Search Results (2,079)

This article introduces the design and implementation of an Unmanned Surface Vehicle (USV), named “AquaMIB”, which introduces a novel and integrated approach for real-time and autonomous water quality monitoring in aquatic environments. The system integrates modular hardware and software, combining sensors for temperature, pH, conductivity, dissolved oxygen, and oxidation reduction potential with GPS, LiDAR, a digital compass, communication modules, and a dedicated power unit. Software components include Python on a Raspberry Pi for navigation and control, C on an Atmega 324P for sensing, C++ on an Arduino Uno for remote control, and C#/JavaScript for the web-based control center. Users assign task points, and the USV autonomously navigates, collects data, and transmits it via RESTful API. Field trials showed 96.5% navigation accuracy over 2.2 km, with 66% of task points reached within 3 m. A total of 120 measurements were processed in real time and visualized as GIS-based spatial maps. The system demonstrates a cost-effective, modular solution for aquatic monitoring. The system’s ability to generate real-time GIS maps enables immediate identification of environmental anomalies, transforming raw sensor data into an actionable decision-support tool for aquatic management. Full article

The subject of this research is the exploration of the potential of remote sensing and Geographic Information Systems (GIS) for basin-scale spatio-temporal monitoring of drought and its impacts in the Ukrina River Basin, Bosnia and Herzegovina (BH), during the last decade (2015–2024). The aim is to integrate meteorological, hydrological, agricultural, and socio-economic drought signals and to delineate areas of long-term drought exposure. Meteorological drought was evaluated using CHIRPS precipitation and the Standardized Precipitation Index (SPI) calculated at 1-, 3-, 6-, and 12- month accumulation scales using Gamma fitting and a fixed long term reference period; hydrological drought was examined using available water-level records complemented by the Standardized Water Level Index (SWLI) and supported by correspondence with standardized ERA5-Land runoff anomalies; agricultural drought was mapped using remote sensing indices—the Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI)—calculated from MODIS satellite data; and socio-economic effects were assessed using municipal crop-production statistics (2015–2019). The results indicate that drought conditions were most pronounced in 2015, 2017, 2021, and especially 2022, showing consistent agreement between precipitation deficits, hydrological responses, and vegetation stress, while 2016, 2018–2020, 2023, and 2024 were generally more favorable. As a key novelty, a persistent drought-prone zone was delineated by intersecting drought-affected areas across major episodes, providing a basin-scale identification of chronic drought hotspots for a river basin in BH. The persistent zone covers 40.02% of the basin and spans nine cities and municipalities, with >93% located in Prnjavor, Derventa, Stanari, and Teslić. Hotspots are concentrated mainly in lowlands below 400 m a.s.l., with a statistically significant concentration across lower elevation classes, indicating higher long-term exposure in the central and northern valley sectors, and land use overlay further highlights high relative exposure of productive land. Overall, the integrated remote sensing and GIS framework strengthens drought monitoring by providing spatially explicit and repeatable evidence to support targeted adaptation planning and drought-risk management. Full article

This study proposes a systematic framework for establishing ethical guidelines for GeoAI (Geospatial Artificial Intelligence), which integrates AI with spatial data science, GIS, and remote sensing. While general AI ethics have advanced through the OECD, UNESCO, and the EU AI Act, ethical standards tailored to GeoAI remain underdeveloped. Geospatial information exhibits unique characteristics, spatiality, contextuality, and spatial autocorrelation—and consequently entails distinct risks such as geo-privacy, spatial fairness and bias, data provenance and quality, and misuse prevention related to mapping and surveillance. Following PRISMA 2020, a systematic review of 32 recent international policy documents and peer-reviewed articles was conducted; through content analysis with intercoder reliability verification (Krippendorff’s α ≥ 0.76), GeoAI ethical principles were extracted and normalized. The analysis identified twelve ethical axes—Geo-privacy, Data Provenance and Quality, Spatial Fairness and Bias, Transparency, Accountability and Auditability, Safety (Security and Robustness), Human Oversight and Human-in-the-Loop, Public Benefit and Sustainability, Participation and Stakeholder Engagement, Lifecycle Governance, Misuse Prevention, and Inclusion and Accessibility—each accompanied by an operational guideline. These axes together form a practical framework that integrates universal AI ethics principles with spatially specific risks inherent in GeoAI and specifies actionable assessment points across the GeoAI lifecycle. The framework is intended for direct use as checklists and governance artifacts (e.g., model/data cards) and as procurement and audit criteria in academic, policy, and administrative settings. Full article

