Unmanned Aerial Vehicle Photogrammetry Research Articles

Accuracy Assessment of UAV Photogrammetry System with RTK Measurements for Direct Georeferencing

Abstract. The direct georeferencing accuracy of unmanned aerial vehicle (UAV) images with real-time kinematic (RTK) measurements is a concerned topic in the community of photogrammetry. This study assesses the capabilities of a multi-rotor platform equipped with RTK technology, specifically a DJI Phantom 4 RTK UAV, for robust direct georeferencing. The UAV surveyed a square and a building at Wuhan University to assess the accuracy and spatial consistency of direct georeferencing in close-range photography. We tested checkpoint errors under various ground control points (GCPs) configurations. The results show that without GCP, an analysis of 71 spatially distributed checkpoints produced a root mean square error (RMSE) of 5.58 cm in the Z direction. This finding indicates that RTK-equipped UAVs can achieve acceptable error margins even without using GCPs, thereby fulfilling the precision requirements for large-scale mapping.

Application of a Soil Erosion Susceptibility Model Using Unmanned Aerial Vehicle Photogrammetry in a Timber Harvesting Area, South Korea

Application of a Soil Erosion Susceptibility Model Using Unmanned Aerial Vehicle Photogrammetry in a Timber Harvesting Area, South Korea

Estimation of Small-Stream Water Surface Elevation Using UAV Photogrammetry and Deep Learning

Unmanned aerial vehicle (UAV) photogrammetry allows the generation of orthophoto and digital surface model (DSM) rasters of terrain. However, DSMs of water bodies mapped using this technique often reveal distortions in the water surface, thereby impeding the accurate sampling of water surface elevation (WSE) from DSMs. This study investigates the capability of deep neural networks to accommodate the aforementioned perturbations and effectively estimate WSE from photogrammetric rasters. Convolutional neural networks (CNNs) were employed for this purpose. Two regression approaches utilizing CNNs were explored: direct regression employing an encoder and a solution based on prediction of the weight mask by an autoencoder architecture, subsequently used to sample values from the photogrammetric DSM. The dataset employed in this study comprises data collected from five case studies of small lowland streams in Poland and Denmark, consisting of 322 DSM and orthophoto raster samples. A grid search was employed to identify the optimal combination of encoder, mask generation architecture, and batch size among multiple candidates. Solutions were evaluated using two cross-validation methods: stratified k-fold cross-validation, where validation subsets maintained the same proportion of samples from all case studies, and leave-one-case-out cross-validation, where the validation dataset originates entirely from a single case study, and the training set consists of samples from other case studies. Depending on the case study and the level of validation strictness, the proposed solution achieved a root mean square error (RMSE) ranging between 2 cm and 16 cm. The proposed method outperforms methods based on the straightforward sampling of photogrammetric DSM, achieving, on average, an 84% lower RMSE for stratified cross-validation and a 62% lower RMSE for all-in-case-out cross-validation. By utilizing data from other research, the proposed solution was compared on the same case study with other UAV-based methods. For that benchmark case study, the proposed solution achieved an RMSE score of 5.9 cm for all-in-case-out cross-validation and 3.5 cm for stratified cross-validation, which is close to the result achieved by the radar-based method (RMSE of 3 cm), which is considered the most accurate method available. The proposed solution is characterized by a high degree of explainability and generalization.

A framework to enhance disaster debris estimation with AI and aerial photogrammetry

This study addresses the critical need to enhance disaster preparedness and response, focusing on hurricane impact assessment and debris estimation. Accurate assessments in this context are critical for post-event search-and-rescue (SAR) operations and resource distribution. Recent computing advancements are revealing the potential of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) technologies in collecting data and assisting with post-hurricane reconnaissance. However, the use of AI and UAV photogrammetry for accurate disaster impact analysis remains underexplored. To this end, this study proposes a damage and debris analysis framework harnessing reality capture through aerial imagery and photogrammetry. Within this framework, a region-based neural network is leveraged to detect debris locations in aerial imagery with favorable performance. In a testbed within the Beaumont-Port Arthur region, in Southeast Texas, this study performs 3D reality captures of the built environment. Since the accuracy of the 3D reality capture is of importance in research areas associated with time-sensitive disaster response, we further investigate the optimal 2D aerial imagery overlap ratio required to generate a sufficiently accurate 3D model for disaster impact analysis and debris volume estimation. Results indicate that, in the case of aerial imagery for infrastructure systems, a minimum of 60 % overlap is recommended for damage assessment and debris analysis. In contrast, for flat green areas, a minimum of 50 % overlap is adequate. Overall, for disaster response applications, our study reveals that an overlap ratio between 60 % and 70 % is optimal for achieving a balance between time efficiency and data quality in aerial data collection. These quantitative recommendations are crucial for enabling efficient disaster response efforts. Additionally, our study outcomes will improve disaster impact analysis and facilitating timely and effective response strategies.

