Search Results (433)

The NOAA IMS (Interactive Multisensor Snow and Ice Mapping System) is a blended snow and ice product based on active and passive satellite sensors, ground observation, and other auxiliary information, providing the daily cloud-free snow cover extent in the Northern Hemisphere (NH) and having great application potential in snow cover monitoring and research in the Tibetan Plateau (TP). However, accuracy assessment of products is crucial for various aspects of applications. In this study, Landsat-8 OLI images were used to evaluate and validate the accuracy of IMS products in snow cover monitoring on the TP. The results show that (1) average overall accuracy of IMS 4 km products is 76.0% and average mapping accuracy is 88.3%, indicating that IMS 4 km products are appropriate for large-scale snow cover monitoring on the TP. (2) IMS 4 km products tend to overestimate actual snow cover on the TP, with an average commission rate of 45.4% and omission rate of 11.7%, and generally present that the higher the proportion of snow-covered area, the lower the probability of omission rate and the higher the probability of commission rate. (3) Mapping accuracy of IMS 4 km snow cover on the TP generally is higher at the high altitudes, and commission and omission errors increase with the decrease of elevation. (4) Compared with less regional representativeness of ground observations, the spatial characteristics of snow cover based on high-resolution remote sensing data are much more detailed, and more reliable verification results can be obtained. (5) In addition to commission and omission error metrics, the overall accuracy and mapping accuracy based on the reference image instead of classified image can better reveal the general monitoring accuracy of IMS 4 km products on the TP area. Full article

Vegetation biodiversity in mountainous regions is controlled by altitudinal gradients and their corresponding microclimate. Higher temperatures, shorter snow cover periods, and high variability in the precipitation regime might lead to changes in vegetation distribution in mountains all over the world. In this study, we evaluate vegetation distribution along an altitudinal gradient (1334–1667 m.a.s.l.) in the Zao Mountains, northeastern Japan, by means of alpha diversity indices, including species richness, the Shannon index, and the Simpson index. In order to assess vegetation species and their characteristics along the mountain slope selected, fourteen 50 m × 50 m plots were selected at different altitudes and scanned with RGB cameras attached to Unmanned Aerial Vehicles (UAVs). Image analysis revealed the presence of 12 dominant tree and shrub species of which the number of individuals and heights were validated with fieldwork ground truth data. The results showed a significant variability in species richness along the altitudinal gradient. Species richness ranged from 7 to 11 out of a total of 12 species. Notably, species such as Fagus crenata, despite their low individual numbers, dominated the canopy area. In contrast, shrub species like Quercus crispula and Acer tschonoskii had high individual numbers but covered smaller canopy areas. Tree height correlated well with canopy areas, both representing tree size, which has a strong relationship with species diversity indices. Species such as F. crenata, Q. crispula, Cornus controversa, and others have an established range of altitudinal distribution. At high altitudes (1524–1653 m), the average shrubs’ height is less than 4 m, and the presence of Abies mariesii is negligible because of high mortality rates caused by a severe bark beetle attack. These results highlight the complex interactions between species abundance, canopy area, and altitude, providing valuable insights into vegetation distribution in mountainous regions. However, species diversity indices vary slightly and show some unusually low values without a clear pattern. Overall, these indices are higher at lower altitudes, peak at mid-elevations, and decrease at higher elevations in the study area. Vegetation diversity indices did not show a clear downward trend with altitude but depicted a vegetation composition at different altitudes as controlled by their surrounding environment. Finally, UAVs showed their significant potential for conducting large-scale vegetation surveys reliably and in a short time, with low costs and low manpower. Full article

