Search Results (747)

Landslides represent a recurrent hazard in tropical mountain environments, where rapid urbanization and extreme rainfall amplify disaster risk. The Sentani region of Papua, Indonesia, is highly vulnerable, as demonstrated by the catastrophic debris flows of March 2019 that caused fatalities and widespread losses. This study developed high-resolution landslide susceptibility maps for Sentani using an ensemble machine learning framework. Three base learners—Random Forest, eXtreme Gradient Boosting (XGBoost), and CatBoost—were combined through a logistic regression meta-learner. Predictor redundancy was controlled using Pearson correlation and Variance Inflation Factor/Tolerance (VIF/TOL). The landslide inventory was constructed from multitemporal satellite imagery, integrating geological, topographic, hydrological, environmental, and seismic factors. Results showed that lithology, Slope Length and Steepness Factor (LS Factor), and earthquake density consistently dominated model predictions. The ensemble achieved the most balanced predictive performance, Area Under the Curve (AUC) > 0.96, and generated susceptibility maps that aligned closely with observed landslide occurrences. SHapley Additive Explanations (SHAP) analyses provided transparent, case-specific insights into the directional influence of key factors. Collectively, the findings highlight both the robustness and interpretability of ensemble learning for landslide susceptibility mapping, offering actionable evidence to support disaster preparedness, land-use planning, and sustainable development in Papua. Full article

Mountainous urban areas like Guiyang face unique fire safety challenges due to rugged terrain and complex road networks, which hinder fire station accessibility. This study proposes a GIS-based framework that integrates nighttime light (NPP/VIIRS) and point of interest (POI) data to assess fire risk and accessibility. Kernel density estimation quantified POI distributions across four risk categories, and the Spatial Appraisal and Valuation of Environment and Ecosystems (SAVEE) model combined these with NPP/VIIRS data to generate a composite fire risk map. Accessibility was evaluated using the enhanced two-step floating catchment area (E2SFCA) method with road network travel times; 80.13% of demand units were covered within the five-minute threshold, while 53.25% of all units exhibited low accessibility. Spatial autocorrelation analysis (Moran’s I) revealed clustered high risk in central basins and service gaps on surrounding hills, reflecting the dominant influence of terrain alongside protected forests and farmlands. The results indicate that targeted road upgrades and station relocations can improve fire service coverage. The approach is scalable and supports more equitable emergency response in mountainous settings. Full article

The study provides valuable insights into the sustainable utilization of edible tuber peels from the high mountains of the Argentinian Puna, which constitutes promising reserves of bioactive phenolic compounds with the potential to enhance the biofunctional properties of lactic acid bacteria. Thirty-two extracts derived from peels of different varieties of tubers, such as Oxalis tuberosa Mol., Ullucus tuberosus Caldas, and Solanum tuberosum L. were incorporated into lactobacilli cultures and individually evaluated. These selectively enhance the development of the probiotic strain Lactiplantibacillus plantarum ATCC 10241 and of Lacticaseibacillus paracasei CO1-LVP105 from ovine origin, without promoting the growth of a pathogenic bacteria set (Escherichia coli O157:H12 and ATCC 35218, Salmonella enterica serovar Typhimurium ATCC 14028, and S. corvalis SF2 and S. cerro SF16), in small amounts. To determine the main phenolic group concentrated in the phytoextracts, a bio-guided study was conducted. The most significant results were obtained by O. tuberosa phytochemicals added to the culture medium at 50 µg/mL, yielding promising increases in biofilm formation (78% for Lp. plantarum and 43% for L. paracasei) and biosurfactant activity (112% for CO1-LVP105 strain). These adaptive strategies developed by bacteria possess key biotechnological significance. Furthermore, the bio-detoxification capacity of phenol and o-phenyl phenol, particularly of the novel strain CO1-LVP105, along with its mode of action and genetic identification, is described for the first time to our knowledge. In conclusion, lactobacilli strains have potential as fermentation starters and natural products, recovered from O. tuberosa peels, and added into culture media contribute to multiple bacterial biotechnological applications in both health and the environment. Full article

