostatic-pressure (HHP) and lytic bacteriophages. The article reviews the current research on the use of HHP and lytic bacteriophages to combat bacteria in food products. The scope includes a comprehensive review of the existing literature on bacterial cell damage following HHP application, aiming to elucidate the synergistic effects of these technologies. Through this in-depth analysis, the article aims to contribute to a deeper understanding of how these innovative techniques can improve food safety and quality. There is no available research on the use of HHP and bacteriophages in the elimination of spore-forming bacteria; however, an important role of the synergistic effect of HHP and lytic bacteriophages with the appropriate adjustment of the parameters has been demonstrated in the more effective elimination of non-spore-forming bacteria from food products. This suggests that, when using this approach in the case of spore-forming bacteria, there is a high chance of the effective inactivation of this biological threat. Full article

In this study, we investigate the usefulness of the spectral whitening procedure, devised by one of the authors as a preprocessing stage of envelope-detected single-look synthetic aperture radar (SAR) images, in application contexts where phase information is relevant. In the first experiment, each of the raw datasets of an interferometric pair of COSMO-SkyMed images, representing industrial buildings amidst vegetated areas, was individually (1) synthesized by the SAR processor without Fourier-domain Hamming windowing; (2) synthesized with Hamming windowing, used to improve the focalization of targets, with the drawback of spatially correlating speckle; and (3) processed for the whitening of complex speckle, using the data obtained in (2). The interferograms were produced in the three cases, and interferometric coherence and phase maps were calculated through 3 × 3 boxcar filtering. In (1), coherence is low on vegetation; the presence of high sidelobes in the system’s point-spread function (PSF) causes the spread of areas featuring high backscattering. In (2), point targets and buildings are better defined, thanks to the sidelobe suppression achieved by the frequency windowing, but the background coherence is abnormally increased because of the spatial correlation introduced by the Hamming window. Case (3) is the most favorable because the whitening operation results in low coherence in vegetation and high coherence in buildings, where the effects of windowing are preserved. An analysis of the phase map reveals that (3) is likely to be facilitated also in terms of unwrapping. Results are presented on a TerraSAR-X/TanDEM-X (TSX-TDX) image pair by processing the interferograms of original and whitened data using a non-local filter. The main results are as follows: (1) with autocorrelated speckle, the estimation error of coherence may attain 16% and inversely depends on the heterogeneity of the scene; and (2) the cleanness and accuracy of the phase are increased by the preliminary whitening stage, as witnessed by the number of residues, reduced by 24%. Benefits are also expected not only for differential InSAR (DInSAR) but also for any coherent analysis and processing carried out performed on SLC data. Full article

Plant ecological strategies are essential for assessing habitat stress and disturbance and evaluating community productivity. These strategies provide theoretical frameworks for maintaining the natural state of vegetation and enhancing productivity. The functional traits of leaves reflect a plant’s responses to environmental changes and contribute to understanding ecosystem stability, providing a basis for species diversity maintenance and effective conservation efforts. The Wuyishan National Park, a biodiversity hotspot in China, is a focal point for ecological research. Its evergreen, broad-leaved forest, the zonal vegetation of Mt. Wuyi, underpins plant diversity protection in the region. This study investigates the CSR (competitor, stress-tolerator, ruderal) strategy of 126 species on Wuyi Mountain to elucidate prevalent ecological strategies. The main ecological strategy of plants in the study area is the CS (competitor, stress-tolerator) strategy. The species exhibit nine categories. The most abundant ecological strategy is S/CS (plants from Fagaceae), accounting for 38%, followed by S/CSR at 23% (plants from Theaceae), CS at 20% (plants from Fagaceae and Theaceae), and the remaining strategies collectively at 19%. The different growth habit categories showed variations in the CSR strategies. The trees clustered around a CS median strategy, with no R-selected trees observed. Shrubs and lianas centered around an S/CSR strategy, while grasses and understory shrubs clustered around CS/CSR. Redundancy analysis results indicate that leaf functional traits are primarily influenced by temperature, suggesting that temperature is the key environmental factor driving the differentiation of plant functional traits. This study provides insights into the ecological strategies of plant species in the Mt. Wuyi region, highlighting the importance of considering both biotic and abiotic factors in maintaining biodiversity and ecosystem stability. Full article

