Search Results (1,655)

This study investigates the spatial relationship between population distribution and tree cover loss in Nepal from 2000 to 2020, using satellite-based forest cover and population data along with statistical and geospatial analysis. Two statistical methods—linear regression (LR) and Geographically Weighted Regression (GWR)—were used to assess the influence of population on forest cover change. The correlation between total population and forest loss at the national level suggested little to no direct impact of population growth on forest loss. However, sub-national analysis revealed localized forest degradation, highlighting the importance of spatial and regional assessments to uncover land cover changes masked by national trends. While LR showed a weak national-level correlation, GWR revealed substantial spatial variation, with the coefficient of determination values increasing from 0.21 in 2000 to 0.59 in 2020. In some regions, local R² exceeded 0.75 during 2015 and 2020, highlighting emerging hotspot clusters where population pressure is strongly linked to deforestation, especially along major infrastructure corridors. Using very high-resolution spatial data enabled pixel-level analysis, capturing fine-scale deforestation patterns, and confirming hotspot accuracy. Overall, the findings emphasize the value of spatially explicit models like GWR for understanding human–environment interactions guiding targeted land use planning to balance development with environmental sustainability in Nepal. Full article

Identifying correspondences of road segments in different road networks, namely road-network matching, is an essential task for road network-centric data processing such as data integration of road networks and data quality assessment of crowd-sourced road networks. Traditional road-network matching usually relies on feature engineering and parameter selection of the geometry and topology of road networks for similarity measurement, resulting in poor performance when dealing with dense and irregular road network structures. Recent development of graph neural networks (GNNs) has demonstrated unsupervised modeling power on road network data, which learn the embedded vector representation of road networks through spatial feature induction and topology-based neighbor aggregation. However, weighting spatial information on the node feature alone fails to give full play to the expressive power of GNNs. To this end, this paper proposes a Spatial Pattern-aware Graph EMbedding learning method for road-network matching, named SP-GEM, which explores the idea of spatially-explicit modeling by identifying spatial patterns in neighbor aggregation. Firstly, a road graph is constructed from the road network data, and geometric, topological features are extracted as node features of the road graph. Then, four spatial patterns, including grid, high branching degree, irregular grid, and circuitous, are modelled in a sector-based road neighborhood for road embedding. Finally, the similarity of road embedding is used to find data correspondences between road networks. We conduct an algorithmic accuracy test to verify the effectiveness of SP-GEM on OSM and Tele Atlas data. The algorithmic accuracy experiments show that SP-GEM improves the matching accuracy and recall by at least 6.7% and 10.2% among the baselines, with high matching success rate (>70%), and improves the matching accuracy and recall by at least 17.7% and 17.0%, compared to the baseline GNNs, without spatially-explicit modeling. Further embedding analysis also verifies the effectiveness of the induction of spatial patterns. This study not only provides an effective and practical algorithm for road-network matching, but also serves as a test bed in exploring the role of spatially-explicit modeling in GNN-based road network modeling. The experimental performances of SP-GEM illuminate the path to develop GeoEmbedding services for geospatial applications. Full article

Contrary to what is represented in geospatial databases, places are dynamic and shaped by events. Point clustering analysis commonly assumes events occur in an empty space and therefore ignores geospatial features where events take place. This research introduces relational density, a novel concept redefining density as relative to the spatial structure of geospatial features rather than an absolute measure. Building on this, we developed Space-Time Plume, a new algorithm for detecting and tracking evolving event clusters as smoke plumes in space and time, representing dynamic places. Unlike conventional density-based methods, Space-Time Plume dynamically adapts spatial reachability based on the underlying spatial structure and other zone-based parameters across multiple temporal intervals to capture hierarchical plume dynamics. The algorithm tracks plume progression, identifies spatiotemporal relationships, and reveals the emergence, evolution, and disappearance of event-driven places. A case study of crime events in Dallas, Texas, USA, demonstrates the algorithm’s performance and its capacity to represent and compute criminogenic places. We further enhance metaball rendering with Perlin noise to visualize plume structures and their spatiotemporal evolution. A comparative analysis with ST-DBSCAN shows Space-Time Plume’s competitive computational efficiency and ability to represent dynamic places with richer geographic insights. Full article

