Search Results (230)

The Upper Niger National Park (UNNP) is the oldest park and one of the most promising conservation areas in Guinea; yet, its mammalian fauna remains poorly documented. Camera trapping has become an essential tool for revealing the diversity and assemblage structure of such communities. This study employed camera traps to improve knowledge about terrestrial and semi-terrestrial mammals with body mass > 0.5 kg (Sciuridae and heavier) in the Mafou Fully Protected Area (Mafou FPA), the principal core zone of the UNNP. The survey was conducted during the dry season, from January to May 2025. Sampling targeted animal trails within forest habitats in the Mafou FPA and involved the deployment of 53 camera traps with an average inter-trap distance of 2 km. Across 4239 camera-days of sampling effort, we collected 10,334 usable images and videos, yielding 2634 independent detection events. Thirty taxa across 15 families and five orders, including six species of high conservation concern according to the IUCN Red List (version 2025-1) were recorded. The mammal assemblage consists of species from the three known trophic levels (prey, mesopredators, and apex predators), with predominance of medium-sized prey species. Our results demonstrate a remarkable richness in frugivorous seed-dispersing species, ecosystem-engineering species, and the presence of a megaherbivore, which all contribute to its ecological dynamics. Despite a notable human activity index of 0.42, occupancy models revealed broad spatial distribution of medium-sized mammals within this protected area. The occupancy patterns demonstrated that occurrence of some species might be sensitive to human disturbances in the area. These findings are an important contribution to Guinean and West African biodiversity assessments. They highlight the critical conservation value of the UNNP within the subregion. Full article

Smart city infrastructures increasingly depend on cloud platforms processing data from billions of IoT devices, managing critical urban services. With IoT connections reaching 1.3 billion globally and cloud attacks rising 75% year-over-year, security breaches threaten public safety. However, traditional AI threat detection systems operate as “black boxes,” preventing municipal stakeholders from understanding automated alerts. This paper presents an interpretable AI framework based on Model Context Protocol (MCP) that bridges automated threat detection with human-centered decision support. Using 1.9 million authentic AWS CloudTrail events, including a 1.3 million-event cryptocurrency mining campaign, we achieved 84.2% detection accuracy (96.8% on real attacks) while generating plain-language threat narratives suitable for diverse stakeholders. The Random Forest classifier with MCP layer provides real-time detection (<13 ms latency) and actionable intelligence (2.3 s explanation generation), meeting smart city operational requirements. SIEM integration enables immediate deployment in municipal Security Operations Centers. This production-ready framework demonstrates that interpretability and accuracy are complementary, not competing, objectives in smart city security. Full article

This study investigated the impact of recreational use on trails in the Atlantic Forest (Ubatuba Municipality, São Paulo State, Brazil) using physical, chemical and geochemical indicators. Five trails with different morphological characteristics were selected, and paired samples were collected from the trail surface (TR) and trail-side slope (TA). The statistical approach combined local analyses for each trail with global clustering (n = 19) using Student’s t-test, along with multivariate modeling through Principal Component Analysis (PCA) and Pearson correlation. The analysis included physical attributes (bulk density, particle size and porosity), chemical attributes (pH, organic matter and macronutrients) and geochemical compositions (major oxides and trace elements determined by XRF). The overall results reveal systematic compaction in the trail surface (TR), with bulk density increasing from 1.32 g/cm³ (TA) to 1.37 g/cm³ (TR) (p = 0.038), and total porosity decreasing from 47.26% to 45.34% (p = 0.016). In contrast, the geochemical oxide composition (SiO₂, Al₂O₃, Fe₂O₃) remained stable (p > 0.05), indicating the resilience of the mineral matrix. However, significant local dynamics (p < 0.05) in K₂O and MgO were observed in more preserved trails, associated with surface compaction and fragmentation of the litter layer, and phosphorus showed strong dependence on organic matter (r = 0.85). Multivariate analysis indicates that degradation is predominantly physical and micromorphological at the local scale, with bulk density and porosity being the most sensitive indicators for environmental monitoring. Full article

