Search Results (450)

Previous approaches to flexible-lined channel design typically isolate least-cost cross-section optimization from parameter uncertainty, or restrict reliability analysis to specific cases, limited failure modes, and proprietary codes. This paper presents VegyRap, an open-source QGIS-based plugin with an intuitive graphical user interface that unites these traditionally disjointed, sequential tasks into a single computational framework. The tool guides designers sequentially through: (i) terrain-driven longitudinal profile optimization using dynamic programming; (ii) least-cost cross-sectional optimization for riprap and vegetated linings; and (iii) multi-mode probabilistic reliability analysis coupled with dual risk–cost Pareto optimization. To seamlessly handle the stochastic behavior of uncertain variables, the framework features built-in statistical distributions and allows users to flexibly evaluate up to four distinct failure modes: overtopping, erosion, sedimentation, and near-critical flow oscillation. The framework’s capabilities are demonstrated through nine diverse design examples, incorporating benchmark validations against published studies and a comprehensive real-world case study in Wadi Al-Arja, Saudi Arabia. Results highlight that for vegetated channels, a hierarchical two-phase design logic is essential to satisfy both establishment-phase stability (Class E) and long-term conveyance (Class B). While benchmark comparisons show VegyRap achieves consistent cost reductions of 10–15% over traditional methods, the case study demonstrates that deterministic least-cost solutions can carry non-negligible failure probabilities. By utilizing marginal efficiency analysis to identify cost-effective enhancements, the integrated Pareto-based dual optimization produces transparent trade-off surfaces, empowering practitioners to transition from a single least-cost solution to a defensible, risk-calibrated preferred alternative. Full article

A continuous integration framework and methodology for hydrological modeling is proposed that integrates model sensitivity analysis with real-time sensor tasking to prioritize data collection in regions and periods of high hydrological variability and drive model refinement. The Cahaba River Watershed in central Alabama serves as a case study to develop this approach. To this end, a benchmark Soil and Water Assessment Tool (SWAT) model (30 m DEM) was refined with high-resolution spatial datasets in QGIS, including 1 m DEMs, NLCD land cover, and SSURGO soil data. The refined model significantly enhanced subbasin delineation, increasing granularity from 8 to 99 subbasins, thereby improving representation of slope, runoff, and storage variability across heterogeneous landscapes. Sensitivity analyses were performed to evaluate the influence of DEM resolution and curve number (CN) perturbations on hydrologic responses, including retention, flow partitioning, and dominant flow direction. High-resolution DEMs (≤5 m) captured microtopographic features that strongly affect infiltration and surface runoff, while coarser DEMs (≥20 m) systematically underestimated retention and smoothed hydrologic gradients. The higher-resolution DEMs can be used to selectively improve the model at certain hotspots/areas of higher sensitivity. Localized flow simulations demonstrated that fine-scale terrain data substantially improve model realism, with up to 58% greater retention captured in 10 m DEMs compared to 30 m DEMs. The results confirm that aligning sensor placement and model refinement with spatially explicit sensitivity zones enhances both predictive accuracy and computational efficiency. The proposed continuous integration approach provides a scalable pathway for coupling high-resolution modeling with adaptive sensing in watershed management and supports future integration of real-time data assimilation for continuous model improvement. Full article

While parameters such as slope and aspect are routinely considered in solar radiation modelling, the role of terrain or surface roughness remains underexplored, with no universally accepted method for its calculation. This study compares several approaches to quantifying terrain or surface roughness in several geographical information system (GIS) environments (ArcGIS, QGIS, WhiteboxTools, and SAGA GIS) and introduces local fractal dimension, computed using a custom Python script, as an additional metric. The aim is to evaluate the influence of surface roughness on potential solar radiation modelling and to examine its relationship with other terrain parameters. The analysis is based on case studies from both a rugged alpine environment in the Tatra Mountains (Tichá and Kôprová dolina (valleys), Kriváň peak; 944–2467 m a.s.l.) and an urban environment (the city of Poprad, near the High Tatras, Slovakia). The results demonstrate that surface roughness can significantly affect potential solar radiation modelling in areas with high surface variability. The findings are applicable not only to solar radiation studies, but also to other fields of spatial modelling, where incorporating surface roughness can improve the accuracy and robustness of spatial analyses and predictions. Full article

