Applied Geomatics最新文献

{"title":"Modelling biomass upscaling uncertainty: Evaluating two machine learning learners under signal saturation constraints","authors":"Bivash Dhali,&nbsp;Abhisek Santra,&nbsp;Sinchan Banerjee,&nbsp;Aniket Saha,&nbsp;Shreyashi S. Mitra,&nbsp;Tuhin Bhadra,&nbsp;Camelia Nandi","doi":"10.1007/s12518-026-00797-w","DOIUrl":"10.1007/s12518-026-00797-w","url":null,"abstract":"<div><p>Accurate estimation of aboveground biomass (AGB) is critical for understanding forest carbon dynamics, particularly in tropical deciduous forests experiencing increasing anthropogenic pressure. The Goaltore deciduous forests of eastern India, dominated by <i>Shorea robusta</i> (Sal), are undergoing rapid degradation due to encroachment, land-use conversion, and unplanned development, leading to habitat fragmentation and loss of carbon storage. To address limitations of plot-based inventories and single-sensor approaches, this study develops a high-precision biomass upscaling framework by integrating RISAT-1A C-band SAR and high-resolution LISS-IV optical data using a two-tier stacked ensemble modelling strategy. Tier-1 base learners (CatBoost, XGBoost, and GBM) were trained using Leave-One-Out Cross-Validation on 62 refined field plots, while Tier-2 meta-learners combined non-linear (LightGBM, Random Forest) and linear (Ridge, Elastic Net) models. The LightGBM meta-learner performed best (R² = 0.697; RMSE = 46.92 Mg/ha), outperforming linear benchmarks and mapping biomass values from 3.98 to 224.89 Mg/ha. Uncertainty mapping highlighted reduced reliability in high-biomass zones affected by SAR saturation. The proposed framework provides a scalable and transferable approach for forest carbon monitoring, supporting REDD+ reporting and contributing to SDG 13 (Climate Action) and SDG 15 (Life on Land).</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149034621","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 remote sensing–based assessment of long-term vegetation dynamics following dam construction in a Mediterranean watershed (Cyprus)","authors":"Marzia Gabriele,&nbsp;Eleni Loulli","doi":"10.1007/s12518-026-00795-y","DOIUrl":"10.1007/s12518-026-00795-y","url":null,"abstract":"<div><p>On Mediterranean intermittent rivers, dams reorganise downstream flow regimes and the shallow groundwater that sustains riparian vegetation. Where no environmental-flow requirement applies and no flood pulses are programmed, disentangling dam-driven vegetation change from background climate variability requires a robust multi-decadal spectral record combined with spatially explicit attribution diagnostics. This study presents a 40-year (1984–2024) Landsat Enhanced Vegetation Index (EVI) time series for the downstream riparian corridor of the Klirou–Malounta–Akaki Dam (central Cyprus; Akaki/Serrachis River; commissioned 2007), constructed by cross-sensor harmonisation of Landsat 5 TM, Landsat 7 ETM + and Landsat 8 OLI Collection 2 Surface Reflectance products using a Random Forest calibration pipeline in Google Earth Engine and R. Hydroclimatic controls were assessed using Mann–Kendall trend tests and Wilcoxon rank-sum tests on precipitation records from two local gauges; no significant long-term trend (τ = − 0.003, <i>p</i> = 0.566) and no significant pre- to post-dam precipitation shift (<i>p</i> = 0.061) were detected. Five-year step-change mapping combined with pixel-wise Wilcoxon level-shift, Pettitt change-point and Mann–Kendall trend attribution, applied under false discovery rate control, identifies a coherent, delayed greening response confined to a ~ 400 m right-bank riparian corridor, where the most responsive pixels show EVI gains of 75–92% relative to the pre-dam baseline, with the strongest signal concentrated in the 2011–2015 period, a lag of approximately four years consistent with post-impoundment groundwater stabilisation. Field validation at four sites confirms species composition dominated by non-native Eucalyptus spp. and phreatophytic understory expanding under reduced flood disturbance and stabilised shallow moisture. These findings demonstrate that spectral greening in a regulated Mediterranean corridor reflects ecological conditions that can inform environmental-flow policy and riparian restoration planning. The study provides a transferable template for dam-impact monitoring across the Mediterranean Basin and other semi-arid regions where long-term riparian vegetation records remain absent.