Russian Snow White (RSW) chickens are characterized by high egg production, extreme resistance to low temperatures, disease resistance, and by the snow-white color of the day-old chicks. Studying the genome of this unique chicken breed will reveal its evolutionary history and help to understand the molecular genetic mechanisms underlying the unique characteristics of this breed, which will open new breeding opportunities and support future studies. We have sequenced and made a de novo assembly of the whole RSW genome using deep sequencing (250×) by the short reads. The genome consists of 40 chromosomes with a total length of 1.1 billion nucleotide pairs. Phylogenetic analysis placed the RSW near the White Leghorn, Fayoumi, and Houdan breeds. Comparison with other chicken breeds revealed a wide pool of mutations unique to the RSW. The functional annotation of these mutations showed the adaptation of genes associated with the development of the nervous system, thermoreceptors, purine receptors, and the TGF-beta pathway, probably caused by selection for low temperatures. We also found adaptation of the immune system genes, likely driven by selection for resistance to viral diseases. Integration with previous genome-wide association studies (GWAS) suggested several causal single nucleotide polymorphisms (SNPs). Specifically, we identified an RSW-specific missense mutation in the RALYL gene, presumably causing the snow-white color of the day-old chicks, and an RSW-specific missense mutation in the TLL1 gene, presumably affecting the egg weight. Full article

The Tianshan Expressway plays a crucial role in China’s “Belt and Road” strategy, yet the extreme climate of the Tianshan Mountains poses significant traffic safety risks, hindering local economic development. Efficient detection of hazardous road surface conditions (RSCs) is vital to address these challenges. The complexity and variability of RSCs in the region, exacerbated by harsh weather, make traditional surveillance methods inadequate for real-time monitoring. To overcome these limitations, a vision-based artificial intelligence approach is urgently needed to ensure effective, real-time detection of dangerous RSCs in the Tianshan road network. This paper analyzes the primary structures and architectures of mainstream neural networks and explores their performance for RSC recognition through a comprehensive set of experiments, filling a research gap. Additionally, T-Net, specifically designed for the Tianshan Expressway engineering project, is built upon the optimal architecture identified in this study. Leveraging the split-transform-merge structure paradigm and asymmetric convolution, the model excels in capturing detailed information by learning features across multiple dimensions and perspectives. Furthermore, the integration of channel, spatial, and multi-head attention modules enhances the weighting of key features, making the T-Net particularly effective in recognizing the characteristics of snow-covered and icy road surfaces. All models presented in this paper were trained on a custom RSC dataset, compiled from various sources. Experimental results indicate that the T-Net outperforms fourteen once state-of-the-art (SOTA) models and three models specifically designed for RSC recognition, with 97.44% accuracy and 9.79% loss on the validation set. Full article

This research studies the characteristics of snow-covered area (SCA) from two vastly different sensors: optical (Moderate-Resolution Imaging Spectroradiometer, or MODIS, equipped on board the Terra satellite) and radar (Synthetic Aperture Radar (SAR) on-board Sentinel-1 satellites). The focus are the five mountain ranges of the Iberian Peninsula (Cantabrian System, Central System, Iberian Range, Pyrenees, and Sierra Nevada). The MODIS product was selected to identify SCA dynamics in these ranges using the Probability of Snow Cover Presence Index (PSCPI). In addition, we evaluate the potential advantage of the use of SAR remote sensing to complete optical SCA under cloudy conditions. For this purpose, we utilize the Copernicus High-Resolution Snow and Ice SAR Wet Snow (HRS&I SWS) product. The Pyrenees and the Sierra Nevada showed longer-lasting SCA duration and a higher PSCPI throughout the average year. Moreover, we demonstrate that the latitude gradient has a significant influence on the snowline elevation in the Iberian mountains (R² ≥ 0.84). In the Iberian mountains, a general negative SCA trend is observed due to the recent climate change impacts, with a particularly pronounced decline in the winter months (December and January). Finally, in the Pyrenees, we found that wet snow detection has high potential for the spatial gap-filling of MODIS SCA in spring, contributing above 27% to the total SCA. Notably, the additional SCA provided in winter is also significant. Based on the results obtained in the Pyrenees, we can conclude that implementing techniques that combine SAR and optical satellite sensors for SCA detection may provide valuable additional SCA data for the other Iberian mountains, in which the radar product is not available. Full article

