World Journal of Geomatics and Geosciences

This study investigates the spatial and temporal dynamics of shoreline deformation along the San Bartholomeo River over 30 years (1995–2025) using an integrated geospatial approach. The research combines Interferometric Synthetic Aperture Radar (InSAR) and Digital Shoreline Analysis System (DSAS) techniques to quantify both vertical land motion and horizontal shoreline change. Results reveal a decline in land area from 32.622 km²... This study investigates the spatial and temporal dynamics of shoreline deformation along the San Bartholomeo River over 30 years (1995–2025) using an integrated geospatial approach. The research combines Interferometric Synthetic Aperture Radar (InSAR) and Digital Shoreline Analysis System (DSAS) techniques to quantify both vertical land motion and horizontal shoreline change. Results reveal a decline in land area from 32.622 km² (56.65%) in 1995 to 30.284 km² (52.59%) in 2025, alongside an increase in water body extent, indicating progressive shoreline retreat. Shoreline change rates varied significantly, with erosion reaching −8.04 m/yr and accretion up to 16.73 m/yr. Net Shoreline Movement (NSM) and Shoreline Change Envelope (SCE) analyses further highlighted zones of persistent erosion and high variability. InSAR-derived deformation patterns confirmed the presence of subsidence contributing to shoreline instability. The findings demonstrate that a combination of hydrodynamic forces, sediment imbalance, and anthropogenic activities drives shoreline deformation in the study area. This study contributes to improved shoreline monitoring by integrating vertical and horizontal deformation metrics, providing a robust framework for sustainable shoreline management in deltaic environments. Read more

This study proposes an integrated approach combining geographic information systems (GIS) and geotechnical analyses to assess the stability of the Oued Ayda mountain lake dam, located in the Siliana Governorate, northwestern Tunisia. The mechanical properties of the embankment and foundation materials were integrated into a Mohr-Coulomb geomechanical model, while the pore water pressure distribution was simulated for various representative hydromechanical... This study proposes an integrated approach combining geographic information systems (GIS) and geotechnical analyses to assess the stability of the Oued Ayda mountain lake dam, located in the Siliana Governorate, northwestern Tunisia. The mechanical properties of the embankment and foundation materials were integrated into a Mohr-Coulomb geomechanical model, while the pore water pressure distribution was simulated for various representative hydromechanical scenarios: end of construction, normal operation, rapid drainage, and short- and long-term empty reservoir conditions. The stability analysis, performed using the Morgenstern-Price method with the SLOPE/W software (GeoStudio), reveals high safety factors on the upstream side (SF > 3 in the short term and SF ≥ 2 in the long term), indicating good resistance of this slope to hydraulic and mechanical stresses. Conversely, the results show that the downstream slope exhibits significantly lower safety factors, ranging from 1.335 to 1.338 under long-term conditions, particularly during normal operating and rapid drainage scenarios. These reduced values indicate a high vulnerability of this slope to persistent saturation and adverse hydraulic gradients. In conclusion, although the dam exhibits satisfactory overall stability, the downstream slope remains the most vulnerable area of the structure. The results underscore the need for rigorous management of water level fluctuations and suggest reinforcing the drainage system or implementing targeted stabilization measures to ensure the long-term safety and durability of the structure. Read more

The Mambaka watershed is extends between latitudes 1 3°45'E and 14°15'E and longitudes 7°16'N and 6°45'N. The geology, various tectonic and structural events that have affected the Adamawa Plateau in Cameroon make it rich in multi-substance mining. The objective of this study is to map rare earth (REE) geochemical anomalies in the sediments of the watershed streams west of Mambaka,... The Mambaka watershed is extends between latitudes 1 3°45'E and 14°15'E and longitudes 7°16'N and 6°45'N. The geology, various tectonic and structural events that have affected the Adamawa Plateau in Cameroon make it rich in multi-substance mining. The objective of this study is to map rare earth (REE) geochemical anomalies in the sediments of the watershed streams west of Mambaka, and to trace their origins and geochemical processes. Predictive maps from inverse distance interpolations (IDW), factor analysis (F1) or principal component analysis (PCA) and hierarchical bottom-up classification maps provided a better understanding of the central tendency, distribution and dispersion of REE in the samples and in the study area, based on standard deviation and variance values that generated two factors F1 (Ho-Tm-Er-Yb-Lu-Dy-Tb-Gd-Eu-Sm) and F2 (Pr-Nd-Ce-La-Sm) representing 92.44% of the total cumulative variance. The ratios Ce/Ce* > 0.78 and Eu/Eu* > 1 demonstrate positive anomalies in Ce and Eu, and clear differentiation. The normalized concentrations used to calculate fractionation ratios show that the values for LaN/YbN (0.58 to 1.34), LaN/SmN (0.61 to 0.88) and LaN/LuN (0.62 to 1.43) suggest higher fractionation in SS09 and lower fractionation in SS01. Similarly, the ratios La/Lu (61.71 to 143.46), La/Yb (9.00 to 20.72), La/Sm (4.02 to 5.83) and La/ Lu (61.71 to 143.46) confirm these higher ratios in SS09 and lower in SS01. The REE in the study area comes from hydrothermal processes based on high lineament densities at sampling points in igneous rocks with a mean ∑REE value of between 174-219 ppm. Read more