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00795-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149034622","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":"Spatio-temporal assessment of agro-ecological covariates modulating puddling intensity in paddy rice systems using ensemble remote sensing and non-parametric frameworks","authors":"Keziah A. Oganyo,&nbsp;Mark K. Boitt,&nbsp;Duncan M. Kimwatu","doi":"10.1007/s12518-026-00796-x","DOIUrl":"10.1007/s12518-026-00796-x","url":null,"abstract":"<div><p>Puddling, the tillage of saturated soils before rice transplanting, is a water and energy-intensive field preparation in irrigated paddy systems which remains poorly quantified. We present a spatio-temporal analysis of covariates affecting puddling intensity across the Mwea Tebere Irrigation Scheme, Kenya between 2016 and 2024. The framework integrates satellite-derived biophysical indices from Sentinel-2; Normalised Difference Vegetation Index, Normalised Difference Water Index, Land Surface Temperature, Normalised Difference Red Edge, Vegetation Stress Index, Soil Moisture Index, Plant Nitrogen Accumulation index and Vegetation Soil Salinity Index; Sentinel-1 C-band surface roughness as a proxy for post-puddling soil microrelief, climatological indices; Precipitation Condition Index, Temperature Condition Index and Temperature Humidity Index. The statistical characterisation was based on 20,000 randomly distributed sample points, encompassing the scheme’s spatial heterogeneity. The declining NDRE (τ = −0.31, <i>p</i> = 0.024) indicated progressive deterioration of canopy nitrogen, linked to declining SMI for optimal nitrogen mineralisation, rising LST accelerating nitrogen volatilisation from flooded soils, and expansion onto inherently lower-fertility soils. The significant declining SMI trend (τ = −0.33, Sen’s slope = − 0.009 yr⁻¹, <i>p</i> = 0.018) is a key finding of this study. The dominant soil texture was Clay Loam (71.1%) which is highly conducive to hardpan formation and soil pH was optimum for rice most points sampled (mean = 6.26). Spearman correlation (ρ = 0.99) showed strong coupling between soil water retention and clay content. The framework offers a reproducible blueprint for monitoring relevant agro-ecological covariates of puddling.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986911","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":"GIS based ecosystem service value change analysis: a case of Barga Watershed, Central Ethiopia","authors":"Muleta Ebissa Feyissa,&nbsp;Heka Yonas,&nbsp;Birhanu Kebede","doi":"10.1007/s12518-026-00802-2","DOIUrl":"10.1007/s12518-026-00802-2","url":null,"abstract":"<div><p>Land use and land cover (LULC) changes severely degrades essential ecological services. This study was aimed at analyzing and quantifying the risk of landscape transformation in Barga Watershed, Ethiopia over a half century. The study used geospatial technology for automated LULC classification, change detection, and benefit transfer-based ecosystem service values (ESVs) trend analysis. The output showed that farmland and settlement areas were expanded by 6,423 and 524.7 hectares, respectively, while forestland, grassland, shrubland, and water bodies declined by 659.7, 3,105, 1,892.8, and 1,241 hectares respectively. These changes caused a sharp decline in the total ESV from US 5,335,760 in 1997, and further to US 11,224,110) occurred between 1973 and 1997. Among ecosystem services, provisioning services decreased by US 2,647,617 loss in water supply. Regulating services experienced the largest losses, totaling US 6,781,875), water treatment (US 715,408). Supporting services declined by US 470,681 and habitat loss (US 105,470 reduction. The study demonstrated the economic risk of landscape transformation showing the loss of high-value ecosystems indicating the requirement for intervention.