Vegetative desiccation tolerance has evolved within the genera Craterostigma and Lindernia. A centre of endemism and diversification for these plants appears to occur in ancient tropical montane rainforests of east Africa in Kenya and Tanzania. Lindernia subracemosa, a desiccation-sensitive relative of Craterostigma plantagineum, occurs in these rainforests and experiences adequate rainfall and thus does not require desiccation tolerance. However, sharing this inselberg habitat, another species, Lindernia brevidens, does retain vegetative desiccation tolerance and is also related to the resurrection plant C. plantagineum found in South Africa. Leaf material was collected from all three species at different stages of hydration: fully hydrated (ca. 90% relative water content), half-dry (ca. 45% relative water content) and fully desiccated (ca. 5% relative water content). Cell wall monosaccharide datasets were collected from all three species. Comprehensive microarray polymer profiling (CoMPP) was performed using ca. 27 plant cell-wall-specific antibodies and carbohydrate-binding module probes. Some differences in pectin, xyloglucan and extension epitopes were observed between the selected species. Overall, cell wall compositions were similar, suggesting that wall modifications in response to vegetative desiccation involve subtle cell wall remodelling that is not reflected by the compositional analysis and that the plants and their walls are constitutively protected against desiccation. Full article

Soil environmental issues in the red bed region are increasingly conspicuous, underscoring the critical importance of assessing soil quality for the region’s sustainable development and ecosystem security. This study examines six distinct land use types of soils—agricultural land (AL), woodland (WL), shrubland (SL), grassland (GL), bare rock land (BRL), and red bed erosion land (REL)—in the Nanxiong Basin of northern Guangdong Province. This area typifies red bed desertification in South China. Principal component analysis (PCA) was employed to establish a minimum data set (MDS) for calculating the soil quality index (SQI), evaluating soil quality, analyzing influencing factors, and providing suggestions for ecological restoration in desertification areas. The study findings indicate that a minimal data set comprising soil organic matter (SOM), pH, available phosphorus (AP), exchangeable calcium (Ca²⁺), and available copper (A-Cu) is most suitable for evaluating soil quality in the red bed desertification areas of the humid region in South China. Additionally, we emphasize that exchangeable salt ions and available trace elements should be pivotal considerations in assessing soil quality within desertification areas. Regarding comprehensive soil quality indicators across various land use types, the red bed erosion soils exhibited the lowest quality, followed by those in bare rock areas and forest land. Within the minimal data set, Ca²⁺ and pH contributed the most to overall soil quality, underscoring the significance of parent rock mineral composition in the red bed desertification areas. Moreover, the combined effects of SOM, A-Cu, and AP on soil quality indicate that anthropogenic land management and use, including fertilization methods and vegetation types, are crucial factors influencing soil quality. Our research holds significant implications for the scientific assessment, application, and enhancement of soil quality in desertification areas. Full article