Rice is one of the most important staple crops in China, and the rapid and accurate extraction of rice planting areas plays a crucial role in the agricultural management and food security assessment. However, the existing rice field identification methods faced the significant challenges in mountainous regions due to the severe cloud contamination, insufficient utilization of multi-dimensional features, and limited classification accuracy. This study presented a novel rice field identification method based on the Graph Convolutional Networks (GCN) that effectively integrated multi-source remote sensing data tailored for the complex mountainous terrain. A coarse-to-fine cloud removal strategy was developed by fusing the synthetic aperture radar (SAR) imagery with temporally adjacent optical remote sensing imagery, achieving high cloud removal accuracy, thereby providing reliable and clear optical data for the subsequent rice mapping. A comprehensive multi-feature library comprising spectral, texture, polarization, and terrain attributes was constructed and optimized via a stepwise selection process. Furthermore, the 19 key features were established to enhance the classification performance. The proposed method achieved an overall accuracy of 98.3% for the rice field identification in Huoshan County of the Dabie Mountains, and a 96.8% consistency compared to statistical yearbook data. The ablation experiments demonstrated that incorporating terrain features substantially improved the rice field identification accuracy under the complex topographic conditions. The comparative evaluations against support vector machine (SVM), random forest (RF), and U-Net models confirmed the superiority of the proposed method in terms of accuracy, local performance, terrain adaptability, training sample requirement, and computational cost, and demonstrated its effectiveness and applicability for the high-precision rice field distribution mapping in mountainous environments. Full article

Mountain settlements represent culturally rich but environmentally fragile landscapes, shaped by enduring processes of ecological adaptation and human resilience. In western Sichuan, Jiarong Tibetan villages, with their distinctive integration of defensive stone towers and settlements, embody this coupling of culture and the environment. We hypothesize that settlement cores in these villages were shaped by natural environmental factors, with subsequent expansion reinforced by the cultural significance of towers. To test this, we applied a micro-scale spatial–environmental framework to four sample villages in Suopo Township, Danba County. High-resolution World Imagery (Esri, 0.5–1 m, 2022–2023) was classified via a Random Forest algorithm to generate detailed land-use maps, and a 100 × 100 m fishnet grid extracted topographic metrics (elevation, slope, aspect) and accessibility measures (distances to streams, roads, towers). Geographically weighted regression (GWR) was then used to examine how slope, elevation, aspect, proximity to water and roads, and tower distribution affect settlement patterns. The results show built-up density peaks on southeast-facing slopes of 15–30°, at altitudes of 2600–2800 m, and within 50–500 m of streams, co-locating with historic watchtower sites. Based on these findings, we propose four zoning strategies—a Core Protected Zone, a Construction And Development Zone, an Ecological Conservation Zone, and an Industry Development Zone—to balance preservation with growth. The resulting policy recommendations offer actionable guidance for sustaining traditional settlements in complex mountain environments. Full article

Cold-air pooling (CAP) and frost risk represent significant climate-related hazards in karstic and agricultural environments, where local topography and surface cover strongly modulate microclimatic conditions. This study focuses on the Mohos sinkhole, Hungary’s cold pole, situated on the Bükk Plateau, to investigate the formation, structure, and persistence of CAPs in a Central European karst depression. High-resolution terrain-based modeling was conducted using UAV-derived digital surface models combined with multiple GIS tools (Sky-View Factor, Wind Exposition Index, Cold Air Flow, and Diurnal Anisotropic Heat). These models were validated and enriched by multi-level temperature measurements and thermal imaging under various synoptic conditions. Results reveal that temperature inversions frequently form during clear, calm nights, leading to extreme near-surface cold accumulation within the sinkhole. Inversions may persist into the day due to topographic shading and density stratification. Vegetation and basin geometry influence radiative and turbulent fluxes, shaping the spatial extent and intensity of cold-air layers. The CAP is interpreted as part of a broader interconnected multi-sinkhole system. This integrated approach offers a transferable, cost-effective framework for terrain-driven frost hazard assessment, with direct relevance to precision agriculture, mesoscale model refinement, and site-specific climate adaptation in mountainous or frost-sensitive regions. Full article

