Search Results (1,844)

A new desmid microalga species, Cosmarium yassinii A.A. Saber, El-Sheekh, Kouwets et Cantonati sp. nov., was isolated from two hyper-arid mountain valleys, so-called “wadis”, in the Eastern Desert of Egypt. The distinctive morphological features of this new species were established using light and scanning electron microscopy observations, and also by documenting its life-cycle stages. Taxonomically, C. yassinii is characterized by a cell wall sculpture consisting of isolated granules or small warts arranged circularly in the swollen mid-region of each semicell, never forming parallel vertical ridges or costae as in morphologically similar species, and the interesting shape of the marginal granules appears as small emarginate “combs” or crenae, including its knobby zygospores. Similarities and differences with the morphologically most closely related species are discussed in detail. Ecologically, C. yassinii seems to prefer alkaline freshwater environments with lower nutrient concentrations and a NaCl/HCO₃ water type. The detailed assessment and documentation of the biodiversity of these peculiar freshwater ecosystems are a fundamental prerequisite to adequately inform their protection strategies. Full article

Accurate precipitation estimation is essential for hydrological modeling and water resource management in arid regions; however, complex terrain and sparse meteorological station networks introduce substantial uncertainties into gridded precipitation datasets. This study evaluates the performance of nine widely used precipitation products in the arid region of Northwest China (ARNC) at both the meteorological station scale and the sub-basin scale, and applies the Bayesian Model Averaging (BMA) approach to merge multi-source precipitation estimates. The results reveal pronounced spatial heterogeneity and significant differences in performance among datasets, with the Integrated Multi-Satellite Retrievals for the Global Precipitation Measurement mission performing best at the station scale and the Famine Early Warning Systems Network Land Data Assimilation System performing best at the sub-basin scale. Compared with individual products, the BMA-merged precipitation demonstrates substantial improvements at both scales, providing higher coefficients of determination and agreement indices, and lower relative mean absolute error and relative root mean square error, indicating enhanced accuracy and robustness. The BMA-merged precipitation product generally exhibits superior and more spatially consistent performance than the individual datasets across the ARNC, thereby providing a more reliable basis for regional hydrological and climate-related applications. The merged dataset shows that the mean annual precipitation in the ARNC during 2000–2024 is approximately 230.4 mm, exhibiting a statistically significant increasing trend of 1.4 mm per year, with the strongest increases occurring in the Tianshan and Qilian Mountains. This study provides a reliable foundation for hydrological modeling and climate-change assessments in data-limited arid environments. Full article

To address the insufficient exploration ability, susceptibility to local optima, and limited convergence accuracy of the standard Student Psychology-Based Optimization (SPBO) algorithm in three-dimensional UAV trajectory planning, we propose an enhanced variant, Enhanced SPBO (ESPBO). ESPBO augments SPBO with three complementary strategies: (i) Time-Adaptive Scheduling, which uses normalized time ($\tau =t/T$) to schedule global step-size shrinking, Gaussian fine-tuning, and Lévy flight intensity, enabling strong early exploration and fine late-stage exploitation; (ii) Mentor Pool Guidance, which selects a top-K mentor set and applies time-varying guidance weights to reduce misleading attraction and improve directional stability; and (iii) Directional Jump Exploration, which couples a differential vector with Lévy flights to strengthen basin-crossing while keeping the differential step bounded for robustness. Numerical experiments on CEC2017, CEC2020 and CEC2022 benchmark functions compare ESPBO with Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Improved multi-strategy adaptive Grey Wolf Optimization (IAGWO), Dung Beetle Optimization (DBO), Snake Optimization (SO), Rime Optimization (RIME), and the original SPBO. We evaluate best path length, mean trajectory length, standard deviation, and convergence curves and assess statistical stability via Wilcoxon rank-sum tests (p = 0.05) and the Friedman test. ESPBO significantly outperforms the comparison algorithms in path-planning accuracy and convergence stability, ranking first on both test suites. Applied to 3D UAV trajectory planning in mountainous terrain with no-fly zones, ESPBO achieves an optimal path length of 199.8874 m, an average path length of 205.8179 m, and a standard deviation of 5.3440, surpassing all baselines; notably, ESPBO’s average path length is even lower than the optimal path length of other algorithms. These results demonstrate that ESPBO provides an efficient and robust solution for UAV trajectory optimization in intricate environments and extends the application of swarm intelligence algorithms in autonomous navigation. Full article

