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.
Accurate and timely burned-area mapping is essential for supporting post-fire assessment, ecological monitoring, and disaster response. In this study, an interactive Google Earth Engine (GEE) application was developed to provide a user-friendly, no-code environment for detecting burned areas using multiple approaches, including ΔNBR (Difference Normalized Burn Ratio) thresholding, unsupervised classification, and a Random Forest (RF) supervised model. The 2025 Bilecik–Sakarya wildfire in northwestern Türkiye was selected as a case study to evaluate the performance of these methods and demonstrate the operational capabilities of the application. Sentinel-2 pre- and post-fire composites were processed within the application to compute ΔNBR, train spectral classifiers, and generate burned-area maps. A comprehensive accuracy assessment using 5,000 stratified reference samples showed notable variation among the methods. The ΔNBR Threshold of 0.20 emerged as the best-performing approach, achieving the highest accuracy with an Overall Accuracy of 0.917, Precision of 0.930, Recall of 0.902, F1-score of 0.916, and Kappa of 0.834. Unsupervised methods (Otsu and K-means) performed comparably, while the RF classifier, despite high precision, underestimated burned pixels due to lower recall. Burned-area extent comparisons further revealed the strong sensitivity of ΔNBR results to threshold selection, underscoring the importance of flexible and visually supported method selection. The proposed GEE application integrates data preprocessing, ΔNBR computation, classification, accuracy assessment, and export functionality within a single guided interface, making advanced remote-sensing techniques accessible to non-expert users. The tool proved effective for rapid post-fire mapping and offers a scalable framework for future developments, including radar integration, additional machine-learning algorithms, and multi-temporal fire severity analysis. Overall, the application represents a practical and accessible solution for operational burned-area monitoring, with strong potential for use in wildfire-prone regions globally
Monitoring of rice crop variability in Dong Thap is limited by reliance on manual surveys, the lack of continuous data series, and difficulty distinguishing changes between farming models. To address this gap, the study aims to (i) identify changes in the structure of double rice crops and triple rice crops in the period 2017–2021, and (ii) assess the impact of such changes on rice yield. Sentinel-1A SAR data series were processed on Google Earth Engine through the following steps: speckle noise filtering, VH reflectance extraction over time, growth curve construction, and crop clustering using the K-means algorithm. The resulting layers were integrated with GIS for overlaying, analyzing spatial-temporal fluctuations, and combining with household survey data to estimate yield. The results show that the two rice-crop areas decreased by 7,038.5 hectares, and the three rice-crop areas decreased by 4,068.88 hectares from 2017 to 2021. Despite the decrease in area, the province's rice output still increased from 2.88 to 2.95 million tons, reflecting productivity improvements and changes in production structure. These findings provide important evidence to support managers in adjusting rice land planning, allocating water resources, reviewing the dike system, and developing climate change adaptation policies for production.
Changes in land use and land cover (LULC) represent a major environmental challenge resulting from rapid population growth, necessitating accurate monitoring and assessment of their impacts. This study aims to evaluate the effectiveness of three machine learning algorithms, namely Support Vector Machines (SVM), Decision Trees (CART), and Random Forests (RF) in classifying land cover patterns and land use in the Ourika Mountain Basin for the periods 1987 and 2025 using Landsat 5 TM and 9 OLI satellite data via Google Earth Engine (GEE). The results showed a clear superiority of the random forest (RF) algorithm in terms of accuracy and consistency, as it recorded the highest values for overall accuracy (OA) and kappa coefficient (KC) for both years, with an overall accuracy of 93% and a kappa coefficient of 0.91 for 1987, and increased to 95% and 0.94, respectively, for 2025. Based on these results, the classification map produced by the RF