Applied Geomatics最新文献

{"title":"Recent (1981–2024) climatic trends in Italy from high-resolution reanalysis data and station-based observations","authors":"Alberto Vavassori,&nbsp;Matej Žgela,&nbsp;Maria Antonia Brovelli","doi":"10.1007/s12518-026-00734-x","DOIUrl":"10.1007/s12518-026-00734-x","url":null,"abstract":"<div><p>Extreme heat is among the most severe climate-related risks affecting Europe, with particularly pronounced impacts in the Mediterranean region, where temperatures are increasing faster than the global average. In recent decades, the frequency and severity of heatwaves (HWs) have intensified, leading to substantial societal and environmental impacts, including increased heat-related mortality. In this study, we provide a comprehensive assessment of recent climatic trends in Italy over 1981–2024, focusing on daily temperature extremes, HWs, and standard climate and heat stress (HS) indicators. The analysis integrates the VHR-REA_IT climate reanalysis dataset (∼ 2.2 km) with ground-based observations from the synoptic network of the Italian Air Force Meteorological Service. Trends are estimated using linear regression and analysed across multiple ecoregions and elevation ranges. Results reveal a clear and spatially coherent warming signal across Italy, characterised by increasing warm extremes, particularly in the frequency of tropical nights (up to + 7 days/decade), a marked rise in HW frequency (+ 2 events/decade), and a widespread reduction in cold extremes. The HS analysis further highlights a growing frequency of thermal discomfort conditions during the warm season, including nighttime periods (up to + 8 days/decade), with the strongest increases occurring in lowland and densely populated areas. These findings demonstrate the added value of very high-resolution reanalysis data for tracking spatial patterns in climate trends and provide a valuable basis for stakeholders to support climate adaptation and heat-risk management strategies in Italy under current and future climate change.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00734-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147937787","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":"Mapping urban cultural functions from high-resolution remote sensing: An MPHN-RT-DETR detection and spatial assessment framework","authors":"Zixuan Guo,&nbsp;Houbin Wang,&nbsp;Sameer Kumar,&nbsp;Lianyun Huang","doi":"10.1007/s12518-026-00737-8","DOIUrl":"10.1007/s12518-026-00737-8","url":null,"abstract":"<div><p>In the context of urban modernization and the construction of digital twin cities, relying on large-scale high-spatial-resolution data from remote sensing technology, rapid and stable identification of traffic-related targets, and further characterization and evaluation of the spatial pattern and evolution laws of urban cultural functional layout have become key technical requirements for refined urban cultural governance and optimized allocation of cultural space. However, the existing detection algorithms still have shortcomings and are unable to support the high-quality identification and stable analysis of the cultural functional space. Therefore, this paper proposes an improved target detection algorithm MPHN-RT-DETR based on RT-DETR, integrating multi-scale attention fusion, Haar wavelet downsampling, lightweight P-RepConv convolution, and a Normalized Wasserstein Distance loss. The function supply field is formed based on the category-function comprehensive weight matrix and the adaptive bandwidth Gaussian kernel density, enabling rapid quantitative assessment of cultural units. Compared with the baseline model, the accuracy of the improved model is increased by 9.07%, the recall rate is increased by 5.16%, the mAP@0.5 rate is increased by 7.58%. In summary, the MPHN-RT-DETR proposed in this paper not only meets the requirements for efficient and precise detection of remote sensing traffic targets, but also, through coupling with the urban spatial analysis module, realizes the automatic extraction and quantitative assessment of cultural functional layout. It can provide data support and decision-making basis for the optimization of public cultural facility location, the planning of cultural tourism routes, and the crowd guidance during major events.