Applied Geomatics最新文献

{"title":"Mangrove habitat suitability mapping integrating hybrid MCDM modelling approach of bay facing Dhanchi Island, Indian Sundarbans","authors":"Sweta Chatterjee,&nbsp;Gupinath Bhandari","doi":"10.1007/s12518-026-00776-1","DOIUrl":"10.1007/s12518-026-00776-1","url":null,"abstract":"<div><p>This unique study develops a GIS and Machine Learning integrated hybrid Multi-Criteria Decision Making (MCDM) framework employing Shannon Entropy and Combined Compromise Solution (CoCoSo) models to map mangrove habitat suitability for regeneration on Dhanchi Island, a Bay-facing continental island in the Indian Sundarbans Biosphere Reserve, which has shrunk from 38.73 km² (1970) to 33.14 km² (2020) due to erosion and tidal dynamics. From 27 initial parameters across physical-scape (slope, land cover, elevation, distance to saltpans), hydro-climatic-scape (rainfall, LST, inundation, drainage density, distance to creeks, TWI), chemical-scape (soil salinity), and environmental-scape (canopy height, DBH, NDVI), 14 were selected post-multi-collinearity testing (all VIF &lt; 10, tolerance &gt; 0.1), with hydro-climatic factors dominating weights (30.64%) followed by environmental (29.65%) and physical (28.68%) scapes. Shannon Entropy objectively derived data-driven weights, while CoCoSo combined weighted sum/product measures for compromise solutions; both classified suitability into five zones (critically unsuitable to optimally suitable) using ArcMap 10.8 and R Studio processing of Copernicus DEM, Landsat 8, NASA rainfall, and field data, validated against &gt; 100 ground-truthed points via ROC-AUC (Shannon Entropy AUC = 0.734; CoCoSo = 0.697). Shannon Entropy allocated larger highly/optimal zones (9.66 km² combined) in southern/western fringes and erosion hotspots, outperforming CoCoSo's conservative estimates (8.10 km²), with central saltpans (~ 5 km²) unsuitable due to hyper-salinity in the saltpans. Dumbbell plots highlighted Shannon Entropy's smoother patterns for regeneration planning of native species (e.g., <i>Avicennia</i> spp.<i>, </i><i>Aegiceras corniculatum</i>, <i>Acanthus ilicifolius</i>, <i>Bruguiera gymnorrhiza</i>, <i>Ceriops tagal</i>). These findings advance nature-based solutions for coastal resilience, prioritizing mudflat restoration via sediment trapping and salinity mitigation to counter shoreline retreat, offering a robust, objective MCDM tool for Sundarbans mangrove conservation.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148465266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative framework for neural rendering and photogrammetry for spherical images","authors":"Emanuele Balloni,&nbsp;Lucrezia Gorgoglione,&nbsp;Marina Paolanti,&nbsp;Adriano Mancini,&nbsp;Roberto Pierdicca","doi":"10.1007/s12518-026-00762-7","DOIUrl":"10.1007/s12518-026-00762-7","url":null,"abstract":"<div><p>Heritage 3D Reconstruction is a digital bridge to the past, providing a vivid window into history by meticulously preserving the intricate details of cultural artifacts and sites and bringing them to life for future generations to explore and appreciate. In recent years, novel Neural Rendering methods, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have shown promising results for 3D reconstruction. Despite their rapid development, a systematic comparison of these approaches in the context of <span>(360^{circ })</span> imagery, particularly within Digital Cultural Heritage (DCH), remains largely unexplored. In this work, we present a novel comparative framework specifically designed to address this gap, performing a comprehensive assessment of traditional <span>(360^{circ })</span> photogrammetry against emerging Neural Rendering approaches. To validate its effectiveness, we conducted a comparative analysis on two different Cultural Heritage scenarios: a large-scale outdoor environment and an object-centered setting. These case studies, which present a variety of challenges in the DCH domain, were selected to evaluate the ability of photogrammetry, NeRF, and 3DGS-based methods to reconstruct and render complex scenes in terms of both geometric accuracy and visual quality. The results highlight the differences between Neural Rendering approaches and conventional methods, particularly in handling spherical geometry, adaptability to varying lighting conditions and efficiency in data acquisition and processing. These findings confirm the continued robustness of spherical photogrammetry for metric reconstruction, while also underscoring the potential of Neural Rendering-based scene representation methods as promising tools for DCH preservation.