algorithm was adopted for temporal change analysis. The change analysis revealed significant environmental shifts, represented by a notable decline in natural areas of forests and pastures by 10% of the total area of the basin (equivalent to 5831 hectares). In contrast, there has been a steady expansion in agricultural land, urban areas, and bare land. These changes highlight the increasing human pressures that are contributing to the acceleration of environmental degradation within the Ourika basin. This study provides an effective methodology for monitoring temporal changes and analyzing environmental transformations and can be a valuable tool to support natural resource management and the development of effective strategies for environmental planning and sustainable management of natural resources in similar mountainous areas
It is quite difficult to benefit from winter tourism in regions where summer tourism is dominant, particularly under the increasing impacts of climate change observed in recent years. In this study, the most suitable locations for ski resort development in the Cameli District of Denizli—located in the Mediterranean region where summer tourism is prevalent—were identified using Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP). The site selection was based on a comprehensive evaluation of environmental and physical factors, including snow cover, elevation, temperature, wind conditions, water resources, and road networks. Shuttle Radar Topography Mission (SRTM) data were used to generate Digital Elevation Models (DEMs); however, higher-resolution DEMs were also obtained using the Interferometric SAR (InSAR) method to improve terrain representation. Analyses were conducted using both SRTM- and InSAR-derived DEMs, and the resulting suitability maps were comparatively evaluated. To define realistic snow cover conditions for seasonal winter tourism in a summer-dominated region, nearby ski resorts located within approximately 150 km of the study area—namely Denizli, Salda, Saklikent, and Davraz—were examined as reference cases, rather than for direct spatial comparison. Based on this integrated GIS–AHP analysis, the most suitable areas for ski resort development in the Cameli District were determined.
Recent progress in remote seining sciences and improving the quality of satellite images in all aspects of spatial, spectral and temporal resolution provided large number of data whit demanded developing automated data driven and machine learning techniques. Thus, machine learning approaches in the scientific community have recived a significant interest,for imgae classification and environmental anlysis. As can be figured out, there are plenty of machine learning algorithms being employed for the image processing tasks. In order to evaluate the efficiency of within this research we intended to apply and compare the efficiency of two best known machine learning algorithm including support vector machine (SVM) and random forest (RF) for time series land use land cover (LULC) monitoring in the vicinity area of Urmia lake in north west of Iran. For this object, we employed time series Landsat satellite images on the platform of Google Earth Engine (GEE). We employed three methods for valiadtaion and accurassy assessment. For this goal, first the validation step performed using overall accuracy, kappa coefficient based on the grand control points collected in field operation as as well Fuzzy Synthetic Evaluation for computing the confidence level of classification in sub category of each data driven approach.In addition, in the second step the the Dumpster Shafer theory (DST) was applied to carry out the spatial uncertainty of obtained LULC maps. Results of accurassy assment and also uncertinity anlysis, pointed out that the SVM algorithm performed classification much efficiently rather than RF algorithm. According to the results of validation through ground control points and spatial uncertainty analysis using the DST, as a best performance the SVM could deliver the LULC classified map with the overall accuracy of 92.57% as well the spatial accuracy of 0.97. While, the best performance of the RF algorithm computed to be 86.20 % in overall accuracy and 0.88 in DST for the spatial uncertainty analysis. As these results from both validation methods confirm, there were extensive LULC change in the study area which essentially contributed to Urmia lake drought and respective environmental degredation.