Satellite remote sensing has rapidly evolved from an experimental support tool into a structural component of preventive archaeology and cultural heritage governance. Drawing on scientific publications and policy-oriented grey literature from 2010–2025, this study provides an integrated review of how optical, SAR, and multi-sensor satellite data are used to detect archaeological sites, monitor landscape and structural change, and support risk-informed planning across diverse legal and institutional contexts. A multi-platform workflow combines AI-assisted semantic querying (Consensus), bibliometric searches (Scopus), and the collaborative management and geospatial visualisation of references through Zotero, VOSviewer (1.6.19), and QGIS (3.44)-based literature mapping, thereby linking thematic trends, co-authorship networks, and geographical patterns of research and regulation. The results show non-linear but marked publication growth, a strongly interdisciplinary profile, and the consolidation of international hubs that drive advances in Sentinel-2-based prospection, Landsat and night-time lights urbanisation metrics, and SAR time series for deformation, looting, and conflict-damage mapping. Parallel analysis of grey literature and institutional initiatives (Copernicus Cultural Heritage Task Force, national “extraordinary plans”, regional declarations, and UNESCO guidelines) reveals the codification of satellite Earth observation within rescue archaeology protocols, emergency archaeology, and long-term conservation strategies. Overall, the evidence indicates a transition towards data-driven, multi-sensor, and multi-scalar research, underpinned by open satellite data, reproducible workflows, and AI-supported evidence synthesis. Full article

Outdoor ecological practice is essential for cultivating ecological literacy; however, there is currently a relative lack of comprehensive outdoor practical teaching case designs for class-based teaching. This study describes the design of an experimental teaching case for ecological education involving UAV-based field data collection. For the scheme, we selected the Xinhui Tangerine Peel Germplasm Resources Conservation Center in Jiangmen City, Guangdong Province as the study area, utilizing the DJI Phantom 4 RTK drone, which serves as the equipment for experimental teaching. The experiment is structured into three phases: indoor preparation, field execution, and data processing. Students from four groups collaboratively conducted aerial surveys across 24 partitioned plots, with flight altitudes stratified between groups to ensure safety and data integrity. (1) In the indoor preparation phase, appropriate single-flight operational units were defined. QGIS software (version 3.26.2) was employed for zonal mission planning, and suitable flight altitudes were estimated using contour data. (2) Field experiment phase. This involved conducting a comprehensive survey of the on-site environment, selecting suitable takeoff and landing points, dividing students into teams to carry out UAV-image-acquisition tasks, and assigning different altitudes for flight routes among the teams. (3) After the fieldwork, students processed imagery using Agisoft Metashape (version 2.0.1) to generate orthomosaics and digital surface models, and engaged in ecological interpretation of the results. The experimental design ensured orderly execution, complete data coverage, and active student participation. The results indicate the approach effectively enhanced students’ UAV operational skills, outdoor problem-solving abilities, and teamwork capabilities, while deepening their ecological understanding through real-world inquiry. This case provides a replicable model for integrating UAV technology into ecological education, contributing to the transformation of ecological awareness into actionable practice. Full article

Underwater cultural heritage represents a fragile and largely unexplored component of historical landscapes, particularly in dynamic fluvial and coastal environments. Despite increasing international attention to its protection, the spatial identification of submerged heritage remains methodologically challenging. This study proposes a geo-historical approach that integrates historical cartography and Geographic Information Systems (GIS) to identify areas of high archaeological potential in underwater contexts. Focusing on the Douro River in Porto (Portugal), a UNESCO World Heritage city with a long maritime and fluvial history, the research analyses a set of key historical maps from the eighteenth and nineteenth centuries, complemented by documentary and archaeological sources. These cartographic materials were georeferenced and critically assessed in QGIS, enabling the digitisation of features associated with land–water interaction, navigation hazards, port infrastructures, and military defences. The resulting spatial dataset was used to generate an interpretative map and a kernel density model highlighting potential underwater heritage hotspots along the riverbed and riverbanks. The findings identify several priority zones, including the river mouth, historic quays, former shipbuilding areas, and sectors linked to nineteenth-century defensive structures. While the study does not include in situ verification, it demonstrates the value of historical maps as predictive tools for guiding targeted underwater surveys and proposes a transferable, cost-effective framework for heritage prospection and management in historically active fluvial–estuarine settings. Full article