The integration of traditional geomorphological approaches with advanced artificial intelligence techniques represents a promising frontier in flood risk assessment for arid regions. This study presents a comprehensive analysis of the Wadi Ranuna basin in Medina, Saudi Arabia, combining detailed morphometric parameters with advanced Geospatial Artificial Intelligence (GeoAI) algorithms to enhance flood susceptibility modeling. Using digital elevation models (DEMs) and geographic information systems (GISs), we extracted 23 morphometric parameters across 67 sub-basins and applied XGBoost, Random Forest, and Gradient Boosting (GB) models to predict both continuous flood susceptibility indices and binary flood occurrences. The machine learning models utilize morphometric parameters as input features to capture complex non-linear interactions, including threshold-dependent relationships where the stream frequency impact intensifies above 3.0 streams/km², and the compound effects between the drainage density and relief ratio. The analysis revealed that the basin covers an area of 188.18 km² with a perimeter of 101.71 km and contains 610 streams across six orders. The basin exhibits an elongated shape with a form factor of 0.17 and circularity ratio of 0.23, indicating natural flood-moderating characteristics. GB emerged as the best-performing model, achieving an RMSE of 6.50 and an R² value of 0.9212. Model validation through multi-source approaches, including field verification at 35 locations, achieved 78% spatial correspondence with documented flood events and 94% accuracy for very high susceptibility areas. SHAP analysis identified the stream frequency, overland flow length, and drainage texture as the most influential predictors of flood susceptibility. K-Means clustering uncovered three morphometrically distinct zones, with Cluster 1 exhibiting the highest flood risk potential. Spatial analysis revealed 67% of existing infrastructure was located within high-risk zones, with 23 km of major roads and eight critical facilities positioned in flood-prone areas. The spatial distribution of GBM-predicted flood susceptibility identified high-risk zones predominantly in the central and southern parts of the basin, covering 12.3% (23.1 km²) of the total area. This integrated approach provides quantitative evidence for informed watershed management decisions and demonstrates the effectiveness of combining traditional morphometric analysis with advanced machine learning techniques for enhanced flood risk assessment in arid regions. Full article

Rental accommodations near European university campuses keep rising in price, while listings remain scattered and opaque. This paper proposes a solution that overcomes these issues by integrating real-time open listing ingestion, zone-level geospatial enrichment, and a reinforcement-learning recommender into one streamlined analysis pipeline. On demand, the system updates price statistics for most districts in Timișoara and returns five budget-safe offers in a short amount of time. By combining adaptive ranking with new spatial metrics, it significantly cuts search time and removes irrelevant offers in pilot trials. Moreover, this implementation is fully open-data, open-source, and free, designed specifically for students to ensure accessibility, transparency, and cost efficiency. Full article

The future of marine sustainability depends on public understanding and trust in the policy recommendations that emerge from scientific research. For common pool marine resource decisions made by the people who depend on these resources for their food, employment, and economic future, understanding the current status of these marine systems and change is essential to ensure these resources will persist into the future. As such, the informal learning infrastructure is essential to increasing marine science literacy in a changing world. This mixed-methods research study analyzed the distribution and accessibility of marine science education and research across Alaska’s five geographic regions. Using the PRISMA framework, we synthesized data from 198 institutions and analyzed peer-reviewed literature on marine ecosystems to identify geographic and thematic gaps in access to informal science learning and research focus. In parallel, we undertook geospatial analysis and resource availability to describe the distribution of resources, types of informal learning infrastructure present across the state, regional presence, and resources to support informal marine science learning opportunities. Findings from this multifactor research revealed a concentration of resources in urban hubs and a lack of consistent access to learning resources for rural and Indigenous communities. The configurative literature review of 9549 publications identified topical underrepresentation of the Bering Sea and Aleutian Islands, as well as a lack of research on seabirds across all regions. Considered together, these results recommend targeted investments in rural engagement with marine science programming, culturally grounded partnerships, and research diversification. This review concludes that disparities in learning resource support and government-funded priorities in marine wildlife research have created conditions that undermine the local people’s participation in the sustainability of sensitive resources and are likely exacerbating declines driven by rapid change in Arctic and sub-Arctic waters. Full article