The catastrophic collapse of the Derna Dam created an urgent need for rapid and reliable mapping of flood extent and building damage to support disaster response and recovery. This study presents a multi-sensor, multi-method change detection framework integrating open-access Sentinel-1 Synthetic Aperture Radar (SAR), Sentinel-2 optical imagery, and Very High-Resolution (VHR) Maxar imagery to detect post-event changes and assess building-level damage. Two methodological approaches were evaluated: semi-supervised and unsupervised. The semi-supervised pipeline utilizes a Multilayer Perceptron (MLP)-based post-classification to generate pseudo-labels for training two U-Net variants: single-encoder early fusion and Siamese double-encoder mid-fusion architectures. The unsupervised methods include Principal Component Analysis (PCA) with Change Vector Analysis (CVA), Kernel Canonical Correlation Analysis (KCCA) implemented efficiently with Random Fourier Features (RFF), and a novel PCA with KCCA hybrid that combines dimensionality reduction with nonlinear correlation analysis. Rapid assessment using Sentinel-2 and OpenStreetMap (OSM) data identified changed areas through Change/No-Change and Normalized Difference Moisture Index (NDMI) masks. A detailed analysis employed VHR Maxar imagery and a pretrained Mask Region-based Convolutional Neural Network (Mask R-CNN) within ArcGIS Pro to extract pre- and post-event building footprints and evaluate structural impacts. Validation against manually labeled VHR samples (131 polygons; 3,371 pixels) confirmed the robustness of the framework. Stacked Sentinel-1 + Sentinel-2 fusion improved pseudo-label quality and segmentation accuracy (semi-supervised Overall Accuracy (OA) ≈95.1%), while the PCA＋KCCA hybrid achieved the best unsupervised performance (OA ≈89.4%). For the assessment of building damage, two methods were utilized: Quick Assessment (QA) and Deliberate Assessment (DA). A total of 2,193 flooded buildings were identified using the Deliberate Assessment, while 2,694 flooded buildings were identified through the Quick Assessment. These findings were validated using data from the United Nations Satellite Center (UNOSAT). The study contributes (i) a reproducible, end-to-end workflow for rapid post-disaster mapping, (ii) an efficient RFF-based KCCA implementation, and (iii) a novel PCA＋KCCA hybrid that optimizes the balance between accuracy, robustness, and computational efficiency for operational-scale change detection.
The application of machine learning algorithms to remote sensing data enables the accurate classification of land cover, which is essential for environmental monitoring, land use planning, and sustainable natural resource management. In this study, enhanced land cover classification has been done using Sentinel 2A remote Sensing imagery by doing a comparison between Random Forest (RF) and Support Vector Machine (SVM) in Google Earth Engine (GEE) environment. We consider three different datasets for the performance assessment, particularly for the district 19 Mayis. Three datasets with spectral bands, spectral indices, and topographical features (elevation and slope) have been employed. We generated the evaluation metrics and calculated the overall accuracy (OA), the Kappa statistic (K), the user's accuracy (UA), and the producer's accuracy (PA). Overall, the RF model consistently outperformed the SVM model on each dataset. The SVM model in Dataset one gave an OA of 0.888 and K value of 0.849 but on the other hand the RF model in Dataset 1 gave OA of 0.927 and K value higher than SVM was 0.900. Based on Dataset 2 was RF with OA and K of 0.943 and 0.922 respectively. SVM model achieved OA of 0.912 and K of 0.880. The RF model achieved an OA of 0.965 and a K value of 0.952 according to Dataset 3 results while the OA of the SVM model was 0.927 and a K of 0.900. The results prove that integrating remote sensing data with advanced machine learning classifiers, particularly Random Forest, provides an effective approach for land cover mapping in complex and heterogeneous environments
Fine particulate matter (PM2.5) remains a major environmental and public health concern worldwide. This study investigates the temporal and spatial dynamics of PM2.5 concentrations across Azerbaijan between 2019 and 2024 and develops forecasts using two transparent time-series models. Monthly PM2.5 fields were derived exclusively from ECMWF CAMS reanalysis products, while Sentinel-5P TROPOMI observations were used only for qualitative spatial interpretation and contextual comparison. All datasets were processed in Google Earth Engine and analyzed in R. Temporal dynamics were modelled using SARIMA and Prophet, while spatial patterns were characterized through stratified random-point sampling followed by inverse distance weighting (IDW) interpolation. The results reveal a pronounced seasonal cycle in which PM2.5 concentrations peak during the warm season rather than in winter. The highest concentrations consistently occurred in June and September, with the absolute maximum reaching 28.9 µg/m³ in June 2019, whereas the lowest values were systematically recorded in January and February. At the interannual scale, 2019 emerged as the most polluted year, followed by a substantial decline during 2020–2023, partly associated with COVID-19–related mobility restrictions, before concentrations increased again during the second half of 2024. Spatial analysis identified persistent hotspots in the central and south-central lowland districts, while the eastern coastal zone—particularly the Absheron Peninsula and greater Baku—experienced intense but episodic pollution events. Getis-Ord hotspot analysis and a highly significant positive Moran’s I statistic (0.922, p < 0.001) confirmed strong and non-random spatial clustering of PM2.5 concentrations. Across most locations and months, Prophet produced lower forecasting errors than SARIMA during the 2024 validation period (MAE = 0.180 µg/m³; RMSE = 0.202 µg/m³). Prophet also reproduced the location and intensity of seasonal pollution maxima more successfully, whereas SARIMA showed systematic spatial displacement of hotspots during several critical months. However, the trend shift observed during late 2024 and the absence of ground-based calibration data indicate that forecasting PM2.5 under non-stationary environmental conditions remains challenging. Overall, the proposed framework—combining freely available reanalysis products, simple spatial interpolation, and interpretable forecasting models—provides a practical and low-cost approach for routine air-quality monitoring, hotspot identification, and policy support in data-scarce regions.