Particle size distribution (PSD) estimation using unmanned aerial vehicle (UAV) photogrammetry for rockfill shear strength characterization

AbstractThe strength of rockfills and waste materials is significantly influenced by their particle size distribution (PSD). For large waste rockfills, PSD is fundamental to determine the shear behavior. Traditionally, PSD for rockfill, used in materials like coarse-grained aggregates, has been obtained through physical sieving. However, the particle sizes in hard rockfills can vary significantly from small particles (< 20 cm diameter) to large blocks or boulders over 100 cm, with the maximum size limited by the in situ ground conditions and blasting performance. Essentially, the sieving process is impractical, considering the scale of the mine waste dumps and the time required. Therefore, in this study, a workflow using digital detection to estimate the PSD is presented, aiming to quantify the waste dump shear strength using Barton–Kjaernsli empirical criterion. PSD from UAV is validated using manual field measurements of individual boulders. The error for coarse characteristic size prediction ranges within ± 4 mm, and the increase in the data collection frequency, area covered, and resolution of fragmentation measurement for rockfills and waste dumps using UAV allows to improve the statistical reliability of the PSD and fragmentation measurement.

Multisource elevations strategy obtaining robust seed points and reference surfaces for ground points extraction in complex terrain area

The appearance of unmanned aerial vehicle photogrammetry and airborne lidar makes it possible to obtain measurement data for complex terrains such as gullies and mountainous regions. However, extracting ground points from these abundant and massive measurement datasets is challenging. In traditional extractions, their essence is to determine the surfaces that can describe the terrain from the seed points in the grid and use them as the basis for separating non-ground points. For effective extraction, this study proposes a multisource elevations strategy (MES) obtaining robust seed points and reference surfaces. First, two-level extended grids were constructed as the basic units. Then, to select more robust values between measurement and interpolation elevations, an elevation-determination rule was established for seed points. After, based fitting and interpolation elevations of grid nodes, the correction range is determined and the elevation is corrected for reference surfaces. In two representative complex terrain areas, when non-ground points were marked as seed points, the MES effectively reduced the phenomenon of seed points moving away from the ground. Reference surfaces can also accurately represent the global change trend and local elevation of the ground in areas where the terrain changes rapidly. This strategy provides a new thinking for ground point extraction from point cloud.

UAV Photogrammetric Surveys for Tree Height Estimation

In the context of precision agriculture (PA), geomatic surveys exploiting UAV (unmanned aerial vehicle) platforms allow the dimensional characterization of trees. This paper focuses on the use of low-cost UAV photogrammetry to estimate tree height, as part of a project for the phytoremediation of contaminated soils. Two study areas with different characteristics in terms of mean tree height (5 m; 0.7 m) are chosen to test the procedure even in a challenging context. Three campaigns are performed in an olive grove (Area 1) at different flying altitudes (30 m, 40 m, and 50 m), and one UAV flight is available for Area 2 (42 m of altitude), where three species are present: oleander, lentisk, and poplar. The workflow involves the elaboration of the UAV point clouds through the SfM (structure from motion) approach, digital surface models (DSMs), vegetation filtering, and a GIS-based analysis to obtain canopy height models (CHMs) for height extraction based on a local maxima approach. UAV-derived heights are compared with in-field measurements, and promising results are obtained for Area 1, confirming the applicability of the procedure for tree height extraction, while the application in Area 2 (shorter tree seedlings) is more problematic.