Maize (Zea mays L.) and soybean (Glycine max L. Merr.) are prevalent summer crops planted widely in the Loess Plateau region of China, which is particularly susceptible to severe soil erosion on the sloping farmland. However, which crop exhibits superior soil and water conservation capabilities while maintaining economic viability, and how their performance in soil and water conservation is affected by slope gradient and rainfall intensity remains unclear. The objective of this study was to compare the impacts of maize and soybean on regulating runoff and sediment through rainfall simulation experiments, and explore the main control factors of soil and water conservation benefits. Five slope gradients (8.7, 17.6, 26.8, 36.4, and 46.6%) and two rainfall intensities (40 and 80 mm h⁻¹) were applied at five respective crop growth stages. Both maize and soybean effectively reduced soil and water losses compared with bare ground, although increasing slope gradient and rainfall intensity weakened the vegetation effect. Compared with slope gradient and rainfall intensity, vegetation coverage was the main factor affecting the performance of maize and soybean in conserving soil and water. The average time delay benefit (TDB), runoff reduction benefit (RRB), and sediment reduction benefit (SRB) of soybean (246.48 ± 11.71, 36.34 ± 2.51, and 54.41 ± 3.42%) were significantly higher (p < 0.05) than those of maize (100.06 ± 6.81, 25.71 ± 1.76, and 43.70 ± 2.91%, respectively) throughout growth. After planting, the increasing rates of vegetation coverage, TDB, RRB, and SRB with time were consistently higher with soybean than maize. Moreover, under the same vegetation coverage, the TDB, RRB, and SRB of soybean were also consistently higher than those of maize. In conclusion, these findings indicate that soybean outperformed maize in terms of soil and water conservation benefits under the experimental conditions, making it more suitable for cultivation on sloping farmland. This finding offers crucial guidance for the cultivation of dry farming in regions plagued by severe soil erosion, facilitating a balance between economic objectives and ecological imperatives. Full article

This paper examines the effects of climatic factors, specifically temperature and precipitation, on land use and land cover (LULC) on the Kerch Peninsula using structural equation modeling (SEM). The Normalized Difference Vegetation Index (NDVI) was used as a mediator in the model to accurately assess the impact of climate change on vegetation and subsequent LULC dynamics. The results indicate that temperature exerts a significant negative influence on LULC in the early periods, inducing stress on vegetation and leading to land degradation. However, this influence diminishes over time, possibly due to ecosystem adaptation and the implementation of resilient land management practices. In contrast, the impact of precipitation on LULC, which is initially minimal, increases significantly, highlighting the need for improved water resource management and adaptation measures to mitigate the negative effects of excessive moisture. The NDVI plays a crucial mediating role, reflecting the health and density of vegetation in response to climatic variables. An analysis of lagged effects shows that both precipitation and temperature exert delayed effects on LULC, underscoring the complexity of water dynamics and ecosystem responses to climatic conditions. These results have important practical implications for land resource management and climate adaptation strategies. Understanding the nuanced interactions between climatic factors and LULC can inform the development of resilient agricultural systems, optimized water management practices, and effective land use planning. Future research should focus on refining models to incorporate nonlinear interactions, improving data accuracy, and expanding the geographic scope to generalize findings. This study highlights the importance of continuous monitoring and adaptive management to develop sustainable land management practices that can withstand the challenges of climate change. Full article

In recent years, with the sustained attention from academia and media to urban air quality and environmental issues, governments and scholars worldwide have been devoted to studying the relationship between air quality and green infrastructure (GI), seeking effective measures to address urban air pollution. This study aims to explore the impact of GI on urban air quality, focusing on analyzing data from Ulsan and Junpo cities in South Korea. Significant statistical significance has been found through correlation analysis between GI area and air pollutants such as nitrogen dioxide, carbon monoxide, particulate matter, and ozone. Specifically, when calculating GI using the Normalized Difference Vegetation Index (NDVI) data, for every 1% increase in GI area, nitrogen dioxide (NO₂) decreases by approximately 0.000000001925 ppm. This finding offers important insights for future air quality research and provides empirical evidence for urban planning, emphasizing the significance of green space planning in improving air quality. Additionally, the study suggests that future research should be more diverse, incorporating data from more cities and conducting an in-depth analysis of GI distribution to understand its impact on air quality comprehensively. Full article