This study investigates rural settlement patterns and land suitability in Rwanda’s Western Highlands, a mountainous region highly vulnerable to geohazards like landslides and flooding. Its primary aim is to inform sustainable, climate-resilient development planning in this fragile landscape. We employed high-resolution satellite imagery, a Digital Elevation Model (DEM), and comprehensive geospatial datasets to analyze settlement distribution, using Thiessen polygons for influence zones and Kernel Density Estimation (KDE) for spatial clustering. The Analytic Hierarchy Process (AHP) was integrated with the GeoDetector model to objectively weight criteria and analyze settlement pattern drivers, using population density as a proxy for human pressure. The analysis revealed significant spatial heterogeneity in settlement distribution, with both clustered and dispersed forms exhibiting distinct exposure levels to environmental hazards. Natural factors, particularly slope gradient and proximity to rivers, emerged as dominant determinants. Furthermore, significant synergistic interactions were observed between environmental attributes and infrastructure accessibility (roads and urban centers), collectively shaping settlement resilience. This integrative geospatial approach enhances understanding of complex rural settlement dynamics in ecologically sensitive mountainous regions. The empirically grounded insights offer a robust decision-support framework for climate adaptation and disaster risk reduction, contributing to more resilient rural planning strategies in Rwanda and similar Central African highland regions. Full article

Ancient temple complexes in China’s mountainous landscapes exemplify a profound synthesis of environmental adaptation and cultural expression. This research investigates the spatial logic underlying the Wudang Mountain temple complex—a UNESCO World Heritage site—through integrated geospatial analysis of environmental factors. Using GIS-based modeling, GeoDetector, and regression analysis, we systematically assess how terrain, hydrology, climate, vegetation, and soil conditions collectively influenced site selection. The results reveal a clear hierarchical clustering pattern, with dense temple cores in the southwestern highlands, ridge-aligned belts, and a dominant southwest–northeast orientation that reflects intentional alignment with mountain ridgelines. Temples consistently occupy zones with moderate thermal, hydrological, and vegetative stability while avoiding geotechnical extremes such as lowland humidity or unstable slopes. Regression analysis confirms that site preferences vary across temple types, with soil pH, porosity, and bulk density emerging as significant influencing factors, particularly for cliffside temples. These findings suggest that ancient temple planning was not merely a passive response to sacred geography but a deliberate process that actively considered terrain, climate, soil, and other environmental factors. While environmental constraints strongly shaped spatial decisions, cultural and symbolic considerations also played an important role. This research deepens our understanding of how environmental factors influenced the formation of historical landscapes and offers theoretical insights and ecologically informed guidance for the conservation of mountain cultural heritage sites. Full article

This study examines the impact of geomagnetic disturbances quantified by the Kp and Dst indices on the accuracy of single-frequency GPS positioning across mid-latitudes and the equatorial zone, with a focus on temporal and spatial positioning errors variability. GNSS data from a globally distributed network of 14 IGS stations were analyzed for September 2017, featuring significant geomagnetic activity. The selection of stations encompassed equatorial and mid-latitude regions (approximately ±45°), strategically aligned with the distribution of the Dst index during geomagnetic storms. Satellite navigation data were processed using RTKLIB software in standalone mode with standardized atmospheric and orbital corrections. The GPS was chosen over GLONASS following preliminary testing, which revealed a higher sensitivity of GPS positional accuracy to variations in geomagnetic indices such as Kp and Dst, despite generally lower total error magnitudes. The ECEF coordinate system calculates the total GPS error as the vector sum of deviations in the X, Y, and Z axes. Statistical evaluation was performed using One-Way Repeated Measures ANOVA to determine whether positional error variances across geomagnetic activity phases were significant. The results of the variance analysis confirm that the variation in the total GPS positioning error is non-random and can be attributed to the influence of geomagnetic storms. However, regression analysis reveals that the impact of geomagnetic storms (quantified by Kp and Dst) displays spatiotemporal variability, with no consistent correlation to GPS positioning error dynamics. The findings, as well as the developed methodology, have qualitative implications for GNSS-dependent operations in sensitive sectors such as navigation, timing services, and geospatial monitoring. Full article

