Search Results (322)

Coastal environments are fragile ecosystems threatened by various factors, both natural and anthropogenic. The preservation and protection of these environments, and in particular, the sand dune systems, which contribute significantly to the defense of the inland from flooding, require continuous monitoring. To this end, high-resolution and high-precision multitemporal data acquired with various techniques can be used, such as, among other things, the global navigation satellite system (GNSS) using the network real-time kinematic (NRTK) approach to acquire 3D points, UAS-based structure-from-motion photogrammetry (SfM), terrestrial laser scanning (TLS), and handheld mobile laser scanning (HMLS)-based light detection and ranging (LiDAR). These techniques were used in this work for the 3D survey of a portion of vegetated sand dunes in the Caleri area (Po River Delta, northern Italy) to assess their applicability in complex environments such as coastal vegetated dune systems. Aerial-based and ground-based acquisitions allowed us to produce point clouds, georeferenced using common ground control points (GCPs), measured both with the GNSS NRTK method and the total station technique. The 3D data were compared to each other to evaluate the accuracy and performance of the different techniques. The results provided good agreement between the different point clouds, as the standard deviations of the differences were lower than 9.3 cm. The GNSS NRTK technique, used with the kinematic approach, allowed for the acquisition of the bare-ground surface but at a cost of lower resolution. On the other hand, the HMLS represented the poorest ability in the penetration of vegetation, providing 3D points with the highest elevation value. UAS-based and TLS-based point clouds provided similar average values, with significant differences only in dense vegetation caused by a very different platform of acquisition and point of view. Full article

This study evaluates the use of UAV-based photogrammetry to document shallow submerged cultural heritage, focusing on the Tirpitz wreck salvage site near Håkøya, Norway. Using a DJI Phantom 4 Multispectral drone, we acquired RGB and multispectral imagery over structures located at depths of up to 5–10 m. Structure-from-motion (SfM) processing enabled the three-dimensional reconstruction of submerged features, including a 52 × 10 m wharf and adjacent debris piles, with an accuracy of the order of 10 cm. Our data represents the first and only accurate mapping of the site yet carried out, with an absolute position uncertainty estimated to be no greater than 3 m. Volumes of imaged debris could be estimated, using a background subtraction method to allow for variable bathymetry, at around 350 m³. Bathymetric data for the sea floor could be derived effectively from an SfM point cloud, though less effectively applying the Stumpf model to the multispectral data as a result of significant spectral variation in the sea floor reflectance. Our results show that UAV-based through-surface SfM is a viable, low-cost method for reconstructing submerged heritage with high spatial accuracy. These findings support the integration of UAV-based remote sensing into heritage and environmental monitoring frameworks for shallow aquatic environments. Full article

Coastal dune systems are dynamic landforms shaped by aeolian processes, in which onshore winds transport and deposit sediments behind natural or artificial barriers. The Çukurova Delta Plain, Turkey’s largest delta along the Eastern Mediterranean, contains extensive dune fields, particularly within the Seyhan and Ceyhan Deltas. Despite technological advances in UAV photogrammetry and Structure-from-Motion (SfM) techniques, studies on coastal dune dynamics in Turkey remain scarce. This study demonstrates the first comprehensive assessment of the spatiotemporal evolution of coastal dunes in the Çukurova Delta Plain. Historical aerial photographs and high-resolution UAV imagery were analyzed to evaluate long-term and seasonal morphological changes. The results indicate notable spatial and temporal variability in sediment budgets, with distinct erosion and accretion patterns across the two deltas. While some dune segments remained stable over decades, others displayed strong seasonal responses to wind and sediment dynamics. These findings enhance the understanding of deltaic coastal geomorphology and provide critical insights for sustainable management of vulnerable dune ecosystems under increasing human and climatic pressures. Full article