Mapping land cover, monitoring its changes, and simulating future alterations are essential tasks for sustainable land management. These processes enable accurate assessment of environmental impacts, support informed policymaking, and assist in the planning needed to mitigate risks related to urban expansion, deforestation, and climate change. This study proposes a GeoAI-based framework leveraging Multilayer Perceptron (MLP), a class of Artificial Neural Networks (ANNs), to predict land cover changes in the Aosta Valley region (NW Italy). The model uses Copernicus Earth Observation data, specifically Sentinel-1 and Sentinel-2 imagery, and is trained and validated on land cover maps derived from different time periods previously validated with ground truth data. The objective is to provide a predictive tool capable of simulating potential future landscape configurations, supporting proactive regional land use planning including regulatory constraints under the current land use plan. Model performance is evaluated using accuracy metrics. The land cover classification methodology follows established approaches in the scientific literature, adapted to the specific geomorphological characteristics of the Aosta Valley. To explore and visualize potential future land cover transitions, Sankey and chord diagrams are used in combination with zonal statistics and thematic plots. These provide detailed insights into the intensity, direction, and magnitude of landscape dynamics. Training data were stratified-sampled across the study area, covering a diverse set of land cover classes to ensure robustness and generalization of the MLP model. This GeoAI approach offers a scalable and replicable methodology for anticipating land cover dynamics, identifying vulnerable areas, and informing adaptive environmental management strategies at the regional scale, while simultaneously considering the latest urban planning regulations. Full article

This study presents a GIS-driven multi-criteria decision analysis (MCDA) framework for regional suitability screening of data center (DC) development in the United Kingdom. The methodology integrates spatial exclusion of constrained zones, raster standardization of climate and infrastructure indicators, Analytic Hierarchy Process (AHP) weighting, and Weighted Linear Combination (WLC) to generate a national suitability surface at 1 km resolution. Climate indicators (temperature, air frost days, humidity, and solar radiation) and infrastructure and environmental constraint indicators (grid access, transport proximity, environmental protections, and population distribution) were standardized and combined within a GIS-based decision framework. Hard constraints such as protected areas and flood zones were applied through binary exclusion, while climatic and infrastructure factors were evaluated using weighted suitability scoring. Five candidate regions were identified from the suitability analysis: the Scottish Highlands, Northeast England, Southwest England (Cornwall), Northwest England, and Eastern England. These regions were further evaluated against key requirements including power infrastructure accessibility, workforce and connectivity availability, and exposure to environmental and hydro-climate constraints. The final comparison identified Lincolnshire as the most suitable region due to strong grid accessibility, favorable composite climate suitability, adequate population proximity, and limited overlap with protected areas. The proposed framework demonstrates how climate-driven cooling suitability can be integrated with infrastructure accessibility and environmental constraints within a unified spatial decision model for national-scale digital infrastructure planning. Full article

Rapid urbanization has transformed the face of Texas by converting agricultural and natural lands into expanding built-up areas. This study analyzes and simulates land-use and land-cover (LULC) changes in Kaufman County, Texas, one of the fastest-growing counties in the United States, using a hybrid Cellular Automata–Artificial Neural Network (CA–ANN) model within the Quantum Geographic Information System (QGIS) Modules for Land-Use Change Evaluation (MOLUSCE) framework. Multitemporal NLCD datasets (2001, 2011, and 2021) and six spatial drivers: Elevation, Slope, Aspect, Distance from Roads and Rivers, and Built-up Density were used in the modeling framework. Transition relationships were calibrated using the 2001–2011 LULC data, and the model was validated by simulating the 2021 LULC map from the 2011 baseline. The calibrated model was then used to simulate future LULC scenarios for 2031, 2041, and 2051. Model validation yielded an overall Kappa value of 0.84 and a correctness of 90.9%, indicating high similarity between the observed and simulated maps. The results indicate simulated urban expansion, with built-up areas increasing by nearly 30% by 2051 at the expense of cropland and open areas, with forest and water bodies slightly increasing, and wetlands remaining stagnant. The CA–ANN model effectively captured the nonlinear, spatially dependent land-transition patterns using open-source tools. These findings provided useful information for sustainable land-use planning and environmental management, with the potential to incorporate spatial modeling into regional development strategies in rapidly urbanizing areas of Texas. Full article