Protected areas increasingly require functional zoning approaches that integrate biodiversity conservation, ecosystem service provision, and human use. This study developed a data-driven functional zoning framework for Jirisan National Park, South Korea, by combining ecosystem service assessment with Self-Organizing Map (SOM)-based spatial typology. Five ecosystem services—water yield, sediment retention, carbon storage, net ecosystem productivity, and habitat quality—were assessed using InVEST, RUSLE, and locally derived carbon-related coefficients. These indicators were integrated with topographic and anthropogenic disturbance variables, including distances to roads and trails. The SOM analysis classified the park into seven functional spatial types with distinct environmental and ecosystem service characteristics. High-altitude areas near major trails were characterized by strong visitor pressure and mismatches among regulating services, whereas interior forest areas showed high multifunctionality and evenness, indicating stable ecosystem service provision. Low-altitude facility-dense and disturbance-adjacent zones showed relatively low habitat quality or service imbalance, highlighting the need for restoration-oriented management. These results suggest that ecosystem service bundles, multifunctionality, and evenness can provide a useful basis for functional zoning and evidence-based management of mountainous national parks. Full article

The risk of wildfires is increasing due to high temperatures and dry weather conditions caused by climate change. Outbreaks and spread of wildfires are usually conditioned by weather, topography, and fuel characteristics. In the Republic of Korea (hereafter, the ROK), most wildfires are caused by anthropogenic factors rather than natural ones. However, the current forest fire forecasting system being operated in the ROK does not account for anthropogenic factors. To analyze the impact of human and physical factors on wildfire occurrence, a binary logistic regression model was constructed using data from the Gangwon and Gyeongbuk provinces from January 2022 to August 2025. The dependent variable was defined as the occurrence of a wildfire, while the independent variables comprised meteorological, seasonal, stand, and anthropogenic factors. To address multicollinearity, variables with high correlation coefficients were excluded from the independent variables, which were selected by three estimating approaches, including logistic regression and two machine learning techniques (namely, Random Forest and XGBoost). With machine learning, the variables with high feature importance were identified. The explanatory power of the logistic regression analysis with independent variables selected by the machine learning models was about 1.3 times higher than that of the model using variables adjusted solely for multicollinearity. The results of logistic regression analysis revealed that weather and coniferous forests are the most important factors fostering wildfires, while the mean stand age was the most significant factor in hindering wildfires. Among the anthropogenic factors, forest road density acted as a suppressor of wildfire spread rather than a promoter of occurrence. Conversely, trail density tends to increase the risk of wildfire occurrence. Among forest management activities, plantation forests may increase the risk of forest fires, although this remains uncertain. These findings suggest that preventing wildfires requires a paradigm shift in forest resource management policies, including extending forest rotation ages and converting coniferous forests to broadleaf forests. Meanwhile, it also indicates the need to restrict the expansion of hiking trails and improve regulations regarding hiker access and behavior to prevent wildfires. Full article

With growing interest in health and leisure, walking tourism has emerged as a significant segment of the tourism market. Coastal trails have gained prominence as attractive tourist attractions offering unique experiences that combine coastal and forest environments. Understanding the experiences of tourists using these trails is essential to their sustainability and the revitalization of nearby regions and tourist destinations. However, the sustainable management of coastal trails and the understanding of the perceptions and evaluations of tourists using them remain limited. Therefore, this study aims to analyze walking tourism experiences on coastal trails using online review data to identify tourists’ perceptions and evaluations. Three representative coastal trails in South Korea were selected as the study sites, and 21,289 reviews (including course information, titles, review content, and posting dates) were collected from Durunubi, a walking tourism application operated by the Korea Tourism Organization. The research methodology employed text mining and sentiment analysis in Python 3.12.13 and spatial analysis using GeoDa 1.22.0.20 and QGIS 3.40.11. This study explores the emotional geography of walking tourism experiences along Korean coastal trails by integrating the analysis of online review data using text mining, sentiment analysis, and spatial analysis. The analysis revealed that positive sentiments were associated with natural landscapes, while negative sentiments were associated with trail management. These emotional experiences exhibit distinct spatial clustering patterns. This finding has important implications for establishing sustainable trail management strategies. Full article

National Forest Trails (NFTs), a key component of forest welfare infrastructure, increasingly require a shift from government-led management to citizen-participatory governance. This study examined the structural relationships among volunteer motivation, activity satisfaction, perceived quality-of-life (QoL) change, and behavioral intention in the context of NFT volunteering. A survey was conducted with 217 adults who had participated in forest trail volunteering programs in Korea, and the data were analyzed using structural equation modeling (SEM). The results showed that volunteer motivation had significant positive effects on reward importance, activity satisfaction, and perceived QoL change. Activity satisfaction positively influenced both Future Participation Intention and Active Participation Intention, whereas perceived QoL change had a significant positive effect only on Active Participation Intention. In addition, activity satisfaction and perceived QoL change mediated the relationship between volunteer motivation and Active Participation Intention. These findings suggest that forest trail volunteers are not merely supplementary labor for trail management, but active participants in forest governance who both contribute to and benefit from the environments they help sustain. Overall, the study indicates that sustainable NFT volunteering depends not only on motivation itself, but also on the quality and personal meaning of the volunteer experience. The findings highlight the importance of experience-centered program design, appropriate recognition systems, and greater attention to participant-centered well-being outcomes in sustainable forest trail governance. Full article

