Search Results (11,521)

Forest volume is an important information for assessing the economic value and carbon sequestration capacity of forest resources and serves as a key indicator for energy flow and biodiversity. Although remote sensing technology is applied to estimate volume, optical remote sensing data have limitations in capturing forest vertical height information and may suffer from reflectance saturation. While LiDAR data can provide more detailed vertical structural information, they come with high processing costs and limited observation range. Therefore, improving the accuracy of volume estimation through multi-source data fusion has become a crucial challenge and research focus in the field of forest remote sensing. In this study, we integrated Sentinel-2 multispectral data, Resource-3 stereoscopic imagery, UAV-based LiDAR data, and field survey data to quantitatively estimate the forest volume in Saihanwula Nature Reserve, located in Inner Mongolia, China, on the southern part of Daxing’anling Mountains. The study evaluated the performance of multi-source remote sensing features by using recursive feature elimination (RFE) to select the most relevant factors and applied four machine learning models—multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF), and gradient boosting regression tree (GBRT)—to develop volume estimation models. The evaluation metrics include the coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (rRMSE). The results show that (1) forest Canopy Height Model (CHM) data were strongly correlated with forest volume, helping to alleviate the reflectance saturation issues inherent in spectral texture data. The fusion of CHM and spectral data resulted in an improved volume estimation model with R² = 0.75 and RMSE = 8.16 m³/hm², highlighting the importance of integrating multi-source canopy height information for more accurate volume estimation. (2) Volume estimation accuracy varied across different tree species. For Betula platyphylla, we obtained R² = 0.71 and RMSE = 6.96 m³/hm²; for Quercus mongolica, R² = 0.74 and RMSE = 6.90 m³/hm²; and for Populus davidiana, R² = 0.51 and RMSE = 9.29 m³/hm². The total forest volume in the Saihanwula Reserve ranges from 50 to 110 m³/hm². (3) Among the four machine learning models, GBRT consistently outperformed others in all evaluation metrics, achieving the highest R² of 0.86, lowest RMSE of 9.69 m³/hm², and lowest rRMSE of 24.57%, suggesting its potential for forest biomass estimation. In conclusion, accurate estimation of forest volume is critical for evaluating forest management practices and timber resources. While this integrated approach shows promise, its operational application requires further external validation and uncertainty analysis to support policy-relevant decisions. The integration of multi-source remote sensing data provides valuable support for forest resource accounting, economic value assessment, and monitoring dynamic changes in forest ecosystems. Full article

Understanding how prey and predator species partition activity patterns across time and space is essential for elucidating behavioral adaptation and ecological coexistence. In this study, we examined the diel and seasonal activity rhythms of two sympatric rodent species—Rhombomys opimus (Great gerbil) and Meriones meridianus (Midday gerbil)—and their primary predators, Otocolobus manul (Pallas’s cat) and Vulpes vulpes (Red fox), in a desert-steppe ecosystem on the northern slopes of the Qilian Mountains, China. Using over 8000 camera trap days and kernel density estimation, we quantified their activity intensity and spatiotemporal overlap. The two rodent species showed clear temporal niche differentiation but differed in their synchrony with predators. R. opimus exhibited a unimodal diurnal rhythm with spring activity peaks, while M. meridianus showed stable nocturnal activity with a distinct autumn peak. Notably, O. manul adjusted its activity pattern to partially align with that of R. opimus, whereas V. vulpes maintained a crepuscular–nocturnal rhythm overlapping more closely with that of M. meridianus. Despite distinct temporal rhythms, both rodent species shared high spatial overlap with their predators (overlap index OI = 0.64–0.83). These findings suggest that temporal partitioning may reduce predation risk for R. opimus, while M. meridianus co-occurs more extensively with its predators. Our results highlight the ecological role of native carnivores in rodent population dynamics and support their potential use in biodiversity-friendly rodent management strategies under arid grassland conditions. Full article