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147937790","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-based estimation of sorghum leaf area index (LAI) using normalized difference vegetation index (NDVI) from Sentinel-2A and evaluating regression models","authors":"H. Raj Chandrakant,&nbsp;K. Boomiraj,&nbsp;S. Pazhnivelan,&nbsp;N. K. Sathyamoorthy,&nbsp;M. Prasanthrajan ,&nbsp;A. Vashisth,&nbsp;P. Krishnan,&nbsp;J. Gayathri,&nbsp;G. Karthikeyan,&nbsp;S. Satheesh","doi":"10.1007/s12518-026-00730-1","DOIUrl":"10.1007/s12518-026-00730-1","url":null,"abstract":"<div><p>Satellite based monitoring within a Geographic Information System (GIS) framework provides an effective approach for identifying actionable target areas in semi-arid regions. Sorghum is a climate-resilient crop widely cultivated in these regions, which are often characterized by erratic rainfall and limited irrigation availability. However, monitoring sorghum growth and condition has remained challenging for informed decision-making. Recent advances in satellite sensors and the improved accessibility of large volumes of satellite data through the Google Earth Engine (GEE) platform have created new opportunities for crop monitoring. Sentinel-2A MSI data (Rabi 2022–23) were used to evaluate regression models for estimating LAI from NDVI. A total of 160 field-based LAI observations were collected from Solapur and Ahmednagar districts, Maharashtra, representing diverse agro-ecological conditions. A secondary objective was to isolate sorghum pixels across the study area in order to assess spatial variability in crop distribution. The results revealed that second-order polynomial regression models outperformed linear, logarithmic, exponential, and power models, with higher coefficients of determination (R² &gt; 0.40) and lower root mean square error (RMSE: 0.83–0.89) when evaluated at the district level. When the datasets from both districts were combined, the model performance was moderate (R² &gt; 0.25; RMSE = 1.20), reflecting the influence of spatial variability. Crop area classification showed good accuracy, with Kappa coefficients exceeding 0.70 and estimates within ± 5% of official government statistics. Overall, the findings highlight the potential of integrating NDVI derived from satellite data with ground-based LAI measurements using polynomial regression models for accurate and near real-time crop monitoring. This approach can support climate-smart agriculture, precision farming, and policy-level planning in semi-arid regions.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147829906","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":"Precision of two topographic methods for generating digital elevation models in the high-andean areas of Peru: study case road fundo bocanegra - Chachapoyas","authors":"Teodoro B. Silva-Melendez,&nbsp;Jorge Soto-Pulce,&nbsp;Rolando Salas López,&nbsp;Jhon A. Zabaleta-Santisteban,&nbsp;Angel J. Medina-Medina,&nbsp;Abner S. Rivera-Fernandez,&nbsp;José A. Sánchez-Vega,&nbsp;Nerci M. Noriega-Salazar,&nbsp;Manuel Oliva-Cruz,&nbsp;Katerin M. Tuesta-Trauco,&nbsp;Aqil Tariq,&nbsp;Jhonsy O. Silva-Lopez,&nbsp;Nilton B. Rojas-Briceño,&nbsp;Elgar Barboza","doi":"10.1007/s12518-026-00721-2","DOIUrl":"10.1007/s12518-026-00721-2","url":null,"abstract":"<div><p>Digital Elevation Models (DEM) are useful for solving various problems in civil works planning, risk assessment, hazard prediction, and spatial modeling. This research evaluated the accuracy of DEM generation produced from images obtained from unmanned aerial vehicles (UAV) in hovering mode (DEMf) and with eight Geodetic Control Points (DEMGCP), contrasted with the DEM generated from data from a Total Station - ST (DEMST) on the Bocanegra - Chachapoyas road, Peru. The photogrammetric images were processed with a spatial resolution of 50 cm. The mean, median, mode, mode, range, and standard deviation parameters were compared between DEMf, DEMGCP, and DEMST. In addition, statistical tests such as t-Student (α = 0.05) were applied for the difference of DEMs. The results showed a higher accuracy of the DEMGCP-derived vertical axis concerning DEMf with a combined error of 69.1 cm and an R2 of 0.999986; this is due to the use of GCPs to rectify the position on all axes of the generated DEM. Indeed, the use of UAVs is of great applicability in the generation of DEMs. They complement the data from a total station for the formulation, development, and implementation of civil works projects.