Applied Geomatics最新文献

{"title":"Satellite-derived bathymetry in optically complex waters: testing an SDB–KED hybrid approach in the Venice Lagoon","authors":"Federica Gerla,&nbsp;Caterina Balletti","doi":"10.1007/s12518-026-00742-x","DOIUrl":"10.1007/s12518-026-00742-x","url":null,"abstract":"<div><p>Accurate and up-to-date bathymetric data are essential for the management and spatial planning of transitional coastal environments, yet conventional survey methods remain costly and difficult to implement at large spatial scales and high temporal frequency. This study investigates the potential of Satellite-Derived Bathymetry (<i>SDB</i>) as a cost-effective monitoring tool, using the Venice Lagoon as a morphologically and optically complex test site. Two methodological axes were developed. The first applies a stratified log-ratio Band Ratio Method (Stumpf et al. 2003) using Sentinel-2 multitemporal imagery, calibrated against the 2002 full-lagoon bathymetric dataset (Sarretta et al. 2010), achieving a Root Mean Square Error (RMSE) of 1.09 m and an R² of 0.87 across the entire depth range. The second axis refines this approach through advanced radiometric pre-processing, including sun-glint correction and clean-water compositing, combined with a Random Forest substrate classification and a stratified <i>SDB</i> calibration for sandy versus muddy seafloors. The resulting <i>SDB</i> was further integrated with Kriging with External Drift (<i>KED</i>), using the 2013 CNR-ISMAR (National Research Council Institute of Marine Sciences) multibeam survey as ground truth. This hybrid <i>SDB-KED</i> model achieved an R² of 0.73, an RMSE of 2.55 m, and a near-zero bias (-0.09 m) over more than 320,000 validation points, demonstrating effective bathymetric reconstruction across the full lagoon depth range. Residual uncertainties are concentrated at channel margins and in areas exceeding 15 m depth, where optical signal extinction limits satellite penetration. The proposed framework, implemented entirely on open-source data and cloud computing platforms, offers a replicable and scalable protocol for supporting spatial planning in dynamic coastal and marine environments.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00742-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323547","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":"The use of point clouds for non-destructive testing in structural health monitoring: a review","authors":"Iane Silva Pereira,&nbsp;Luis Augusto Koenig Veiga,&nbsp;Samir de Souza Oliveira Alves","doi":"10.1007/s12518-026-00753-8","DOIUrl":"10.1007/s12518-026-00753-8","url":null,"abstract":"<div><p>In recent years, research in Structural Health Monitoring (SHM) has intensified due to the growing demand for efficient inspection, monitoring, and maintenance of civil engineering structures. To meet this demand, studies have focused on developing non-destructive techniques (NDT) capable of providing accurate and continuous assessments. Among these, point clouds have emerged as a robust approach to capture comprehensive environmental data and provide detailed information for further analysis. This paper presents a state-of-the-art review summarizing advancements over the last ten years in the use of point clouds obtained by laser scanning and photogrammetry in SHM. Initially, an overview of the data acquisition techniques and their operational principles is presented. Subsequently, relevant studies selected through a systematic literature review are analyzed, emphasizing their methodologies, advantages, and limitations. Finally, the paper discusses the current challenges in integrating point cloud technologies into SHM frameworks and outlines potential future research directions aimed at enhancing their precision and automation capabilities.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00753-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148281801","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":"Built-up area mapping for sustainable urban planning: a novel satellite imagery based approach for detection of urbanization and landscape changes","authors":"Prema Somanathan Smitha,&nbsp;Kesavan Maya,&nbsp;Kulamulla Parambath Sudheer,&nbsp;Vijayalekshmi Muraleedharan Bindhu,&nbsp;Krishnan Sreelash,&nbsp;Damodharan Padmalal","doi":"10.1007/s12518-026-00754-7","DOIUrl":"10.1007/s12518-026-00754-7","url":null,"abstract":"<div><p>Accurate estimation of built-up areas provides valuable information on urbanization trends and infrastructure development, which enables policymakers to formulate decisions in the planning and management of urban infrastructure, resource allocation, and environmental management. Although