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986680","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 remote sensing image selection scheme based on tree-type grid partitioning","authors":"Yufan Luo,&nbsp;Hui Zhao,&nbsp;Lingyi Han,&nbsp;Wenji Li,&nbsp;Lu Hou","doi":"10.1007/s12518-026-00794-z","DOIUrl":"10.1007/s12518-026-00794-z","url":null,"abstract":"<div><p>Selecting optimal remote sensing (RS) images from massive datasets to cover regions of interest is critical for large-scale RS applications. This paper proposes a Hierarchical Tree-Based Grid Partitioning (HTGP) method that efficiently identifies minimal image subsets while balancing coverage, cloud ratio, and overlap. HTGP employs a divide-and-conquer strategy to recursively decompose target regions into multi-resolution subgrids, forming a hierarchical spatial structure. A dynamic elimination mechanism is integrated to prevent selection from being trapped in local optima, while the tree-based design naturally supports parallel processing across subtrees, significantly accelerating computation. Experiments on three large-scale real-world regions demonstrate that HTGP achieves full coverage and consistently outperforms baseline methods in terms of cloud ratio, overlap, and execution time. These results indicate that the proposed method is both scalable and adaptable, providing a practical solution for RS image selection tailored to diverse application requirements and dataset characteristics.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986681","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":"Urban accessibility in historic built environments: A geomatics-based decision-support framework from mobile mapping data","authors":"Daniele Treccani,&nbsp;Abel Getachew Assefa,&nbsp;Andrea Adami","doi":"10.1007/s12518-026-00801-3","DOIUrl":"10.1007/s12518-026-00801-3","url":null,"abstract":"<div><p>Urban accessibility is a key component of inclusive and sustainable development, particularly in historic urban environments where complex spatial configurations and preservation constraints challenge standardised approaches. Traditional accessibility assessments are often based on regulatory thresholds and qualitative surveys, which limit their scalability and their ability to capture real spatial conditions. This paper presents a geomatics-based framework for the analysis of urban accessibility, integrating survey data acquired through Handheld Mobile Mapping Systems (HMMS), semi-automated feature extraction, and Geographic Information System (GIS) modelling. The proposed methodology focuses on the identification and measurement of accessibility-related features, including sidewalk geometry, obstacles, and covered areas, which are translated into structured indicators and associated with a pedestrian network. The extracted data are integrated into a GIS environment, where accessibility conditions are encoded as attributes and constraints of network segments. This enables the implementation of accessibility-oriented routing analyses, supporting the computation of paths adapted to different user needs. The methodology is tested in the historic city centre of Mantua (Italy), where the workflow demonstrates its ability to represent local accessibility conditions and to generate alternative routing scenarios based on measurable parameters. Results show that HMMS data provide an effective balance between acquisition efficiency and geometric detail, allowing the extraction of relevant features at the urban scale. The integration of geomatics and GIS enables the transformation of spatial data into operational tools for accessibility analysis, supporting both planning activities and real-world applications. The proposed framework contributes to bridging the gap between data acquisition and decision-making processes, offering a replicable approach for the analysis and management of accessibility in complex urban environments.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00801-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986770","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":"Geospatial assessment of environmental quality indicators in Annaba Port using sentinel-5P and sentinel-2 data","authors":"Latifa Boulahia,&nbsp;Radia Bouarroudj,&nbsp;Ahmed Alliouche,&nbsp;Mohamed Ikbal Farah,&nbsp;Serine Belhani,&nbsp;Imtiyaz Akbar Najar,&nbsp;Haythem Dinar,&nbsp;Nouh Rebouh","doi":"10.1007/s12518-026-00799-8","DOIUrl":"10.1007/s12518-026-00799-8","url":null,"abstract":"<div><p>Port cities and coastal urban areas worldwide face mounting pressure from combined atmospheric and marine pollution, driven by shipping traffic, industrial activity, and urban runoff, which together degrade air quality and threaten coastal ecosystem health. The Port of Annaba, northeastern Algeria, exemplifies this challenge, where vessel emissions and land-based discharges converge in a semi-enclosed coastal environment, yet systematic, spatially resolved monitoring of these pressures remains limited. This study assesses the spatial and temporal variability of atmospheric pollution and coastal