Extreme precipitation events, increasingly driven by climate change, are becoming more frequent and pose significant challenges to both the ecological environment and human society. Using the MSWEP data, this study constructed eight event-based extreme precipitation indicators so as to systematically analyze the spatiotemporal characteristics and dominant types of extreme precipitation across Central Asia and its three sub-regions from 1979 to 2023. The results revealed the following: (1) Extreme precipitation events exhibit a pronounced spatial preference for high-altitude areas, with the total number of events reaching up to 698 in these regions. (2) From 1979 to 1991, the frequency of extreme precipitation events has decreased in Central Asia (by 1.742 events per 13 years), while their duration has however increased (by 0.52 days per 13 years). The period from 1992 to 2009 experienced the most significant and widespread decline in the magnitude of extreme precipitation indicators. In contrast, from 2010 to 2023, all indicators—except for the event frequency (EF) and event intensity (EI)—have shown rising tendencies across the region. (3) Regarding the dominant event types, based on the proportion of extreme precipitation frequency across areas, the Southwestern Desert (SD) and northern Kazakhstan (NK) regions are characterized by a more prominent combination of rear-peak (TDP2) and front-peak (TDP1) events, whereas the southeastern mountains (SM) region is rather dominated by a combination of rear-peak (TDP2) and balanced-type (TDP3) events. (4) The EF and event duration (ED) are strongly associated with the Digital Elevation Model (DEM) and Aridity Index (AI). The spatial patterns of EF and ED are closely linked, with the sub-humid and mountainous regions demonstrating the highest frequency and longest duration of extreme precipitation events. Full article

Microplastic (MP) pollution is an emerging environmental concern, yet its occurrence in remote high-mountain ecosystems remains poorly understood. This study investigated MP contamination in fish from Nero Lake, an alpine lake in northwestern Italy. Between 2023 and 2024, a total of 33 specimens of Salmo trutta, Phoxinus lumaireul, and Salvelinus fontinalis were analyzed. MPs were detected in 84% of specimens in 2023 and in 93% in 2024. Filaments were the predominant particle type, while polyethylene, polypropylene, and polyethylene terephthalate were the most common polymers. In 2024, polyamide was also detected and showed the highest Polymer Hazard Index (PHI = 12.22), indicating a high hazard risk (Grade III) and elevated toxicological potential. Contamination Factor values exceeded 10 in S. trutta, and Pollution Load Index values frequently surpassed 1, both suggesting established contamination. However, the limited number of specimens, particularly for P. lumaireul and S. fontinalis, reduces statistical power and increases the risk of Type II errors. Although no significant interspecific differences in MP counts were observed, results should be interpreted with caution. Larger sample sizes are recommended but remain difficult to obtain in alpine environments. These findings highlight the vulnerability of remote lakes to both local and long-range MP pollution sources. Full article

The Lop sheep (LOP), a unique local breed from Xinjiang, exhibits remarkable resilience to the harsh conditions of a desert arid climate and frequent sandstorms, alongside notable fecundity characteristics. This study aims to investigate the adaptability of LOP within this challenging environment by collecting whole blood samples from 110 LOP individuals in the Lop Nur region of Xinjiang for genome resequencing. The resulting data will be compared with whole genome resequencing information from 22 local sheep breeds worldwide to analyze the origin and evolution of LOP. Additionally, comparisons will be made with HUS sheep from warm and humid regions to identify genomic differences through selection signal analysis, thereby assessing the impact of a desert arid climate on the extreme living conditions of LOP. Finally, qPCR was used to preliminarily analyse the impact of the desert arid climate on the genome of the Bactrian sheep. Genetic diversity results indicate that LOP exhibits a relatively stable genetic structure alongside high genetic diversity. The results of population structure analysis and gene flow indicate that we can tentatively posit that LOP is a breed that originated from the Middle East, subsequently mixing with MGS upon its arrival in Xinjiang. Chinese local sheep breeds trace their origins to AMS, with the gene flow evolving from west to east, progressing through mountainous hills (BSBS), basins (LOP, HTS, CLHS, DLS), plains (MGS, TANS), and coastal areas (HUS). LOP is associated with ALTS, BSBS, HTS, CLHS, and DLS, as well as with MGS, HUS, TANS, WDS, and SSSP, in a context of gene exchange, with the degree of exchange diminishing in that order. Selection signal analysis revealed that the candidate genes identified are closely related to adaptation to desert arid climates and disease resistance (PDGFD, NDUFS3, ATP1B2, ITGB8, and CD79A), using HUS as the reference group. qPCR results demonstrated that LOP was significantly upregulated in cardiac, splenic, and lung tissues compared to HUS, suggesting that LOP plays a crucial role in cardiac function, immune response, and respiratory capacity. Full article