many remote sensing-based indices are widely used to map built-up areas and monitor the impact of urbanization on climate change, the close resemblances in the spectral features of built-up areas with bare soil areas pose a great challenge in the precise delineation of built-up areas, which may result in their overestimation. Moreover, most of the existing built-up indices use thresholding techniques to discriminate built-up areas from bare soil, but their effectiveness is limited due to the spatio-temporal variability that affects mapping accuracy. The present research introduces a novel constraint-based built-up indexing (AMCBI) method that improves the distinction between target and background features with similar spectral signatures, eliminating the need for thresholding, and demonstrates its potential over existing spectral indices for precise mapping applications. The qualitative and quantitative analysis of AMCBI revealed overall agreement values of above 97% and an F1 score above 0.95 across all study sites. Further, the weak correlation between AMCBI and existing indices indicates reduced spectral confusion and eliminates the need for thresholding or masking techniques. The superiority of the AMCBI can be mainly attributed to the ability of the constraint-based indexing technique to de-correlate built-up areas from bare soil. The findings of the study suggest that AMCBI is a viable alternative in mapping built-up areas, especially in heterogeneous urban landscapes. Clinical trial number: not applicable.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148281659","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 google earth engine–driven machine learning framework for mustard crop mapping and suitability index assessment","authors":"Ahamed Ibrahim Abdul Rahim,&nbsp;Prabhakaran Moorthy,&nbsp;Bharathi Balu,&nbsp;Muthusankar Gowrappan,&nbsp;Lakshumanan Chokkalingam","doi":"10.1007/s12518-026-00760-9","DOIUrl":"10.1007/s12518-026-00760-9","url":null,"abstract":"<div><p>In India, mustard (Brassica juncea) is a significant cash and oilseed crop. This work aims to evaluate the different mustard crop classification algorithms for mapping mustard crop in Bharatpur District, Rajasthan, India, using Google’s Earth Engine (GEE) cloud platform and integrates socio-economic, economic, and terrain-topography parameters into the crop suitability analysis. Several methods were used, including unsupervised and supervised classification, HDRBC, RF, and machine learning-based classification, over multi-temporal satellite data captured 15 days apart from October 2022 until March 2023-Rabi, and geospatial indicators generated include FCC, NDVI, land use/land cover crop masks, and time series vegetation dynamics through custom GEE codes. According to the government statistics report, the area under mustard cultivation for the 2022–2023 season was around 274,500 ha. According to the model predictions, the areas under mustard cultivation are unsupervised, 296,763 ha; supervised, 290,954 ha; HDRBC, 280,534 ha; and RF, 278,541 ha. The model with the best accuracy (93.67%) was the RF, followed by HDRBC (87.20%), supervised (79.24%), and unsupervised (67.65%). To further ensure sustainable expansion in production, a crop suitability analysis of 20 parameters, 10 of which were socio-economic and 10 economic, was integrated with terrain and topographic parameters. The suitability map generated classified the study area into very low (9.27%), low (18.74%), moderate (27.27%), high (19.60%), and very high (25.11%) suitability areas. The results revealed a region such as Bharatpur, Sikri Ratti, Nagar, Semli, Pahari, Gopalgarh, Rarah, Chak Sana, and Jagiria, where mustard cultivation is currently supported under less favorable conditions. Pathena, Halena, Bhasawar, Wer, Karsado, Nithor, Luharu, Uchen, Khanua, and Kurka are areas exhibiting high to very high suitability. This study establishes that integrating GEE-based ML crop mapping with socio-economic and terrain-based suitability analysis effectively classifies optimal zones for mustard cultivation, supporting sustainable strategies to improve productivity.