Collecting accurate and reliable beach morphology data is essential for informed coastal management. The beach adjacent to the seawall on North Padre Island, Texas, USA has experienced increased erosion and disrupted natural processes. City ordinance mandates the placement of bollards to restrict vehicular traffic when the beach width from the seawall toe to mean high water (MHW) is less than 45.7 m. To aid the City of Corpus Christi’s understanding of seasonal beach changes, mobile lidar scanning (MLS) surveys with a mapping-grade system were conducted in February, June, September, and November 2023, and post-nourishment in March 2024. Concurrent uncrewed aircraft system (UAS) photogrammetry surveys were performed in February and November 2023, and March 2024 to aid beach monitoring analysis and for comparative assessment to the MLS data. MLS-derived digital elevation models (DEMs) were used to evaluate seasonal geomorphology, including beach slope, width, shoreline position, and volume change. Because MHW was submerged during all surveys, highest astronomical tide (HAT) was used for shoreline analyses. HAT-based results indicated that bollards should be placed from approximately 390 to 560 m from the northern end of the seawall, varying seasonally. The March 2024 post-nourishment survey showed 102,462 m³ of sand was placed on the beach, extending the shoreline by more than 40 m in some locations. UAS photogrammetry-derived DEMs were compared to the MLS-derived DEMs, revealing mean HAT position differences of 0.02 m in February 2023 and 0.98 m in November 2023. Elevation and volume assessments showed variability between the MLS and UAS-SfM DEMs, with neither indicating consistently higher or lower values. Full article

Understanding the sediment grain size distribution in riverbeds is essential for analyzing sediment transport, riverbed morphology, and ecological habitats. Previous studies have shown that riverbed grain size can be inferred from surface roughness using linear relations between manually sampled grain sizes and percentile roughness derived from point-cloud data. However, these relations are often established within narrow grain-size ranges, causing regression coefficients to vary across percentiles and limiting their applicability to broader grain-size variability. This study conducted field investigations and UAV (Unmanned Aerial Vehicle) surveys to examine grain size–roughness relations across four coarse-grained mountainous river reaches in Taiwan, characterized by a wide grain-size distribution (D₁₆–D₈₄: 2.3–525 mm). High-resolution 3D point clouds were generated using UAV-SfM (Structure-from-Motion) techniques for roughness metric computation. Linear relations between grain size D_i (i = 16, 25, 50, 75, and 84) and their corresponding percentile roughness RH_i were developed and evaluated. Results indicate that D_i-RH_i relations exhibit moderate to strong correlations (R² = 0.60–0.94), and the regression slope increases exponentially with grain size. To address cross-percentile variability, an integrated power-law relation was proposed by pooling all paired D_i-RH_i data from Reach R1, yielding a single, continuous reach-scale grain size–roughness correlation. Applicability tests using data from the remaining three reaches show that the integrated relation performs better for coarser grains (D₅₀–D₈₄) than for finer grains. Future work incorporating more sampling sites across diverse river types will help further refine the integrated relation and improve its cross-reach applicability. Full article

Coral reefs play a crucial role in tropical coastal ecosystems, even though these environments are difficult to monitor due to their diversity and morphological complexity and due to their shallowness in some cases. This study used two approaches for acquiring very-high-resolution bathymetric data: underwater structure-from-motion (SfM) photogrammetry collected from a low-cost platform and unmanned/uncrewed aerial vehicle (UAV)-based SfM photogrammetry. While underwater photogrammetry avoids the distortions caused by refraction at air/water interface, it remains limited in spatial coverage (about 0.04 ha in 1 h of survey). In contrast, UAV photogrammetry allows for covering extensive areas (more than 20 ha/h) but requires applying refraction correction in order to accurately compute bathymetry and roughness values. An analytical approach based on Snell laws and an empirical approach based on linear regression (calibrated using a batch of points whose depths are representative of the depth range of the surveyed areas) are tested to correct the apparent depth on the raw UAV digital elevation model (DEM). Comparison to underwater photogrammetry shows that correcting refraction reduces the root mean square error (RMSE) by more than 50% (up to 62%) on bathymetric models, with RMSE lower than 0.13 m for the analytical approach and down to 0.09 m for the regression method. The linear-regression-based refraction correction proved most effective in restoring accurate seabed roughness, with a mean error on roughness lower than 17% (vs. 30% for analytical refraction correction and 48% for apparent bathymetry). Full article

