Search Results (307)

Small towns in mountainous regions face significant challenges in formulating effective landscape zoning strategies due to pronounced landscape fragmentation, which is driven by both the dominance of large-scale forest resources and the lack of coordination between administrative planning departments. To tackle this problem, this study focused on Yuqian, a quintessential small mountainous town in Hangzhou, Zhejiang Province. The town’s layout was divided into a grid network measuring 70 m × 70 m. A two-step cluster process was employed using ArcGIS and SPSS software to analyze five landscape variables: altitude, slope, land use, heritage density, and visual visibility. Further, eCognition software’s semi-automated segmentation technique, complemented by manual adjustments, helped delineate landscape character types and areas. The overlay analysis integrated these areas with administrative village units, identifying four landscape character types across 35 character areas, which were recategorized into four planning and management zones: urban comprehensive service areas, agricultural and cultural tourism development areas, industrial development growth areas, and mountain forest ecological conservation areas. This result optimizes the current zoning types. These zones closely match governmental sustainable development zoning requirements. Based on these findings, we propose integrated landscape management and conservation strategies, including the cautious expansion of urban areas, leveraging agricultural and cultural tourism, ensuring industrial activities do not impact the natural and village environment adversely, and prioritizing ecological conservation in sensitive areas. This approach integrates spatial and administrative dimensions to enhance landscape connectivity and resource sustainability, providing key guidance for small town development in mountainous regions with unique environmental and cultural contexts. Full article

The agricultural sector faces significant sustainability, productivity, and environmental impact challenges. In this context, geographic information systems (GISs) have become a key tool to optimize resource management and make informed decisions based on spatial data. These data support planning the best cotton planting and harvest dates based on agroclimatic conditions, such as temperature, precipitation, and soil type, as well as identifying areas with a lower risk of water or thermal stress. As a result, cotton productivity is optimized, and costs associated with supplementary irrigation or losses due to adverse conditions are reduced. However, data from automatic weather stations in Mexico are scarce and incomplete. Instead, grid meteorological databases (DMM, in Spanish) were used with daily temperature and precipitation data from 1983 to 2020 to determine the heat units (HUs) for each cotton crop development stage; daily and accumulated HU; minimum, mean, and maximum temperatures; and mean annual precipitation. This information was used to determine areas that comply with environmental, geographic, and regulatory conditions (NOM-059-SEMARNAT-2010, NOM-026-SAG/FITO-2014) to delimit areas with agricultural potential for planting genetically modified (GM) cotton. The methodology made it possible to produce thirty-four maps at a 1:250,000 scale and a digital GIS with 95% accuracy. These maps indicate whether a given agricultural parcel is optimal for cultivating GM cotton. Full article

This study investigates the anthropogenic transformation of small- and medium-sized river basins within the Caspian Sea catchment. The basins of seven rivers—Sunzha, Sulak, Ulluchay, Karachay, Atachay, Haraz, and Gorgan—were selected as key study areas. For both the broader Caspian region, particularly its southwestern sector, and the selected study sites, trends in land cover types were analyzed, natural resource use practices were assessed, and population density dynamics were examined. Furthermore, a range of indices were calculated to quantify the degree of anthropogenic transformation, including the coefficient of anthropogenic transformation, the land degradation index, the urbanity index, the degree of anthropogenic transformation, coefficients of absolute and relative tension of the ecological and economic balance, and the natural protection coefficient. The study was conducted using geoinformation research methods and sets of geodata databases—the global LandScan population density database, the GHS Population Grid database, the ESRI land cover type dynamics database, and OpenStreetMap (OSM) data. The analysis was performed using the geoinformation programs QGIS and ArcGIS, and a large amount of literary and statistical data was additionally analyzed. It is shown that within the studied region, there has been a decrease in the number and density of the population, as a result of which the territories of river basins are experiencing an increasing anthropogenic impact, the woody type of land cover is decreasing, and the agricultural type is increasing. The most anthropogenically transformed river basins are Karachay, Haraz, and Gorgan. Full article

