Search Results (636)

Geomorphological change is a fundamental consequence of high-magnitude flood events, as extreme hydraulic forcing can rapidly reshape river channels, redistribute sediment, and alter floodplain connectivity. This study applies multi-temporal UAS-based Structure-from-Motion (SfM) photogrammetry to quantify flood-induced geomorphological changes along two representative reaches of the Lilas River (Evia Island, Central Greece) affected by the extreme August 2020 flash flood. High-resolution aerial surveys were conducted prior to the event (June 2018) and shortly after the flood (September 2020), producing Digital Surface Models (DSMs) and orthomosaics with a ground sampling distance of ~2.5 cm. Differential DSM analysis reveals pronounced spatial heterogeneity in erosion and deposition, with net erosional lowering locally exceeding 7 m and depositional aggradation reaching up to ~5 m after accounting for vegetation effects. Channel widening was the dominant response, with cross-sectional widths increasing by a factor of three to nine at selected locations, driven primarily by lateral bank erosion. The results highlight the strong interaction between extreme hydrological forcing, loose alluvial sediments, vegetation removal, and human interventions such as roads and engineered terraces. The study demonstrates how repeatable UAS–SfM workflows can provide quantitative evidence to support post-flood assessment, guide infrastructure adaptation, and inform river restoration and flood risk management in Mediterranean catchments prone to extreme events. Full article

In recent years, digital technologies have become increasingly prevalent in the field of heritage protection. In addition to geomatic techniques like laser scanning (LiDAR) and Structure-from-Motion (SfM), geophysical methods, especially Ground-Penetrating Radar (GPR), offer added value for investigating protected buildings and objects. Additionally, chemical analysis (e.g., X-ray fluorescence, XRF) and mineral magnetic methods can be utilized to investigate specific research topics. All these methods are completely non-invasive and leave the heritage site untouched. Furthermore, they are cost-efficient and fast to use. Within this paper, we want to present an integrated study of a medieval sarcophagus in Bamberg Cathedral. The geophysical surveys via GPR and magnetic susceptibility (MS) measurements should answer open questions regarding the construction and internal layout of the sandstone sarcophagus, dated to the Early or High Middle Ages. The susceptibility data indicated an inner lead coffin in the lower part behind the stone slabs due to an unusual diamagnetic response in these parts. In contrast, the GPR data gave no such indication and revealed that the interior is too small for a direct burial of the bishop. Hence, an additional XRF survey was conducted to help solve this contradiction. The latter data indicate that the lead could be due to remains of a former painting on the sarcophagus with colours containing lead white pigments. Due to the porous sandstone, the moist environmental conditions, and the high weight of the lead elements, these could have accumulated at the bottom of the sarcophagus, creating the diamagnetism detected by the magnetic susceptibility measurements. Full article

Accurate assessment of rice resistance to Sogatella furcifera (Horváth) is essential for breeding insect-resistant cultivars. Traditional assessment methods rely on manual scoring of damage severity, which is subjective and inefficient. To overcome these limitations, this study proposes an automated resistance evaluation approach based on multi-view 3D reconstruction and deep learning–based point cloud segmentation. Multi-view videos of rice materials with different resistance levels were collected over time and processed using Structure from Motion (SfM) and Multi-View Stereo (MVS) to reconstruct high-quality 3D point clouds. A well-annotated “3D Rice WBPH Damage” dataset comprising 174 samples (15 rice materials, three replicates each, 45 pots) was established, where each sample corresponds to a reconstructed 3D point cloud from a video sequence. A comparative study of various point cloud semantic segmentation models, including PointNet, PointNet++, ShellNet, and PointCNN, revealed that the PointNet++ (MSG) model, which employs a Multi-Scale Grouping strategy, demonstrated the best performance in segmenting complex damage symptoms. To further accurately quantify the severity of damage, an adaptive point cloud dimensionality reduction method was proposed, which effectively mitigates the interference of leaf shrinkage on damage assessment. Experimental results demonstrated a strong correlation (R² = 0.95) between automated and manual evaluations, achieving accuracies of 86.67% and 93.33% at the sample and material levels, respectively. This work provides an objective, efficient, and scalable solution for evaluating rice resistance to S. furcifera, offering promising applications in crop resistance breeding. Full article

Monitoring steep slopes in mountainous canyon areas has always been a challenging problem, especially during the construction of large hydropower projects. Effective monitoring is crucial for construction safety and operational security. However, under complex terrain conditions, existing monitoring methods have significant limitations and cannot comprehensively and accurately cover steep slopes. To address the above challenges, this study proposes a multi-temporal UAV-based photogrammetric offset tracking (POT) monitoring method assisted by terrestrial laser scanning (TLS), which is primarily applicable to rocky and texture-rich steep slopes. This method utilizes TLS point cloud data to provide supplementary ground control points (TLS-GCPs) for UAV image modeling, effectively overcoming the difficulty of deploying conventional RTK ground control points (RTK-GCPs) on high and steep slopes, thereby significantly improving the accuracy of UAV-based Structure-from-Motion (SfM) models. In a case study at a hydropower station, we employed TLS-assisted UAV modeling to produce high-precision UAV images. Using POT technology, we successfully identified signs of slope deformation between January 2024 and December 2024. Comparative experiments with traditional algorithms demonstrated that in areas where RTK-GCPs cannot be deployed, this method greatly enhances UAV modeling accuracy, fully meeting the monitoring requirements for steep slopes in complex terrains. Full article