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00721-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147937929","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":"Multi-satellite remote sensing, GIS, and bathymetric survey-based assessment of reservoir sedimentation in Damsal Dam, Punjab, India","authors":"Mahesh Chand Singh,&nbsp;Jaswinder Singh,&nbsp;Sukhdeep Kumar","doi":"10.1007/s12518-026-00732-z","DOIUrl":"10.1007/s12518-026-00732-z","url":null,"abstract":"<div><p>Reservoir sedimentation poses a critical threat to water resource sustainability, particularly in erosion-prone regions such as the <i>Shivalik</i> foothills of Punjab. The main aim of this study was to quantitatively assess long-term reservoir sedimentation, live storage loss, and sediment yield in Damsal Dam, Hoshiarpur, using an integrated satellite (Sentinel-2 and Landsat-8)-based and Geographic Information System (GIS)-supported approach validated with bathymetric survey measurements. Sentinel-2 and Landsat-8 datasets were analysed to enable cross-validation of results and enhance the reliability of sedimentation and storage loss estimates. The Modified Normalized Difference Water Index (MNDWI) was applied to delineate water spread areas from multi-temporal imagery (1990–2026), and elevation-area-capacity curves were generated to estimate live storage capacity. The results revealed a substantial reduction in reservoir capacity due to long-term sedimentation. At the maximum elevation of 412.0 m, live storage capacity declined from the designed 315.0 ha-m to 251.9 ha-m (bathymetric survey), 227.4 ha-m (Sentinel-2), and 209.0 ha-m (Landsat-8). Over 36 years, this corresponds to live storage losses of 27.2% and 33.1%, with annual depletion rates of 0.76% and 0.92% as estimated from Sentinel-2 and Landsat-8 data, respectively, with the slight variation primarily attributed to differences in spatial resolution, revisit frequency, and sensor characteristics between the two satellite datasets. Sediment yield from the catchment was significantly higher during the post-monsoon season, increasing from 12.03 to 15.04 Mg ha⁻¹ yr⁻¹ (Sentinel-2) and 14.57 to 18.21 Mg ha⁻¹ yr⁻¹ (Landsat-8). Water quality analysis showed that all parameters (pH, EC, TDS, Ca²⁺, Mg²⁺, Cl⁻, HCO₃⁻) were within permissible limits for irrigation, while catchment soil exhibited moderate fertility with improved post-monsoon water-holding capacity. Overall, the study confirms the effectiveness of integrated satellite and bathymetric approaches for quantifying reservoir sedimentation and supports their application for sediment management and sustainable water resource planning in small earthen dams of the <i>Kandi</i> region.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147829180","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":"Explainable geo-informatics for spatial solar suitability analysis in Gauteng Province, South Africa","authors":"Mohlehli George Mohlehli,&nbsp;Godwell Nhamo","doi":"10.1007/s12518-026-00738-7","DOIUrl":"10.1007/s12518-026-00738-7","url":null,"abstract":"<div><p>The global transition toward low-carbon energy systems requires spatially explicit, transparent, and sustainable approaches for identifying suitable renewable energy sites, particularly in rapidly urbanising regions. This study develops an explainable geospatial artificial intelligence framework to support sustainable solar energy planning in Gauteng Province, South Africa, where competing land uses and limited ground-based solar data constrain evidence-based decision-making. The objective was to identify environmentally and operationally suitable areas for solar development while ensuring model transparency and spatial interpretability. Remote sensing-derived vegetation, surface moisture, built-up land, and topographic variables were integrated into a gradient boosting regression model, and feature contributions were interpreted using explainable artificial intelligence techniques. Spatial clustering was applied to classify suitability zones. The model demonstrated strong internal predictive performance (area under the curve = 0.98). Results show that surface moisture conditions and elevation exert the strongest influence on suitability, with large portions of the province exhibiting high relative suitability and distinct spatial hotspots emerging in central regions. The findings demonstrate that urban and peri-urban landscapes offer substantial sustainable solar potential. This study provides a transparent, scalable, and policy-relevant decision-support framework that advances sustainable energy planning in data-scarce environments, particularly across rapidly developing regions of the Global South.