The Ahmedabad Metropolitan Region in India has witnessed rapid, spatially uneven urbanisation over the past two decades, profoundly altering land use and land cover (LULC) patterns and generating significant environmental and planning challenges. This study integrates advanced geospatial analytics and machine learning techniques to evaluate past and future LULC transitions from 2000 to 2020, with projections through 2045. Landsat imagery was processed in Google Earth Engine (GEE), and land cover was classified into five major classes: built-up, vegetation, agricultural land, barren land, and water bodies. This classification was achieved using the Random Forest algorithm, which proved highly effective in handling complex, heterogeneous urban environments. Classification accuracy was assessed using a confusion matrix, yielding an overall accuracy of 92.8% and a Kappa coefficient of 0.89, confirming the robustness of the results. Future simulations were conducted using the QGIS-MOLUSCE plugin based on a Cellular Automata–Artificial Neural Network (CA–ANN) model to capture the spatial dynamics of urban expansion. Results indicate a significant 38.34% increase in built-up area (from 161.40 km² in 2000 to 223.39 km² in 2020) alongside declines in vegetation, agriculture, and barren land, reflecting mounting ecological stress and thermal vulnerability. The model projects that by 2045, built-up areas could expand to 310.22 km², potentially encroaching upon vital green and hydrological systems. While the model demonstrates high predictive accuracy, it remains constrained by the spatial resolution of input data and the exclusion of socio-economic variables. The study highlights the pressing need for integrated urban policies, enhanced green infrastructure, and the establishment of a Metropolitan Land Use Observatory to ensure continuous monitoring, data-informed governance, and sustainable urban development across Ahmedabad’s expanding metropolitan landscape.
Türkiye was once regarded as a country rich in water resources; however, rapid population growth, climate change, and increasing water demand have accelerated the transition toward water scarcity. Water losses in distribution networks have reached critical levels, with annual averages often exceeding 50%. Mitigating these physical leakages through proactive infrastructure management is therefore essential. The primary aim of this study is to develop a robust spatial susceptibility model for water distribution network failures using an integrated Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) approach. Focusing on the Yenişehir district of Mersin Province, nine environmental and anthropogenic conditioning factors were identified for failure prediction: soil type, lithology, slope, elevation, aspect, building density, population density, lineament density, and distance to roads. These factors were spatially analyzed and weighted by coupling data-driven FR statistics with expert-based AHP evaluations. The developed model was validated against an independent testing dataset comprising 30% of the actual historical failure inventory, achieving a reliable predictive accuracy rate of 92.40%. Ultimately, this research presents an effective geospatial decision support tool for municipalities to prioritize maintenance interventions and minimize urban water losses.
The Urban Heat Island (UHI) effect has intensified due to rapid urbanization and the expansion of impervious surfaces, a phenomenon particularly critical in semi-arid cities. This study investigates the relationship between Land Surface Temperature (LST) and vegetation density (NDVI) during the 2025 summer season (May–September) in the Diyarbakır metropolitan area, Türkiye. Landsat 8–9 thermal data and Sentinel-2 imagery were processed on the Google Earth Engine platform using spatial and statistical analyses. Quantitative results reveal that the mean surface temperature in urban areas was 53.7°C, compared to 45.6°C in vegetated rural reference areas. The study identified a maximum mean UHI intensity of 8.1°C during the peak summer month of July. Furthermore, regression analysis demonstrated a robust negative correlation between NDVI and LST (R² = 0.757), indicating that densely vegetated zones (NDVI > 0.3) were up to 10–15°C cooler than the urban core. These findings underscore the pivotal function of urban green spaces in moderating UHI intensity and provide a scientific basis for thermal comfort strategies in semi-arid urban contexts.
Coral reef ecosystems, often described as the “rainforests of the sea,” play a vital role in maintaining marine biodiversity and coastal protection but are increasingly threatened by climate change and human-induced pressures. This study integrates Remote Sensing (RS) and Geographic Information Systems (GIS) within the Google Earth Engine (GEE) cloud-computing framework to assess the degradation of coral reefs along the western coast of the Gulf of Suez, Egypt. A robust, reproducible methodology was developed to analyze a 33-year time series (1986–2019) of Landsat and Sentinel-2 imagery. Key environmental stressors, including sea surface temperature (SST), chlorophyll-a concentration, and water turbidity, were quantified using validated algorithms. Coral reef extent was mapped using a supervised Random Forest classification, achieving an overall accuracy of 91.2%. Results revealed a net loss of 1.15 km² of coral reef cover over the study period, decreasing from 57.15 km² in 1986 to 55.99 km² in 2019, with an estimated uncertainty of ±2.8 km². Spatial analysis showed distinct regional variations: the most pronounced losses occurred in Zone 2 (Ras El Adabia–Ain Sokhna Port; –4.89 km²) and Zone 4 (Ras Abu Darge–Ras El Zaafrana; –4.37 km²), mainly due to maritime operations, port expansion, and coastal urbanization. Conversely, Zone 7 (Ras Bakar–Al-Ghardaqa) exhibited a notable increase of +7.4 km², likely linked to favorable hydrodynamic conditions and limited anthropogenic disturbance. These findings demonstrate how localized human activities and natural processes jointly shape reef dynamics. The study underscores the critical importance of integrating validated geospatial technologies into early warning systems, conservation planning, and adaptive management strategies to enhance the long-term sustainability of coral reef ecosystems amid accelerating global environmental change.
Continuous and reliable monitoring of water quality is crucial for the sustainable management of lake ecosystems. Suspended Solids (SS) concentration is a key indicator, yet traditional measurement methods are costly and offer limited spatial and temporal coverage. Remote sensing addresses these constraints by providing wide area, repeatable observations. This study estimated SS concentrations in Lake Mogan using a hybrid remote sensing approach with Landsat-8 OLI imagery. First, scarce in-situ data were augmented with a Random Forest (RF) model to create a more robust training set. This dataset then supported Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) models, using B2, B3, and B4 bands along with spectral indices. The CNN model yielded the highest accuracy (R² = 0.97, RMSE = 0.17, MAE = 0.13) and was used to generate lake wide SS maps. Overall, the RF–ANN–CNN framework significantly improves SS estimation in small lakes with limited field data, demonstrating the strong potential of remote-sensing-based deep learning for sustain-able water-quality monitoring.