Agriculture faces a dual challenge in the context of climate change, serving as both a significant contributor to greenhouse gas (GHG) emissions and a sector highly vulnerable to its impacts. Addressing this requires a transition toward climate-resilient agriculture (CRA). Emerging technologies, including geospatial tools (e.g., Geographic Information Systems (GISs) and remote sensing (RS)), as well as artificial intelligence (AI), offer promising methods to support this transition. However, their individual capabilities, limitations, and appropriate applications are not always well understood or clearly delineated in the literature. A common issue is the frequent overlap between GISs and RS, with many studies assessing GIS contributions while concurrently employing RS techniques, without explicitly distinguishing between the two (or vice versa). In this sense, the objective of this review is to conduct a critical analysis of the existing state of the art in terms of the distinct roles, limitations, and complementarities of GISs, RS, and AI in advancing CRA, guided by an original definition we propose for CRA (structured around three key dimensions and their corresponding targets). Furthermore, this review introduces a synthesis matrix that integrates both the individual contributions and the synergistic potential of these technologies. This synergy-focused matrix offers not just a summary, but a practical decision support matrix that could be used by researchers, practitioners, and policymakers in selecting the most appropriate technological configuration for their objectives in CRA-related work. Such support is increasingly needed, especially considering that RS and AI have experienced exponential growth in the past five years, while GISs, despite being the more established “big brother” among these technologies, remain underutilized and is often insufficiently understood in agricultural applications. Full article

The present study implements novel innovative geostatistical imaging using precision agriculture (PA) under sugarbeet field conditions. Two driplines layout designs (d.l.d.) and soil water content (SWC)–irrigation treatments (A: d.l.d. = 1.00 m driplines spacing × 0.50 m emitters inline spacing; B: d.l.d. = 1.50 m driplines spacing × 0.50 m emitters inline spacing) were applied, with two subfactors of clay loam and clay soils (laboratory soil analysis) for modeling (evaluation of seven models) TDR multi-sensor network measurements. Different sensor calibration methods [method 1(M1) = according to factory; method 2 (M2) = according to Hook and Livingston] were applied for the geospatial two-dimensional (2D) imaging of accurate GIS maps of rootzone soil moisture profiles, soil apparent dielectric Ka profiles, and granular and hydraulic parameters profiles, in multiple soil layers (0–75 cm depth). The modeling results revealed that the best-fitted geostatistical model for soil apparent dielectric Ka was the Gaussian model, while spherical and exponential models were identified to be the most appropriate for kriging modelling, and spatial and temporal imaging was used for accurate profile SWC ${\theta v}_{TDR}$(m³·m⁻³) M1 and M2 maps using TDR sensors. The resulting PA profile map images depict the spatio-temporal soil water and apparent dielectric Ka variability at very high resolutions on a centimeter scale. The best geostatistical validation measures for the PA profile SWC ${\theta v}_{TDR}$ maps obtained were MPE = −0.00248 (m³·m⁻³), RMSE = 0.0395 (m³·m⁻³), MSPE = −0.0288, RMSSE = 2.5424, ASE = 0.0433, Nash–Sutcliffe model efficiency NSE = 0.6229, and MSDR = 0.9937. Based on the results, we recommend d.l.d. A and sensor calibration method 2 for the geospatial 2D imaging of PA GIS maps because these were found to be more accurate, with the lowest statistical and geostatistical errors, and the best validation measures for accurate profile SWC imaging were obtained for clay loam over clay soils. Visualizing sensors’ soil moisture results via geostatistical maps of rootzone profiles have practical implications that assist farmers and scientists in making informed, better and timely environmental irrigation engineering decisions, to save irrigation water, increase water use efficiency and crop production, optimize energy, reduce crop costs, and manage water resources sustainably. Full article