This study analyzes spatiotemporal aridity dynamics in Central Kazakhstan (1960–2022) using a monthly Aridity Index (AI = P/PET), where P is precipitation and PET is potential evapotranspiration, Mann–Kendall trend analysis, and climate zone classification. Results reveal a northeast–southwest aridity gradient, with Aridity Index ranging from 0.11 to 0.14 in southern deserts to 0.43 in the Kazakh Uplands. Between 1960–1990 and 1991–2022, southern regions experienced intensified aridity, with Aridity Index declining from 0.12–0.15 to 0.10–0.14, while northern mountainous areas became more humid, where Aridity Index increased from 0.40–0.44 to 0.41–0.46. Seasonal analysis reveals divergent patterns, with winter showing improved moisture conditions (52.4% reduction in arid lands), contrasting sharply with aridification in spring and summer. Summer emerges as the most extreme season, with hyper-arid zones (8%) along with expanding arid territories (69%), while autumn shows intermediate conditions with notable dry sub-humid areas (5%) in northwestern regions. Statistical analysis confirms these observations, with northern areas showing positive Aridity Index trends (+0.007/10 years) against southwestern declines (−0.003/10 years). Key drivers include rising temperatures (with recent degradation) and variable precipitation (long-term drying followed by winter and spring), and PET fluctuations linked to temperature. Since 1991, arid zones have expanded from 40% to 47% of the region, with semi-arid lands transitioning to arid, with a northward shift of the boundary. These changes are strongly seasonal, highlighting the vulnerability of Central Kazakhstan to climate-driven aridification. Full article

We conducted a mark–release–recapture study of the ringlet butterfly, Erebia pronoe glottis, in the Pyrenees to study population density, flight activity, dispersal, and nectar plant preferences. We found differences between both sexes in population density (males: 48/ha; females: 23/ha), sex ratio (2.1), and behaviour (75.4 vs. 20.5% flying). Both sexes used a wide range of nectar plants (Asteraceae, 40.6%; Apiaceae, 34.4%; Caprifoliaceae, 18.8%). However, local abundance appeared to be limited by the availability of nectar plants. Compared to a population of an extensively used pasture in the Alps, a significant increase in flight activity, but not in range, was observed. Movement patterns showed the establishment of home ranges, which significantly limited the dispersal potential, being low for both sexes (mean fight distances-males: 101 m ± 73 SD; females: 68 m ± 80 SD). A sedentary taxon such as E. pronoe glottis does not seem to be able to avoid the pressure of resource shortage by dispersal. As a late-flying pollinator, Erebia pronoe competes seasonally for scarce resources. These are further reduced by grazing pressure and are exploited by honey bees as a superior competitor, resulting in low habitat quality and, consequently, in comparatively low abundance of E. pronoe glottis. Full article

With the global economic restructuring and the consequent population mobility, urban shrinkage has become a common phenomenon. The Qinling–Daba Mountains, a zone with a key ecological function in China, have long experienced population decline and functional degradation. Clarifying the dynamics and influencing factors of urban shrinkage plays a vital role in supporting the sustainable development of the region. This study, using permanent resident population growth rates and nighttime light data, classified cities in the region into four spatial patterns: expansion–growth, intensive growth, expansion–shrinkage, and intensive shrinkage. It further examined the spatial characteristics of shrinkage across four periods (2005–2010, 2010–2015, 2015–2020, and 2020–2022). A Geographically and Temporally Weighted Regression (GTWR) model was applied to examine core influencing factors and their spatiotemporal heterogeneity. The results indicated the following: (1) The dominant pattern of urban shrinkage in the Qinling–Daba Mountains shifted from expansion–growth to expansion–shrinkage, highlighting the paradox of population decline alongside continued spatial expansion. (2) Three critical indicators significantly influenced urban shrinkage: the number of students enrolled in general secondary schools (X₅), the per capita disposable income of urban residents (X₇), and the number of commercial and residential service facilities (X₁₂), with their effects exhibiting significant spatiotemporal heterogeneity. Temporally, X₁₂ was the most influential factor in 2005 and 2010, while in 2015, 2020, and 2022, X₅ and X₇ became the dominant factors. Spatially, X₇ significantly affected both eastern and western areas; X₅’s influence was most pronounced in the west; and X₁₂ had the greatest impact in the east. This study explored the patterns and underlying drivers of urban shrinkage in underdeveloped areas, aiming to inform sustainable development practices in regions facing comparable challenges. Full article