water-quality indicators around the Port of Annaba using Sentinel-5P TROPOMI and Sentinel-2 MSI imagery processed within Google Earth Engine (GEE) and analysed in a Geographic Information System (GIS) environment. Atmospheric analyses were based on four representative monthly Sentinel-5P composites acquired between 2019 and 2023. Sentinel-5P products were used to examine carbon monoxide (CO), formaldehyde (HCHO), nitrogen dioxide (NO₂), ozone (O₃), and the ultraviolet aerosol index, whereas Sentinel-2 imagery was used to derive the Modified Normalized Difference Water Index (MNDWI), the Normalized Difference Chlorophyll Index (NDCI), and the Normalized Difference Turbidity Index (NDTI). Atmospheric class extents are reported as percentages of valid classified cells within an area of interest centred on the Port of Annaba, using class boundaries held constant across all four composites so that the dates are directly comparable. The high-CO class decreased from 35.1% in 2019 to 16.6% in 2020, increased to 27.5% in 2022, and declined to 14.8% in 2023, whereas the high-NO₂ class accounted for 24.6%, 15.1%, 14.4%, and 33.2% of valid cells in the corresponding composites. NDCI and NDTI revealed persistent spatial contrasts within the harbour, with the highest values occurring in semi-enclosed basins and port-adjacent waters. These results demonstrate the capability of integrated Sentinel-5P/Sentinel-2 observations and cloud-based geospatial processing to support regional-scale environmental screening of port areas. However, the satellite-derived indicators should be interpreted as relative screening-level information rather than calibrated surface concentrations, and future studies should incorporate comprehensive in situ observations to strengthen validation and source attribution.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986683","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 ground deformation in the Greater Accra Region of Ghana using SBAS-InSAR and Sentinel-1 time series","authors":"Emmanuel Adu-Afari,&nbsp;Caleb Kelly,&nbsp;John Ayer,&nbsp;Yaw Mensah Asare,&nbsp;Monica Naa Morkor Cudjoe,&nbsp;Samuel Ato Andam-Akorful,&nbsp;Gordon Foli","doi":"10.1007/s12518-026-00805-z","DOIUrl":"10.1007/s12518-026-00805-z","url":null,"abstract":"<div><p>The Greater Accra Region of Ghana is experiencing rapid urban expansion, increasing infrastructure loading, and growing hydrological stress, all of which heighten susceptibility to ground deformation. This study integrates multi-temporal Landsat-8 land use/land cover (LULC) mapping for 2017, 2020, and 2024, Sentinel-1 SBAS-InSAR time-series analysis for January 2018 to March 2025, GNSS validation, and an Analytic Hierarchy Process (AHP)-based susceptibility model to characterize deformation patterns and their controlling factors across the region. The LULC results reveal pronounced urban growth, with built-up area increasing from 508.78 km² (15.68%) in 2017 to 850.95 km² (26.22%) in 2024, while bare land declined from 1372.63 km² (42.30%) to 982.54 km² (30.28%) over the same period. SBAS-InSAR results indicate predominantly low-magnitude motion across much of the region, interrupted by six spatially coherent subsidence hotspots covering approximately 573.95 km² (17.69% of the region). Pixel-scale LOS velocities ranged from − 14.23 to + 4.13 mm/yr, while hotspot mean velocities ranged from − 3.76 to − 2.44 mm/yr, with within-zone standard deviations of 1.17–2.80 mm/yr. GNSS-based validation showed close agreement with the SBAS-InSAR time series at the monitored stations, providing independent site-specific support for the satellite-derived deformation estimates. Variance Inflation Factor screening reduced the initial conditioning variables from ten to eight prior to AHP weighting, and the resulting susceptibility map shows that high to very high susceptibility accounts for 1384.37 km² (42.66%) of the region. A quantitative spatial-overlap assessment showed that 66.11% of the valid susceptibility pixels within the six SBAS-InSAR deformation hotspots occurred within the High and Very High susceptibility classes, compared with 42.66% of the mapped region classified as High or Very High susceptibility. This provides quantitative support for the spatial consistency between the observed deformation hotspots and the modelled susceptibility pattern. The principal contribution of the study is an integrated observation–validation–susceptibility framework that links measured SBAS-InSAR deformation, independent GNSS observations, multi-temporal land-surface change and GIS-based susceptibility modelling, providing a transferable basis for deformation screening and risk-informed planning in data-sparse urban environments.