Applied Geomatics最新文献

{"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}

{"title":"Automatic tree detection in varying urban environments using airborne LiDAR data","authors":"Renato César dos Santos,&nbsp;Cleber Junior Alencar,&nbsp;Henrique Cândido de Oliveira,&nbsp;Mauricio Galo","doi":"10.1007/s12518-026-00720-3","DOIUrl":"10.1007/s12518-026-00720-3","url":null,"abstract":"<div>\u0000 \u0000 <p>Tree detection in urban environments is an essential stage for applications in urban mapping, city management, and forest inventory. In recent decades, airborne LiDAR data have gained prominence owing to their ability to represent objects with high geometric quality. In this paper, we propose a straightforward geometry-based tree detection approach exploring the local omnivariance feature and K-means clustering. The main contribution can be summarized as an automatic tree detection approach based on point cloud geometry, which does not require sample data for training, as in machine-learning approaches. In addition, the approach uses a set of parameters/thresholds that can be intuitively defined. The experiments were conducted across three datasets with distinctive urban characteristics. Obtained results indicate that the proposed approach has strong potential for detecting trees from airborne LiDAR data with different point densities and acquired in urban environments of varying complexities, yielding an average <i>F</i>_<i>score</i> of around 95%. Compared with related approaches, the results are similar to machine learning-based strategies and, generally, resulted in superior performance in terms of completeness, as evidenced by the low occurrence of omission errors.</p>\u0000 </div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00720-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738006","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 urban land use dynamics implication on land surface temperature using remote sensing data: The case of Addis Ababa Sub-Cities, Ethiopia","authors":"Esubalew Nebebe Mekonnen,&nbsp;Ephrem Gebremariam,&nbsp;Aramde Fetene,&nbsp;Liku Workalemahu","doi":"10.1007/s12518-026-00714-1","DOIUrl":"10.1007/s12518-026-00714-1","url":null,"abstract":"<div>\u0000 \u0000 <p>Rapidly growing cities like Addis Ababa are facing dynamic changes in the land-use land cover (LULC), resulting in a significant increase in the land surface temperature (LST), exacerbating the vulnerability to calamities of climate change. The objective of this paper was to investigate the urban heat island (UHI) effect driven by land-use/land-cover dynamics in Addis Ababa sub-cities, Ethiopia. In this study, LULC, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI) were extracted from Landsat 5TM (1985) and Landsat 8 OLI/TIRS (2020) using Geospatial technologies and JMP statistical software. The land surface temperature anomaly was calculated using mono-window algorithm. Brightness temperature, Land Surface Emissivity (LSE), and NDVI were computed to estimate land surface temperature (LST). An object-oriented supervised classification with the maximum likelihood method was applied to detect the LULC change between 1985 and 2020. The results revealed that the built-up area increased from 195 km² (37.5%) in 1985 to 326.3km² (62.8%) in 2020. While forest, cultivated land, and bare land were reduced from 61.1 km² (11.7%), 115.6 km² (22.3%), 147.4 km² (28.4%) in 1985 to 32.2 km² (6.4%), 38.6 km² (7.4%), 118.3km² (22.8%) in 2020 respectively. These land cover dynamics contributed to the increasing trend of mean LST in the study area from 27.2 °C in 1985 to 29 °C in 2020, indicating an increase of 1.8 °C over the study period. The findings from the correlations of LST with NDVI, NDBI, and digital elevation model (DEM) signposted that there was a negative correlation between vegetation coverage, elevation, and mean LST, while a linear positive correlation was found with the built-up index. All 11 sub-cities have seen an increase in land surface temperature, although Kirkos, Arada, Addis Ketema, and Lideta experienced the most, ranging from 2.79 °C to 4.72 °C. The maximum mean LST increment for both periods (1985 and 2020) was detected around built-up areas (rose by 2.68 °C) and bare land (ramped up by 2.64 °C), whereas the minimum temperature was recorded around cultivated land, water bodies, and forests. Thus, an amplified surface urban heat island in Addis Ababa city was observed, and this eventually could disturb the urban microclimate and trigger multifarious environmental and human health impacts.