Small Mediterranean islands relying on the sun–sea–sand (3S) tourism model face growing climate risks that threaten their tourism-dependent economies. This study evaluates climate suitability for 3S tourism on the Island of Vis by integrating the Climate Index for Tourism (CIT) with land- use and land-cover (LU/LC) spatial analysis. The integration is operationalized by overlaying CIT-derived seasonal suitability windows with LU/LC-based spatial vulnerability maps, enabling identification of micro-zones where natural buffers (forest cover and elevation) can offset thermal discomfort during peak heat stress periods. Observed data reveals declining ideal 3S conditions from July to October, with the island already exceeding 50 days per year of Physiologically Equivalent Temperature (PET) above 35.1 °C, increasing by 0.7 days per year. Regional climate models tend to exhibit a cold bias over small Adriatic islands, largely related to their limited spatial horizontal resolution (12.5 km grid spacing). However, they robustly reproduce the direction of recent and projected warming trends. Future projections indicate that the annual number of strong heat stress days with PET above 35.1 °C increase from approximately one per year in the reference period to six under RCP4.5 and nine under RCP8.5, with both scenarios reducing ideal peak-summer conditions while extending favorable periods into transitional seasons. Spatial analysis shows that coastal zones have higher sealed surfaces and less forest cover, reducing natural shade and cooling capacity, while the island interior offers higher elevations, forest buffers, hiking trails, and a UNESCO Global Geopark. Drawing on social–ecological resilience theory, we conceptualize the island’s tourism system as an adaptive unit whose long-term viability depends on spatially diversified resource use and temporally extended seasonality. The integrated analytical framework identifies not only when conditions deteriorate but where alternative tourism resources exist, enabling more targeted adaptation planning and supporting diversification toward outdoor tourism forms. The novelty of this study lies in the systematic spatial integration of bioclimatic suitability assessments (CIT and PET) with LU/LC analysis at the micro-island scale. Such an approach moves beyond temporally focused climate–tourism indices to produce actionable, location-specific adaptation strategies. Full article

Forests in Poland play a key recreational role, and the growing interest in sylvaturism requires optimized management. Despite the growing body of research on forest recreation, existing studies rarely address the role of small-scale infrastructure in shaping user preferences and its integration into spatial planning frameworks, which constitutes a research gap in this study. This study aimed to identify user preferences for small infrastructure and to develop an application-oriented, socially sensitive model for forest trail design that supports sustainable management. The research was conducted in 2021–2024 using the CAWI method on a group of 402 adult Poles. Data analysis included descriptive statistics, Pearson’s chi-square tests to assess demographic differences, and correspondence analysis to identify user preference profiles. The results not only confirmed a clear hierarchy of needs but also demonstrated that differences between user groups relate primarily to the intensity rather than the structure of preferences. A clear hierarchy of needs was confirmed, with route map boards (86.32%), educational boards (72.64%), and benches (71.14%) dominating. Based on the results, a modular design model was developed (modules: basic, comfort, accessibility, and activity), which constitutes a conceptual advancement over existing planning approaches by introducing a flexible, user-oriented framework that links social preferences with spatial decision-making. By integrating empirical social data into the planning process, the proposed framework extends current knowledge on recreation planning and provides a structured basis for adaptive forest trail design. This tool could help managers efficiently channel tourist traffic, protect ecosystems, and promote public health. Full article