This study explores the integration of point cloud data obtained from unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) to address limitations in photogrammetry and to create comprehensive models of aquatic environments. The UAV platform (AUTEL EVO II) employs structure-from-motion (SfM) photogrammetry using optical imagery, while the USV (equipped with a NORBIT iWBMS multibeam sonar system) collects underwater bathymetric data. UAVs commonly face constraints in battery life and image-processing capacity, making it necessary to merge smaller UAV point clouds into larger, more complete models. The USV-derived bathymetric data are integrated with UAV-derived surface data to construct unified terrain models that include both above-water and underwater features. This study evaluates three coordinate transformation (CT) methods—4-parameter, 6-parameter, and 7-parameter—across three study areas in Taiwan to assess their effectiveness in registering USV–UAV and UAV–UAV point clouds. For USV–UAV integration, all CT methods improved alignment accuracy compared with results without CT, achieving decimeter-level precision. For UAV–UAV integrations, the 7-parameter method provided the best accuracy, especially in areas with low terrain roughness such as rooftops and pavements, while improvements were less pronounced in areas with high roughness such as tree canopies. These findings demonstrate that the 7-parameter CT method offers an effective and straightforward approach for accurate point cloud integration from different platforms and sensors. Full article

Accurate geodetic surveys are essential for road design, with altimetric accuracy being particularly critical. UAV photogrammetry offers faster and safer data acquisition than conventional methods, but its applicability depends on whether it can meet engineering accuracy standards. This study investigates the altimetric accuracy of UAV photogrammetry through a comparative assessment of surveys conducted on the same urban roundabout in Osijek, Croatia, in 2016 and 2024. By conducting the surveys eight years apart at the same location, the study allows for an assessment of how technological and methodological developments affect survey outcomes. The research evaluates different UAVs and multiple SfM software packages in a comparative framework, highlighting how UAV–software combinations affect results, rather than attributing accuracy solely to hardware or processing. The results of the conducted research indicate a significant increase in the accuracy of the UAV photogrammetric survey method. Through a proper combination of UAVs and SfM processing software, it is possible to achieve an accuracy within 2 cm and an RMSE of 1.2 cm, which is in line with the accuracy of a standard survey method like GNSS CROPOS. The results underline that UAV photogrammetry, when properly planned and executed, can now deliver altimetric accuracy sufficient for most road construction tasks, providing a reliable and cost-effective alternative to conventional geodetic surveys. Full article

Habitat complexity plays a critical role in coral reef ecosystems by enhancing habitat availability, increasing ecological resilience, and offering coastal protection. Structure-from-motion (SfM) photogrammetry has become a standard approach for quantifying habitat complexity in reef monitoring programs. However, a major bottleneck remains in the two-dimensional (2D) classification of benthic cover in three-dimensional (3D) models, where experts are required to manually annotate individual colonies and identify coral species or taxonomic groups. With recent advances in deep learning and computer vision, automated classification of benthic habitats is possible. While some semi-automated tools exist, they are often limited in scope or do not provide semantic segmentation. In this investigation, we trained a convolutional neural network with the ResNet101 architecture on three years (2015, 2017, and 2019) of human-annotated 2D orthomosaics from Kiritimati, Kiribati. Our model accuracy ranged from 71% to 95%, with an overall accuracy of 84% and a mean intersection of union of 0.82, despite highly imbalanced training data, and it demonstrated successful generalizability when applied to new, untrained 2023 plots. Successful automation depends on training data that captures local ecological variation. As coral monitoring efforts move toward standardized workflows, locally developed models will be key to achieving fully automated, high-resolution classification of benthic communities across diverse reef environments. Full article