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00762-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148380154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated lamp post extraction using drone based photogrammetry","authors":"Alya Farzana Mohd Ayub,&nbsp;Khairul Nizam Tahar","doi":"10.1007/s12518-026-00774-3","DOIUrl":"10.1007/s12518-026-00774-3","url":null,"abstract":"<div><p>Utility poles, particularly lamp posts, are essential for urban planning, infrastructure management, and road safety. Accurate roadside asset inventories are increasingly required to support maintenance and safety assessments. UAV photogrammetry has emerged as a potentially cost-effective and efficient approach for infrastructure mapping due to its rapid data acquisition and high spatial resolution. However, extracting pole-specific information from UAV-derived point clouds remains challenging and labour-intensive, especially in newly developed areas. While Light Detection and Ranging (LiDAR) has been widely applied for pole detection, limited studies have explored UAV photogrammetry as a standalone solution. This study proposes a proof-of-concept automated workflow for extracting lamp posts from UAV-derived point clouds using clustering-based segmentation techniques. The method integrates planar grid filtering for feature isolation and K-means clustering for object identification, with preprocessing steps including voxelisation and statistical outlier removal. The workflow was implemented using geometrically defined parameters, which are expressed in metres in accordance with the coordinate system of the dataset. Parameter values, including voxel size (0.1 m), neighbour distance (10 m), grid resolution (0.1 m), standard deviation (2.0), and lower-bound height (8 m), were determined through empirical evaluation. The method was applied to a dataset of 402 UAV images to generate a dense point cloud. Clustering analysis identified six distinct objects, consistent with the number of lamp posts observed in the study area. The extracted heights showed a mean difference of 0.02 m when compared with field measurements. The results demonstrate the potential feasibility of the proposed approach for automated extraction of pole-like structures from UAV photogrammetric point clouds under the tested controlled conditions. However, the validation is limited to a single study area and should be interpreted as a proof-of-concept. Further evaluation using larger and more diverse datasets is required to assess robustness, scalability, and applicability across different environments.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148380972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emerging trends in remote sensing technologies for mineral resource mapping: an updated review","authors":"Mubashir Hussain,&nbsp;Hai Li,&nbsp;Matloob Hussain,&nbsp;Peng Yuan","doi":"10.1007/s12518-026-00773-4","DOIUrl":"10.1007/s12518-026-00773-4","url":null,"abstract":"<div><p>Remote sensing (RS) technologies have increasingly become essential in mapping mineral resources, as they offer innovative and cutting-edge solutions crucial for detecting and comprehensively assessing mineral deposits across diverse geological settings. This in-depth review thoroughly explores several prominent and newly emerging trends, encompassing significant advancements in satellite imagery, strategic combination of artificial intelligence (AI) and machine learning (ML) methodologies along with relevant case studies, the use of Light detection and ranging (LiDAR), RADAR (Radio Detection and Ranging) systems, drone and unmanned aerial vehicles (UAVs), real time data analysis and data fusion techniques. These sophisticated technologies markedly enhance the efficacy and accuracy of data collection, processing, and analysis, ultimately facilitating the formulation and execution of more effective exploration strategies that can considerably diminish the time and resources necessary for successful mineral exploration. Unlike previous reviews, we synthesize these trends to chart the evolution towards predictive mineral systems modeling, identifying key challenges and future pathways for the industry. By addressing both the innovative features and the constraints of remote sensing (RS) technologies, this analysis seeks to present a balanced viewpoint that enriches the ongoing dialogue regarding their future role in mineral resource exploration. Ultimately, this review functions not only as a reference for comprehending the current state of remote sensing (RS) technologies but also as a framework for future research and development initiatives to address existing challenges and optimize the potential advantages of these revolutionary tools.