This study develops a voxel-based leaf area estimation framework and validates it using a three-year multi-temporal dataset (2022–2024) of pergola-trained grapevines. The workflow integrates 2D image analysis, ExGR-based leaf segmentation, and 3D reconstruction using Structure-from-Motion (SfM). Multi-angle canopy images were collected repeatedly during the growing seasons, and destructive leaf sampling was conducted to quantify true leaf area across multiple vines and years. After removing non-leaf structures with ExGR filtering, the point clouds were voxelized at a 1 cm³ resolution to derive structural occupancy metrics. Voxel-based leaf area showed strong within-vine correlations with destructively measured values (R² = 0.77–0.95), while cross-vine variability was influenced by canopy complexity, illumination, and point-cloud density. In contrast, optical LAI tools (DHP and LAI–2000) exhibited negligible correspondence with true leaf area due to multilayer occlusion and lateral light contamination typical of pergola systems. This expanded, multi-year analysis demonstrates that voxel occupancy provides a robust and scalable indicator of canopy structural density and leaf area, offering a practical foundation for remote-sensing-based phenotyping, yield estimation, and data-driven management in perennial fruit crops. Full article

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

Unmanned Aerial Vehicles (UAVs) equipped with low-cost RGB and near-infrared (NIR) cameras represent efficient and scalable technology for monitoring sugarcane crops. This study evaluated the potential of UAV imagery and three-dimensional crop modeling to estimate sugarcane height and yield under different nitrogen fertilization levels. The experiment comprised 28 plots subjected to four nitrogen rates, and images were processed using a Structure from Motion (SfM) algorithm to generate Digital Surface Models (DSMs). Crop Height Models (CHMs) were obtained by subtracting DSMs from Digital Terrain Models (DTMs). The most accurate CHM was derived from the combination of the reference DTM and the NIR-based DSM (R² = 0.957; RMSE = 0.162 m), while the strongest correlation between height and yield was observed at 200 days after cutting (R² = 0.725; RMSE = 4.85 t ha⁻¹). The NIR-modified sensor, developed at a total cost of USD 61.59, demonstrated performance comparable with commercial systems that are up to two hundred times more expensive. These results demonstrate that the proposed low-cost NIR sensor provides accurate, reliable, and accessible data for three-dimensional modeling of sugarcane. 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

This study introduces a versatile seed 3D reconstruction method that is applicable to multiple crops—including maize, wheat, and rice—and designed to overcome the inefficiency and subjectivity of manual measurements and the high costs of laser-based phenotyping. A panoramic video of the seed is captured and processed through frame sampling to extract multi-view images. Structure-from-Motion (SFM) is employed for sparse reconstruction and camera pose estimation, while 3D Gaussian Splatting (3DGS) is utilized for high-fidelity dense reconstruction, generating detailed point cloud models. The subsequent point cloud preprocessing, filtering, and segmentation enable the extraction of key phenotypic parameters, including length, width, height, surface area, and volume. The experimental evaluations demonstrated a high measurement accuracy, with coefficients of determination (R²) for length, width, and height reaching 0.9361, 0.8889, and 0.946, respectively. Moreover, the reconstructed models exhibit superior image quality, with peak signal-to-noise ratio (PSNR) values consistently ranging from 35 to 37 dB, underscoring the robustness of 3DGS in preserving fine structural details. Compared to conventional multi-view stereo (MVS) techniques, the proposed method can achieve significantly improved reconstruction accuracy and visual fidelity. The key outcomes of this study confirm that the 3DGS-based pipeline provides a highly accurate, efficient, and scalable solution for digital phenotyping, establishing a robust foundation for its application across diverse crop species. Full article

Detecting topographic change in mountainous areas using historical aerial imagery is challenging due to complex terrain and variable data quality. This study evaluates the potential of Structure from Motion (SfM) for deriving 3D information from archival photograms in the Rabbia basin (Central Italian Alps), a catchment with a well-documented history of debris flow activity. The aim is to assess the impact of input configurations and photogrammetric processing strategies on the quality and interpretability of 3D reconstructions from historical aerial imagery, as a basis for further geomorphological analyses. A 1999 aerial dataset was processed via SfM workflow to generate a point cloud and orthomosaic, and then co-registered with a 2021 LiDAR-derived dataset. Multi-temporal analysis was conducted using point cloud distance computations and visual interpretation of orthomosaics. Additional aerial images spanning nearly 80 years expanded the temporal scale of the analysis, providing valuable retrospective insight into long-term terrain evolution. The results, although considered semi-quantitative due to data quality limitations, are consistent with geomorphological trends in the area. The study confirms that historical SfM-derived products, when supported by robust co-registration and quality checks, can contribute to sediment dynamics and hazard evaluation in alpine environments, though result interpretation should remain cautious due to dataset-specific uncertainties. 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