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00738-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147829867","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":"Urban expansion mapping: an analysis of land cover change and sustainability in Delhi using GIS (2000–2025)","authors":"Vikrant Pachouri,&nbsp;Jigyasa Parmar,&nbsp;Prafull Kothari,&nbsp;Rajesh Singh,&nbsp;Anita Gehlot,&nbsp;Amit Kumar Thakur,&nbsp;Sudhanshu Dogra","doi":"10.1007/s12518-026-00735-w","DOIUrl":"10.1007/s12518-026-00735-w","url":null,"abstract":"<div><p>The environmental structure of Delhi, India, has changed significantly within the last two decades due to rapid urbanisation, which has caused significant concerns about land transformation, the health of vegetation, and long-term urban sustainability. The study examines the change in land use and land cover (LULC) in Delhi between 2000 and 2025 and explores it through a multi-temporal remote sensing and Geographic Information System (GIS) framework. The 2000, 2010, 2015, and 2025 Landsat images were examined to produce built-up maps, harmonised LULC classifications, as well as spectral indices such as the Normalised Difference Vegetation Index (NDVI) and the Normalised Difference Built-up Index (NDBI). The findings show that the settlement area increased significantly, by 60,065.92 ha in 2000 and 76,704.76 ha in 2025, whereas the barren/fallow land decreased significantly by 42,813.83 ha to 8,679.84 ha. Water bodies and vegetation were non-linear, and they portrayed the spatial complexity of environmental change in a fast-growing urbanising megacity. The patterns of Multi-temporal NDBI reveal the gradual urbanisation and growing dominance of the impervious surface, whereas NDVI outcomes reveal the fact that the state of vegetation has become more fragmented and spatially uneven, and the stronger green signatures are concentrated predominantly in the peripheral areas. The results indicate that the urbanisation in Delhi has involved both outward and internal reorganisation to impose substantial ecological restructuring in the urban environment. The study creates a new insight into the urban change in Delhi and proves the importance of monitoring using GIS to evaluate land cover dynamics and sustainability pressure in fast-growing cities.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796833","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":"Toponymy as an environmental indicator: GIS and geostatistical approaches to cultural landscape dynamics","authors":"Oihana Mitxelena-Hoyos,&nbsp;José-Lázaro Amaro-Mellado","doi":"10.1007/s12518-026-00726-x","DOIUrl":"10.1007/s12518-026-00726-x","url":null,"abstract":"<div>\u0000 \u0000 <p>Landscape results from the prolonged interaction between humans and territory, while toponymy reflects the perceptions of its inhabitants. This study develops a transferable methodological framework that integrates Geographic Information Systems (GIS) with geostatistical tools to analyze the relationship between landscape classifications and the semantic dimension of toponymy as complementary indicators for environmental monitoring. The research was carried out in the Urola-Kosta (Basque Country, Spain) region, using high-resolution open data provided by public administrations. The most detailed available landscape typology was employed, together with a dense toponymic database in which place names were categorized according to semantic roots. The analytical workflow incorporated nearest-neighbor analysis, kernel density estimation, spatial autocorrelation, multiple correspondence analysis, and co-occurrence analysis. Results show that toponymic density and texture provide measurable indicators of land use and landscape dynamics: urban areas concentrate higher toponymic density, phytonymy dominates agrarian and forest contexts, and temporal dynamics emerge in the persistence of vineyard-related names versus the absence of names for recent forest plantations. The study reinforces the concept of the Toponymic Cultural Landscape (TCL) and demonstrates that toponymy can serve as a replicable proxy for monitoring land use change and cultural landscape dynamics. This approach provides new metrics for environmental assessment by linking intangible heritage to spatial analysis. Finally, it also illustrates how digital methods can help integrate cultural dimensions into sustainable territorial development, in line with Sustainable Development Goal 11.4.</p>\u0000 </div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00726-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147797116","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":"Assessing the applicability of Sentinel-1 derived DEMs for forest terrain and canopy studies","authors":"Elizaveta Avoiani,&nbsp;Zahra Dabiri,&nbsp;Dominika Krausková,&nbsp;Kristýna Šleglová,&nbsp;Zdeněk Patočka,&nbsp;Petr Vahalík","doi":"10.1007/s12518-026-00716-z","DOIUrl":"10.1007/s12518-026-00716-z","url":null,"abstract":"<div>\u0000 \u0000 <p>Digital Elevation Models (DEMs) are indispensable tools in geospatial analysis, especially for applications in forested areas. The main aim of this research is to assess the applicability, accuracy, and usability of DEMs derived from Sentinel-1 Synthetic Aperture Radar (SAR) for forest studies, in comparison to the commercial high-resolution DEMs. Using the interferometric