A comprehensive analysis of cross-country skiing races is a pivotal step in establishing effective training objectives and tactical strategies. This study aimed to develop a method of classifying sub-techniques and analyzing skiing characteristics during cross-country skiing skating style timed races on snow using high-precision kinematic GNSS devices. The study involved attaching GNSS devices to the heads of two athletes during skating style timed races on cross-country ski courses. These devices provided precise positional data and recorded vertical and horizontal head movements and velocity over ground (VOG). Based on these data, sub-techniques were classified by defining waveform patterns for G2, G3, G4, and G6P (G6 with poling action). The validity of the classification was verified by comparing the GNSS data with video analysis, a process that yielded classification accuracies ranging from 95.0% to 98.8% for G2, G3, G4, and G6P. Notably, G4 emerged as the fastest technique, with sub-technique selection varying among skiers and being influenced by skiing velocity and course inclination. The study’s findings have practical implications for athletes and coaches as they demonstrate that high-precision kinematic GNSS devices can accurately classify sub-techniques and detect skiing characteristics during skating style cross-country skiing races, thereby providing valuable insights for training and strategy development. Full article

Albedo is a primary driver of the glacier surface energy balance and consequent melting. As glacier albedo decreases, it further accelerates glacier melting. Over the past 20 years, glaciers on the Tibetan Plateau have experienced significant melting. However, our understanding of the variations in glacier albedo and its driving factors in this region remains limited. This study used MOD10A1 data to examine the average characteristics and variations in glacier albedo on the Tibetan Plateau from 2001 to 2022; the MOD10A1 snow cover product, developed at the National Snow and Ice Data Center, was employed to analyze spatiotemporal variations in surface albedo. The results indicate that the albedo values of glaciers on the Tibetan Plateau predominantly range between 0.50 and 0.60, with distinctly higher albedo in spring and winter, and lower albedo in summer and autumn. Glacier albedo on the Tibetan Plateau decreased at an average linear regression rate of 0.06 × 10⁻² yr⁻¹ over the past two decades, with the fastest declines occurring in autumn at an average rate of 0.18 × 10⁻² yr⁻¹, contributing to the prolongation of the melting period. Furthermore, significant variations in albedo change rates with altitude were found near the snowline, which is attributed to the transformation of the snow and ice surface. The primary factors affecting glacier albedo on the Tibetan Plateau are temperature and snowfall, whereas in the Himalayas, black carbon and dust primarily influence glacier albedo. Our findings reveal a clear decrease in glacier albedo on the Tibetan Plateau and demonstrate that seasonal and spatial variations in albedo and temperature are the most important driving factors. These insights provide valuable information for further investigation into surface albedo and glacier melt. Full article

In recent years, there has been growing concern about the condition of snow leopards. The snow leopard (Panthera uncia), an apex predator of alpine ecosystems, is essential for the structural and functional stability of ecosystems. Monitoring of snow leopards’ activity patterns based on camera traps in the Qilian Mountain National Park (Qinghai area) between August 2020 to October 2023 was performed. The results showed that autumn is the peak period of snow leopard activity, especially in September when the frequency of activity is the highest, and there is one peak in the frequency of snow leopard daily activity in the time period of 18:00–22:00, while the highest overlap of the daily activity curves of snow leopards in different months was from spring to autumn (Δ = 0.97), and there were significant differences in diurnal activity rhythm between spring and autumn (p = 0.002). Snow leopards prefer sunny days, and they tend to be active at temperatures of −10–9 °C. Our research aimed to uncover the activity patterns of snow leopards at different scales within the study area and provide data for further studies on snow leopards and other wildlife by researchers. This study can be used to gain a comprehensive understanding of the ecological characteristics of snow leopards and to assess their habitats, and it will also serve as a reference for the local wildlife management authorities in formulating snow leopard conservation measures. Full article

Strawberry wine production is a considerable approach to solve the problem of the Chinese concentrated harvesting period and the short shelf life of strawberries, but the appropriative strawberry cultivars for fermentation are still undecided. In this study, the strawberry juice and wines of four typical strawberry cultivars named Akihime (ZJ), Sweet Charlie (TCL), Snow White (BX), and Tongzhougongzhu (TZ) were thoroughly characterized for their physicochemical indicators, bioactive compounds, and volatile organic components (VOCs) to determine the optimal strawberry cultivars for winemaking. The results showed that there were significant differences in the total sugar content, pH, total acid, and other physicochemical indexes in the strawberry juice of different cultivars, which further affected the physicochemical indexes of fermented strawberry wine. Moreover, the content of polyphenols, total flavonoids, vitamin C, and color varied among the four strawberry cultivars. A total of 42 VOCs were detected in the strawberry juice and wines using headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS), and 3-methyl-1-butanol, linalool, trans-2-pinanol, hexanoic acid, and hexanoic acid ethyl ester were the differential VOCs to identify the strawberry wine samples of different cultivars. Overall, strawberry cultivar ZJ had a relatively high VOC and bioactive compound content, indicating that it is the most suitable cultivar for strawberry wine fermentation. In addition to determining the relatively superior fermentation characteristics of cultivar ZJ, the results may provide a theoretical basis for the raw material quality control and quality improvement of strawberry wine. Full article