Accurate stockpile volume estimation is essential for industries that manage bulk materials across various stages of production. Conventional ground-based methods such as walking wheels, total stations, Global Navigation Satellite Systems (GNSSs), and Terrestrial Laser Scanners (TLSs) have been widely used, but often involve significant safety risks, particularly when accessing hard-to-reach or hazardous areas. Unmanned Aerial Systems (UASs) provide a safer and more efficient alternative for surveying irregularly shaped stockpiles. This study evaluates UAS-based methods for estimating the volume of coal stockpiles at a storage facility near Cadiz, Ohio. Two sensor platforms were deployed: a Freefly Alta X quadcopter equipped with a Real-Time Kinematic (RTK) Light Detection and Ranging (LiDAR, active sensor) and a WingtraOne UAS with Post-Processed Kinematic (PPK) multispectral imaging (optical, passive sensor). Three approaches were compared: (1) LiDAR; (2) Structure-from-Motion (SfM) photogrammetry with a Digital Surface Model (DSM) and Digital Terrain Model (DTM) (SfM–DTM); and (3) an SfM-derived DSM combined with a kriging-interpolated DTM (SfM–intDTM). An automated boundary detection workflow was developed, integrating slope thresholding, Near-Infrared (NIR) spectral filtering, and Canny edge detection. Volume estimates from SfM–DTM and SfM–intDTM closely matched LiDAR-based reference estimates, with Root Mean Square Error (RMSE) values of 147.51 m³ and 146.18 m³, respectively. The SfM–intDTM approach achieved a Mean Absolute Percentage Error (MAPE) of ~2%, indicating strong agreement with LiDAR and improved accuracy compared to prior studies. A sensitivity analysis further highlighted the role of spatial resolution in volume estimation. While RMSE values remained consistent (141–162 m³) and the MAPE below 2.5% for resolutions between 0.06 m and 5 m, accuracy declined at coarser resolutions, with the MAPE rising to 11.76% at 10 m. This emphasizes the need to balance the resolution with the study objectives, geographic extent, and computational costs when selecting elevation data for volume estimation. Overall, UAS-based SfM photogrammetry combined with interpolated DTMs and automated boundary extraction offers a scalable, cost-effective, and accurate approach for stockpile volume estimation. The methodology is well-suited for both the high-precision monitoring of individual stockpiles and broader regional-scale assessments and can be readily adapted to other domains such as quarrying, agricultural storage, and forestry operations. Full article

Lump charcoal is used in various applications, with combustion performance reliant on physical properties including apparent density. Currently, apparent density is measured by liquid displacement using Archimedes’ principle, which can yield inconsistent results for porous, irregular materials. This study investigates structure-from-motion (SfM) photogrammetry as a non-destructive alternative for estimating the apparent density of lump charcoal. Ninety fragments from 15 commercial samples were analyzed. Mass was measured using an analytical balance, and volume was estimated independently via Archimedes’ method and photogrammetry. Apparent density was calculated as the ratio of mass to volume. Results showed strong agreement between the two methods. Mean density values ranged from 284.2 to 751.6 kg/m³ for photogrammetry and from 267.2 to 765.7 kg/m³ for Archimedes. No significant differences were found (Wilcoxon test, p > 0.05), and a strong correlation was observed (Spearman’s ρ = 0.94, p < 0.001). Photogrammetry also demonstrated low estimation errors, with a mean absolute error of 38.8 kg/m³, a percentage error of 9.9%, and a root mean squared error of 50.2 kg/m³. Beyond methodological innovation, this approach strengthens sustainability by supporting accurate fuel properties control, allowing better use of the resource and maximizes combustion efficiency. In this way, it contributes to United Nations Sustainable Development Goal 7 (SDG7) on affordable, reliable, and sustainable energy. Full article

Soil erosion is a critical ecological challenge in semi-arid regions of China, particularly in the Yellow River Basin, where Pisha sandstone slopes undergo rapid degradation. Rill erosion, driven by rainfall and overland flow, destabilizes slopes and accelerates ecosystem degradation. To address this, we developed a multi-view stereo observation system that integrates Structure-from-Motion (SFM) and multi-view stereo (MVS) for high-precision, dynamic monitoring of rill erosion. Laboratory rainfall simulations were conducted under four inflow rates (2–8 L/min), corresponding to rainfall intensities of 30–120 mm/h. The erosion process was divided into four phases: infiltration and particle rolling, splash and sheet erosion, incipient rill incision, and mature rill networks, with erosion concentrated in the middle and lower slope sections. The SFM-MVS system achieved planimetric and vertical errors of 3.1 mm and 3.7 mm, respectively, providing approximately 25% higher accuracy and nearly 50% faster processing compared with LiDAR and UAV photogrammetry. Infiltration stabilized at approximately 6.2 mm/h under low flows (2 L/min) but declined to less than 4 mm/h under high flows (≥6 L/min), leading to intensified rill incision and coarse-particle transport (up to 21.4% of sediment). These results demonstrate that the SFM-MVS system offers a scalable and non-invasive method for quantifying erosion dynamics, with direct implications for field monitoring, ecological restoration, and soil conservation planning. Full article