Suyukou Gully, located on the eastern slope of the Helan Mountains in northwest China, is a typical debris-flow-prone catchment characterized by a steep terrain, fractured bedrock, and abundant loose colluvial material. The area is subject to intense short-duration convective rainfall events, which often trigger destructive debris flows that threaten the Suyukou Scenic Area. To investigate the dynamics and risks associated with such events, this study employed the FLO-2D two-dimensional numerical model to simulate debris flow propagation, deposition, and hazard distribution under four rainfall return periods (10-, 20-, 50-, and 100-year scenarios). The modeling framework integrated high-resolution digital elevation data (original 5 m DEM resampled to 20 m grid), land-use classification, rainfall design intensities derived from regional storm atlases, and detailed field-based sediment characterization. Rheological and hydraulic parameters, including Manning’s roughness coefficient, yield stress, dynamic viscosity, and volume concentration, were calibrated using post-event geomorphic surveys and empirical formulations. The model was validated against field-observed deposition limits and flow depths, achieving a spatial accuracy within 350 m. Results show that the debris flow mobility and hazard intensity increased significantly with rainfall magnitude. Under the 100-year scenario, the peak discharge reached 1195.88 m³/s, with a maximum flow depth of 20.15 m and velocities exceeding 8.85 m·s⁻¹, while the runout distance surpassed 5.1 km. Hazard zoning based on the depth–velocity (H × V) product indicated that over 76% of the affected area falls within the high-hazard zone. A vulnerability assessment incorporated exposure factors such as tourism infrastructure and population density, and a matrix-based risk classification revealed that 2.4% of the area is classified as high-risk, while 74.3% lies within the moderate-risk category. This study also proposed mitigation strategies, including structural measures (e.g., check dams and channel straightening) and non-structural approaches (e.g., early warning systems and land-use regulation). Overall, the research demonstrates the effectiveness of physically based modeling combined with field observations and a GIS analysis in understanding debris flow hazards and supports informed risk management and disaster preparedness in mountainous tourist regions. Full article

This study presents an experimental and comprehensive data-driven framework to gain deeper insights into the effect of acidic pretreatment in enhancing the biomethane yield of Xyris capensis. The experimental workflow involves subjecting the Xyris capensis to different concentrations of HCl, exposure times, and digestion retention time in mesophilic anaerobic conditions. Key insights were gained from the experimental dataset through correlation mapping, feature importance assessment (FIA) using the Gini importance (GI) metric of the decision tree regressor, dimensionality reduction using Principal Component Analysis (PCA), and operational cluster analysis using k-means clustering. Furthermore, different clustering techniques were tested with an Adaptive Neuro-Fuzzy Inference System (ANFIS) tuned with particle swarm optimization (ANFIS-PSO) for biomethane yield prediction. The experimental results showed that HCl pretreatment increased the biomethane yield by 62–150% compared to the untreated substrate. The correlation analysis and FIA further revealed exposure time and acid concentration as the dominant variables driving biomethane production, with GI values of 0.5788 and 0.3771, respectively. The PCA reduced the complexity of the digestion parameters by capturing over 80% of the variance in the principal components. Three distinct operational clusters, which are influenced by the pretreatment condition and digestion set-up, were identified by the k-means cluster analysis. In testing, a Gaussian-based Grid-Partitioning (GP)-clustered ANFIS-PSO model outperformed others with RMSE, MAE, and MAPE values of 5.3783, 3.1584, and 10.126, respectively. This study provides a robust framework of experimental and computational data-driven methods for optimizing the biomethane production, thus contributing significantly to sustainable and eco-friendly energy alternatives. Full article