Snow detection is imperative in remote sensing for various applications, including climate change monitoring, water resources management, and disaster warning. Recognizing the limitations of current deep learning algorithms in cloud and snow boundary segmentation, as well as issues like detail snow information loss and mountainous snow omission, this paper presents a novel snow detection network based on Swin-Transformer and U-shaped dual-branch encoder structure with geographic information (SD-GeoSTUNet), aiming to address the above issues. Initially, the SD-GeoSTUNet incorporates the CNN branch and Swin-Transformer branch to extract features in parallel and the Feature Aggregation Module (FAM) is designed to facilitate the detail feature aggregation via two branches. Simultaneously, an Edge-enhanced Convolution (EeConv) is introduced to promote snow boundary contour extraction in the CNN branch. In particular, auxiliary geographic information, including altitude, longitude, latitude, slope, and aspect, is encoded in the Swin-Transformer branch to enhance snow detection in mountainous regions. Experiments conducted on Levir_CS, a large-scale cloud and snow dataset originating from Gaofen-1, demonstrate that SD-GeoSTUNet achieves optimal performance with the values of 78.08%, 85.07%, and 92.89% for $IoU\_s$, $F1\_s$, and $MPA$, respectively, leading to superior cloud and snow boundary segmentation and thin cloud and snow detection. Further, ablation experiments reveal that integrating slope and aspect information effectively alleviates the omission of snow detection in mountainous areas and significantly exhibits the best vision under complex terrain. The proposed model can be used for remote sensing data with geographic information to achieve more accurate snow extraction, which is conducive to promoting the research of hydrology and agriculture with different geospatial characteristics. Full article

Snow avalanche susceptibility (AS) mapping is a crucial step in predicting and mitigating avalanche risks in mountainous regions. The conditioning factors used in AS modeling are diverse, and the optimal set of factors depends on the environmental and geological characteristics of the region. Using a sub-optimal set of input features with a data-driven machine learning (ML) method can lead to challenges like dealing with high-dimensional data, overfitting, and reduced model generalization. This study implemented a robust framework involving the Sequential Backward Selection (SBS) algorithm and a decision-tree based ML model, CatBoost, for the automatic selection of predictive variables for AS mapping. A comprehensive inventory of a large avalanche period, previously derived from satellite images, was used for the investigations in three distinct catchment areas in the Swiss Alps. The integrated SBS-CatBoost approach achieved very high classification accuracies between 94% and 97% for the three catchments. In addition, the Shapley additive explanations (SHAP) method was employed to analyze the contributions of each feature to avalanche occurrences. The proposed methodology revealed the benefits of integrating advanced feature selection algorithms with ML techniques for AS assessment. We aimed to contribute to avalanche hazard knowledge by assessing the impact of each feature in model learning. Full article

Understanding the dietary preferences and ecological niche characteristics of mammals not only reveals their adaptive strategies under environmental changes but also reveals the interspecific relationships and coexistence mechanisms among sympatric species. Nevertheless, such data are scarce for rodents inhabiting areas spanning a wide altitude range. This study employed DNA metabarcoding technology to analyze the stomach contents of Apodemus ilex, Apodemus chevrieri, and Niviventer confucianus, aiming to investigate their dietary compositions and diversity in the Meili Snow Mountain in Yunnan Province, China. Levins’s and Pianka’s indices were used to compare the interspecific niche breadth and niche overlaps. The results revealed the following: (1) Insecta (relative abundance: 59.4–78.4%) and Clitellata (relative abundance: 5.2–25.5%) were the primary animal food sources for the three species, while Magnoliopsida (relative abundance: 90.3–99.9%) constitutes their main plant food source. Considerable interspecific differences were detected in the relative abundance of primary animal and plant foods among the three species; (2) There was partial overlap in the genus-level animal food between A. ilex and N. confucianus (O_jk = 0.4648), and partial overlap in plant food between A. ilex and A. chevrieri (O_jk = 0.3418). However, no overlap exists between A. chevrieri and N. confucianus, either in animal or plant food; (3) There were no significant interspecific differences in the α-diversity of animal and plant foods among the three species. The feeding strategies and ecological niche variations of these rodents support the niche differentiation hypothesis, indicating that they have diversified in their primary food sources. This diversification may be a strategy to reduce competition and achieve long-term coexistence by adjusting the types and proportions of primary foods consumed. Full article