Shorelines in Bangladesh are among the most vulnerable regions to the compounded impacts of climate change, sea-level rise, and intensified natural hazards. Although sediment deposition in Bengal Delta’s promotes accretion in certain areas, many coastal zones are experiencing severe erosion, driving large scale population displacement and heightening socioeconomic vulnerability. This study examines shoreline change dynamics and geotechnical soil properties along the Kalapara coast in Patuakhali District over a 31-year period (1989–2020) through the integration of multi-temporal Landsat imagery and field-based geotechnical analysis. A total of 1,533 transects were evaluated using the Digital Shoreline Analysis System (DSAS), revealing that 77.9% of the shoreline experienced erosion, while only 22.1% showed accretion. Dhulasar union exhibited the most extreme rates of both erosion (33.57 m/yr) and accretion (22.88 m/yr), while Nilganj remained comparatively stable. Geotechnical testing of thirty shoreline soil samples from six unions indicated low specific gravity values (2.21–2.68), suggesting poor compaction and high susceptibility to erosion. The plasticity index ranged from -26.77 to 39.1, with Mohipur, Lalua, and Champapur dominated by non-plastic to low-plastic soils, and Dhulasar and Nilganj characterized by high plasticity and cohesive soils. Particle size analyses showed that most soils comprisedmedium to fine sand (up to 98.34%), with minimal silt and clay fractions; however, Nilganj, Latachapli, and Dhulasar contained higher proportions of silty-clayey material, contributing to enhanced slope stability and erosion resistance. These findings emphasize the critical role of soil mechanical properties in governing shoreline evolution and highlight the urgent need for integrated coastal zone management strategies that couple geotechnical stabilization with continuous remote sensing-based monitoring to protect vulnerable areas like Kuakata beach from irreversible coastal degradation.
Natural hazards such as earthquakes can cause significant loss of life and extensive damage worldwide each year. Search and Rescue (SAR) operations in such critical situations require an urgent need for speed and efficiency, but are also complex challenges, given their dynamic and uncertain nature. This challenge requires approaches that integrate realistic environmental modeling with intelligent decision-making. Geospatial information systems are a powerful tool that can provide robust representations of complex terrains under time-critical circumstances, and artificial intelligence techniques can simplify decision-making and optimize decision-making under time-sensitive circumstances. In addition, context-aware SAR enables the precise identification and quantification of situational parameters, allowing better tailoring of operations to the real world. A context-aware SAR framework was developed in this study to identify and estimate key parameters in rescue operations, activities, and environmental conditions, and to model the problem within a spatial information system. And by integrating ant colony optimization (ACO), which is well-suited to modeling cooperative search behaviors, with the SARSA reinforcement learning algorithm, which is well-suited to discrete decision-making environments, a hybrid method was proposed. As part of this integration, a pheromone-like reinforcement mechanism was utilized to enhance overall performance and adaptability. A comparison of this hybrid approach against a standalone ACO model revealed that it outperformed it in both operational efficiency and solution quality, underscoring its potential for real-world SAR applications.