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148381029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remote sensing and GIS-based mapping of tree fall risk and hazard along railway lines in Germany","authors":"Miguel Cueva,&nbsp;Daniel Rutte,&nbsp;Sonja Szymczak,&nbsp;Peter Borrmann","doi":"10.1007/s12518-026-00769-0","DOIUrl":"10.1007/s12518-026-00769-0","url":null,"abstract":"<div><p>Trees located along railway corridors pose a significant risk to transport infrastructure, particularly in the context of increasingly frequent and intense storm events driven by climate change. Proactive vegetation management strategies are therefore essential for reducing the risk of disruptions caused by falling trees. This study applies an existing GIS-based assessment framework to analyse tree fall risk and hazard along railway infrastructure. The approach is demonstrated along a corridor in the Sachsenwald forest, Germany, using high-resolution individual tree crown (ITC) data derived from the Digital Twin Germany (DigiZ-DE) project. By integrating high-resolution airborne LiDAR data and openly available geospatial datasets, the methodology enables spatially explicit assessments at the level of individual trees. It combines two complementary analytical perspectives: a basic risk-oriented exposure analysis, which identifies trees that may directly affect railway infrastructure based on height and distance to the track, and an advanced hazard-oriented susceptibility analysis, which incorporates site- and tree-specific factors. This dual structure enables both rapid operational screening and more comprehensive, precautionary vegetation management. The results can inform targeted interventions, enabling resource-efficient and ecologically sensitive mitigation strategies. While the accuracy of the outputs depends on the timeliness and quality of the input data, the modularity of the approach supports its transferability across regions and infrastructure types. Although applied here to railway systems, the framework is readily adaptable to other transport or utility corridors and can contribute to climate-resilient infrastructure planning and natural hazard management.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00769-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Management of risk assessment for cultural heritage on GIS based solution for the historic center of Venice","authors":"Caterina Balletti,&nbsp;Enrico Gobbi,&nbsp;Greta Bruschi,&nbsp;Francesco Trovò","doi":"10.1007/s12518-026-00771-6","DOIUrl":"10.1007/s12518-026-00771-6","url":null,"abstract":"<div><p>This contribution aims to analyze the specific vulnerabilities of the historic center of Venice and to develop an evaluative method capable of taking into account both the intrinsic and extrinsic characteristics of the built environment through the use of georeferenced information systems. In particular, the study focuses on the analysis of vulnerabilities related to the gradual phenomenon of mean sea level rise and on an approach for the identification of site-specific surface temperatures based on remote sensing data. Finally, the work addresses the integration of the different types of datasets into a single spatial management platform (GIS), referenced to individual historic buildings.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward sustainable groundwater management in Arid regions: GIS–AHP integration of DRASTIC models and machine learning–based GQI assessment in the Medenine Aquifer, Tunisia","authors":"Bachaer Ayed,&nbsp;Nadia Khlif,&nbsp;Amna Bouri,&nbsp;Zied Haj-Amor,&nbsp;Salem Bouri","doi":"10.1007/s12518-026-00770-7","DOIUrl":"10.1007/s12518-026-00770-7","url":null,"abstract":"&lt;div&gt;&lt;p&gt;Groundwater vulnerability assessment is critical for sustainable management. However, previous research in arid regions, generally rely on traditional index-based models and their methodological adaptations. Combining conventional parametric approaches and machine learning (ML) methods, the objective of this study is to conduct a comparative assessment of groundwater vulnerability and water quality in the Medenine aquifer (Southeastern Tunisia) covering about 3100 km&lt;sup&gt;2&lt;/sup&gt;. Using GIS-based spatial analysis, four vulnerability models were applied: standard DRASTIC, modified DRASTIC, AHP-DRASTIC and AHP-modified DRASTIC. Geological, hydrogeological, and land-use factors were integrated to generate vulnerability maps. Moreover, the Groundwater Quality Index (GQI) was computed and integrated with ML models to analyze the hydrochemical parameters influence on groundwater quality and