Marine biodiversity is of critical importance to global ecosystems. The Indo-Pacific Convergence Zone (IPCZ), a global marine biodiversity hotspot, faces escalating threats from human activities and climate change. This underscores the pressing need to develop effective conservation strategies for marine biodiversity in the IPCZ. This study integrates spatial analysis of ecological sensitivity (coral reefs, mangroves, and seagrass) and anthropogenic pressures (shipping/fishing intensity) to identify biodiversity hotspots and conservation gaps. Using datasets from UNEP-WCMC, OBIS, and Global Fishing Watch, we applied GIS-based multi-criteria evaluation to 5408 grid cells (0.5° resolution) across the IPCZ. Results revealed that 14.7% of the study area constitutes biodiversity hotspots, primarily in coastal Philippines, Indonesia’s Lesser Sunda Islands, and northern Australia. However, only 6% of the IPCZ is currently protected, with merely 13.88% of hotspots overlapping existing marine protected areas (MPAs). Anthropogenic pressure hotspots (e.g., Malacca Strait) showed limited spatial overlap with biodiversity hotspots, suggesting species displacement from high-disturbance zones. Priority conservation areas were delineated by balancing ecological significance and economic activity conflicts. We propose targeted strategies, including buffer zones, seasonal no-take areas, and green shipping technologies, to reconcile conservation with sustainable development. This framework provides actionable insights for enhancing MPA networks in biogeographic transition zones. Full article

In this work, an innovative process chain is set up for the regular provision of fertilization consultation services to farmers for a variety of crops, within a precision agriculture framework. The central hub of this mechanism is a geographic information system (GIS), while a 5 × 5 m point grid is the information carrier. Potential data sources include soil samples, satellite imagery, meteorological parameters, yield maps, and agronomic information. Whenever big data are available per crop, decision-making is supported by machine learning systems (MLSs). All the map data are uploaded to a farm management information system (FMIS) for visualization and storage. The recipe maps are transmitted wirelessly to variable rate technologies (VRTs) for applications in the field. To a large degree, the process chain has been automated with programming at many levels. Currently, four different service modules based on the new process chain are available in the market. Full article

The Huaihe River Basin stands as a vital grain-producing base in China. Predicting the dynamic evolution of its carbon storage (CS) is of great theoretical value and practical significance for maintaining regional ecological security, guaranteeing food production capacity, and coping with climate change. This study established a multi-dimensional analysis framework of “scenario simulation–reservoir assessment–value quantification”. Using a sample of 195 cities, the PLUS-InVEST-GIS method was combined to explore the overall CS, spatial differentiation, and value changes in future scenarios. The results indicate that the following: (1) From 2000 to 2020, CS kept on declining, with cultivated land and forest land being the dominant carbon pools, accounting for over 86% of the total CS. (2) From a “city–grid–raster” perspective, the spatial pattern of high-value hot spots of CS remained stable, and the overall pattern remained unchanged under multi-scenario simulation, yet the overall carbon sink center of gravity shifted to the southwest. (3) The top five driving factors are elevation, slope, NDVI, GDP per capita, and population density, accounting for 77.2% of the total driving force. (4) The carbon sequestration capacity at the county scale continued to weaken, and the overall capacity presented the following order: 2035 Farmland protection scenario (FPS) > 2035 Natural development scenario (NDS) > 2035 Urban development scenario (UDS). The resulting carbon economic losses were USD 2.28 × 10⁸, 4.57 × 10⁸, and 6.90 × 10⁸, respectively. The research results will provide scientific land use decision-making support for the realization of the “double-carbon” goals in the Huaihe River grain-producing area. Full article

Deep learning-based models have achieved considerable success in partial discharge (PD) fault diagnosis for power systems, enhancing grid asset safety and improving reliability. However, traditional approaches often rely on centralized training, which demands significant resources and fails to account for the impact of noisy operating conditions on Intelligent Electronic Devices (IEDs). In a gas-insulated switchgear (GIS), PD measurement data collected in noisy environments exhibit diverse feature distributions and a wide range of class representations, posing significant challenges for trained models under complex conditions. To address these challenges, we propose a Self-Supervised Asynchronous Federated Learning (SSAFL) approach for PD diagnosis in noisy IED environments. The proposed technique integrates asynchronous federated learning with self-supervised learning, enabling IEDs to learn robust pattern representations while preserving local data privacy and mitigating the effects of resource heterogeneity among IEDs. Experimental results demonstrate that the proposed SSAFL framework achieves overall accuracies of 98% and 95% on the training and testing datasets, respectively. Additionally, for the floating class in IED 1, SSAFL improves the F1-score by 5% compared to Self-Supervised Federated Learning (SSFL). These results indicate that the proposed SSAFL method offers greater adaptability to real-world scenarios. In particular, it effectively addresses the scarcity of labeled data, ensures data privacy, and efficiently utilizes heterogeneous local resources. Full article