During wintertime temperature inversion episodes, ozone in the Uinta Basin sometimes exceeds the standard of 70 ppb set by the US Environmental Protection Agency. Since ozone formation depends on sunlight, and less sunlight is available during winter, wintertime ozone can only form if snow cover and albedo are high. Researchers have encountered difficulties replicating high albedo values in 3-D weather and photochemical transport model simulations for winter episodes. In this study, a process to assimilate MODIS satellite data into WRF and CAMx models was developed, streamlined, and tested to demonstrate the impacts of data assimilation on the models’ performance. Improvements to the WRF simulation of surface albedo and snow cover were substantial. However, the impact of MODIS data assimilation on WRF performance for other meteorological quantities was minimal, and it had little impact on ozone concentrations in the CAMx photochemical transport model. The contrast between the data assimilation and reference cases was greater for a period with no new snow since albedo appears to decrease too rapidly in default WRF and CAMx configurations. Overall, the improvement from MODIS data assimilation had an observed enhancement in the spatial distribution and temporal evolution of surface characteristics on meteorological quantities and ozone production. Full article

The dynamic changes of sea ice exhibit spatial clustering, and this clustering has characteristics extending from its origin, through its development, and to its dissipation. Current research on sea ice change primarily focuses on spatiotemporal variation trends and remote correlation analysis, and lacks an analysis of spatiotemporal evolution characteristics. This study utilized monthly sea ice concentration (SIC) data from the National Snow and Ice Data Center (NSIDC) for the period from 1979 to 2022, utilizing classical spatiotemporal clustering algorithms to analyze the clustering patterns and evolutionary characteristics of SIC anomalies in key Arctic regions. The results revealed that the central-western region of the Barents Sea was a critical area where SIC anomaly evolutionary behaviors were concentrated and persisted for longer durations. The relationship between the intensity and duration of SIC anomaly events was nonlinear. A positive correlation was observed for shorter durations, while a negative correlation was noted for longer durations. Anomalies predominantly occurred in December, with complex evolution happening in April and May of the following year, and concluded in July. Evolutionary state transitions mainly occurred in the Barents Sea. These transitions included shifts from the origin state in the northwestern margin to the dissipation state in the central-north Barents Sea, from the origin state in the central-north to the dissipation state in the central-south, and from the origin state in the northeastern to the dissipation state in the central-south Barents Sea and southeastern Kara Sea. Various evolutionary states were observed in the same area on the southwest edge of the Barents Sea. These findings provide insights into the evolutionary mechanism of sea ice anomalies. Full article

Plastics are produced, consumed, and disposed of worldwide, with more than eight million tons of plastic litter entering the ocean each year. Plastic litter accumulates in marine and terrestrial environments through a variety of pathways. Large plastic debris can be broken down into micro- and nano-plastic particles through physical/mechanical mechanisms and biologically or chemically mediated degradation. Their toxicity to aquatic organisms includes the scavenging of pollutant compounds and the production of reactive oxygen species (ROS). Higher levels of ROS cause oxidative damages to microalgae and bacteria; this triggers the release of large amounts of exopolymeric substances (EPSs) with distinct molecular characteristics. This review will address what is known about the molecular mechanisms phytoplankton and bacteria use to regulate the fate and transport of plastic particles and identify the knowledge gaps, which should be considered in future research. In particular, the microbial communities react to plastic pollution through the production of EPSs that can reduce the plastic impacts via marine plastic snow (MPS) formation, allowing plastics to settle into sediments and facilitating their removal from the water column to lessen the plastic burden to ecosystems. Full article