To address the challenges of over-reliance on single-source data, strong spatial heterogeneity in scenic areas, and difficulty in dynamically capturing spatial topology and heterogeneous node relationships in forestry tourism resource carrying capacity prediction, this paper constructs a carrying capacity prediction framework that integrates a multi-source data fusion algorithm with an attention mechanism and a GAT-Transformer model. This framework employs a modal-level multi-head cross-attention mechanism to conditionally weight and fuse multi-source heterogeneous data in the node and time dimensions. It adaptively allocates the contribution of each information source based on the spatiotemporal context, suppressing noise and redundant interference. A weighted spatial graph is constructed based on fusion distance, trail connectivity, and traffic similarity. Neighborhood information is aggregated through a graph attention network to characterize spatial heterogeneity. The spatially enhanced node sequence is then input into a multi-layer Transformer encoder to capture the long-term temporal dependence and periodic patterns of carrying capacity. Finally, the prediction results are output through a regression layer. Systematic experiments were conducted using two years of multi-source observation data from Wulingyuan National Forest Park. The results show that the proposed method has low prediction error and good stability, exhibiting excellent performance in temporal scale adaptation, spatial generalization, and resistance to missing data and noise. Simultaneously, the model structure is lightweight, with low inference latency, achieving a good balance between prediction accuracy, interpretability, and engineering deployment. Full article

Sea ice classification is of fundamental importance for polar monitoring and global climate research. Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a frontier technology in polar remote sensing due to its high spatiotemporal resolution and cost-effectiveness. Based on BeiDou System Reflectometry (BDS-R) data acquired from the Fengyun-3E (FY-3E) satellite, this study introduces a classification approach that integrates multi-dimensional sea ice information. A comprehensive feature set was constructed by integrating the Spectral Entropy (SE) of the Normalized Integrated Delay Waveform (NIDW) First-order Differential Curve to characterize the oscillatory complexity of the trailing edge power decay process as a scattering dynamic property, the Root Mean Square height (RMS) to characterize the attenuation magnitude of scattering intensity arising from surface roughness and related factors as a scattering intensity attenuation property, and salinity (S) and L-band brightness temperature (TB) data from SMOS to describe dielectric and radiative properties. These novel features are combined with traditional GNSS-R features. After selecting the optimal feature set via an ablation study, the features were used to train a Random Forest (RF) classifier for sea ice classification. Validated against Ocean and Sea Ice Satellite Application Facility (OSI SAF) sea ice type products, the proposed method yielded an overall accuracy of 93.86% and a Kappa coefficient of 0.8061. The integration of multi-dimensional features notably improved the identification of Multi-Year Ice (MYI), achieving a Recall of 85.11% and an F1-score of 84.43%. These results indicate that the proposed multi-dimensional feature set provides an effective solution for GNSS-R-based sea ice classification. Full article

The rapid growth of hiking tourism presents a critical challenge for balancing visitor safety with the sustainable management of ecologically fragile mountain environments. Traditional models developed in urban settings struggle to capture the highly non-linear, heterogeneous, and zero-inflated characteristics of wilderness trekking behavior. In order to quantify the nonlinear and threshold-based effects of environmental variables on hikers’ spatial decisions in unstructured wilderness and to identify distinct behavioral regimes for segmented management, this study introduces an explainable machine learning framework to reconstruct hikers’ spatial decision-making in a complex mountainous system in Inner Mongolia, China. Random Forest (RF), XGBoost, and LightGBM were compared in predicting trail density and the Euclidean distance to the nearest trail. Results show that transforming behavioral traces into continuous proximity surfaces dramatically improves model performance, with XGBoost achieving the highest predictive accuracy for Trail_Dist. By integrating the SHapley Additive exPlanations framework, this study moves beyond black-box prediction to reveal the nonlinear mechanisms driving hiker behavior. Key findings include: (1) Nighttime light range exhibits a U-shaped threshold effect as the primary anthropogenic attractor. (2) Elevation shows an exponential inhibitory trend above 1238 m. (3) Strong spatial coupling exists between elevation and slope, alongside a landscape compensation effect where high Normalized Difference Vegetation Index (NDVI) areas attract off-trail movements. This research provides a robust methodological pathway for predicting behavior in unstructured outdoor environments. It offers a scientific foundation for smart scenic area management, including optimized route planning, precise ecological protection zoning, and targeted emergency rescue preparedness. Full article

Mechanized timber harvesting on steep slopes causes soil disturbance; however, comprehensive post-harvest assessment remains challenging because terrain complexity and safety constraints render traditional field-based methods labor-intensive, spatially limited, and difficult to implement systematically. In this study, we developed and evaluated a convolutional neural network-based semantic segmentation model for detecting soil disturbances using high-resolution unmanned aerial vehicle (UAV) imagery in a steep-slope harvested area (2.50 ha, mean slope of 53.4%) in Republic of Korea. A U-Net semantic segmentation model was trained on manually annotated orthomosaic tiles incorporating RGB and digital elevation model (DEM) inputs. Ensemble predictions at an optimized threshold of 0.65 achieved Intersection over Union (IoU) of 0.55 and F1-score of 0.71. Although moderate, these values reflect the inherently challenging conditions of steep-slope forest terrain compared to similar studies conducted under gentler terrain. DEM-derived depth estimation enabled severity classification of the detected disturbances, with light disturbances predominating. Field validation using 38 pinboard measurements demonstrated reliable spatial detection (ρ = 0.567, RMSE = 6.45 cm). This approach provides an effective alternative to traditional monitoring practices in mountainous forests, where systematic trail planning is impractical, and may support evidence-based assessment of harvesting impacts for sustainable forest management. Full article

