Applied Geomatics最新文献

{"title":"AI-assisted terrain-based gis framework for spatial coherence assessment: Least-cost path and viewshed analysis","authors":"Fabio Crosilla","doi":"10.1007/s12518-026-00814-y","DOIUrl":"10.1007/s12518-026-00814-y","url":null,"abstract":"<div><p>Historical-territorial models propose spatial relationships among routes, nodes, terrain constraints and landmarks, but their geographical coherence is often assessed mainly through qualitative interpretation. This study develops a reproducible terrain-based GIS framework through an explicitly AI-assisted research process. Accessibility and landmark visibility are treated as two independent spatial tests. OpenAI's ChatGPT was used as an interactive methodological interlocutor to explore analytical alternatives, support the refinement of Python-based computational procedures and improve scientific clarity; all methodological choices, computational outputs, interpretations and conclusions were critically reviewed by the author. The terrain inputs, GIS settings and principal numerical results were independently checked by Dr Eleonora Maset of the University of Udine. The author retains responsibility for the study and its conclusions. The framework is applied to the Serbonis-Har Karkom corridor, proposed by Emmanuel Anati in the context of studies on a possible geographical reconstruction of the biblical Exodus. The historical interpretation is not tested here; the corridor is used as a spatially explicit model whose internal geographical organization can be evaluated independently. A NASA Shuttle Radar Topography Mission (SRTM) elevation model, with an effective working grid spacing of approximately 50 m in the study area, provides the common terrain basis. Functional accessibility is evaluated by comparing an unconstrained Least-Cost Path with a route constrained to pass sequentially through five predefined waypoints. The computation yields a reference-route length of 164.25 km and a constrained-route length of 188.79 km, corresponding to a 14.94% increase in length and a 16.89% increase in accumulated geomorphological cost. Landmark prominence is evaluated through terrain viewshed analysis from Har Karkom. Both independent tests support the spatial coherence of the investigated model while remaining explicitly distinct from historical validation.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149189582","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":"Assessing river-induced land area dynamics and NDVI-based vegetation conditions in Dibru-Saikhowa National Park (1964–2024) using remote sensing and geospatial techniques","authors":"Kuldeep Bora,&nbsp;Raju Gogoi,&nbsp;Kastauv Das,&nbsp;Jubilee Goswami","doi":"10.1007/s12518-026-00817-9","DOIUrl":"10.1007/s12518-026-00817-9","url":null,"abstract":"<div><p>Dibru-Saikhowa National Park (DSNP), a dynamic riverine protected landscape within the Brahmaputra floodplain, is subject to persistent geomorphological and vegetation changes driven by fluvial processes. This study assesses long-term land-area dynamics and vegetation-cover changes in DSNP using Remote Sensing (RS) and Geographic Information System (GIS) techniques. A 1964 Survey of India (SOI) toposheet and Landsat imagery from 1994, 2004, 2014 and 2024 were used to analyse changes in park area, erosion, accretion and Normalised Difference Vegetation Index (NDVI)-based vegetation classes. The mapped park area decreased from 428.90 km² in 1964 to 291.72 km² in 2024, representing a net reduction of 137.18 km². Erosion exceeded accretion over the longer observation period, although during 2014–2024, erosion (44.04 km²) and accretion (43.23 km²) were nearly balanced. NDVI analysis revealed substantial temporal variation in vegetation condition: moderate vegetation cover declined from 197.86 km² to 98.92 km², while low vegetation cover increased to 183.70 km²; the high vegetation density class was detected in 2004 but was not detected after 2004. Analysis of initial vegetation condition and subsequent land persistence showed that 64.19% of the 1994 park area persisted until 2024, while 35.81% was eroded. Moderate vegetation exhibited the highest persistence (87.82%), whereas 85.86% of initially non-vegetated areas were subsequently eroded. Field observations further documented siltation, vegetation mortality, natural regeneration and floodwater marks. The findings provide a geospatial baseline supporting Sustainable Development Goals (SDGs) 15 (Life on Land) and 13 (Climate Action) through evidence-based conservation and adaptive management of this dynamic riverine ecosystem.