This study presents a comprehensive evaluation of the behavior of fluoroelastomer (FKM) compounds reinforced with graphene nanoplatelets of various sizes such as 15 μm (GN15) and 5 μm (GN5). The study evaluates the mechanical, dynamic mechanical, thermal, wetting, and photothermal properties of the compounds when irradiated with an 808 nm laser. The results demonstrate that the size of the graphene nanoplatelets significantly impacts the mechanical properties, with smaller sizes exhibiting a stronger reinforcing effect compared to larger nanoplatelets. Additionally, clear evidence of an influence on dynamic mechanical properties was observed, particularly through the broadening of the damping factor (tan δ) peak. This suggests modifications to the material’s viscoelastic behavior. Regarding the photothermal response, it was found that smaller nanoplatelets (GN5) dispersed in the rubber matrix allow higher temperatures to be reached and thermal equilibrium to be achieved more efficiently under irradiation. Overall, the results suggest that FKM compounds containing graphene nanoplatelets can attain high temperatures with low-energy infrared irradiation. This makes them promising materials for technological applications in extreme environments, such as the Arctic, high mountains, or space, where materials with controlled thermal responses and high mechanical performance are required. Full article

The rugged terrain and dense vegetation in the mountainous area of Luding after the strong earthquake have made geologic hazards hidden and difficult to verify, and there are limitations in the fine-resolution monitoring of small-scale landslides, especially in the area covered by high vegetation. Currently, there is a lack of research on the application of L-band LuTan-1 (LT-1) for landslide detection in the dense vegetation-covered area of the Luding strong earthquake zone, and it is necessary to carry out the analysis of the detection capability of LT-1 for small-scale landslide hazards under the complex terrain and dense vegetation area. In this study, the Stacking-InSAR method was employed using LT-1 and Sentinel-1 satellites to conduct deformation monitoring and landslide detection in the Luding seismic area and to investigate the small-scale landslide detection capability of LT-1 in vegetation-covered areas. The results show that LT-1 and Sentinel-1 identified 23 landslide hazards, and their obvious deformation and landslide characteristics indicate that they are still in an unstable state with a continuous deformation trend. At the same time, through the detection analysis of LT-1’s landslide detection capability under high vegetation cover and small-scale landslide detection capability, the results show that the long wavelength LT-1 can be more effective in landslide hazard identification and monitoring than the short wavelength, and LT-1 with high spatial resolution can be more refined to depict the landslide deformation characteristics in space, which demonstrates the great potential of LT-1 in the refinement of landslide detection. It shows the significant potential of the LT-1 satellite data in landslide detection. Finally, the effects of geometric distortion on landslide detection under different satellite orbits are analyzed, and it is necessary to adopt the combined monitoring method of elevating and lowering orbits for landslide detection to ensure the integrity and reliability of landslide detection. This study highlights the capability of the LT-1 satellite in monitoring landslides in complex mountainous terrain and underscores its potential for detecting small-scale landslides. The findings also offer valuable insights for future research on landslide detection using LT-1 data in similar challenging environments. Full article

In high-density built environments, perceived density (PD)—shaped by physical, socio-cultural, and perceptual factors—often induces sensations of crowding, stress, and spatial oppression. Although green spaces are recognised for their stress-reducing effects, the influence of built-environment characteristics on public sentiment under stringent mobility restrictions remains inadequately explored. This study takes Chongqing, a representative mountainous metropolis in China, as a case to examine how natural and built environmental elements modulate emotional valence across varying PD levels. Using housing data (n = 4865) and geotagged Weibo posts (n = 120,319) collected during the 2022 lockdown, we constructed a PD-sensitive sentiment dictionary and applied Python’s Jieba package and natural language processing (NLP) techniques to analyse emotional scores related to PD. Spatial and bivariate autocorrelation analyses revealed clustered patterns of sentiment distribution and their association with physical density. Using entropy weighting, building density and floor area ratio were integrated to classify residential built environments (RBEs) into five tiers based on natural breaks. Key factors influencing positive sentiment across PD groups were identified through Pearson correlation heatmaps and OLS regression. Three main findings emerged: (1) Although higher-PD areas yielded a greater volume of positive sentiment expressions, they exhibited lower diversity and intensity compared to low-PD areas, suggesting inferior emotional quality; (2) Environmental and socio-cultural factors showed limited effects on sentiment in low-PD areas, whereas medium- and high-PD areas benefited from a significantly enhanced cumulative effect through the integration of socio-cultural amenities and transportation facilities—however, this positive correlation reversed at the highest level (RBE 5); (3) The model explained 20.3% of the variance in positive sentiment, with spatial autocorrelation effectively controlled. These findings offer nuanced insights into the nonlinear mechanisms linking urban form and emotional well-being in high-density mountainous settings, providing theoretical and practical guidance for emotion-sensitive urban planning. Full article