This paper addresses the problem of imbalance between coastal and inland areas and recognises the reuse of abandoned buildings as an evolutionary regeneration strategy which, through specific interventions linked by a system of routes for tourism and sport, can gradually trigger sustainable development on a regional scale. It presents research conducted in recent years on behalf of local administrations and continued in national and European projects. The reference context is the Abruzzo region, where coastal, hilly and mountainous areas are a short distance apart and include both densely built-up and populated urban centres and small depopulated towns surrounded by landscapes of high environmental value. The objective is to define, through the responsible use of built resources, viable and sustainable strategies for regeneration and rebalancing oriented towards the concept of transformative resilience. The methodology adopted is divided into phases and includes both theoretical developments and case study applications according to an approach that networks building restoration and reuse interventions in the region. The key results consist of defining a reuse logic that considers the regional territory as a whole, linking different resources, functions and environments. This logic, which envisages the organisation of new functions on a regional scale, emphasises the capacity of building reuse to produce positive effects on the territory and trigger socio-economic development dynamics. This research forms part of the experience underlying a project of significant national interest (PRIN 2022 TRIALs), which will provide guidelines for activating the transformative resilience capacities of inland areas of central Italy. Full article

Hilly and mountainous areas are important agricultural production regions globally. Their dramatic topography, dense fruit tree planting, and steep slopes severely restrict the application of traditional plant protection machinery. Pest and disease control has long relied on manual spraying, resulting in high labor intensity, low efficiency, and pesticide utilization rates of less than 30%. Plant protection UAVs, with their advantages of flexibility, high efficiency, and precise application, provide a feasible technical approach for plant protection operations in hilly and mountainous areas. However, steep slopes and dense orchard environments place higher demands on key technologies such as drone positioning and navigation, attitude control, trajectory planning, and terrain following. Achieving accurate identification and adaptive following of the undulating fruit tree canopy while maintaining a constant spraying distance to ensure uniform pesticide coverage has become a core technological bottleneck. This paper systematically reviews the key technologies and research progress of plant protection UAVs in hilly and mountainous operations, focusing on the principles, advantages, and limitations of core methods such as multi-sensor fusion positioning, intelligent SLAM navigation, nonlinear attitude control and intelligent control, three-dimensional trajectory planning, and multimodal terrain following. It also discusses the challenges currently faced by these technologies in practical applications. Finally, this paper discusses and envisions the future of plant protection UAVs in achieving intelligent, collaborative, and precise operations on steep slopes and in dense orchards, providing theoretical reference and technical support for promoting the mechanization and intelligentization of mountain agriculture. Full article

Efficient route planning technology is the core support for ensuring the successful execution of unmanned aerial vehicle (UAV) flight missions. In this paper, the coordination issue of global route planning and local real-time obstacle avoidance in complex mountainous environments is studied. To deal with this issue, a hierarchical route planning framework is designed, including global route planning and AI-based local route re-planning using deep reinforcement learning, exhibiting both flexible versatility and practical coordination and deployment efficiency. Throughout the entire flight, the local route re-planning task triggered by dynamic threats can be executed in real time. Meanwhile, a multi-model DQN (MMDQN) agent with a Monte Carlo traversal iterative learning (MCTIL) strategy is designed for local route re-planning. Compared to existing methods, this agent can be directly used to generate local obstacle avoidance routes in various scenarios at any time during the flight, which simplifies the complicated structure and training process of conventional deep reinforcement learning (DRL) agents in dynamic, complex environments. Using the framework structure and MMDQN agent for local route re-planning ensures the safety and efficiency of the mission, as well as local obstacle avoidance during global flights. These performances are verified through simulations based on actual terrain data. Full article