Coastal settlements worldwide face increasing threats from erosion, and the Monrovia coastline in Liberia is no exception. This study investigates shoreline dynamics along a 20.5 km stretch of Monrovia’s coast, which is characterized by low-lying elevations, gentle slopes, and sandy beaches. Using Landsat satellite imagery (1986–2025), supported by Sentinel-2 MSI and qualitative validation drone data, we analyzed historical shoreline change with remote sensing and GIS techniques. Shorelines were extracted using a band-ratio thresholding method and quantified with the Digital Shoreline Analysis System (DSAS 5.0), applying end-point rate (EPR), linear regression rate (LRR), and net shoreline movement (NSM). Exploratory projections for 2036 and 2046 were generated using a Kalman Filter model integrated into DSAS. Results show maximum historical erosion rates of up to 3.8 m/yr and accretion rates of up to 5.9 m/yr, with shoreline retreat reaching 150 m and advance up to 194 m. Erosion hotspots are projected for Hotel Africa, Westpoint, New Kru Town, and the JFK–ELWA corridor, while areas near the St. Paul and Mesurado estuaries are expected to accrete. These findings confirm historical trends and suggest that Monrovia will continue to face significant shoreline change, with implications for natural habitats, infrastructure, land loss, and population displacement. Full article

This observation-based and explainable approach demonstrates the applicability of multi-source remote sensing for flood assessment in data-scarce regions, offering a robust scientific basis for flood management and spatial planning in monsoon-affected areas. Floods are among the most frequent and devastating natural hazards, particularly in developing countries such as Myanmar, where monsoon-driven rainfall and inadequate flood-control infrastructure exacerbate disaster impacts. This study presents a satellite-driven and interpretable framework for high-resolution flood susceptibility and risk assessment by integrating multi-source remote sensing and geospatial data with ensemble machine-learning models—Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM)—implemented on the Google Earth Engine (GEE) platform. Eleven satellite- and GIS-derived predictors were used, including the Digital Elevation Model (DEM), slope, curvature, precipitation frequency, the Normalized Difference Vegetation Index (NDVI), land-use type, and distance to rivers, to develop flood susceptibility models. The Jenks natural breaks method was applied to classify flood susceptibility into five categories across Myanmar. Both models achieved excellent predictive performance, with area under the receiver operating characteristic curve (AUC) values of 0.943 for XGBoost and 0.936 for LightGBM, effectively distinguishing flood-prone from non-prone areas. XGBoost estimated that 26.1% of Myanmar’s territory falls within medium- to high-susceptibility zones, while LightGBM yielded a similar estimate of 25.3%. High-susceptibility regions were concentrated in the Ayeyarwady Delta, Rakhine coastal plains, and the Yangon region. SHapley Additive exPlanations (SHAP) analysis identified precipitation frequency, NDVI, and DEM as dominant factors, highlighting the ability of satellite-observed environmental indicators to capture flood-relevant surface processes. To incorporate exposure, population density and nighttime-light intensity were integrated with the susceptibility results to construct a natural–social flood risk framework. This observation-based and explainable approach demonstrates the applicability of multi-source remote sensing for flood assessment in data-scarce regions, offering a robust scientific basis for flood management and spatial planning in monsoon-affected areas. Full article

Soil contamination by potentially toxic elements (PTEs) is a growing environmental concern, particularly in agricultural regions where soil quality directly affects crop safety and human health. This study evaluates PTE concentrations and ecological risks in agricultural soils of Hautat Sudair, central Saudi Arabia, using contamination indices, multivariate statistics, and GIS-based spatial modeling supported by RS-derived land use/land cover (LULC) mapping. The results show that the mean concentrations of Ni (35.97 mg/kg) and Mn (1230 mg/kg) exceed international thresholds in several locations, while Pb (8.34 mg/kg), Cr (33.00 mg/kg), Zn (60.09 mg/kg), and As (4.25 mg/kg) remain within permissible limits in most samples. Contamination indices, including the Enrichment Factor (EF), Contamination Factor (CF), and Geo-Accumulation Index (Igeo), highlight hotspot behavior, with isolated sites showing elevated concentrations approaching screening levels (e.g., Pb up to 32.0 mg/kg and Cr up to 52.0 mg/kg), whereas Ni and Mn exhibit the most pronounced local enrichment. The Pollution Load Index (PLI) varies from 0.24 to 0.80, indicating low to moderate contamination levels, while the Risk Index (RI) ranges from 10.43 to 41.38, signifying low ecological risk. Multivariate statistical analyses, including correlation matrices and principal component analysis (PCA), reveal that Ni, Cr, and Mn share a common source, possibly linked to anthropogenic inputs and natural geological background. Kaiser–Meyer–Olkin (KMO) and Bartlett’s test confirm the adequacy of the dataset for PCA (KMO = 0.797; χ² = 563.845, p < 0.001). Spatial distribution maps generated using GIS and RS highlight contamination hotspots, reinforcing the necessity for periodic monitoring. By integrating indices, multivariate patterns, and spatial context, this study provides a replicable, research-driven framework for interpreting PTE controls in arid agricultural soils. Full article