SAR (InSAR) technique, two DEMs were generated in 2018 using descending-orbit Sentinel-1 SAR (C-band) data for each SAR polarisation band (VH and VV). Elevation errors (LiDAR − DEM) were analysed using multivariate ordinary least squares regression with heteroscedasticity-robust (HC3) standard errors (<i>n</i> = 5228). Both Sentinel-1 VV and VH models explained a modest proportion of error variability (R² = 0.056, <i>p</i> &lt; 0.001). For VV, elevation error increased significantly with slope (β = 2.72 m deg⁻¹, <i>p</i> &lt; 0.001) and aspect (β = 18.78 m, <i>p</i> &lt; 0.001), while crown density reduced error magnitude (β = −0.40 m %⁻¹, <i>p</i> &lt; 0.001). The VH model showed similar explanatory power (R² = 0.056), but larger absolute deviations, with slope (β = 2.93 m deg⁻¹, <i>p</i> = 0.001) and aspect (β = 18.10 m, <i>p</i> &lt; 0.001) again acting as dominant predictors. Tree species categories were not statistically significant in contributing to elevation errors. WorldDEM elevation errors showed stronger systematic relationships with terrain predictors, resulting in higher explained variance (R² = 0.124, <i>p</i> &lt; 0.001) and greater statistical stability, with weaker but significant relationships to slope (β = 0.16 m deg⁻¹, <i>p</i> = 0.017) and crown density (β = −0.11 m %⁻¹, <i>p</i> &lt; 0.001). Overall, Sentinel-1-derived DEMs showed stronger sensitivity to terrain geometry and canopy structure and substantially higher variability than WorldDEM. Despite the observed variations in Sentinel-1-derived DEMs within the forest application, Sentinel-1 SAR data remains a valuable data resource due to its free and globally available data, updated every six days (6–12 days depending on constellation availability), with an extensive archive spanning nearly a decade. This accessibility makes it a useful option for large-scale studies and regions where commercial SAR datasets are not feasible. The results of this study can enhance our understanding of errors associated with each Sentinel-1 polarisation when generating DEMs in forested areas. Further research should focus on post-processing of Sentinel-1-derived DEMs, such as combining DEMs derived from ascending and descending flight directions. Overall, this study provides an in-depth assessment of Sentinel-1-derived DEMs and highlights trade-offs between cost, accessibility, and accuracy when selecting DEMs for forest applications.</p>\u0000 </div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00716-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796785","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":"Satellite-based modeling of mangrove forest degradation in the coastal waters of Bintan Island, Indonesia","authors":"Money Carattri Kusuma Werdani,&nbsp;Anang Dwi Purwanto,&nbsp;Mariska Astrid Kusumaningtyas,&nbsp;Aprizon Putra,&nbsp;Emma Rahmawati","doi":"10.1007/s12518-026-00725-y","DOIUrl":"10.1007/s12518-026-00725-y","url":null,"abstract":"<div>\u0000 \u0000 <p>Bintan Island is an island in Indonesia with high potential for mangrove ecosystems. However, the surrounding environment has disturbed the mangrove’s condition in the location. This study aims to analyse the extent and density of mangrove vegetation in the coastal waters of Bintan Island based on satellite image data. The data used for this study were Satellite Pour l’Observation de la Terre (SPOT)-4 and SPOT-7 images acquired in 2012 and 2019, respectively, and field measurement data. The image classification to separate mangrove objects used the maximum likelihood method, while the calculation of mangrove density used the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Optimized Soil Adjusted Vegetation Index (OSAVI) methods. We also used the linear regression method to determine the relationship between the vegetation index and mangrove density. The results showed that there was a decrease in mangrove distribution area by 70 hectares (ha), where the mangrove forests have been converted mostly into buildings and settlements. NDVI is the most effective vegetation index for mapping the mangrove density in the study area. The calculation results of mangrove density using NDVI showed that there was a decrease in very dense and dense classes of 86.65 ha and 32.59 ha, respectively. The accuracy assessment obtained an overall accuracy of 84.6% and a kappa value of 85.7%. Overall, SPOT-based mapping revealed a 70-ha loss and declining high-density stands, while the NDVI best tracked field density, enabling practical monitoring to support targeted conservation in the coastal waters of Bintan.</p>\u0000 </div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738284","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}