As rapid urbanization intensified pressure on coastal ecosystems, balancing economic development with ecological preservation remained a critical challenge. This study developed a multi-criteria framework for integrated marine spatial planning and applied it to Shenzhen, China—a rapidly expanding coastal metropolis overseeing 1145 km² of marine territory with a 260.5 km coastline, 61.47% of which has been anthropogenically modified. The methodology combined ecological, environmental, and socioeconomic indicators through a hierarchical evaluation system, using entropy-weighted indices and GIS-based spatial analysis to assess marine space suitability across three functional categories: ecological protection, urban development, and biological resource utilization. The results revealed that 38.53% of Shenzhen’s coastline retains natural geomorphology, while 23.7% annual growth in maritime infrastructure projects since 2015 highlights urgent development pressures. Marine spatial zoning identified priority areas for ecological conservation, urban–industrial expansion, and biological resource utilization through a 1 km × 1 km grid-based analysis, integrating water quality monitoring data. The framework demonstrated how adaptive zoning of underutilized coastal areas could enhance resource efficiency while balancing economic and environmental goals. These findings provide empirical evidence for optimizing marine resource allocation in coastal megacities, emphasizing the importance of data-driven planning anchored in quantitative metrics (e.g., shoreline development intensity and fisheries resource carrying indices) to achieve long-term sustainability. Full article

With the acceleration of urbanization, the diffusion mechanism of urban non-point source (NPS) pollution caused by extreme rainfall is not clear, which leads to high cost and difficulty in water environment treatment. In view of the shortcomings of dynamic diffusion simulations of mesoscale pollution, this paper proposes a simulation framework based on cellular automata, GIS geographic technology, and a two-dimensional shallow water model. Taking the 500 m × 500 m grid as the unit, we explore the migration laws of nitrogen and phosphorus pollutants and the response relationship between pollutant diffusion and land use under extreme rainfall scenarios. The results show that (i) the pollution risk increases significantly with diffusion, with the maximum pollution load in high-risk areas increasing by 181%, and the diffusion rate is positively correlated with the rate of change in rainfall intensity; (ii) forest land has the highest grid pollution load loss rate, whereas the water grid has the highest accumulation rate; (iii) this method can accurately identify the hot spots of pollution diffusion, providing a basis for the precise control of high-risk areas. This study can support the targeted governance of pollution sources and land planning optimization in urban storm and flood management, and help reduce environmental health risks in extreme climates. Full article

Urban parks are essential for enhancing public health and environmental sustainability, as they reduce urban heat, improve air quality, and provide spaces for physical activity. Inequalities in park allocation, however, lead to access discrepancies, disproportionately impacting populations already struggling socially. The spatial disparity between park supply and demand in Busan, South Korea, is examined in this study through a quantitative approach incorporating socio-economic indicators and GIS(Geographic Information System)-based analysis. First, we divided Busan into 100 m × 100 m grid cells and applied a modified Huff model, setting a kind of distance-decay exponent β, to estimate park supply against baseline demand (the planning standard of 6 m² per person), and overlaid a composite need index of six socio-economic indicators to pinpoint underserved areas. Our first stage grid-based arithmetic analysis revealed that 100 of Busan’s 205 communities are undersupplied. Given a composite need index of six socio-economic indicators, sixty-two cells remained imbalanced, and we finally identified the ten communities with the highest need for targeted park provision. The findings indicate that Busan’s park planning policies, aimed at enhancing per capita green space, do not adequately address localized disparities. Accordingly, as opposed to a uniform expansion plan, this study stresses the importance of prioritizing park provision according to community-specific needs. These results suggest that policymakers could enhance public health outcomes and advance social equity by considering socio-economic vulnerabilities when planning cities. Specifically, this research highlights the significance of including environmental justice in urban sustainability frameworks and gives actionable ideas for fair park allocation. Full article