Coastal lakes require monitoring approaches that capture spatial and temporal variability beyond the limits of conventional in situ measurements. In this study, a SIGMaL framework (Satellite–In situ–GIS-multicriteria decision analysis (MCDA)–Machine Learning (ML)) was developed, a unified methodology that integrates in situ monitoring, GIS MCDA-derived water quality index (WQI), satellite imagery, and ML models for comprehensive coastal lake water quality assessment. A WQI, derived from a 12-month series of in situ measurements and environmental parameters, was used alongside four physicochemical parameters measured by a multiparameter probe. First, satellite reflectance from each sensor was used to train a set of nine regression models for modelling electrical conductivity (EC), turbidity, water temperature (WT), and dissolved oxygen (DO). Second, convolutional neural networks (CNNs) with spectral and temporal inputs were trained to classify WQI classes, enabling a cross-sensor evaluation of their suitability for lake water quality monitoring. Third, the trained CNNs were applied to generate WQI maps for a subsequent 12-month period without in situ data. Across all analyses, WQI-based models provided more stable and accurate models than those trained on raw parameters. Sentinel-2 achieved the most consistent WQI performance (AUC ≈ 1.00, R² ≈ 0.84), PlanetScope captured fine-scale spatial detail (R² ≈ 0.77), while Landsat 8–9 was most effective for WT but less reliable for multi-class WQI discrimination. Sentinel-2 is recommended as the primary satellite sensor for WQI mapping within the SIGMaL framework. These findings demonstrate the advantages of WQI-based modelling and highlight the potential of ML–remote sensing integration to support coastal lake water quality monitoring. Full article

Crabronid wasps (Hymenoptera: Crabronidae) are ecologically important predators that provide various ecological services by regulating the arthropod populations, enhancing soil processes through nesting, serving as sensitive indicators of habitat condition, and providing pollen transfer for plants. However, as other invertebrates face biodiversity threats, these wasps might be under threat from environmental changes, and we need to assess the biodiversity patterns of these wasps in Yunnan Province. Unfortunately, no information is currently available about the pattern and factors responsible for the assemblages of these wasps within our study region. This study provides the first province-level assessment of habitat suitability, species richness, assemblage structure, and environmental determinants for Crabronidae in Yunnan by integrating species distribution modeling (SDM), multivariate clustering, and ordination analyses. More than 50 species were studied to assess habitat suitability in Yunnan using MaxEnt. Model performance was robust (AUC > 0.7). Suitability patterns varied distinctly among regions. Species richness peaked in southern Yunnan, particularly in the counties of Jinghong, Mengla, Menghai, and Jiangcheng Hani & Yi. Land use/land cover (LULC) variables were the dominant predictors for 90% of species, whereas precipitation-related variables contributed most strongly to the remaining 10%. Ward’s hierarchical clustering grouped the 125 counties into three community assemblage zones, with Zone III comprising the most significant area. A unique species composition was found within a particular zone, and clear separation among zones based on environmental variation was supported by Principal Component Analysis (PCA), which explained more than 70% variability among zones. Furthermore, Canonical Correspondence Analysis (CCA) indicated that both LULC and climatic factors shaped community structure assemblages, with axes 1 and 2 explaining 70% of variance (p = 0.001). The most relevant key factors in each zone were precipitation variables (bio12, bio14, bio17), which were dominant in Zone I; for Zone II, temperature and vegetation variables were most important; and urban, wetland, and water variables were most important in Zone III. Full article