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148281700","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":"LOD-2 roof models reconstruction assisted by topological constraints using UAV point cloud","authors":"Meng-Qi Zhou,&nbsp;Iradaf Mandaya,&nbsp;Jiann-Yeou Rau","doi":"10.1007/s12518-026-00761-8","DOIUrl":"10.1007/s12518-026-00761-8","url":null,"abstract":"<div><p>With the rapid development of smart cities worldwide, the demand for 3D Geographic Information Systems (3D GIS) is increasing rapidly. This study focuses on the feasibility of reconstructing Level-of-detail 2 (LOD-2) 3D roof models. Unmanned aerial vehicles (UAVs) imagery was used to generate point cloud, providing three-dimensional coordinate observation information. Using true orthophotos and DSM, the two-dimensional polygons of roof structures were manually digitized to determine their planar boundaries. Least-squares adjustment with blunder error detection were then applied to perform roof plane fitting, ultimately determining the position and distribution of each roof plane in three-dimensional space. In the meantime, the least-squares adjustment with topological constraints was conducted. Corrections for various geometric conditions were applied to further refine the geometric relationships between roof polygons while avoiding topological errors, resulting in an optimized three-dimensional roof model. In the experiment, several buildings with various roof types were evaluated. Accuracy analysis was performed using manually measured 3D coordinates of roof corners. The reconstructed roof models have achieved a planimetric error of 20 centimeters and an elevation error of 15 centimeters, meeting the accuracy requirement of CityGML LOD-2. This confirms the applicability of the proposed roof model reconstruction method.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148281698","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":"Spatio-temporal variation of building morphology in Indian Cities from 2018 to 2023","authors":"Nishchaya Kumar Mishra,&nbsp;Sameer Patel,&nbsp;Sushobhan Sen","doi":"10.1007/s12518-026-00765-4","DOIUrl":"10.1007/s12518-026-00765-4","url":null,"abstract":"<div><p>By 2050, about two-thirds of the world’s population will live in urban areas, intensifying resource consumption in cities. Depending on urban sprawl and building morphology, this can lead to adverse environmental and public health effects, including increased energy consumption and the Urban Heat Island effect. While urban sprawl in India has been studied in terms of land-use changes, systematic assessments of spatio-temporal variability in building morphology remain limited. The current study addresses this gap by examining the evolution of building morphology in 11 major Indian cities over two years: 2018 and 2023. Remote sensing data, coupled with a deep learning model, Simultaneous building Height And footprinT extraction from Sentinel imagery (SHAFTS), produces building height and footprint maps at 100 m resolution. Variability index (VI), defined as the rate of change in spatial autocorrelation with shift distance, was used to quantify urban heterogeneity. In 2023, the mean footprint and height VIs were 0.0481 km⁻¹ and 0.0598 km⁻¹, indicating that height variability was ~ 24% greater than footprint variability. Between 2018 and 2023, the footprint variability decreased by ~ 5%, whereas height variability increased by ~ 4%. Chandigarh (0.0557 km⁻¹) and Mumbai (0.0351 km⁻¹) have the highest and the lowest footprint variability, respectively, while Bangalore (0.0739 km⁻¹) and Mumbai (0.0441 km⁻¹) have the highest and lowest height variability, respectively, in 2023. The divergence between footprint and height variability suggests increasing vertical densification. These findings provide insights into evolving urban form and a scalable approach for linking building morphology with urban heat, airflow, and building energy demand.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148281766","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":"Geospatial assessment of urban environmental and mitigation strategies for thermal stress in Lahore, Karachi, and Islamabad","authors":"Fizza Ejaz,&nbsp;Anila Kausar,&nbsp;Intikhab Ulfat,&nbsp;Sergij Vambol,&nbsp;Vladyslava Vambol,&nbsp;Olena Lyndiuk","doi":"10.1007/s12518-026-00759-2","DOIUrl":"10.1007/s12518-026-00759-2","url":null,"abstract":"<div><p>Rapid urbanization in South Asia has significantly altered urban land surface characteristics, intensifying thermal stress and environmental degradation. This study aims to conduct a comparative geospatial assessment of urban environmental conditions and thermal stress across three major Pakistani cities: Lahore, Karachi, and Islamabad. The analysis is based on Landsat 8 satellite imagery (June 2024) and integrates four remote sensing indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Bareness Index (NDBaI), Land Surface Temperature (LST), and Urban Thermal Field Variance Index (UTFVI). Spatial analysis was performed using ArcGIS, with data acquired from the USGS Earth Explorer platform to ensure consistent cross-city comparison under uniform seasonal conditions. Islamabad demonstrated the most environmentally