Background. The growth of urban populations leads to swift urban development. Consequently, much of the unplanned expansion of new neighborhoods results in the loss of public open space. The deficiency of public open space, particularly Urban Green Space (UGS), diminishes the quality of life and adversely affects community health. This study seeks to provide an overview of the significance of UGS. Furthermore, it aims to evaluate the accessibility of UGS in the context of Famagusta city in Cyprus.Method. The methodology employed in this study integrates geographical information systems, questionnaires, and field surveys to assess UGS accessibility within the study area. Additionally, the principles of English Nature concerning UGS accessibility were utilized as a foundation for this evaluation.Result. The findings of the study indicate that the rapid urbanization of the area has led to a reduction in UGS. Indeed, the existing UGS in Famagusta is both physically and visually inaccessible. The absence of paved pedestrian pathways, lighting, recreational amenities, and seating furniture renders the UGS ineffective. The current circumstances necessitate immediate action from the relevant authorities.
This study examines the underexplored physical disparities within the urban structures of historical cities shaped by religious communities, focusing on the Muslim and Christian quarters of the Old City of Jerusalem. As a city that was both newly developed and transformed during the Islamic period1, Jerusalem provides a unique perspective for analyzing these distinctions. Employing space syntax, the research investigates the macro-and micro-level spatial configurations of the quarters, uncovering variations in street networks, accessibility, and urban design. The findings reveal how Christian and Muslim communities navigated and shaped their shared environment, offering a deeper understanding of the Islamic contribution to the planning of historical Jerusalem. This study not only advances the discourse on urban morphology in historical cities during the Islamic period but also highlights the relationship between administrative efficiency and Islamic principles of tolerance, as reflected in the physical fabric of the Old City.
Urbanization has significantly increased over the past decades, making monitoring of urban growth and urban texture essential for urban planning and sustainable development. In this context, the classification of different urban textures has gained importance, leveraging advancemnts insatellite image processing and methods such as machine learning and object-based image analysis (OBIA), as well as their integration. The present study aims to apply and evaluate different object-based methods to map urban texture in different part of Tabriz city in Iran. To this end, five area with distinct urban texture patterns were selected and analyzed using OBIA’s spectral and spatial features. A semiautomated OBIA approach was developed and applied to map urban textures, and its robustness and efficiency was examinedOur analysis indicated that combining the average, shape, and gray level co-occurrence matrix methods enhances the ability to identify objects in urban environments. The results highlight the high potential of OBIA algorithms and features in detecting and classifying urban areas. This study provides valuable insights for urban planners, offering a useful tool for informed decision-making in future urban development.
Seasonal fluctuations in water levels can significantly influence geodynamic processes in tectonically active reservoir regions. This study investigates such effects around the Charvak Reservoir (Uzbekistan) using lineament analysis derived from Landsat 9 satellite imagery for the period 2022–2024. Lineaments were extracted using the pyLefa tool across three seasonal phases—March, July, and October—to capture deformation patterns associated with minimum, peak, and declining water levels. Results reveal a strong correlation between reservoir volume and lineament density, with peak values observed during high water levels. Orientation changes further indicate stress redistribution and possible reactivation of surface faults. Seismic records confirm spatial alignment between active fault zones and areas of high lineament density. These findings provide new insight into how hydrological loading interacts with crustal structures and demonstrate the value of multi-temporal lineament mapping as a tool for monitoring reservoir-induced deformation and supporting geohazard risk assessment.
Oases are complex, dynamic, and inherently fragile ecosystems. Understanding their functionality is vital for their sustainable management. This study presents findings on the spatiotemporal evolution of dune units in the Middle Draa oasis, located in southeastern Morocco. The analysis is based on the use of the Normalized Difference Enhanced Sand Index (NDESI) derived from Landsat satellite imagery (5 TM and 8 OLI). Landsat 5 TM images from 1988 and 2011, as well as Landsat 8 OLI images from 2023, were selected. Validation of the results was carried out using Corona archive images (1980), high-resolution Google Earth Pro images, and field observations. The study highlights the relevance of combining Landsat data with the NDESI index to monitor, analyze, and assess dune dynamics in arid environments. The results indicate an intensification of sand encroachment in the Middle Draa, with potential implications for ecosystems and socio-economic activities. This intensification is reflected in the concentration of 95% of dunes in the downstream part, particularly in M’hamid El Ghizlane, and a significant extension of dunes between 1988 and 2023, with an evolution rate of approximately 21 km² per year. This study provides a solid database for planning protection and environmental restoration measures, as well as for the sustainable management of sand systems in similar environments.