This study examines the motivations, socio-demographic profiles, and behavioural orientations of residents in Northern Italy toward mountain and forest visitation, with a focus on their propensity to engage in forest-based health and wellness activities. The analysis draws on a large stratified survey conducted between December 2023 and January 2024, involving 1218 respondents, of whom 976 reported regular forest visitations. Exploratory factor analysis identifies two main attitudinal dimensions: “Health and Wellness-Driven Forest Engagement”, centred on psychophysical restoration, and “Comfort-Oriented Forest Use”, related to accessibility and low physical effort. Regression models show that wellness-oriented engagement is strongly associated with psychological well-being, walking and hiking habits, and gender, while comfort-oriented use reflects seasonal patterns and preferences for easily accessible forests. A small subset of respondents reports discomfort in forest environments, forming a distinct attitudinal barrier. Overall, the results indicate substantial potential for forest-based wellness tourism to support healthier lifestyles and diversify mountain economies. Policy implications highlight the need for accessible infrastructures, targeted communication, and the integration of wellness-oriented services into regional development strategies. Full article

Traditional villages are vital carriers of cultural heritage and key foundations for rural revitalization and sustainable development, yet rapid urbanization increasingly threatens their survival, making it necessary to clarify their spatial distribution and driving mechanisms to support effective conservation and rational utilization. Yunnan Province, home to 777 nationally recognized traditional villages and the highest number in China, offers a representative context for such analysis. Methodologically, this study uses a 12 km × 12 km geographic grid (3005 cells) rather than administrative units. The count of catalogued traditional villages in each cell is taken as the dependent variable, and nine indicators selected from five dimensions (traffic accessibility, natural topography, climatic conditions, socioeconomic factors, and historical and cultural factors) serve as explanatory variables. Assuming that relationships between villages and their environment are spatially nonstationary and operate at multiple spatial scales, we combine spatial autocorrelation analysis with a multiscale geographically weighted regression (MGWR) model to detect clustering patterns and estimate location-specific coefficients and bandwidths. The results indicate that: (1) traditional villages in Yunnan exhibit significant clustering, with over 60% concentrated in Dali, Baoshan, Honghe, and Lijiang; (2) the spatial pattern follows a “more in the northwest, fewer in the southeast, dense in mountainous areas” distribution, shaped by both natural and socioeconomic factors; (3) natural geographic factors show the strongest associations, with sunshine duration and water availability strongly promoting village presence, while slope exhibits regionally differentiated effects; (4) socioeconomic development and transportation accessibility are generally negatively associated with village distribution, but in tourism-driven areas such as Dali and Lijiang, road improvements have facilitated protection and revitalization; and (5) historical and cultural factors, particularly proximity to nationally protected cultural heritage sites, contribute to spatial clustering and long-term preservation. The MGWR model achieves strong explanatory power (R² = 0.555, adjusted R² = 0.495) and outperforms OLS and standard GWR, confirming its suitability for analyzing the spatial mechanisms of traditional villages. Finally, the study offers targeted recommendations for the conservation and sustainable development of traditional villages in Yunnan. Full article

Urban energy systems in Andean cities face growing pressure to accommodate rising electricity demand while progressing toward decarbonization and grid modernization. Residential rooftop photovoltaic (PV) generation offers a promising pathway to enhance transformer utilization, reduce emissions, and improve distribution network performance. However, most GIS-based rooftop solar assessments remain disconnected from operational constraints of urban electrical networks, limiting their applicability for distribution planning. This study examines the technical and environmental feasibility of integrating residential PV distributed generation into the urban distribution network of an Andean city by coupling high-resolution geospatial solar potential analysis with monthly aggregated electricity consumption (MEC) and transformer loadability (LD) information. A GIS-driven framework identifies suitable rooftops based on solar irradiation, orientation, slope, shading, and three-dimensional urban geometry, while MEC data are used to perform energy-balance and planning-level transformer LD assessments. Results indicate that approximately 1.16 MW of rooftop PV capacity could be integrated, increasing average transformer LD from 21.5% to 45.8% and yielding an annual PV generation of about 1.9 GWh. This contribution corresponds to an estimated avoidance of 1143 metric tons of CO₂ per year. At the same time, localized reverse power flow causes some transformers to reach or exceed nominal capacity, highlighting the need to explicitly consider network constraints when translating rooftop solar potential into deployable capacity. By explicitly linking rooftop solar resource availability with aggregated electricity consumption and transformer LD, the proposed framework provides a scalable and practical planning tool for distributed PV deployment in complex mountainous urban environments. Full article