The integration of LiDAR and Artificial Intelligence (AI) has revolutionized feature detection in urban environments. LiDAR systems, which utilize pulsed laser emissions and reflection measurements, produce detailed 3D maps of urban landscapes. When combined with AI, this data enables accurate identification of urban features such as buildings, green spaces, and infrastructure. This synergy is crucial for enhancing urban development, environmental... The integration of LiDAR and Artificial Intelligence (AI) has revolutionized feature detection in urban environments. LiDAR systems, which utilize pulsed laser emissions and reflection measurements, produce detailed 3D maps of urban landscapes. When combined with AI, this data enables accurate identification of urban features such as buildings, green spaces, and infrastructure. This synergy is crucial for enhancing urban development, environmental monitoring, and advancing smart city governance. LiDAR, known for its high-resolution 3D data capture capabilities, paired with AI, particularly deep learning algorithms, facilitates advanced analysis and interpretation of urban areas. This combination supports precise mapping, real-time monitoring, and predictive modeling of urban growth and infrastructure. For instance, AI can process LiDAR data to identify patterns and anomalies, aiding in traffic management, environmental oversight, and infrastructure maintenance. These advancements not only improve urban living conditions but also contribute to sustainable development by optimizing resource use and reducing environmental impacts. Furthermore, AI-enhanced LiDAR is pivotal in advancing autonomous navigation and sophisticated spatial analysis, marking a significant step forward in urban management and evaluation. The reviewed paper highlights the geometric properties of LiDAR data, derived from spatial point positioning, and underscores the effectiveness of machine learning algorithms in object extraction from point clouds. The study also covers concepts related to LiDAR imaging, feature selection methods, and the identification of outliers in LiDAR point clouds. Findings demonstrate that AI algorithms, especially deep learning models, excel in analyzing high-resolution 3D LiDAR data for accurate urban feature identification and classification. These models leverage extensive datasets to detect patterns and anomalies, improving the detection of buildings, roads, vegetation, and other elements. Automating feature extraction with AI minimizes the need for manual analysis, thereby enhancing urban planning and management efficiency. Additionally, AI methods continually improve with more data, leading to increasingly precise feature detection. The results indicate that the pulse emitted by continuous wave LiDAR sensors changes when encountering obstacles, causing discrepancies in measured physical parameters. Read more

In this study, we have investigated the status of faults in terms of precession and subsequence, and their relationship with vein mineralization in Kaybarkuh intrusive body in East of Iran. At least, three generations of faults are evidenced in Dasht-e-Bayaz (DB) fault terminal. During formation of faults, the stress orientation in the region has changed at least once probably due... In this study, we have investigated the status of faults in terms of precession and subsequence, and their relationship with vein mineralization in Kaybarkuh intrusive body in East of Iran. At least, three generations of faults are evidenced in Dasht-e-Bayaz (DB) fault terminal. During formation of faults, the stress orientation in the region has changed at least once probably due to DB fault evolution. Mineralization, especially gold and copper, is formed along the third-generation faults and sometimes on the fault surfaces. It can be predicted that mineralization also happened in the tensioned area of Kal-Shur covered by salt playa and Quaternary sediments, which requires subsurface and geophysics studies. Read more

First, based on the many levels and their cycles in geoscience, we research the hypercycle of geoscience. This is the hypercycle as a tool of self-organization applied to geoscience. It may form from a level to other higher levels. These levels influence each other and the co-evolution. Second, we discuss some possible mathematical methods, which include graph, vector, matrix, some... First, based on the many levels and their cycles in geoscience, we research the hypercycle of geoscience. This is the hypercycle as a tool of self-organization applied to geoscience. It may form from a level to other higher levels. These levels influence each other and the co-evolution. Second, we discuss some possible mathematical methods, which include graph, vector, matrix, some equations, similar theories, etc. This method can be developed and perfected. Third, we propose the nonlinear whole geoscience and its three basic laws. Fourth, we discuss thermodynamics of geoscience, and in which possible entropy decrease under some sates, such as evolution and cycles of Earth, etc. Sustainable development of society must study the mode from high entropy to low entropy. Various cycles in geoscience cannot all be entropy increases, and cannot all be originated from the external interactions. Read more