This paper examines public architecture in Baghdad during the late nineteenth and early twentieth centuries, focusing on how public buildings contributed to the formation of urban centrality and how this process interacted with local architectural practices. Rather than approaching public construction solely through administrative or ideological frameworks, the study conceptualizes public buildings as structuring components in the reconfiguration of the urban fabric. Methodologically, the research adopts a two-stage, multi-scalar approach. First, public buildings in Beirut, Damascus, and Baghdad are identified and comparatively analyzed using QGIS-based spatial analysis, employing Kernel Density Estimation and DBSCAN clustering to examine patterns of spatial concentration, distribution, and relationships with major urban axes. This comparative stage establishes a comparative spatial framework for understanding urban centrality in provincial capitals. In the second stage, Baghdad is examined as a focused case study through building-scale architectural analysis, incorporating plan organization, construction techniques, material use, and environmental adaptation based on archival documents, historical maps, and visual sources. The results indicate that public buildings in Baghdad were not isolated institutional entities but integral components in the formation of new urban focal areas structured along river-oriented and infrastructural axes. Architecturally, these buildings exhibit a hybrid character, combining standardized public building programs with locally embedded materials, construction methods, and spatial adaptations. The study concludes that public architecture in late Ottoman Baghdad emerged through a negotiated process between centralized planning principles and local architectural knowledge, producing a distinct yet contextually grounded form of urban centrality. Full article

Global warming has significantly intensified the risks of summer heatwaves, making outdoor thermal comfort during extreme heat periods a critical research focus. Under centralized rural village reconstruction policies, traditional settlements are being replaced by regularized modern communities characterized by new materials and standardized layouts. However, the impact of these morphological transitions on the micro-scale thermal environment remains under-researched, with a notable lack of comparative perspectives between traditional organic and modern standardized typologies. This study identifies six representative zones based on spatial configuration. By integrating UAV photogrammetry (Pix4Dmapper v4.5), AutoCAD 2019, and QGIS (v3.22), morphological characteristics were quantified, followed by microclimate simulations using ENVI-met v5.9. The results reveal that while peak daytime Physiological Equivalent Temperature (PET) in the standardized zones (49.2–51.8 °C) is slightly lower than in traditional zones (53.5–55.2 °C), a phenomenon of thermal homogenization emerges in the former. Specifically, values in standardized zones are highly concentrated around the median (53.5 °C), contributing to a significant upward trend in the minimum PET values, with nearly all sampling points exceeding 47.0 °C. Quantitative analysis identifies green coverage and perviousness as primary cooling drivers, while spatial openness and imperviousness promote thermal homogenization. In contrast, traditional zones retain critical cool refuges due to their spatial heterogeneity. This research provides an empirical foundation and quantitative reference for understanding the thermal performance differences across different rural spatial typologies. The findings offer insights for planners to optimize street layouts and shading strategies, ultimately mitigating heat stress and fostering climate-resilient modern countryside development. Full article

Systematic, spatially explicit tree inventories are increasingly implemented in cities worldwide, as they are crucial for evidence-based green infrastructure planning. Currently, different approaches are adopted, which differ in methodological framework and parameter standardization, limiting comparative assessments and coordinated monitoring. This study presents a replicable protocol for a field-based digital street tree census, applied in a densely built central area and in a low-density suburban area of Rome. Field surveys documented a set of 15 parameters, including species identity, dendrometric and tree pit parameters, acquired using open-source QGIS/QField tools. Subsequent analysis evaluated floristic diversity, population structure, and climate suitability at the neighborhood scale, enabling the identification of context-specific vulnerabilities. The testing of the methodology shown in this work involved 13,017 georeferenced tree pits, pointing out substantial pit restoration needs and insufficient soil conditions in the most densely urbanized area, whereas the suburban area shows optimal conditions with extensive road verge green spaces. Joint interpretation of the considered parameters reveals that high floristic diversity alone does not guarantee climate resilience: high-diversity neighborhoods can exhibit substantial non-climate-resilient species and limited alignment with local species recommendations, demonstrating that comprehensive evaluation of street tree populations requires integrated analysis. The operationalized protocol establishes a replicable, municipally scalable methodological framework, providing policymakers with fine-scale, actionable insights enabling differentiated urban forestry strategies addressing both infrastructure deficits and long-term species climate suitability. Full article