balanced conditions, with NDVI ranging from − 0.1338 to + 0.5777 and LST between 19.73 °C and 36.69 °C, indicating strong vegetation cover and lower thermal stress. Karachi exhibited the highest environmental degradation, with low NDVI (–0.1381 to + 0.5947), high impervious surface dominance, and elevated LST values (25.48 °C to 41.19 °C), reflecting severe urban heat stress. Lahore showed intermediate but more extreme thermal conditions, with the highest recorded LST range (26.34 °C to 48.17 °C), indicating significant urban heat island intensity despite moderate vegetation cover. UTFVI patterns further confirmed strong spatial variability in thermal stress across all cities. The study is based on a single-season Landsat dataset, which provides a spatially representative but temporally limited snapshot of urban thermal conditions. Additionally, uncertainties related to spatial resolution and emissivity correction may influence absolute temperature values.</p><p><b>Practical value</b>: The findings provide actionable insights for urban planners, supporting green infrastructure development, reflective building materials, and heat mitigation strategies aligned with sustainable urban development goals (SDGs 11 and 13).</p><p><b>Originality/Significance</b>: This study contributes a comparative multi-city geospatial framework that integrates vegetation, bare land, and thermal indicators, addressing a critical gap in spatially explicit urban environmental assessments in Pakistan.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148281420","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":"Cloud computing and geospatial approach for assessment of soil erosion in lower Shiwalik foothills of Punjab, India","authors":"Pravin Dahiphale,&nbsp;Abrar Yousuf","doi":"10.1007/s12518-026-00736-9","DOIUrl":"10.1007/s12518-026-00736-9","url":null,"abstract":"<div><p>The <i>Shivalik</i> foothills of Punjab are highly susceptible to soil erosion due to their uneven topography, fragile geological formations, and intense rainfall events. The land degradation is going on in alarming rate. A Google Earth Engine-based cloud computing framework was utilized to implement the Revised Universal Soil Loss Equation (RUSLE) model for spatial assessment of soil erosion. The present study aims to assess and map soil erosion risk in the Shivalik foothill watersheds using the Revised Universal Soil Loss Equation (RUSLE) integrated with cloud-based geospatial techniques. The watersheds were delineated using the ALOS PALSAR Digital Elevation Model (DEM). The RUSLE parameters were derived from multiple geospatial datasets: rainfall erosivity (R) was estimated from CHIRPS rainfall data, soil erodibility (K) from FAO ISRIC SoilGrids, slope length and steepness (LS) were generated from DEM-based flow accumulation equations, the cover management (C) factor was derived from Sentinel-2 imagery, and the conservation practice (P) factor was obtained from a land use/land cover (LULC) map, prepared using the Random Forest classification algorithm. The classification accuracy was evaluated using a confusion matrix, and the model performance was validated through the AUC/ROC curve method. The results showed that the R factor ranged from 600.24 to 824.35, with the highest values observed in watersheds 6 and 8. The K factor varied from 0.0082 to 0.027, while the LS factor ranged from 0 to 12.13, indicating the presence of steep slopes susceptible to erosion. The C and P factors varied from 0 to 1, reflecting differences in vegetation cover and conservation practices across the watersheds. Integration of all RUSLE parameters revealed soil loss rates ranging from 0 to 13.31 t ha⁻¹ yr⁻¹, with watershed 7 experiencing the highest soil erosion. Spatial analysis indicated that upstream areas exhibited greater erosion due to steep slopes and sparse vegetation, whereas downstream areas showed relatively lower erosion because of gentler slopes and denser vegetation cover. The study demonstrates that cloud-based geospatial modelling provides an efficient approach for identifying erosion-prone zones and prioritizing watershed management strategies. The true AUC/ROC curve shows the value 0.837 indicates good accuracy of model for erosion estimation. The findings highlight the need for site-specific soil and water conservation measures such as contour bunding, check dams, and afforestation to reduce soil loss and promote sustainable watershed management in the <i>Shivalik</i> foothills.