Accurate estimation of hourly near-surface air temperature (NSAT) is critical for climate analysis, environmental monitoring, and urban thermal studies. However, existing temperature datasets remain constrained by coarse spatial resolution and limited hourly accuracy. This study systematically evaluates four widely used land surface temperature (LST) datasets—MODIS, ERA5-Land, FY-2F, and CGLS—and five machine learning models (RF, MDN, DNN, XGBoost, and GTNNWR) for NSAT estimation across two contrasting regions in Shaanxi, China: a complex-terrain region in southwestern Shaanxi and the urban area of Xi’an. Results demonstrate that single-source LST inputs outperform multi-source LST stacking, largely due to compounded systematic biases across heterogeneous datasets. MODIS provides the best performance in the mountainous region, while CGLS excels in the urban environment. Among all models, GTNNWR—which explicitly captures spatiotemporal non-stationarity—consistently achieves the highest accuracy, reducing RMSE by 44.8% and 44.2% relative to the second-best model in the two study areas, respectively, whereas the remaining four models exhibit broadly comparable performance. This work identifies effective data–model configurations for generating high-resolution hourly NSAT products and provides methodological insights for climate and environmental applications in regions with complex terrain or strong urban heterogeneity. Full article

Catastrophic mass flows originating from the high mountain cryosphere often cause cascading hazards. With increasing human activities in the alpine region and the sensitivity of the cryosphere to climate warming, cryospheric hazards are becoming more frequent in the mountain regions. Monitoring the evolution and impact of the glaciers and ice-rock avalanches and hazard consequences in the mountain regions is crucial to understand nature and drivers of mass flow process in order to prevent and mitigate potential hazard risks. In this study, the glacier and ice-rock avalanches that occurred in the Tibetan Plateau (TP) were investigated based on the Sentinel-2 satellite data and in situ observations, and the main driving forces and impacts on the regional environment, landscape, and geomorphological conditions were also analyzed. The results showed that the avalanche deposit of Arutso glacier No. 53 completely melted away in 2 years, while the deposit of Arutso glacier No. 50 melted in 7 years. Four large-scale ice-rock avalanches in the Sedongpu basin not only had significant impacts on the river flow, landscape, and geomorphologic shape in the basin, but also caused serious disasters in the region and beyond. These glacier and ice-rock avalanches were caused by temperature anomaly, heavy precipitation, climate warming, and seismic activity, etc., which act on the specific glacier properties in the high mountain regions. The study highlights scientific advances should support and benefit the remote and vulnerable mountain communities to make mountain regions safer. Full article

The uncoordinated water–sediment relationship, fragile eco-environment and unbalanced economic development in the Wei River Basin (WRB) pose serious challenges to its high-quality development. Most existing studies focus on static structures or single elements, making it difficult to systematically reveal the complex interrelationships among ecosystem services (ESs) supply, transmission and demand. To address this issue, this paper innovatively combines the “system perspective” with the “flow network model”. From the perspective of flood-sediment transport, eco-environmental and socio-economic (FES) subsystems, we take the WRB as its research object and systematically analyzes the supply–demand relationship of ESs, the pathways of the ESs flows and ecological compensation (EC) strategies at multiple scales. By constructing a supply–demand assessment model for six types of ESs combined with the water-related flows model, the enhanced two-step floating catchment area method and the gravity model, this paper simulates the ESs flows driven by different transmission media (water, road and atmosphere). The results showed the following: (1) a significant spatial mismatch was observed between the high-supply areas at the northern foothills of the Qinling Mountains and the high-demand areas in the Guanzhong Plains. Furthermore, the degree of this mismatch increased with decreasing scale. (2) The pathways of different ESs flows were influenced by their respective transmission media. The water-related flows passed through areas along the Wei River and the Jing River. The carbon sequestration flows were identified in the upper reaches of the Luo River and between the core urban agglomerations of the Guanzhong Plains. The crop production flows were significantly influenced by the scale of urban crop demand, radiating outward from Xi’an City. (3) At the county and watershed scales, The EC fund pools of 7.5 billion yuan and 2.6 billion yuan were formed, respectively. These EC funds covered over 90% of the areas. These findings verify the applicability of the “FES subsystems” framework for multi-scale EC and provide a theoretical basis for developing an integrated EC mechanism across the entire basin. Full article