Poultry waste composting is a necessary technique for agricultural farm sustainability. Composting is a dynamic process influenced by multiple variables. Humidity and temperature play fundamental roles in analyzing its different phases according to the environment and composting technique. Current developments for monitoring these variables include automation via intelligent Internet of Things (IoT)-based sensor networks for variable tracking. These advancements serve as efficient tools for modeling that facilitate the simulation and prediction of composting process variables to improve system efficiency. Therefore, this paper presents the dynamic modeling of composting via forced aeration processes in high-mountain climates, with the intent of estimating biomass temperature dynamics in different phases using system identification techniques. To this end, four dynamic model estimation structures are employed: transfer function (TF), state space (SS), process (P), and Hammerstein–Wiener (HW). The and model quality, fitting results, and standard error metrics of the different models found in each phase are assessed through residual analysis from each structure by validation with real system data. Our results show that the second-order underdamped multiple-input–single-output (MISO) process model with added noise demonstrates the best fit and validation performance. Full article

This study examines the effect of oxygen concentration and sputtering power on the surface morphology of TiO₂ thin films deposited by DC reactive magnetron sputtering. Surface roughness parameters were obtained using MountainsMap^® software(10.2) from SEM images, while fractal dimensions and texture descriptors were extracted via Python-based image processing. Fractal dimension was calculated using the box-counting method applied to binarized images with multiple threshold levels, and texture analysis employed Gray-Level Co-occurrence Matrix (GLCM) statistics to capture local anisotropies and spatial heterogeneity. Four samples were analyzed, previously prepared with oxygen concentrations of 50% and 75%, and sputtering powers of 500 W and 1000 W. The results have shown that films deposited at higher oxygen levels and sputtering powers exhibited increased roughness, higher fractal dimensions, and stronger GLCM contrast, indicating more complex and heterogeneous surface structures. Conversely, films produced at lower oxygen and power settings showed smoother, more isotropic surfaces with lower complexity. This integrated analysis framework links deposition parameters with morphological characteristics, enhancing the understanding of surface evolution and enabling better control of TiO₂ thin film properties. Full article

A number of phenomena were observed in experiments on the study of cosmic rays at mountain altitudes and in the stratosphere at ultra-high energies; in particular, the coplanarity of the most energetic particles and local subcascades in the so-called families of γ-rays and hadrons in the cores of extensive air showers at E₀ ≳ 2·10¹⁵ eV (√s ≳ 2 TeV). These effects are not described by theoretical models. To explain this phenomenon, it may be necessary to introduce a new process of generating the most energetic particles in the interactions of hadrons with the nuclei of atmospheric atoms. A new experimental array of cosmic ray detectors, including the ADRON-55 ionization calorimeter, has been created to study processes in EAS cores at ultra-high energies. The possibility of using it to study the coplanarity effect is being considered. Full article

For further investigation of the properties of the muon component in the core regions of extensive air showers (EASs), a new underground hodoscopic set-up with a total sensitive area of 22 m${}^2$ was built at the Tien Shan High Mountain Cosmic Ray Station. The hodoscope is based on a set of large-sized scintillation charged particle detectors with an output signal of analog type. The installation ensures a (5–8) GeV energy threshold of muon registration and a ∼${10}^4$ dynamic range for the measurement of the density of muon flux. A program facility was designed that uses modern machine learning techniques for automated search for the typical scintillation pulse pattern in an oscillogram of a noisy analog signal at the output of the hodoscope detector. The program provides a ∼99% detection probability of useful signals, with a relative share of false positives below 1%, and has a sufficient operation speed for real-time analysis of incoming data. Complete verification of the hardware and software tools was performed under realistic operation conditions, and the results obtained demonstrate the correctness of the proposed method and its practical applicability to the investigation of the muon flux in EASs. In the course of the installation testing, a preliminary physical result was obtained concerning the rise of the multiplicity of muon particles around an EAS core in dependence on the primary EAS energy. Full article