Unveiling chromaticity and Milankovitch cycles in sedimentary rocks via unmanned aerial vehicle photogrammetry

Unveiling chromaticity and Milankovitch cycles in sedimentary rocks via unmanned aerial vehicle photogrammetry

Quick Extraction of Joint Surface Attitudes and Slope Preliminary Stability Analysis: A New Method Using Unmanned Aerial Vehicle 3D Photogrammetry and GIS Development

The present study proposes a preliminary analysis method for rock mass joint acquisition, analysis, and slope stability assessment based on unmanned aerial vehicle (UAV) photogrammetry to extract the joint surface attitude in Geographic Information Systems (GIS). The method effectively solves the difficulties associated with the above issues. By combining terrain-following photogrammetry (TFP) and perpendicular and slope surface photogrammetry (PSSP), the three-dimensional (3D) information can be efficiently obtained along the slope characteristics’ surface, which avoids the information loss involved in traditional single-lens aerial photography and the information redundancy of the five-eye aerial photography. Then, a semi-automatic geoprocessing tool was developed within the ArcGIS Pro 3.0 environment, using Python for the extraction of joint surfaces. Multi-point fitting was used to calculate the joint surface attitude. The corresponding attitude symbols are generated at the same time. Finally, the joint surface attitude information is used to perform stereographic projection and kinematic analysis. The former can determine the dominant joint group, and the latter can obtain the probability of four types of failure, including planar sliding, wedge sliding, flexural toppling, and direct toppling. The integrated stability evaluation method studied in this paper, which combines a 3D interpretation of UAV and GIS stereographic projection statistical analysis, has the advantages of being efficient and user-friendly, and requires minimal prior knowledge. The results can aid in the geological surveys of slopes and guide engineering practices.

Quality Analysis of Unmanned Aerial Vehicle Images Using a Resolution Target

Unmanned aerial vehicle (UAV) photogrammetry is an emerging means of acquiring high-precision rapid spatial information and data because it is cost-effective and highly efficient. However, securing uniform quality in the results of UAV photogrammetry is difficult due to the use of low-cost navigation devices, non-surveying cameras, and rapid changes in shooting locations depending on the aircraft’s behavior. In addition, no specific procedures or guidelines exist for performing quantitative quality tests or certification methods on UAV images. Additionally, test tools for UAV image quality assessment only use the ground sample distance (GSD), often resulting in a reduced image quality compared with that of manned aircraft images. In this study, we performed a modulation transfer function (MTF) analysis using a slanted edge target and a GSD analysis to confirm the necessity of MTF analysis in UAV image quality assessments. In this study, we aimed to address this issue by conducting a modulation transfer function (MTF) analysis using a slanted edge target and a ground sample distance (GSD) analysis. This was carried out to confirm the necessity of MTF analysis in evaluating UAV image quality. Furthermore, we analyzed the impact of flight height and mounted sensors on image quality at different study sites.

Assessment of loess terraced slope erosion using unmanned aerial vehicle photogrammetry: Topographic and hydrological effects

AbstractTerraced slopes are widely used in the Loess Plateau to control gully erosion, yet their potential to induce new erosion patterns remains overlooked. Here we used unmanned aerial vehicle (UAV) photogrammetry to obtain centimeter‐resolution digital surface models (DSMs) and derived hydrological networks, slopes, aspects, and curvatures of the terraced slopes. Our analysis reveals terraces disrupted natural water pathways, spurring new rill formations during each rainy season—a process neglected by models. Intriguingly, despite gentler gradients, terraced slopes exhibited higher erosion rates than steeper gullies, with widespread rills on terrace ridges. Vegetation‐induced deposition surpassed erosion by 1.52 times, but moisture evaporation on sunny slopes limited plant growth and enhanced erosion. We propose targeted strategies tailored to these erosion mechanisms, including terrace redesign, runoff diversion, slope strengthening, and suitable vegetation, advancing sustainable land management. However, this study faces limitations due to the reliance on drone photogrammetry, which may be influenced by environmental factors like varying solar angles and vegetation growth, and the lack of extensive field validation to support the UAV‐derived data. Our study spotlights drone photogrammetry's potential for elucidating complex erosion dynamics on terraced slopes.