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132564","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":"Seasonal compatibility of RGB and multispectral vegetation indices in Amazon Cacao","authors":"Charlyngton da Silva e Silva,&nbsp;Luiz Antonio Soares Cardoso,&nbsp;Ronilson de Souza Santos,&nbsp;Carlos Rodrigo Tanajura Caldeira,&nbsp;Norberto Conejo Noronha","doi":"10.1007/s12518-026-00815-x","DOIUrl":"10.1007/s12518-026-00815-x","url":null,"abstract":"<div><p>Precision agriculture in cacao cultivation is constrained by multispectral sensor costs, limiting accessibility for smallholder farmers. This study evaluated the seasonal compatibility between RGB-derived visible reflectance and multispectral vegetation indices (NDVI, GNDVI, NDRE) to establish a low-cost monitoring approach for Eastern Amazon cacao plantations. We collected spectral data from 1,431 random sampling points across a 0.9-ha commercial cacao plantation during four seasonal periods (February, May, August, and November 2025) using UAV-mounted RGB and multispectral sensors. Pearson correlations and linear regression models assessed relationships between mean visible reflectance [(R + G+B)/3] and multispectral indices. Geostatistical analysis characterized spatial dependence structure across seasons. GNDVI demonstrated superior compatibility across all seasons (mean R² = 59.3%; range: 52.5–62.9%), outperforming NDRE (44.2%) and NDVI (40.8%) by 15–19% points. Temporal stability was 2.9× higher for GNDVI (SD = 4.7 pp) than NDRE (14.1 pp) and NDVI (12.7 pp). Peak compatibility occurred in May (R² = 62.9%) during wet-to-dry transition. The conversion equation (GNDVI = 0.509–0.000012 × Visible Reflectance; RMSE = 0.077) enables reliable estimation, retaining 59% of multispectral information. Strong spatial dependence (Co/(Co + C) &lt; 10%) was confirmed across all periods, with August providing optimal structure (range = 11.4 m) for management zone delineation. GNDVI is the most reliable RGB proxy for cacao monitoring, democratizing precision agriculture access through affordable technology while maintaining robust spatial and temporal performance year-round.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00815-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132995","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":"Tracing the invisible footprint of mining in Iran: spectral mapping of heavy metal contamination in arid soils with a remote sensing approach","authors":"Javad Momeni Damaneh,&nbsp;Ehsan Tamassoki,&nbsp;Mohammed Achite,&nbsp;Seyed Mohammad Tajbakhsh,&nbsp;Kusum Pandey,&nbsp;Celso Augusto Guimarães Santos","doi":"10.1007/s12518-026-00809-9","DOIUrl":"10.1007/s12518-026-00809-9","url":null,"abstract":"<div><p>This research applies an interdisciplinary framework to investigate the interactions between mining activities, heavy metal contamination, and vegetation response in the Sangan mining areas of Khaf County, Iran. Geochemical analyses of 24 surface soil samples were integrated with remote sensing data from Landsat 8-OLI to develop a robust methodology for monitoring stressed ecosystems. Chemical analysis revealed substantial accumulation of cadmium (Cd, barium (Ba) and arsenic (As). The mean Cd concentration (26.5 mg kg⁻¹) was about 88 times higher than the global shale average (0.3 mg kg⁻¹), which is likely associated with to intensive iron ore mining. This finding underscores the risk of these metals entering the food chain and threatening human health, especially in children, who exhibit higher absorption rates. From a remote-sensing perspective, soil-adjusted vegetation indices such as RVI (<i>R</i> = 0.82) and MSAVI2 (<i>R</i> = 0.79) outperformed NDVI (<i>R</i> = 0.40) in arid environments with sparse vegetation, providing a methodological advance for ecological monitoring. Correlation analyses between heavy metals and vegetation indices revealed a strong negative association between copper (Cu) and most indices, particularly IPVI (<i>R</i> = − 0.69), indicating copper’s phytotoxic effects on chlorophyll and biomass. In contrast, chromium (Cr) showed a weak positive correlation with NDVI (<i>R</i> = 0.36). These results highlight the need to select vegetation indices according to local environmental conditions and offer a practical framework for enhancing remote sensing–based monitoring of contaminated ecosystems. The study improves understanding of mining impacts and provides a replicable model for sustainable management of mining-affected landscapes worldwide.