This article presents a comprehensive assessment of mudflow risk in the Talgar River basin through the application of Multi-Criteria Decision Analysis (MCDA) methods and numerical modeling using the Rapid Mass Movement Simulation (RAMMS) environment. The first part of the study involves a spatial assessment of mudflow hazard and susceptibility using GIS technologies and MCDA. The key condition for evaluating mudflow hazard is the identification of factors influencing the formation of mudflows. The susceptibility assessment was based on viewing the area as an object of spatial and functional analysis, enabling determination of its susceptibility to mudflow impacts across geomorphological zones: initiation, transformation, and accumulation. Relevant criteria were selected for analysis, each assigned weights based on expert judgment and the Analytic Hierarchy Process (AHP). The results include maps of potential mudflow hazard and susceptibility, showing areas of hazard occurrence and risk impact zones within the Talgar River basin. According to the mudflow hazard map, more than 50% of the basin area is classified as having a moderate hazard level, while 28.4% is subject to high hazard, and only 1.8% falls under the very high hazard category. The remaining areas are categorized as very low (4.1%) and low (14.7%) hazard zones. In terms of susceptibility to mudflows, 40.1% of the territory is exposed to a high level of susceptibility, 35.6% to a moderate level, and 5.5% to a very high level. The remaining areas are classified as very low (1.8%) and low (15.6%) susceptibility zones. The predictive performance was evaluated through Receiver Operating Characteristic (ROC) curves, and the Area Under the Curve (AUC) value of the mudflow hazard assessment is 0.86, which indicates good adaptability and relatively high accuracy, while the AUC value for assessing the susceptibility of the territory is 0.71, which means that the accuracy of assessing the susceptibility of territories to mudflows is within the acceptable level of model accuracy. To refine the spatial risk assessment, mudflow modeling was conducted under three scenarios of glacial-moraine lake outburst using the RAMMS model. For each scenario, key flow parameters—height and velocity—were identified, forming the basis for classification of zones by impact intensity. The integration of MCDA and RAMMS results produced a final mudflow risk map reflecting both the likelihood of occurrence and the extent of potential damage. The presented approach demonstrates the effectiveness of combining GIS analysis, MCDA, and physically-based modeling for comprehensive natural hazard assessment and can be applied to other mountainous regions with high mudflow activity. Full article

The cessation of traditional mountain grazing has emerged as a principal driver of habitat degradation and the local extinction of Parnassius apollo (Linnaeus, 1758) in Serbia. While previous studies have cited multiple contributing factors, our research provides evidence that the abandonment of extensive livestock grazing has triggered vegetation succession, the disappearance of the larval host plant (Sedum album), and a reduction in microhabitat heterogeneity—conditions essential for the persistence of this stenophagous butterfly species. Through satellite-based analysis of vegetation dynamics (2015–2024), we identified clear structural differences between habitats that currently support populations and those where the species is no longer present. Occupied sites were characterized by low levels of exposed soil, moderate grass coverage, and consistently high shrub and tree density, whereas unoccupied sites exhibited dense encroachment of grasses and woody vegetation, leading to structural instability. Furthermore, MODIS-derived indices (2010–2024) revealed a consistent decline in vegetation productivity (GPP, FPAR, LAI) in succession-affected areas, alongside significant correlations between elevated land surface temperatures (LST), thermal stress (TCI), and reduced photosynthetic capacity. A wildfire event on Mount Stol in 2024 further exacerbated habitat degradation, as confirmed by remote sensing indices (BAI, NBR, NBR2), which documented extensive burn scars and post-fire vegetation loss. Collectively, these findings indicate that the decline of P. apollo is driven not only by ecological succession and climatic stressors, but also by the abandonment of land-use practices that historically maintained suitable habitat conditions. Our results underscore the necessity of restoring traditional grazing regimes and integrating ecological, climatic, and landscape management approaches to prevent further biodiversity loss in montane environments. Full article