With the advancement of UAV trajectory planning and sensing technologies, unmanned aerial vehicles (UAVs) are now capable of performing high-performance ground detection and search tasks. Mountainous regions, due to their complex terrain, have long been a focal point in the field of remote sensing. Effective UAV search tasks in such areas must consider not only horizontal coverage but also variations in detection range and angle caused by changes in elevation. Conventional algorithms typically require complete prior knowledge of the environment for trajectory optimization and often depend on scenario-specific policy models, limiting their generalizability. To address these challenges, this paper proposes a Feature-Aware Elite Imitation Multi-Agent Reinforcement Learning (FA-EIMARL) algorithm that leverages partial terrain information to construct a feature extraction network. This approach enables batch training across diverse terrains without the need for full environmental maps. In addition, an elite imitation mechanism has been proposed for convergence acceleration and task performance enhancement. Simulation results demonstrate that the proposed method achieves superior reward performance, convergence rate, and computational efficiency while maintaining strong adaptability to varying terrains. Full article

Urban Tourism Areas (UTAs) face growing challenges in balancing tourism development with ecological preservation, particularly under the pressures of rapid urbanization and intensified land use. However, systematic approaches to optimizing landscape patterns in urban tourism contexts remain limited. The aim of this study is to develop and apply an integrated framework that combines ecological sensitivity evaluation and landscape eco-ethics to guide sustainable landscape optimization. Using Shihe District in Xinyang City, China—a region marked by diverse natural landscapes and intensive human–environment interactions—as a case study, we applied a multi-indicator ecological sensitivity assessment together with landscape pattern analysis, supported by Geographic Information Systems (GIS) and FRAGSTATS software. The results revealed significant spatial heterogeneity in ecological sensitivity across the district. High- and very-high-sensitivity zones accounted for 23.2% of the total area, primarily located in southwestern mountainous regions, while low-sensitivity zones covered 53.8%, concentrated in urban plains and lowlands. The landscape exhibited a Shannon’s Diversity Index (SHDI) of 0.8617 and an Edge Density (ED) of 17.05, reflecting a moderately fragmented spatial structure. Based on these findings, a hierarchical optimization strategy was proposed, delineating three protection zones: primary conservation zones (23.2%), secondary buffer zones (22.9%), and development-prioritized zones (53.8%). This framework promotes ecological integrity, supports balanced tourism development, and accommodates the needs of both tourists and local communities. The model has potential applicability to other global UTAs facing similar conflicts between ecological protection and tourism expansion. Full article

Ship waterway tunnels are a new and special type of navigation facility that has emerged in the construction of complex hubs in high mountain valleys and rivers, and they have demonstrated broad applications worldwide. Due to their characteristics of long length, a dim visual background, and enclosed space, waterway tunnels are prone to causing tension and cognitive fatigue in ship officers on watch, affecting their decision-making and control abilities. This study constructs the visual navigation environment of a typical waterway tunnel in China using a ship maneuvering simulator. By monitoring the physiological data of ship officers, such as through electroencephalograms (EEGs) and electrocardiograms (ECGs), the temporal and spatial patterns of their physiological and psychological characteristics are analyzed systematically. Based on this, a quantitative model of the cognitive load of a ship officer working in a waterway tunnel is constructed. At the same time, the navigation risk of waterway tunnels of different lengths is quantized based on the entropy weight TOPSIS method, and finally, high-risk sections in waterway tunnels are identified and visualized, providing theoretical support for the management of safety in waterway tunnels. Full article