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986771","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":"Integrating environmental sustainability certification data into a municipal spatial data infrastructure: a case study from Porto Alegre, Brazil","authors":"Wanessa Mezzomo,&nbsp;Paulo Galvão,&nbsp;Andrea Lopes Iescheck","doi":"10.1007/s12518-026-00808-w","DOIUrl":"10.1007/s12518-026-00808-w","url":null,"abstract":"<div><p>The increasing demand for sustainable and smart cities has reinforced the need to integrate environmental information into municipal Spatial Data Infrastructures (SDIs) to support urban planning and evidence-based decision-making. Although the Brazilian National Spatial Data Infrastructure (INDE) and municipal SDIs provide standardized mechanisms for managing geospatial information, they remain largely focused on two-dimensional data and lack methodological approaches for the semantic integration of environmental sustainability certification data into three-dimensional urban models based on international standards. This study proposes a methodological framework for structuring and semantically modeling data from the Environmental Sustainability Certification Program (PCSA) of Porto Alegre, Brazil, enabling its future integration into the municipal SDI through the CityGML conceptual model at the Level of Detail 2 (LoD2). The methodology comprised three stages: (i) collection and organization of 43 sustainability assessment criteria distributed across seven thematic dimensions; (ii) semantic conceptual modeling based on the CityGML standard; and (iii) demonstration of the proposed approach through a three-dimensional visualization to assess its applicability and identify implementation challenges. The resulting conceptual model organizes certification data into interoperable semantic structures associated with the CityGML <i>Building</i> and <i>BuildingPart</i> entities, providing a consistent basis for future implementation in municipal SDIs. Although the current stage of the municipal SDI and limitations in the available building dataset prevented a complete three-dimensional implementation, the proposed framework demonstrated its potential to support spatial analysis, environmental management, and urban planning while identifying the technical requirements for future deployment. The proposed methodology provides a replicable framework for integrating environmental sustainability certification data into CityGML-based SDIs, contributing to the advancement of three-dimensional urban information management and Smart City initiatives.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00808-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986457","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":"Procedure for transforming ETRS/ITRS into a locally deformed national reference system","authors":"Jakub Kostelecky","doi":"10.1007/s12518-026-00803-1","DOIUrl":"10.1007/s12518-026-00803-1","url":null,"abstract":"<div><p>With the intensive use of Global Navigation Satellite Systems (GNSS) methods in surveying, the transformation of measurement coordinates from geocentric reference coordinate systems to national reference coordinate systems is an urgent issue. If the national system exhibits large local distortions, it is necessary to determine local transformation parameters, which leads to ambiguity, or to apply a distortion grid in which it is interpolated. To define the distortion grid, a procedure is proposed using a network of triangles between unevenly spaced identical points, and the most suitable method is to use a 2D 4-parameter transformation in individual triangles with Jung’s transformation of residual deviations. At the same time, an optimal method of interpolation and distortion grid step is proposed. The proposed procedure was tested on a part of the Czech Republic with local deformations of the S-JTSK (System of the Unified Trigonometric Cadastral Network) national reference system, and a mean error of 3 mm was achieved on identical points and 8 mm on check points. Compared to the original procedure, the accuracy was improved by 38%.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148985354","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}