The aspiring United Nations Educational, Scientific and Cultural Organization (UNESCO) Global Geopark of Chefchaouen includes part of the Talassemtane National Park (TNP), classified by UNESCO as an exceptional natural heritage site within the Intercontinental Mediterranean Biosphere Reserve (RBIM). The other section corresponds to the Ghomara Coast (GC), characterized by an outstanding succession of metamorphic rocks. This study identifies and highlights the most significant sites of geological interest (geosites and geodiversity sites) in the territory. Forty-two sites are proposed as geological heritage sites, thirty of which are organized into four accessible georoutes (Oued Laou Valley, Ghomara Coast, Talambote–Akchour, and Chaouen–Ametrasse), while the other twelve are located along trails and forest tracks inside or near the TNP. These sites cover a wide range of geological typologies, including structural geology, stratigraphy–sedimentology, paleontology, geomaterials, petrology, geomorphology, and hydrogeology. To classify and rank the sites objectively, a numerical methodology based on the recent literature was applied. Scientific value (SV), Potential Educational Use (PEU), and Potential Touristic Use (PTU) were quantified using multiple criteria, facilitating route selection according to user needs. Degradation Risk (DR) was also measured, providing managers with essential guidance for an appropriate geoconservation plan. Actions consistent with UNESCO Global Geoparks Network criteria are proposed to improve conservation, support education, and promote sustainable tourism, thereby enhancing economic activity in the region. The initiative aims to promote the region’s exceptional geological, cultural, and natural heritage. The Chefchaouen Geopark was designated a deferred candidate during the UNESCO Global Geoparks Council meeting of 8–9 September 2024. According to Section 5.5 of its guidelines, the Council may defer an application for up to two years to allow improvements without requiring a second field evaluation. To consolidate the Chefchaouen candidacy, we developed a strategy to strengthen compliance with UNESCO requirements, reduce the risk of final rejection, and maintain the territory’s credibility with international networks and partners. This work presents an operational, costed, and scheduled roadmap enabling stakeholders at all levels to converge toward a complete and coherent application. Full article

Urban wetlands are acoustic hotspots where vegetation structure, hydrological dynamics, and anthropogenic noise interact, yet multi-season assessments of how vegetation influences avian soundscapes are limited. This study explored bird soundscape dynamics across forest, open forest grassland, and meadow habitats in Nanjing Xinjizhou National Wetland Park, eastern China, using passive acoustic monitoring during spring and autumn 2023. Twelve sampling points (four per vegetation type) were established, and six acoustic indices were calculated, including the Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), Acoustic Evenness Index (AEI), Bioacoustic Index (BIO), Normalized Difference Soundscape Index (NDSI), and Acoustic Entropy Index (H). were calculated from 48-h recordings each season. Random forest models and redundancy analysis assessed the relationships between acoustic indices, fine-scale vegetation parameters (e.g., crown width, tree height, species richness), and anthropogenic factors (e.g., distance to roads/trails, surface hardness). Vegetation structure, particularly crown width, was the primary driver of avian acoustic diversity, with broad-crowned forests consistently exhibiting the highest acoustic complexity. In spring, anthropogenic factors such as trail and road proximity dominated soundscape variation, suppressing biological sounds. In autumn, with reduced human presence, vegetation structure emerged as the dominant factor, while bioacoustic activity remained elevated despite reduced peaks in acoustic complexity. Proximity to roads increased low-frequency (1–2 kHz) noise and suppressed mid-frequency (4–8 kHz) bird vocalizations, but trees with crown widths ≥4 m maintained higher acoustic diversity even near disturbance sources. This study demonstrates that vegetation structure mediates both resource availability and sound propagation, buffering the effects of anthropogenic disturbance in frequency-specific ways. Multi-season sampling is crucial for understanding the dynamic interplay between vegetation phenology and human activity that shapes urban wetland soundscapes. Full article