Against the backdrop of rapid urbanization and land development, the degradation of regional ecosystem services and the intensification of ecological risks have become prominent challenges. This study takes the Pinglu Canal Economic Belt—a region characterized by the triple pressures of “large-scale engineering disturbance, karst ecological vulnerability, and port economic agglomeration”—as a case study. Based on remote sensing image data from 2000 to 2020, a landscape ecological risk index was constructed, and regional landscape ecological risk levels were assessed using ArcGIS spatial analysis tools. On this basis, ecological sources were identified by combining the InVEST model with morphological spatial pattern analysis (MSPA),and an ecological resistance surface was constructed by integrating factors such as land use type, elevation, slope, distance to roads, distance to water bodies, and NDVI. Furthermore, the circuit theory method was applied to identify ecological corridors, ecological pinch points, and barrier points, ultimately constructing the ecological security pattern of the Pinglu Canal Economic Belt. The main findings are as follows: (1) Ecological risks were primarily at low to medium levels, with high-risk areas concentrated in the southern coastal region. Over the past two decades, an overall optimization trend was observed, shifting from high risk to lower risk levels. (2) A total of 15 ecological sources (total area 1313.71 km²), 31 ecological corridors (total length 1632.42 km), 39 ecological pinch points, and 15 ecological barrier points were identified, clarifying the key spatial components of the ecological network. (3) Based on spatial analysis results, a zoning governance plan encompassing “ecological protected areas, improvement areas, restoration areas, and critical areas” along with targeted strategies was proposed, providing a scientific basis for ecological risk management and pattern optimization in the Pinglu Canal Economic Belt. Full article

Environmental Impact Assessments (EIAs) are crucial for mitigating flood risks in vulnerable ecosystems, yet their effective application remains inconsistent. This study synthesises global literature to systematically map EIA methodologies, evaluate the extent of hydrological integration, and analyse the evolution of practices against policy frameworks for flood-prone areas. A scoping review of 144 peer-reviewed articles, conference papers, and one book chapter (2005–2025) was conducted using PRISMA protocols, complemented by bibliometric analysis. Quantitative findings reveal a significant gap where 72% of studies lacked specialised hydrological impact assessments (HIAs), with only 28% incorporating them. Post-2016, advanced tools like GIS, remote sensing, and hydrological modelling were used in less than 32% of studies, revealing reliance on outdated checklist methods. In South Africa, despite wetlands covering 7.7% of its territory, merely 12% of studies applied flood modelling. Furthermore, 40% of EIAs conducted after 2016 excluded climate adaptation strategies, undermining resilience. The literature is geographically skewed, with developed nations dominating publications at a 3:1 ratio over African contributions. The study’s novelty is its systematic global mapping of global EIA practices for flood-prone areas and its proposal for mandatory HIAs, predictive modelling, and strengthened policy enforcement. Practically, these reforms can transform EIAs from reactive compliance tools into proactive instruments for disaster risk reduction and climate resilience, directly supporting Sustainable Development Goals 11 (Sustainable Cities), 13 (Climate Action), and 15 (Life on Land). This is essential for guiding future policy and improving EIA efficacy in the face of rapid urbanisation and climate change. Full article

Over the past several centuries, many lakes in the Yunnan Plateau disappeared or are disappearing due to climate change and human activities; the developments of these lakes are thus crucial for understanding their evolutions and underlying causes. Here we present a near 260-year history of water area changes in Lake Zhehai, a disappearing lake in northeastern Yunnan of Southwest China, based on historical documents such as local and regional annals and gazetteers, water conservancy records, and old maps using GIS and remote sensing techniques, to identify the dominant drivers of the lake disappearing. Results show that the reconstructed water area of Lake Zhehai was ca. 1500, 710, 370, 340, and 110 ha in 1761, 1912, 1935, 1950, and 1975 AD; this indicates that Lake Zhehai experienced three-phase lake evolution over the past 260 years, i.e., large lake in 1761–1920 AD, shrinking lake in 1921–1980 AD, and disappearing lake since 1981. Significant changes in the water area of Lake Zhehai were mainly attributed to both climate change and human activities, especially human activities as dominant drivers during the last two phases of lake evolution. Our findings provide a reference for both understanding the driving mechanisms of large shallow lake evolution during historical times in Yunnan, as well as assessing strategies of lake environmental protection under global warming and increasing human activities. Full article