Land subsidence significantly threatens vulnerable coastal environments. This study aims to explore how Semarang’s government, local communities, and researchers address land subsidence and its role in exacerbating flood risk, against the backdrop of ongoing efforts within flood risk governance. Employing an integrated mixed-methods approach, the research combined quantitative geospatial analysis (InSAR and land cover change detection) with qualitative socio-political and governance analysis (interviews, FGDs, field observations). Findings show high subsidence rates in Semarang. Line of sight displacement measurements revealed a continuous downward trend from late 2014 to mid-2023, with rates varying from −8.8 to −10.1 cm/year in Karangroto and Sembungharjo. Built-up areas concurrently expanded from 21,512 hectares in 2017 to 23,755 hectares in 2023, largely displacing cropland and tree cover. Groundwater extraction was identified as the dominant driver, alongside urbanization and geological factors. A critical disconnect emerged: community views focused on flooding, often overlooking subsidence’s fundamental role as an exacerbating factor. The study concluded that multi-level collaboration, improved risk communication, and sustainable land management are critical for enhancing urban coastal resilience against dual threats of subsidence and flooding. These insights offer guidance for similar rapidly developing coastal cities. Full article

Plant–endophyte symbioses are widespread in grasslands. While symbiotic interactions often provide hosts with major fitness enhancements, the role of the endophyte Alternaria oxytropis, which produces swainsonine in locoweeds (Oxytropis and Astragalus spp.), remains enigmatic. We compared endophyte-infected (E+) and endophyte-free (E−) plants of three main Chinese locoweed species (O. kansuensis, O. glabra, and O. ochrocephala) under controlled conditions, and analyzed environmental factors at locoweed poisoning hotspots for herbivores. The results demonstrated significant species-specific effects: E+ plants of O. glabra and O. ochrocephala exhibited 26–39% reductions in biomass, net photosynthetic rate, and stomatal conductance, with elevated CO₂ levels, while O. kansuensis showed no measurable impacts. Swainsonine concentrations were 16–20 times higher in E+ plants (122.6–151.7 mg/kg) than in E− plants. Geospatial analysis revealed that poisoning hotspots for herbivores consistently occurred in regions with extreme winter conditions (minimum temperatures ≤ −17 °C and precipitation ≤ 1 mm during the driest month), suggesting context-dependent benefits under abiotic stress. These findings suggest that the ecological role of A. oxytropis may vary depending on both host species and environmental context, highlighting a trade-off between growth costs and potential stress tolerance conferred by A. oxytropis. The study underscores the need for field validation to elucidate the adaptive mechanisms maintaining this symbiosis in harsh environments. Full article

This study presents a comprehensive coastal vulnerability assessment framework by integrating a range of physical, environmental, and climatic parameters. Key criteria include shoreline changes, coastal geomorphology, slope, elevation, bathymetry, tidal range, wave height, shoreline change rates, population density, land use and land cover (LULC), temperature, precipitation, and coastal inundation factors. By synthesizing these parameters with real-time coastal monitoring data, the framework enhances the accuracy of regional risk evaluations. The study employs Multi-Criteria Spatial Analysis (MCSA) to systematically assess and prioritize vulnerability indicators, enabling a data-driven and objective approach to coastal zone management. The findings aim to support coastal planners, policymakers, and stakeholders in designing effective, sustainable adaptation and mitigation strategies for regions most at risk. This integrative approach not only strengthens the scientific understanding of coastal vulnerabilities but also serves as a valuable tool for informed decision-making under changing climate and socioeconomic conditions. Full article

The rising demand for freshwater, driven by population growth, economic development, and climate change, necessitates proactive watershed management. This study focuses on prioritizing the watersheds of the Doon Valley in the Indian Himalayan Region (IHR) using geospatial techniques. It involves a detailed morphometric analysis incorporating hydrological and topographical parameters, ranking the watersheds using the compound factor value (CFV), and prioritizing them based on the given CFV. The Doon Valley watersheds exhibit dendritic to parallel drainage patterns and moderate relief. The study identifies the Suswa watershed as the most susceptible, necessitating urgent conservation attempts to mitigate soil erosion and ensure sustainable land use. In contrast, the Song watershed, characterized by steep slopes and high relief, requires targeted management strategies to control rapid runoff and prevent potential flooding. The Asan watershed, with a medium priority classification, also requires intervention to prevent ecological degradation. Prioritization based on the CFV provides a strategic framework for targeted management, offering valuable insights for policymakers and planners. This research supports sustainable watershed management by guiding effective conservation practices and addressing the specific needs of each watershed. Full article