</p><h3>Graphical Abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00809-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132446","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":"Correction: Procedure for transforming ETRS/ITRS into a locally deformed national reference system","authors":"Jakub Kostelecky","doi":"10.1007/s12518-026-00813-z","DOIUrl":"10.1007/s12518-026-00813-z","url":null,"abstract":"","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132040","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 transformation of semi-arid landscapes under declining rainfall trends: a multi-decadal LULC assessment using google earth engine","authors":"Shiphali Preeti Aind,&nbsp;A. R. Quaff","doi":"10.1007/s12518-026-00810-2","DOIUrl":"10.1007/s12518-026-00810-2","url":null,"abstract":"<div><p>Distinguishing human-driven degradation from climate variability remains a critical challenge for sustainable land management. This study introduces a Sustainability Index (SI) model using Google Earth Engine to analyze Landsat imagery and CHIRPS rainfall data (2000–2025) in Khammam, India. The findings reveal significant landscape shifts, including a 24.5% loss of water bodies and a 56.6% decline in agricultural land, both linked to localized rainfall decline rates ranging from − 3 to − 8 mm·yr⁻¹ (Sen’s slope). By mapping eco-hydrological resilience and vulnerability, the SI identified hotspots nearing irreversible degradation. This scalable framework provides a robust methodology for data-scarce, semi-arid regions, directly supporting UN Sustainable Development Goals 13 (Climate Action) and 15 (Life on Land) by identifying high-risk zones for targeted intervention.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132402","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 analysis of urbanization and land use/land cover change in Kishoreganj Sadar Upazila, Bangladesh (2014–2024)","authors":"Pranta Bhowmik,&nbsp;Md. Aminul Haque Laskor,&nbsp;Md. Mohaimenul Islam","doi":"10.1007/s12518-026-00811-1","DOIUrl":"10.1007/s12518-026-00811-1","url":null,"abstract":"<div><p>Rapid urbanization has significantly altered land use and land cover (LULC) patterns in many developing regions, particularly in Bangladesh. This research examines the spatio-temporal dynamics of LULC in Kishoreganj Sadar Upazila between 2014 and 2024, using remote sensing and GIS to classify major land cover categories, quantify changes, and assess associated environmental implications. Landsat 8 (2014) and Landsat 9 (2024) OLI/TIRS satellite imagery from the United States Geological Survey (USGS) were analyzed using a supervised classification approach based on the Maximum Likelihood Classifier in ArcMap 10.8. Five major LULC classes were identified: Built-up Area, Vegetation, Waterbody, Agricultural Land, and Barren Land. Classification accuracy was assessed using confusion matrix analysis and the Kappa coefficient. The results revealed substantial LULC transformations during the study period. Built-up areas increased from 5.31% to 8.58%, while barren land expanded from 7.29% to 14.57%, reflecting rapid urban expansion and land surface modification. In contrast, waterbodies declined from 16.98% to 8.14%, and agricultural land decreased from 36.10% to 31.16%, indicating increasing pressure on natural and productive landscapes. Vegetation cover showed a moderate increase from 34.32% to 37.55%, mainly associated with homestead vegetation and seasonal land cover variability. The classified maps achieved overall accuracies of 82.99% and 85.48% for 2014 and 2024, respectively, with corresponding Kappa coefficients of 0.81 and 0.83, indicating reliable classification performance. The findings highlight the importance of sustainable land-use planning, wetland conservation, and environmentally informed urban management strategies to support long-term regional sustainability in the study area.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149035374","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":"Vertical accuracy of satellite-derived bathymetry developed using ICESat-2 ATL03 data for Brazilian coastal waters","authors":"Laura Coelho de Andrade,&nbsp;Kim Lowell,&nbsp;Nilcilene das Graças Medeiros,&nbsp;Arthur Amaral e Silva,&nbsp;Italo Oliveira Ferreira","doi":"10.1007/s12518-026-00812-0","DOIUrl":"10.1007/s12518-026-00812-0","url":null,"abstract":"<div><p>Satellite-Derived Bathymetry (SDB) offers a cost-effective solution for mapping and monitoring coastal environments, especially where in situ data are limited or outdated. The ICESat-2 mission, through its ATL03 photon-counting lidar product, provides high-resolution geolocated photon data suitable for depth retrieval in optically shallow waters. This study evaluates the vertical accuracy of ATL03-derived depths along four tropical coastal regions of Brazil by comparing photon returns, corrected for geophysical effects and empirical refraction, with reference depths from nautical charts referenced to Mean Sea Level (MSL). Accuracy metrics including RMSE, MAE, R², and Lin’s Concordance Correlation Coefficient (LCCC) were employed for each region and acquisition. Results showed the