Optimal camera focal length detection method for GPS-supported bundle adjustment in UAV photogrammetry

The global positioning system (GPS) supported bundle adjustment (BA) method of unmanned aerial vehicle (UAV) photogrammetry significantly minimises the number of ground control points (GCPs); however, there are often systematic errors in the vertical axis direction of the 3D reconstruction results. In this study, a method that relies only on a small number of GCPs for detecting and estimating the optimal focal length of the camera is proposed. First, the scene structure information is obtained based on the conventional GPS-supported BA optimisation method, and the camera focal length is redefined based on a single GCP. Second, a second-order polynomial is used to fit the redefined camera focal lengths of multiple GCPs and the corresponding positioning accuracy. The highest accuracy position is selected as the optimal focal length of the camera. Finally, the optimal focal length of the camera is taken as a constant, and the conventional GPS-supported BA method is used to complete the optimisation again. The experimental results show that under the same number and distribution of GCPs, the camera detection method’s total positioning accuracy proposed in this paper is improved by an average of 37.64%. The optimal camera focal length obtained by the method can correct the systematic error in the vertical direction while keeping the vertical accuracy consistent with the horizontal accuracy.

Residential greenspace exposure, particularly green window-views, is associated with improved sleep quality among older adults: Evidence from a high-density city

Preliminary evidence suggests a potential link between greenspace exposure and better sleep quality. Here we aimed to investigate the specific associations between residential greenspace exposure, green-window views, and sleep quality among older adults and determine whether these associations were mediated through mitigation (i.e., perceived noise annoyance, air pollution, and artificial light pollution) and restoration (i.e., psychological stress) pathways. A total of 1007 older adults with an average age >70 years were recruited from 40 residential communities in the Old City District of Nanjing, China. We developed a set of fine-grained greenspace exposure indices based on 3D point cloud data derived from unmanned aerial vehicle photogrammetry, incorporating vegetation coverage and green volume ratio at the community level and green window ratio at the individual level. Sleep quality was assessed using the Pittsburgh Sleep Quality Index. We examined the complex interrelationships between residential greenspace exposure, environmental stressors (air pollution, artificial light pollution, and noise), perceived mental stress, and sleep quality using multilevel linear regression models. The results suggested the following: (1) Vegetation coverage, green volume ratio, and green window ratio were significantly associated with sleep quality in older adults. (2) The green window ratio was more strongly associated with improved sleep quality than other greenspace exposure metrics. (3) These associations may be partially explained by noise mitigation and mental stress reduction. In conclusion, it seems advantageous to consider immediate residential greenspace, particularly through green-window views, as part of the interventions aimed at supporting sleep quality improvement among older adults.

GNSS-supported direct georeferencing for UAV photogrammetry without GCP in Antarctica: a case study in Larsemann Hills

Unmanned aerial vehicle (UAV) has become an increasingly popular remote sensing platform in Antarctica. Due to the challenging natural conditions and lack of global navigation satellite system (GNSS) references in Antarctica, GNSS-supported direct georeferencing holds great potential for remote sensing applications in this region. Based on UAV surveys and GNSS observations we performed in Larsemann Hills, Antarctica, four GNSS-supported direct georeferencing schemes for UAV photogrammetry without ground control point (GCP) were designed and evaluated. Three of the schemes can generate high-accuracy photogrammetric products (horizontal accuracy: ∼0.7 ground sampling distance (GSD), vertical accuracy: ∼2.6 GSD). The fourth scheme, while exhibiting a lower accuracy at the meter-level, could offer high flexibility, and the accuracy of its derived products could be improved by post-flight transformation. The selection of an appropriate georeferencing scheme should be contingent upon the given application scenario, which can enhance the quality and accuracy of UAV photogrammetry in Antarctica. Potential applications of UAV remote sensing in Antarctica were discussed. It’s proven that UAV photogrammetry constitutes a reliable tool for Antarctic expedition path planning and ice morphology evolution monitoring. Our study demonstrated that direct georeferencing can generate high-accuracy UAV products in a reliable and feasible way in Antarctica.