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148281299","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":"IndiGeo: an open-source Geospatial platform for multi-indicator SDG 15 environmental monitoring in Ecuador","authors":"Marcelo Rea-Guamán,&nbsp;Andrés Almeida-Jara,&nbsp;Brandon Masacela-Atupaña,&nbsp;Andrea López-López,&nbsp;Danilo Martínez-Espinoza","doi":"10.1007/s12518-026-00766-3","DOIUrl":"10.1007/s12518-026-00766-3","url":null,"abstract":"<div><p>Environmental monitoring for Sustainable Development Goal 15 (Life on Land) in resource-constrained institutions faces critical challenges: fragmented workflows, dependency on proprietary software, and limited reproducibility across institutions. Ecuador, despite being one of the world’s 17 megadiverse countries, lacks integrated tools, to operationalize the indicators developed by the NASA Life on Land project, which is a regional collaboration with Colombia and Peru for SDG 15 reporting. This paper presents IndiGeo, an integrated open-source geospatial platform that addresses these gaps by implementing two of the three NASA Life on Land indicators adopted by Ecuador: Human Footprint (HF) and Riparian Forest Cover (RFC). Unlike existing solutions such as Google Earth Engine that require cloud dependencies and advanced GIS expertise, IndiGeo provides reproducible ETL workflows in Python and R, interactive Shiny dashboards, and a React interface—all containerized via Docker for deployment on local or cloud infrastructure. Applied to continental Ecuador, IndiGeo processed HF data (90 m resolution, 2014–2022) and RFC data (30 m resolution, 1990–2020), revealing concerning trends: the \"Very Low\" HF category decreased from 60.17% to 57.43% of national territory, while RFC declined from 74.06% (2000) to 41.80% (2020). These results evidence IndiGeo’s capability to process multiple environmental indicators. Developed in collaboration with Ecuador’s Ministry of Environment (MAATE), IndiGeo implements official national methodologies, providing a replicable model for reproducible SDG 15 monitoring, aligned with official MAATE methodologies, in similar institutional contexts.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148281028","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 GNSS-GIS approach for high-resolution topographic mapping and resource assessment of a Mesozoic limestone quarry in Unaizah province, Saudi Arabia","authors":"Tarek Slama,&nbsp;Mohamed Hafedh Hamza,&nbsp;Noamen Baccari,&nbsp;Mohamed Montassar Ben Slama,&nbsp;Khadeijah Yahya Faqeih,&nbsp;Somayah Moshrif Alamri,&nbsp;Eman Rafi Alamery,&nbsp;Abdelaziz Sebei","doi":"10.1007/s12518-026-00756-5","DOIUrl":"10.1007/s12518-026-00756-5","url":null,"abstract":"<div><p>Topography is a decisive parameter usually taken into account during mining investigation and exploration process, particularly in shallow material reserve estimation within open pit mines or quarries. Consequently, obtained results and proposed exploration strategies and mining planning will substantially be dependent on used data quality. A workflow leading to high-resolution topographic mapping and accurate reserve estimation based on global navigation satellite system (GNSS) techniques and GIS, combined with data assessment and adjustment procedures is thereby proposed. The tested site belongs a mining industrial complex (several open pits) in the central province of Saudi Arabia. GNSS post-processing kinematic (PPK) was the conducted technique with controlled implementation of a ground reference station or GCP allowing post-processing operation. This latter was performed by the least-squares method and a suggested solution based on both single baselines and multi-baselines adjustment procedures. Data accuracy was thus maximized and sub-centimetric precision was yielded with a well-adjusted observation. A sub-metric grid-cell DEM was therefore produced by kriging interpolation of processed GNSS points, and high-resolution topographic maps were also extracted. DEM Error model has been analyzed using statistical parameters and residual cartography. Furthermore, non-extracted volume and the mass of limestone rocks into the investigated mining site were carried out using spatial gridding approach. In fact, spatial distribution of volumetric values was obtained and analyzed by considering the chosen topographic level for mining exploration and material extraction. In addition, a 2D block-mass estimation was readily conducted showing distribution of potential mass of the limestone rocks within the gridded surface of the tested site. Several 2D profiling were therefore extracted and used as additional and valuable tools for mining planning and extraction scenarios.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 3","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148281027","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}