Out-of-hospital cardiac arrest (OHCA) is a major cause of death during mountain activities in the Alpine regions. Due to the time-critical nature of these emergencies and the logistical challenges of remote terrain, emergency medical services (EMS) are investigating the use of unmanned aerial vehicles (UAVs) to deliver automated external defibrillators (AEDs). This study presents a geospatial strategy for optimising AED delivery by UAVs in mountainous environments, using the Province of South Tyrol, Italy, as a model region. A Geographic Information System (GIS) framework was developed to identify suitable sites for vertical drone ports based on terrain, infrastructure, and regulatory constraints. A Low-Altitude-Flight Elevation Model (LAFEM) was implemented to generate obstacle-avoiding, regulation-compliant 3D flight paths using least-cost path analysis. The results identified 542 potential vertical-port locations, covering approximately 49% of South Tyrol within ten minutes of flight, and demonstrated significant time savings for AED delivery in field tests compared with manual and Euclidean routing. These findings show that integrating GIS-based vertical-port placement and terrain-adaptive UAV routing can substantially improve AED accessibility and response times in mountainous regions. The LAFEM model aligns with U-space airspace regulations and supports safe, automated AED deployment for improved outcomes in OHCA emergencies. Full article

The Eurasian Griffon Vulture (Gyps fulvus) is the most abundant obligate scavenger in Europe. It depends on wild and domestic carcasses whose availability and location are relatively unpredictable in terms of space and time, but also on predictable sources of anthropogenic origin. In this study, satellite and accelerometer data from 10 adult individuals captured in the Basque Country (N Spain) were analysed with the aims of identifying feeding sites and determining the types of resources used. The annual cycle of the species was subdivided into three phases: pre-laying and incubation (December–March), rearing (April–July) and post-rearing (August–November). Our results showed that 64% of trophic resources were consumed in mountain pastures and on extensive or semi-extensive livestock farms, highlighting the importance of these farming systems for the species in the study area. However, 36% of the resources were exploited in more predictable anthropic environments, such as landfills and supplementary feeding stations and, to a much lesser extent, intensive farms. Individual variability was detected in terms of trophic behaviour. On semi-extensive farms, the most consumed carcasses were sheep (48%) and horses (37%), while on intensive farms, it was pigs (81%). During the pre-laying and incubation phase, feeding events detected in landfills were reduced, with vultures focusing on resources close to the colony. We observed that the population studied differed from other Spanish populations in its greater use of trophic resources from extensive and semi-extensive livestock farms, as expected from their spatial-temporal distribution and local availability. Full article

The article analyses the direction and intensity of changes occurring in agriculture in mountain areas in Europe between 2000 and 2022. For the calculations, the ESA CCI Land Cover global land-use map set was used. This dataset was established by the European Space Agency (ESA) through the classification of satellite images from sources (MERIS, AVHRR, SPOT, PROBA, and Sentinel-3). In the next step, the organisational features and economic performance of farms located in mountain areas of the European Union were determined for the period 2004–2022. For this purpose, data from the European Farms Accountancy Data Network (FADN-FSDN) were used. Subsequently, using Poland as a case study, the capacity of mountain agriculture to implement key environmental interventions under the Common Agricultural Policy (CAP) 2023–2027 was assessed. The results highlight the varying directions and intensity of organisational changes occurring in mountain agriculture across Europe. They also show that farms can operate successfully in these areas, although their economic situation varies between EU countries. The findings indicate the need for further adaptation of CAP instruments to better reflect the ecological and economic conditions of mountain areas. Strengthening support mechanisms for these regions within the current and future CAP is of crucial importance for protecting biodiversity, promoting sustainable land use, and maintaining the socio-environmental functions of rural mountain landscapes. Our study highlights that the CAP for mountain farms should be targeted, long-term, and compensatory, so as to compensate for the naturally unfavorable farming conditions and support their multifunctional role. The most important assumptions of CAP for mountain farms are a fair system of compensatory payments (LFA/ANCs), support for local and high-quality production, income diversification, and investments adapted to mountain conditions. Full article