The mountainous rivers of Central Asia host diverse ichthyofauna threatened by increasing anthropogenic pressures, particularly water pollution, abstraction, and hydropower development. This study provides valuable morphometric and ecological data for Schizothorax eurycephalus (snow trout) and Triplophysa ferganaensis (stone loach) in the Shakhimardan River basin, Uzbekistan. S. eurycephalus exhibited positive allometric growth, while T. ferganaensis showed negative near-isometric growth. The mean Fulton’s Condition Factor was 1.0 for S. eurycephalus and 0.7 for T. ferganaensis, with site-specific variations. Strong correlations among morphometric parameters, particularly length–height relationships, support non-invasive monitoring techniques. Dietary analysis revealed S. eurycephalus was predominantly herbivorous, with around 70% algae consumption. Early sexual maturity was observed in S. eurycephalus males, whereas T. ferganaensis showed no clear maturity signs, but swollen bellies suggested ongoing or recent reproductive activity. These baseline morphometric and ecological data establish a solid foundation for future ecological assessments, conservation strategies, and the design and monitoring of mitigation measures to address anthropogenic impacts in this vulnerable region. Full article

Motor vehicle crashes are a persistent cause of unintentional deaths in the United States. Scholarship on how manmade interventions and natural phenomena interact to effectuate such calamitous outcomes is longstanding. One manmade intervention of interest in the literature is daylight saving time (DST). Unfortunately, results on how the natural phenomena attributable to DST interact with driver behavior are inconsistent. To advance knowledge on DST-safety interactions, this study adopts a multilevel model approach to fatal motor vehicle crash outcomes in the contiguous United States. Results from a national analysis contextualize results from zonal analyses to unmask within- and between-time zone differences in DST-safety interactions. In the national analysis, motor vehicle crash fatalities decrease somewhat during DST (−0.10%). In the zonal analyses, motor vehicle crash fatalities decrease more so in the Central and Eastern time zones (−2.00% and −2.00%, respectively), but increase somewhat in the Pacific and Mountain time zones (+0.30%) during DST. The spatiotemporal context of the national analysis highlights specific policy implications from the zonal analyses to decrease the lethality of motor vehicle crashes. Specifically, interdictions to target alcohol and/or drug involvement in the northern latitudes of the Pacific and Mountain time zones during DST, the Central time zone at dawn or dusk before or after DST, and the northern latitudes in the Eastern time zone before or after DST are important. Generally, national DST-safety benefits mask zonal DST-safety costs in the Pacific and Mountain time zones. Full article

Tianshanhong (TSH), black tea products originating from the Ningde Tianshan Mountain, has gained significant recognition in the market. However, the chemical characteristics contributing to the flavor of TSH have not yet been reported. To systematically investigate the non-volatile and volatile compounds in TSH, four grades of TSH were evaluated using national standard sensory methods, revealing that overall quality improved with higher grades. Based on the detection of ultra-performance liquid chromatography–mass spectrometry (UPLC-MS), the content of ester-type catechins was relatively high and decreased with lower grades. A total of 19 amino acids (AAs) were clustered, among them, three amino acids, L-Theanine (L-Thea), Arg, and GABA, showed highly significant correlations with the refreshing taste of TSH. Notably, the content of Arg had the highest correlation with TSH grade, with a coefficient of 0.976 (p < 0.01). According to gas chromatography mass spectrometry (GC-MS) analysis, a total of 861 kinds of volatile compounds were detected, with 282 identified and aroma-active compounds across grades selected using the PLS model. Methyl salicylate and geraniol were particularly notable, showing strong correlations with TSH grades at 0.975 and 0.987 (p < 0.01), respectively. Our findings show that non-volatile and volatile compounds can rationally grade TSH and help understand its flavor quality. Full article