to explore nonlinear relationships. Based on the standard DRASTIC, the area was divided into low (2%), moderate (80%), and high (18%) vulnerability zones. However, the incorporation of the land use factor in the modified DRASTIC significantly increases the high vulnerability zones significantly to 57%. The AHP-modified DRASTIC model also classifies the area into low (28%), moderate (54%), and high (18%) vulnerability zones. The validation using nitrate concentration shows that the modified DRASTIC best matches contamination hotspots, while AHP-modified DRASTIC gives the most consistent vulnerability pattern. The incorporation of ML-based analysis combined with GQI results is useful for improving water quality assessment and understanding hydrochemical controls. The suggested strategy shows the value of integrating ML techniques and multi-criteria decision analysis for groundwater vulnerability and quality evaluation. The resulting maps represent valuable tools for groundwater protection and management, providing essential guidance for land and water resource management in arid regions.&lt;/p&gt;&lt;h3&gt;Graphical Abstract&lt;/h3&gt;&lt;p&gt;This graphical abstract delivers a concise visual synthesis of the methodological framework and key findings of the study, showcasing the transition from the classical DRASTIC approach to more advanced and better-performing vulnerability assessment models. The workflow begins with the seven intrinsic DRASTIC parameters, which constitute the foundation of traditional groundwater vulnerability evaluation. To enhance the model’s representativeness for the Midyan Basin, a land-use parameter is introduced to capture geomorphological characteristics and human-induced pressures that influence contamination risk. The Analytical Hierarchy Process (AHP) is then applied to refine the rating and weighting system, providing a more objective, consistent, and data-driven assessment of parameter significance. This methodological enhancement leads to the generation of four vulnerability maps: the standard DRASTIC, the land-use–integrated modified DRASTIC, and tw","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated remote sensing and google earth engine analysis of groundwater changes under climate and human influences: a case study of Iran","authors":"Hamed Shahidinia","doi":"10.1007/s12518-026-00764-5","DOIUrl":"10.1007/s12518-026-00764-5","url":null,"abstract":"<div><p>Groundwater (GW) is recognized as the second-largest freshwater reservoir in the world, following surface water. Over the years, it has been subjected to immense pressure to meet human demands. Additionally, one of the most significant consequences of human activities is climate change, which can alter the physical and chemical properties of GW, thereby impacting its ecological functions. This study examined GW patterns in Iran from 2000 to 2022, emphasizing the roles of both climatic and anthropogenic factors in altering GW levels. The present study evaluated various climatic and anthropogenic variables, including air temperature (AT), precipitation, snow cover, urban development, agricultural land, and surface water storage to explore their relationships with changes in GW. The Mann-Kendall (MK) trend test was employed to analyze these relationships. The MK trend analysis revealed a significant declining trend in GW (S = − 167, Z = − 4.04), accompanied by decreasing trends in surface water (Sen’s slope = − 15.29) and cropland area (Sen’s slope = − 337.25). Snow cover, precipitation, AT, and built-up areas exhibited non-significant trends over the study period. The analysis also revealed a correlation coefficient of -0.43 between AT and GW. Precipitation and snow cover were found to have a relatively moderate effect on GW levels, with a correlation coefficient of 0.43 and 0.54, respectively. A significant negative correlation of -1.00 was observed between built-up areas and GW from 2000 to 2022, indicating that an increase in settlement development has led to decrease in GW. The findings also indicated a positive correlation coefficient of 0.75 and 0.872 between cropland, surface water and GW, respectively, demonstrating that a decrease in cropland is linked to a decrease in GW and surface water. Overall, the results underscore the substantial influence of anthropogenic factors, such as urban expansion, agriculture, and surface water on GW in Iran. Anthropogenic factors, as well as climatic factors contributed to a reduction in GW by 33 mm, with a marked decline starting in 2009. Before this pivotal year, the average GW was around 629 mm, which dropped to 596 mm afterward.