River-ice, a significant element of the cryosphere, plays a crucial role in hydrological processes. However, the effectiveness of current river-ice monitoring techniques on the Qinghai–Tibet Plateau is limited due to the complex interplay of environmental and topographical factors in this extensively ice-covered region. To overcome the inadequacies of traditional monitoring approaches in plateau settings, this research introduces a 4D-SfM photogrammetry method for river-ice monitoring. Experimental measurements of river-ice thickness were conducted on the upper reaches of the Heihe River in the Qilian Mountains during the freezing period of 2023–2024. The study evaluated accuracy variations across three different shooting distances: close-range (0.5 m–1.5 m), mid-range (3 m–10 m), and long-range (25 m–60 m). In this study, 4D-SfM photogrammetry not only accurately represents the nonlinear processes of river-ice formation and melting but also sensitively detects abrupt changes in thickness. Between 6 February and 4 April 2024, river-ice underwent a cumulative melt of 77.8 cm, followed by a cumulative growth of 72.2 cm between 26 November and 26 December 2024. Notably, between 24 and 25 December 2024, 4D-SfM photogrammetry successfully captured an extreme event in which river-ice thickness surged by approximately 30 cm. Measurement accuracy decreased with increasing shooting distance, as indicated by an increase in RMSE from 0.43 cm to 3.97 cm. Additionally, factors such as image brightness and ice surface irregularities significantly impact measurement precision. Moreover, the measurement area expanded from 11.38 m² to 2642 m² with increased shooting distances. Therefore, achieving a balance between shooting distance and measurement accuracy is essential when employing 4D-SfM photogrammetry for river-ice monitoring. This study provides a valuable resource for utilizing 4D-SfM photogrammetry to monitor river-ice thickness on the Qinghai–Tibet Plateau. Full article

The Oymaağaç Höyük Project (2005–today) investigates a 6,500-year-oldmulti-period settlement in the district of Vezirköprü at the southern edge of the Black Sea province of Samsun in northern Türkiye. According to cuneiform texts, the site can be associated with the Hittite cult city of Nerik (17th–12th century BC). Automatic multi-image photogrammetry, also known as Structure from Motion (SfM), has proven to be a powerful and flexible means for the three-dimensional documentation of objects and finds of different shapes and sizes. Data products were created in the form of 3D point clouds, textured surface models, orthophotos, sections, and 3D prints (physical 3D models). Visualization of 3D data was realized via an internet browser (Potree Viewer, Babylon.js) and virtual reality (VR) techniques. Photogrammetry is very flexible in its application because the accuracy depends essentially on the scale of the images. On the other hand, the constantly growing volume of data as a result of the evolving technical possibilities requires sustainable data management, which is difficult to realize in practice due to limited financial resources. The article provides an overview of the use of photogrammetry in the project. Full article

Structural traits of vegetation, derived from the three-dimensional distribution of plant elements, are closely linked to ecosystem functions such as productivity and habitat provision. While extensively studied in forest ecosystems, these traits remain understudied in low-stature systems such as Mediterranean-type shrublands. In this study we explore the use of structural metrics derived from small unmanned aerial system (UAS)-based 3D point clouds, generated using the structure-from-motion (SfM) photogrammetry technique, to assess post-fire vegetation structure and biodiversity in the fynbos biome of the Cape Floristic Region (CFR), South Africa. Fynbos is a fire-adapted shrubland that represents nearly 80% of plant species in the CFR, making post-disturbance monitoring critical for conservation. We extracted three structural metrics—canopy height, top rugosity, and surface gap ratio—and achieved ~85% accuracy in classifying 5 × 5 m subplots by burn year using a Multi-Layer Perceptron (MLP), with canopy height as the strongest predictor. Additionally, top rugosity and gap ratio significantly contributed to modeling percentage cover-based species diversity. Our findings demonstrate that UAS-derived structural metrics provide valuable information for characterizing vegetation recovery and biodiversity patterns in low-stature, fire-prone ecosystems. This approach can support ecological monitoring and inform conservation strategies in Mediterranean-type shrublands. Full article