</p>\u0000 </div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738192","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":"Horizontal displacement monitoring in mining areas using UAV data from photogrammetry and from laser scanning","authors":"Natalia Wielgocka,&nbsp;Grzegorz Jóźków,&nbsp;Norbert Pfeifer","doi":"10.1007/s12518-026-00724-z","DOIUrl":"10.1007/s12518-026-00724-z","url":null,"abstract":"<div>\u0000 \u0000 <p>In monitoring surface deformation related to underground mining activities, analyzing both vertical (dU) and horizontal (dN, dE) displacements is essential. Most studies focus on 1-dimensional vertical movements, overlooking horizontal displacements, which are crucial for understanding the full 3-dimensional deformation pattern caused, which reduces the effectiveness of risk assessment. This study explores four approaches for analyzing horizontal displacements using image-based, point cloud-based, and surface-based methods, utilizing photogrammetric and laser scanning data from unmanned aerial vehicles (UAVs). The methods were evaluated in terms of accuracy, usability, and limitations. The accuracy analysis was conducted using Global Navigation Satellite Systems (GNSS) as reference data. The results showed that the highest accuracy was achieved with UAV photogrammetry, using automatic analysis of corresponding points from raw images (RMSE: 0.030 m, 0.020 m, 0.032 m for the dN, dE, and dU displacement components, and 0.031 m for the horizontal displacement magnitude). However, this method is most effective in urban areas with consistent lighting and stable objects. For point clouds and digital surface models (DSMs), the results achieved accuracies of 0.029 m, 0.027 m, and 0.047 m for the dN, dE, and dU displacement components, respectively, and 0.031 m for the horizontal displacement magnitude. Despite this, point clouds and DSMs are recommended for displacement monitoring, as they provide more reliable data in varying lighting conditions and natural environmental changes, offering a more consistent and reliable approach than RGB imagery.</p>\u0000 </div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738191","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":"InfraHBIM/HBIM–GIS integration for the reconstruction and remote access of the First Songhua River Bridge heritage complex along the Chinese Eastern Railway (1901–1961)","authors":"Jianzhuo Xu,&nbsp;Manuel Garramone,&nbsp;Fangming Li,&nbsp;Haitao Zhang,&nbsp;Marco Scaioni","doi":"10.1007/s12518-026-00727-w","DOIUrl":"10.1007/s12518-026-00727-w","url":null,"abstract":"<div>\u0000 \u0000 <p>The First Songhua River Bridge of the Chinese Eastern Railway (CER) entered service in 1901 and was protected by three military blockhouses constructed between 1915 and 1917. As the first metal bridge in the country exceeding one kilometre in length, the bridge represents a major achievement in the engineering history of the CER. Although the bridge preserves a substantial and diverse collection of historical archives, these materials have not been systematically organised, while archival records for the blockhouses remain scarce. In 1961, the original curved-chord through trusses and deck trusses were replaced and upgraded. To address these issues, this study proposes an Infrastructure HBIM (InfraHBIM) and HBIM–GIS integration methodology for this transport infrastructure heritage to support archival management, documentation, phase-based three-dimensional reconstruction and web-based remote access. The InfraHBIM model of the bridge representing its state between 1901 and 1961 was reconstructed using a forward modelling strategy based on historical archives, while the HBIM models of the blockhouses were developed through reverse modelling driven by point-cloud data acquired by drone photogrammetry. During the integration phase, heterogeneous datasets were organised within a GIS database derived from historical records and field investigations, and the integrated models were published via ArcGIS ^® Online to enable interactive visualisation, attribute query and remote access. The proposed approach provides a reusable reference for the digital management of large-scale linear heritage systems such as railways, canals and defensive networks, while supporting professional decision making as well as public interpretation and engagement.</p>\u0000 </div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00727-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738193","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":"Geoenvironmental Assessment of flood risk and soil dynamics in parts of Southeastern Nigeria","authors":"Francis Begianpuye Akiang,&nbsp;Alexander Iheanyichukwu Opara,&nbsp;Chikwendu Njoku Okereke,&nbsp;Diugo Okereke Ikoro,&nbsp;Maureen Chioma Umeh,&nbsp;Charles Uchenna Emmanuel,&nbsp;Chukwuebuka Nnamdi Onwubuariri,&nbsp;Michael Maduabuchi Emeh","doi":"10.1007/s12518-026-00728-9","DOIUrl":"10.1007/s12518-026-00728-9","url":null,"abstract":"<div>\u0000 \u0000 <p>Flooding remains a critical environmental challenge, particularly in regions with rapid urbanization and poor drainage infrastructure. Understanding flood susceptibility through geospatial analysis is essential for effective risk management and disaster preparedness. This study assesses flood risk and soil erosion dynamics in Southeastern Nigeria using Geographic Information Systems (GIS) and Remote Sensing (RS). The objective is to classify flood-prone areas and propose suitable mitigation strategies based on environmental parameters. A multi-criteria approach was applied using Digital Elevation Models (DEM), slope analysis, Topographic