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00764-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating geomatics and mixed reality for heritage visualization and experiential learning in an educational context","authors":"Ashraquet Bastawrous,&nbsp;Anna Forte,&nbsp;Alessandro Lambertini,&nbsp;Gabriele Bitelli","doi":"10.1007/s12518-026-00755-6","DOIUrl":"10.1007/s12518-026-00755-6","url":null,"abstract":"<div><p>eXtended Reality (XR) technologies are increasingly employed for the visualization and communication of cultural heritage (CH), offering new opportunities for interpretation, documentation, and education. Within this context, the integration of XR with rigorous geomatic methodologies remains a relevant research topic, particularly for engineering-oriented applications. This paper presents a Mixed Reality (MR) application developed through a fully metric and integrated geomatic workflow in a university education context. The case study is the façade of the remaining structure of the San Salvatore ad Calchi medieval church, formerly identified as part of the Theodoric Palace, in Ravenna, Italy. Terrestrial Laser Scanning (TLS), close-range and unmanned aerial vehicle (UAV) photogrammetry, GNSS, and GIS data were systematically combined to produce an accurate three-dimensional documentation of the structure. A historical drawing from 1800’s – representing a previous constructive phase of the façade – was rectified using the obtained georeferenced orthophoto and transformed into a metrically consistent 3D model, enabling its in-situ superimposition onto the existing building within a MR environment. The application was developed in Unity and deployed on a Meta Quest 3 headset, permitting mutual visualization of real and virtual elements. Beyond the technical implementation, the study emphasizes the educational perspective. The complete Survey-to-XR workflow was integrated into a master’s-level Geomatics Engineering course focused on cultural heritage. Pre- and post-experience surveys indicate that XR enhances students’ understanding of integrated geomatic processes, spatial relationships, and data visualization and interpretation. The results support the use of MR as an effective educational tool when grounded in robust geomatic frameworks.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00755-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating machine learning and geomatics in modelling gully erosion susceptibility in Awka and Idemili areas of Anambra state, Nigeria","authors":"Nelson Onyebuchi Nwobi","doi":"10.1007/s12518-026-00763-6","DOIUrl":"10.1007/s12518-026-00763-6","url":null,"abstract":"<div><p>Gully erosion represents a severe geomorphic threat to tropical landscapes, yet multi-temporal assessments of its expansion and comparative susceptibility modelling remain under-researched. This study implements an integrated geomatics pipeline to quantify gully dynamics and model erosion susceptibility in two contrasting formations in Anambra State, Nigerian: the gully-prone Nanka sands formation of Idemili and the stable Imo clay formations of Awka area. Utilising the high-resolution Google Earth imagery of years 2000 and 2025, gully inventories were manually digitised, revealing a significant intensification of land degradation. In Idemili, gully frequency rose by 69%, with a 181.5% increase in surface area, while Awka experienced a 169.6% increase in surface area. To isolate the environmental drivers of this instability, an eleven-variable predictor suite comprising geomorphometric and pedological indices was integrated via Google Earth Engine and SoilGrids v2.0. Susceptibility was then modelled through a comparative framework, leveraging the non-linear predictive power of Random Forest (RF) alongside the inferential precision of Binary Logistic Regression (BLR). Performance metrics demonstrated that RF significantly outperformed BLR, achieving a superior recall of 0.9057 and an Area Under Curve (AUC) of 0.825. Variable importance analysis identified elevation, Topographic Wetness Index (TWI), and slope as primary topographic drivers, while high sand-to-clay ratios in Idemili served as critical pedological triggers. Susceptibility mapping confirmed that 29.43% and 28.65% of the Ameki Group falls within Moderate to High-risk zones respectively. On the other hand, for Awka the moderate to high-risk zones is 28.25% and 10.38% respectively. These results validate a transferable geomatics framework for erosion monitoring and underscore the necessity of integrating catchment-scale hydrology with soil-mineralogical properties to effectively reduce disaster risk.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}