best performance in Pernambuco and Rio de Janeiro, with RMSE values between 1.04 and 1.38 m. In contrast, Ceará and Rio Grande do Norte displayed larger discrepancies. These regional differences may reflect variations in local environmental conditions, seabed characteristics, and the representativeness of available reference data, which can affect photon penetration and bottom detection. Subtrack-level analysis revealed considerable variability among ICESat-2 beams, highlighting the importance of individualized assessment and beam selection. Overall, ICESat-2 ATL03 data proved viable for bathymetric mapping in Brazilian coastal waters, with reliable performance to depths of 17–24 m, providing a critical baseline for future SDB research and operational applications. </p><h3>Graphical Abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00812-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149035425","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":"Applications of drone-based photogrammetry and multi-resolution raster data processing with fine detail for fish pass analysis","authors":"Andrej Šoltész,&nbsp;Lea Čubanová,&nbsp;Ján Rumann,&nbsp;Adela Rutzká,&nbsp;Alexandra Vidová","doi":"10.1007/s12518-026-00793-0","DOIUrl":"10.1007/s12518-026-00793-0","url":null,"abstract":"<div><p>High-resolution topographic data are crucial for accurately modeling hydraulic structures, particularly fish passes, where subtle geomorphic features govern flow heterogeneity and fish navigation. Conventional remote sensing approaches often lack the spatial resolution to resolve such microrelief. This study demonstrates the application of unmanned aerial vehicle (UAV) photogrammetry using Structure-from-Motion (SfM), which generates dense point clouds and digital elevation models (DEMs) with sub-centimeter accuracy. The UAV-SfM workflow enables rapid, non-invasive acquisition under controlled field conditions, followed by point-cloud processing in CloudCompare and rasterization into hydraulically suitable elevation models. These datasets support high-fidelity numerical modeling in 1D, 2D, and 3D, as well as the creation of physical models via 3D printing for laboratory hydraulic experiments. The study highlights the advantages of UAV-SfM in capturing complex terrain, estimating grain characteristics, and improving hydraulic model reliability, while also noting limitations such as restricted visibility areas and computational demands of large point-cloud datasets. By integrating high-resolution UAV-based topographic data, new opportunities for routine, high-detail hydraulic modeling and environmental engineering practice are opened.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00793-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149035421","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":"Identification of traffic accident hotspots in Hamirpur District using GIS","authors":"Sudhir Singh Rana,&nbsp; Shahrukh,&nbsp;Chander Prakash","doi":"10.1007/s12518-026-00806-y","DOIUrl":"10.1007/s12518-026-00806-y","url":null,"abstract":"<div><p>Worldwide, road traffic accidents remain a major public safety concern, particularly in mountainous regions where complex terrain and challenging roadway geometry increase crash risk. This study identifies traffic accident hotspots along National Highway 88 (NH-88) and State Highway 42 (SH-42) in Hamirpur District, Himachal Pradesh, India, using Geographic Information System (GIS)-based spatial analysis. Police-reported crash data collected over a ten-year period (January 2008 to December 2017) were combined with Global Positioning System (GPS)-based spatial information to prepare accident hotspot maps using Point Density Analysis (PDA) and Kernel Density Analysis (KDA). The analysis identified major accident hotspots at Hamirpur Bus Stand, Didwin Tikker, Dosarka, Pakka Bharo, Bhota, and Salouni, indicating locations requiring priority safety interventions. Field observations revealed that inadequate roadway geometry, sharp curves, insufficient sight distance, poor pavement condition, and driver-related factors such as rash driving were the major contributors to crash occurrence. The study demonstrates that GIS-based hotspot mapping provides an effective and practical approach for identifying accident-prone locations and supporting road safety planning in mountainous regions. Based on the identified hotspots, the study recommends improvements in roadway geometry, installation of appropriate traffic signs and safety devices, pavement maintenance and stricter enforcement of traffic regulations to reduce crash risk and support evidence-based road safety management.</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":"149034616","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}