Validation of Unmanned Aerial Vehicle Photogrammetry for Landslide Mapping

Unmanned Aerial Vehicle (UAV) photogrammetry offers quick and effective landslide monitoring. UAV named Polines 01-GD has been developed for photogrammetry. The UAV is designed flexibly in its gimbal to change the camera accordingly. However, UAV validation is needed to evaluate the quality of the device. This study aims to validate the performance of UAV Polines 01-GD for photogrammetry from quantitative and qualitative analysis. The quantitative analysis was performed by RMSE and area accuracy in m2 with the reference DJI Phantom 4 Pro. Meanwhile, the qualitative analysis was done using the DEM (Digital Elevation Model) result. 3D Ground Control Points (GCPs) size 2 m x 2 m were used and placed in the landslide area, UNNES park, Semarang. The camera assembled for an experiment in Polines 01-GD is GoPro Hero 3. The results show that the RMSE of Polines 01-GD is 0,0000234193, and the area accuracy to the real measurement of GCP is 97,8%. The results of landslide indicated by the data was taken in March 2022 compared to the first flight on August 2021, showing landslides in 79.4 m and 60.3 m. Even so, the DEM of DJI Phantom 4 Pro result is clearer than Polines 01-GD. In conclusion, UAV Polines 01-GD can be used for photogrammetry. However, the flight time can be a challenge for future research as Polines 01-GD can only fly in 15 minutes, which is half of DJI Phantom 4 Pro.

Reservoir landslide monitoring and mechanism analysis based on UAV photogrammetry and sub-pixel offset tracking: a case study of Wulipo landslide

Introduction: Reservoir landslides undergo large deformations during the early stages of impoundment and maintain long-term persistent deformations during the operational period of the reservoir. The management of reservoir landslides mostly focuses on the early identification, risk assessment during the large deformations, and long-sequence monitoring during long-term persistent deformations, which requires sufficient continuity and integrity of the landslide monitoring data.Methods: Taking the Wulipo (WLP) landslide in Baihetan Reservoir as example, this paper proposes a reservoir landslide monitoring method that integrates field survey, unmanned aerial vehicle (UAV) photogrammetry and global navigation satellite system (GNSS) monitoring, which can effectively eliminate the practical monitoring gaps between multiple monitoring methods and improve the continuity and completeness of monitoring data.Results and discussion: First, this study determined the initiation time of the landslide through the field investigation and collected five period of UAV data to analyze the overall displacement vector of the WLP landslide using sub-pixel offset tracking (SPOT). On the basis of the above data, we compensated for the missing data in GNSS system due to the practical monitoring vacancies by combining the field survey and the landslide-water level relationship. Based on these monitoring data, this paper points out that the WLP landslide is a buoyancy-driven landslide, and whether or not accelerated deformation will occur is related to the maximum reservoir water level. Finally, this study analyzed and discussed the applicability of UAV photogrammetry for reservoir landslide monitoring in the absence of ground control points (GCPs), and concluded that this method can be quickly and flexibly applied to the stage of large deformation of reservoir landslides.

Dynamics of beach scarp formation behind detached breakwaters

Detached (offshore) breakwaters are widely used as beach stabilization measures. However, there is potential for erosion at the openings between detached breakwaters, which may result in the formation of beach scarps. This study evaluated the formation of beach scarps around breakwaters using field surveys and numerical simulations. The evaluation was conducted using a combination of a recent advanced field survey method and a numerical modelling system combining GPV-MSM, SWAN, and XBeach. The unmanned aerial vehicle photogrammetry results revealed the height of the beach scarp generated behind the detached breakwaters to be approximately 3.0 m at Aoyama beach, Niigata coast, Japan, although sand deposition occurred below sea level towards the detached breakwaters. The numerical simulation using the results of the field survey showed that a combination of high waves and increased water level owing to radiation stress between the shoreline and breakwaters resulted in a steeper slope in the beach morphology. Second, avalanching occurred on the beach, leading to the formation of beach scarps. This study showed that detached breakwaters may lead to high water levels, resulting in the generation of beach scarps behind the breakwaters. This study implied that large beach scarps can be generated even when detached breakwaters are constructed.