The coastal zone is abundant in natural resources but has become increasingly fragile in recent years due to climate change and extensive, improper exploitation. Accurate land use and land cover (LULC) mapping of coastal zones using remotely sensed data is crucial for monitoring environmental changes. Traditional classification methods based on statistical learning require significant spectral differences between ground objects. However, state-of-the-art end-to-end deep learning methods can extract advanced features from remotely sensed data. In this study, we employed ResNet50 as the feature extraction network within the U-Net architecture to achieve accurate classification of coastal areas and assess the model’s performance. Experiments were conducted using Gaofen-2 (GF-2) high-resolution remote sensing data from Shuangyue Bay, a typical coastal area in Guangdong Province. We compared the classification results with those obtained from two popular deep learning models, SegNet and DeepLab v3+, as well as two advanced statistical learning models, Support Vector Machine (SVM) and Random Forest (RF). Additionally, this study further explored the significance of Gray Level Co-occurrence Matrix (GLCM) texture features, Histogram Contrast (HC) features, and Normalized Difference Vegetation Index (NDVI) features in the classification of coastal areas. The research findings indicated that under complex ground conditions, the U-Net model achieved the highest overall accuracy of 86.32% using only spectral channels from GF-2 remotely sensed data. When incorporating multiple features, including spectrum, texture, contrast, and vegetation index, the classification accuracy of the U-Net algorithm significantly improved to 93.65%. The major contributions of this study are twofold: (1) it demonstrates the advantages of deep learning approaches, particularly the U-Net model, for LULC classification in coastal zones using high-resolution remote sensing images, and (2) it analyzes the contributions of spectral and spatial features of GF-2 data for different land cover types through a spectral and spatial combination method. Full article

This study collects the results obtained using the Getis-Ord local spatial autocorrelation index (Gi) with the aim of improving the classification of burned area detection maps generated from spectral indices (i.e., dNBR index) derived from Sentinel-2 satellite data. Therefore, the work proposes an adaptive thresholding approach that also includes the application of a similarity index (Sorensen–Dice Similarity Index) with the aim of adaptively correcting classification errors (false-positive burned pixels) related to the spectral response of burned/unburned areas. In this way, two new indices derived from the application of the Getis-Ord local autocorrelation analysis were created to test their effectiveness. Three wildfire events were considered, two of which occurred in Southern Italy in the summer of 2017 and one in Sardinia in the summer of 2019. The accuracy assessment analysis was carried out using the CEMS (Copernicus Emergency Management Service) on-demand maps. The results show the remarkable performance of the two new indices in terms of their ability to reduce the false positives generated by dNBR. In the three sites considered, the false-positive reduction percentage was around 95–96%. The proposed approach seems to be adaptable to different vegetation contexts, and above all, it could be a useful tool for mapping burned areas to support post-fire management activities. Full article

The flow structure around limited-size vegetation patches is crucial for understanding sediment transport and vegetation succession trends. While the influence of vegetation density has been extensively explored, the impact of the relative diameter of vegetation stems remains relatively unclear. After validating the reliability of the numerical model with experimental data, this study conducted 2D-URANS simulations (SST k-ω) to investigate the impact of varying relative diameters d/D under different vegetation densities λ on the hydrodynamic characteristics and drag force of vegetation patches. The results show that increasing d/D and decreasing λ are equivalent, both contributing to increased spacing between cylinder elements, allowing for the formation of element-scale Kármán vortices. Compared to vegetation density λ, the non-dimensional frontal area aD is a better predictor for the presence of array-scale Kármán vortex streets. Within the parameter range covered in this study, array-scale Kármán vortex streets appear when aD ≥ 1.4, which will significantly alter sediment transport patterns. For the same vegetation density, increasing the relative diameter d/D leads to a decrease in the array drag coefficient ${\overline{C}}_D$ and an increase in the average element drag coefficient ${\overline{C}}_d$. When parameterizing vegetation resistance using aD, all data points collapse onto a single curve, following the relationships ${\overline{C}}_D=0.34\ln \left(aD\right)+0.78$ and ${\overline{C}}_d=-0.42\ln \left(aD\right)+0.82$. Full article