GIS circuit breakers play a critical role in maintaining the reliability of modern power systems. However, mechanical failures, such as spring fatigue, transmission rod jamming, and loosening of structural components, can significantly impact their performance. Traditional diagnostic methods struggle to identify these issues effectively due to the enclosed nature of GIS equipment. This study explores the use of vibration signal analysis, specifically during the closing transient phase of the GIS circuit breaker. The proposed method combines wavelet packet decomposition, rough set theory for feature extraction and dimensionality reduction, and the S_Kohonen neural network for fault type identification. Experimental results demonstrate the robustness and accuracy of the method, achieving a diagnostic accuracy of 96.7% in identifying mechanical faults. Compared with traditional methods, this approach offers improved efficiency and accuracy in diagnosing GIS circuit breaker faults. The proposed method is highly applicable for predictive maintenance and fault diagnosis in power grid systems, contributing to enhanced operational safety and reliability. Full article

Understanding fine-scale spatial ecology is essential for defining effective conservation priorities, particularly at the range margins of vulnerable species. Here, we investigate the spatial ecology and habitat associations of the Persian squirrel (Sciurus anomalus) on Lesvos Island, Greece, representing the species’ westernmost distribution. Using a randomized grid-based survey, we recorded 424 presence records across the island and applied a suite of spatial analyses, including Kernel Density Estimation, Getis-Ord Gi*, and Anselin Local Moran’s I, to detect hotspots, coldspots, and spatial outliers. Binomial Logistic Regression, supported by Principal Component Analysis, identified key ecological drivers of habitat use, while spatial regression models (Spatial Lag and Spatial Error Models) quantified the influence of land-use characteristics and spatial dependencies on hotspot intensity and clustering dynamics. Our results showed that hotspots were primarily associated with olive-dominated and broadleaved landscapes, while coldspots and Low–Low clusters were concentrated in fragmented or degraded habitats, often outside protected areas. Spatial outliers revealed fine-scale deviations from broader patterns, indicating local habitat disruptions and emerging conservation risks not captured by existing Natura 2000 boundaries. Spatial regression confirmed that both hotspot intensity and clustering patterns were shaped by specific land-use features and spatially structured processes. Collectively, our findings underscore the fragmented nature of suitable habitats and the absence of cohesive population cores, reinforcing the need for connectivity-focused, landscape-scale conservation. Full article

This study evaluates the techno-economic feasibility of solar-based green hydrogen potential for off-grid and utility-scale systems in Niger. The geospatial approach is first employed to identify the area available for green hydrogen production based on environmental and socio-technical constraints. Second, we evaluate the potential of green hydrogen production using a geographic information system (GIS) tool, followed by an economic analysis of the levelized cost of hydrogen (LCOH) for alkaline and proton exchange membrane (PEM) water electrolyzers using fresh and desalinated water. The results show that the electricity generation potential is 311,617 TWh/year and 353,166 TWh/year for off-grid and utility-scale systems. The hydrogen potential using PEM (alkaline) water electrolyzers is calculated to be 5932 Mt/year and 6723 Mt/year (5694 Mt/year and 6454 Mt/year) for off-grid and utility-scale systems, respectively. The LCOH production potential decreases for PEM and alkaline water electrolyzers by 2030, ranging between 4.72–5.99 EUR/kgH₂ and 5.05–6.37 EUR/kgH₂ for off-grid and 4.09–5.21 EUR/kgH₂ and 4.22–5.4 EUR/kgH₂ for utility-scale systems. Full article