Multiple Close-Range Geomatic Techniques for the Kinematic Study of the La Paúl Rock Glacier, Southern Pyrenees

Rock glaciers are one of the most representative elements of mountain permafrost. Their study can contribute to modelling climate change and its effect on natural and anthropogenic environments. Therefore, it is crucial to understand the evolution and quantify the changes in these periglacial landforms at a global level. This study aims to present the monitoring work carried out on the Pyrenean rock glacier of La Paúl (42°39′40″N, 0°26′34″E) from 2013 to 2020, employing in situ geomatics techniques to determine the landform surface kinematics accurately. For this purpose, global navigation satellite systems (GNSS), terrestrial laser scanners (TLS), and unmanned aerial vehicles (UAV) photogrammetry techniques were used simultaneously to evaluate their compatibility in quantifying displacements. Based on 2D and 3D analyses, the results demonstrate the high surface activity of the rock glacier, with mean variations reaching 36 cm/year (GNSS) and a distribution of deformations that, although intensified on its western side, are present on the entire surface of La Paúl. This study uses state-of-the-art geomatics techniques to present dependable and updated quantitative data on a periglacial landform’s recent development in under-researched areas, such as the Pyrenean temperate high mountain.

Assessing Shallow Soft Deposits through Near-Surface Geophysics and UAV-SfM: Application in Pocket Beaches Environments

This study employs a multimethod approach to investigate the sediment distribution in two pocket beaches, Ramla Beach and Mellieha S Beach, in Malta. Both study sites were digitally reconstructed using unmanned aerial vehicle (UAV) photogrammetry. For each case, an ERT and a dense network of ambient seismic noise measurements processed through a horizontal-to-vertical spectral ratio (HVSR) technique were acquired. Electrical resistivity tomography (ERT) analysis enables the estimation of sediment thickness in each beach. HVSR analysis revealed peaks related to beach sediments overlying limestone rocks in both sites and also indicated a deeper stratigraphic contact in Mellieha S Beach. Based on ERT measurements, sediment thickness is calculated for each HVSR measurement. Interpolation of results allows for bedrock surface modelling in each case study, and when combined with digital terrain models (DTMs) derived from photogrammetric models, sediment volumes are estimated for each site. The geometry of this surface is analyzed from a geological perspective, showing structural control of sediment distribution due to a normal fault in Mellieha S Beach and stratigraphic control facilitated by a highly erodible surface in Ramla Beach. The results emphasize the importance of adopting a three-dimensional perspective in coastal studies for precise sediment volume characterization and a deeper understanding of pocket beach dynamics. This practical multimethod approach presented here offers valuable tools for future coastal research and effective coastal management, facilitating informed decision making amidst the growing vulnerability of coastal zones to climate change impacts.

UAV Photogrammetry for Estimating Stand Parameters of an Old Japanese Larch Plantation Using Different Filtering Methods at Two Flight Altitudes.

Old plantations are iconic sites, and estimating stand parameters is crucial for valuation and management. This study aimed to estimate stand parameters of a 115-year-old Japanese larch (Larix kaempferi (Lamb.) Carrière) plantation at the University of Tokyo Hokkaido Forest (UTHF) in central Hokkaido, northern Japan, using unmanned aerial vehicle (UAV) photogrammetry. High-resolution RGB imagery was collected using a DJI Matrice 300 real-time kinematic (RTK) at altitudes of 80 and 120 m. Structure from motion (SfM) technology was applied to generate 3D point clouds and orthomosaics. We used different filtering methods, search radii, and window sizes for individual tree detection (ITD), and tree height (TH) and crown area (CA) were estimated from a canopy height model (CHM). Additionally, a freely available shiny R package (SRP) and manually digitalized CA were used. A multiple linear regression (MLR) model was used to estimate the diameter at breast height (DBH), stem volume (V), and carbon stock (CST). Higher accuracy was obtained for ITD (F-score: 0.8-0.87) and TH (R2: 0.76-0.77; RMSE: 1.45-1.55 m) than for other stand parameters. Overall, the flying altitude of the UAV and selected filtering methods influenced the success of stand parameter estimation in old-aged plantations, with the UAV at 80 m generating more accurate results for ITD, CA, and DBH, while the UAV at 120 m produced higher accuracy for TH, V, and CST with Gaussian and mean filtering.