Peri-urban areas in Eastern Europe are undergoing rapid land transformation driven by suburban housing expansion and infrastructure development, yet the processes through which vegetation is progressively degraded and built-up areas intensify remain insufficiently documented. This study analyses vegetation loss and urban expansion in the peri-urban belt of Timișoara, Western Romania, between 2020 and 2025 using Sentinel-2 multispectral imagery, two key spectral indices (NDVI and NDBI), and a Random Forest (RF) classifier. The results reveal a gradual, multi-stage transformation trajectory, where dense vegetation transitions first into sparse vegetation and bare soil before consolidating into built-up surfaces, rather than being replaced abruptly. Substantial vegetation decline is accompanied by notable increases in built-up land, with strong spatial differences between communes depending on development pressure. The integration of RF classification with spectral index analysis allows these transitions to be validated and interpreted more reliably, helping distinguish structural suburbanisation from short-term spectral variability. Overall, the study demonstrates the value of combining NDVI, NDBI and AI-supported land-cover classification to capture nuanced peri-urban transformation dynamics and provides actionable insights for spatial planning and sustainable land management in rapidly growing metropolitan regions. Full article

Wildfires, which encompass all fires that occur outside urban areas, represent one of the most frequent forms of natural disaster worldwide. This study presents the wildfire occurrence across the territory of Southeastern Europe, covering an area of 800,000 km² (Greece, Romania, Serbia, Slovenia, Croatia, Bosnia and Herzegovina, Montenegro, Albania, North Macedonia, Bulgaria, and Moldova). The research applies geospatial artificial intelligence techniques, based on the integration of machine learning (Random Forest (RF), XGBoost), deep learning (Deep Neural Network (DNN), Kolmogorov–Arnold Networks (KAN)), remote sensing (Sentinel-2, VIIRS), and Geographic Information Systems (GIS). From the geospatial database, 11 natural and anthropogenic criteria were analyzed, along with a wildfire inventory comprising 28,952 historical fire events. The results revealed that areas of very high susceptibility were most prevalent in Greece (10.5%), while the smallest susceptibility percentage was recorded in Slovenia (0.2%). Among the applied models, RF demonstrated the highest predictive performance (AUC = 90.7%), whereas XGBoost, DNN, and KAN achieved AUC values ranging from 86.7% to 90.5%. Through a SHAP analysis, it was determined that the most influential factors were global horizontal irradiation, elevation, and distance from settlements. The obtained results hold international significance for the implementation of preventive wildfire protection measures. Full article

Lavender is a perennial Mediterranean plant that has been cultivated throughout history for medicinal, aromatic, and cosmetic purposes. Due to its high economic and commercial value, it has become an important agricultural product worldwide. The low production cost, adaptability to environmental conditions, and demand for its versatile use in the global market make it a significant potential source of income for developing Mediterranean countries. This study aims to identify commercially suitable cultivation sites for Lavandula angustifolia Mill. using remote sensing (RS) and geographic information systems (GIS) technologies to support rural development. Within this scope, suitable cultivation habitat parameters for the species in open fields and natural conditions were determined; these parameters were weighted according to their importance using multi-criteria decision analysis (MCDA), and thematic maps were created for each parameter. The created maps were combined using weighted overlay analysis, and a final map was generated according to the suitability class. The results indicate that within the study area, 75,679.45 ha is mostly suitable, 388,832.71 ha is moderately suitable, 24,068.43 ha is marginally suitable, and 229,327.20 ha is not suitable. As a result, it has been observed that Lavandula angustifolia Mill., which is currently cultivated on approximately 4045 ha of land and contributes 429 tons of product to the regional economy, covers only a relatively small portion of the suitable cultivation sites identified in the study and is not utilized to its full potential. It is understood that the expansion of lavender cultivation in determined suitable sites has significant potential to substantially develop the region and its rural population in terms of both yield and production volume, and to involve women and youth entrepreneurs in agricultural employment. Full article

The study and understanding of spatial and temporal changes in the landscape is essential for assessing environmental trends and predicting future developments in the area. Changes in land cover and vegetation dynamics are key indicators of the ecological stability of an area. This study analyzes long-term changes in land cover and vegetation dynamics in Jelšava and neighboring municipalities. The selected area has long been classified as one of the areas with poor air quality in Slovakia. The analysis is based on data from the CORINE Land Cover program for the period 1990–2018 and Landsat data from 1990 to 2025. The condition and vitality of vegetation were assessed using the Normalized Difference Vegetation Index (NDVI), while temporal trends were assessed using non-parametric Mann–Kendall and Sen’s slope tests. The results show a decrease in the area of class 31—Forests between 2012 and 2018, accompanied by an increase in the area of class 324—Transitional woodland–shrub. Analysis of the NDVI confirmed a slightly positive trend in vegetation cover development, with statistically significant growth (p < 0.05) recorded on approximately 43% of the territory. The combination of remote sensing data and spatial analysis in a GIS environment has proven to be an effective approach to monitoring ecological dynamics and provides valuable insights for regional environmental management and sustainable land use planning. Full article