Land use and land cover change (LULC) driven by agricultural expansion has become a major environmental challenge in tropical regions, particularly in coffee-producing landscapes, where economic growth often conflicts with forest conservation. This study integrates multi-temporal land cover analysis and future scenario modeling to assess LULC dynamics associated with coffee expansion in the district of Ocumal, in the Amazona Peru. Land cover classes were identified using a Random Forest classification approach applied to Landsat imagery from 2000, 2010, and 2020 processed in Google Earth Engine (GEE), while future scenarios for 2030 and 2040 were simulated using the MOLUSCE plugin in QGIS 2.18. Cross-tabulation matrices and annual rates of change were calculated using IDRISI SELVA 17.0. The results show increases of 12.6% and 7.4% in coffee crop area during 2000–2010 and 2010–2020, respectively, alongside a significant reduction in forest and grassland cover (−5.06% and −2.10% during 2010–2020), mainly driven by agricultural expansion facilitated by transportation infrastructure and market accessibility. This study contributes to the international literature by providing empirical evidence from the Peruvian Amazon on the long-term impacts of coffee expansion on land use and land cover, supporting land-use planning and sustainable agriculture in tropical regions. Full article

Air pollution from industrial point sources remains a major concern in urban environments, highlighting the need for accessible tools that support both education and preliminary environmental assessment. This study presents the development and intercomparison of an open-source, QGIS-based geospatial model for simulating atmospheric pollutant dispersion from fixed point sources using the Gaussian plume formulation. The model integrates emission parameters, meteorological conditions, and terrain data within a fully spatial workflow implemented through the QGIS graphical modeler, enabling the generation of ground-level concentration fields without advanced programming expertise. Dispersion is simulated with and without inclusion of a ground reflection term, allowing comparative analysis of boundary condition effects. The model was applied to a representative urban industrial source at the National University of Science and Technology POLITEHNICA Bucharest, using CO₂ emissions treated as a passive tracer. Model outputs were evaluated through descriptive statistics and quantitative comparison with two established open-source Gaussian plume implementations developed in Python. Ground reflection leads to an increase of approximately 60% in modeled near-surface concentrations, particularly in the upper tail of the distribution, underscoring its importance for screening-level exposure assessment. The proposed model provides a transparent, reproducible, and user-friendly framework suitable for teaching activities, rapid screening analyses, and exploratory air quality assessments. Full article

Seagrass meadows are essential coastal ecosystems that provide key ecological services, including carbon sequestration, sediment stabilization, and shoreline protection. Increasing threats from natural and anthropogenic stressors highlight the need for efficient, reproducible, and non-invasive monitoring solutions. This study evaluates the performance of low-cost commercial drones for seagrass assessment in shallow coastal waters, with an emphasis on freely accessible mission-planning and photogrammetric workflows. Field surveys were conducted along the Calabrian coast (southern Italy), where automated flight paths were generated using the software WaypointMap, and high-resolution orthophotos were generated using the WebODM software and subsequently analyzed in QGIS for seagrass patch detection, mapping, and surface estimation. The methodological pipeline is described in detail to facilitate full reproducibility. Compared with traditional diver-based methods, this workflow offers faster data collection, broader spatial coverage, and minimal environmental disturbance. Although some limitations remain, the results demonstrate that combining low-cost drones with open-source tools provides a practical and scalable solution for routine monitoring. This approach has strong potential for integration into routine coastal habitat assessment, supports early impact detection, and contributes to evidence-based conservation and management strategies. Full article