Wetness Index (TWI), drainage density, land use/land cover (LULC), and annual rainfall data. The weighted overlay method in ArcGIS was used to classify the study area into five flood risk categories: very low, low, moderate, high, and very high. The analysis revealed that moderate-risk areas constituting 41.10% cover the most significant portion (106.12 km²) of the study area, while high-risk zones span 51.14 km² (19.80%), primarily in low-lying regions with impervious surfaces. Very high-risk areas, totaling 6.03 km² (2.33%), were concentrated along riverbanks and poorly drained areas. In contrast, low-risk areas covered 81.74 km² (31.64%), benefiting from adequate drainage, and very low-risk zones accounted for 5.03% (13.30 km²). The study highlights the influence of urbanization, topography, and hydrology on flood susceptibility. Effective land-use planning, afforestation, and improved drainage infrastructure are critical for mitigating flood risks. The integration of geospatial techniques provides valuable insights for flood risk assessment, aiding in proactive flood management strategies and urban planning. This research offers a spatially explicit flood risk classification, integrating multiple environmental variables to enhance predictive accuracy and inform sustainable flood mitigation efforts.</p>\u0000 </div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737675","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 fast method for detecting rock blocks deposits and calculating volumes and 3D surface areas","authors":"Ali Polat","doi":"10.1007/s12518-026-00729-8","DOIUrl":"10.1007/s12518-026-00729-8","url":null,"abstract":"<div><p>Unmanned aerial vehicle (UAV) imagery and deep learning-based approaches offer effective solutions for accurately identifying rocks and performing volume and surface area analyses because traditional methods are time-consuming and costly. This study aims to automatically detect rock blocks in a region and calculate their volumes and 3D surface areas. U-Net, Mask R-CNN, and YOLOv8n-seg methods were applied for the detection of rock blocks. Mask R-CNN and YOLOv8n-seg models were found unsuitable for this study due to overlapping issues and false positive detections. Considering these limitations, U-Net with DenseNet121 transfer learning method was preferred as it provided more balanced and reliable results. To further assess the robustness and generalization capability of the proposed model, a three-fold cross-validation experiment was conducted, yielding an average validation IoU of 0.85 and a training IoU of 0.84. The trained model was applied to the study area, and 3111 rock blocks were detected. The volumes and 3D surface areas of the rock blocks were calculated with a Python script as 540.13 m<span>(^3)</span> and 4284.42 m<span>(^2)</span>, respectively. This study demonstrates a fast, cost-effective, and reliable approach for rock segmentation and volume/surface area calculations, providing direct contributions to engineering applications through the determination of physical rock properties. In addition, the codes used in this study can automatically detect different geological formations from ortophotos. Also, volume and 3D surface area algorithms developed in this study can be used to calculate different types of objects.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737450","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":"Exploring the sensitivity of SAOCOM L-band SAR to forest biomass under severe spatial uncertainty","authors":"José Miguel Febles Díaz,&nbsp;Zhaofei Fan","doi":"10.1007/s12518-026-00723-0","DOIUrl":"10.1007/s12518-026-00723-0","url":null,"abstract":"<div><p>Synthetic aperture radar remote sensing has long been recognized as a valuable tool for estimating aboveground biomass density in forest ecosystems. In this study, we specifically evaluated the sensitivity of the SAOCOM mission to forest biomass under conditions of high spatial uncertainty in reference data (± 1.6 km). A complex single-look SAOCOM acquisition was processed to obtain a comprehensive set of polarimetric backscatter and decomposition metrics, which were used as predictors in Random Forest regression models calibrated with biomass estimates from the U.S. Forest Inventory and Analysis program. The optimized model achieved a concordance correlation coefficient of 0.50, with a mean square error of 59 Mg ha⁻¹ and a mean absolute error of 47 Mg ha⁻¹ on independent test data. Beyond predictive performance, the results demonstrate that SAOCOM observations retain significant sensitivity to forest structural variability, even when the spatial correspondence between satellite measurements and ground-based biomass references is substantially degraded. These findings highlight SAOCOM’s potential for biomass-oriented applications, even under challenging conditions.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":"18 2","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-026-00723-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737443","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}