Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning

Low-cost tools for virtual reconstruction of traffic accident scenarios

Investigations into traffic accidents that lead to the determination of their causes and consequences are useful to all interested parties, both in the public and private sectors. One of the phases of investigation is the capture of data enabling the complete reconstruction of the accident scene, which is usually the point at which a conflict arises between the slow process of information gathering and the need to restore normal traffic flow. To reduce to a minimum the time the traffic is halted, this paper follows a methodology to reconstruct traffic accidents and puts forward a series of procedures and tools that are applicable to both large and small scenarios. The methodology uses low-cost UAV-SfM in combination with UAS aerial image capture systems and inexpensive GNSS equipment costing less than €900. This paper describes numerous tests and assessments that were carried out on four potential work scenarios (E-1 and E-2 urban roads with several intersections; E-3, an urban crossing with medium slopes; and E-4, a complex road section with different land morphologies), assessing the impact of using simple or double strip flights and the number of GCPs, their spacing distance and different distribution patterns. From the different configurations tested, the best results were achieved in those offset-type distributions where the GCPs were placed on both sides of the working area and at each end, with a spacing between 100 and 50 m and using double strip flights. Our conclusion is that the application of this protocol would be highly efficient and economical in the reconstruction of traffic accidents, provide simplicity in implementation, speed of capture and data processing, and provide reliable results quite economically and with a high degree of accuracy with RMSE values below 5 cm.

Measurement Accuracy and Improvement of Thematic Information from Unmanned Aerial System Sensor Products in Cultural Heritage Applications

In the context of producing a digital surface model (DSM) and an orthophotomosaic of a study area, a modern Unmanned Aerial System (UAS) allows us to reduce the time required both for primary data collection in the field and for data processing in the office. It features sophisticated sensors and systems, is easy to use and its products come with excellent horizontal and vertical accuracy. In this study, the UAS WingtraOne GEN II with RGB sensor (42 Mpixel), multispectral (MS) sensor (1.2 Mpixel) and built-in multi-frequency PPK GNSS antenna (for the high accuracy calculation of the coordinates of the centers of the received images) is used. The first objective is to test and compare the accuracy of the DSMs and orthophotomosaics generated from the UAS RGB sensor images when image processing is performed using only the PPK system measurements (without Ground Control Points (GCPs)), or when processing is performed using only GCPs. For this purpose, 20 GCPs and 20 Check Points (CPs) were measured in the field. The results show that the horizontal accuracy of orthophotomosaics is similar in both processing cases. The vertical accuracy is better in the case of image processing using only the GCPs, but that is subject to change, as the survey was only conducted at one location. The second objective is to perform image fusion using the images of the above two UAS sensors and to control the spectral information transferred from the MS to the fused images. The study was carried out at three archaeological sites (Northern Greece). The combined study of the correlation matrix and the ERGAS index value at each location reveals that the process of improving the spatial resolution of MS orthophotomosaics leads to suitable fused images for classification, and therefore image fusion can be performed by utilizing the images from the two sensors.

Image Mapping Accuracy Evaluation Using UAV with Standalone, Differential (RTK), and PPP GNSS Positioning Techniques in an Abandoned Mine Site

Global navigation satellite systems (GNSSs) provide a common positioning method that utilizes satellite signals to determine the spatial location of a receiver. However, there are several error factors in standalone GNSS positioning due to instrumental, procedural, and environmental factors that arise during the signal transmission process, and the final positioning error can be up to several meters or greater in length. Thus, real-time kinematic (RTK) correction and post-mission precise point positioning (PPP) processing technologies are proposed to improve accuracy and accomplish precise position measurements. To evaluate the geolocation accuracy of mosaicked UAV images of an abandoned mine site, we compared each orthomosaic image and digital elevation model obtained using standalone GNSS positioning, differential (RTK) GNSS positioning, and post-mission PPP processing techniques. In the three types of error evaluation measure (i.e., relative camera location error, ground control points-based absolute image mapping error, and volumetric difference of mine tailings), we found that the RTK GNSS positioning method obtained the best performance in terms of the relative camera location error and the absolute image mapping error evaluations, and the PPP post-processing correction effectively reduced the error (69.5% of the average total relative camera location error and 59.3% of the average total absolute image mapping error) relative to the standalone GNSS positioning method. Although differential (RTK) GNSS positioning is widely used in positioning applications that require very high accuracy, post-mission PPP processing can also be used in various fields in which it is either not feasible to operate expensive equipment to receive RTK GNSS signals or network RTK services are unavailable.

High-precision plant height measurement by drone with RTK-GNSS and single camera for real-time processing

Conventional crop height measurements performed using aerial drone images require 3D reconstruction results of several aerial images obtained through structure from motion. Therefore, they require extensive computation time and their measurement accuracy is not high; if the 3D reconstruction result fails, several aerial photos must be captured again. To overcome these challenges, this study proposes a high-precision measurement method that uses a drone equipped with a monocular camera and real-time kinematic global navigation satellite system (RTK-GNSS) for real-time processing. This method performs high-precision stereo matching based on long-baseline lengths (approximately 1 m) during the flight by linking the RTK-GNSS and aerial image capture points. As the baseline length of a typical stereo camera is fixed, once the camera is calibrated on the ground, it does not need to be calibrated again during the flight. However, the proposed system requires quick calibration in flight because the baseline length is not fixed. A new calibration method that is based on zero-mean normalized cross-correlation and two stages least square method, is proposed to further improve the accuracy and stereo matching speed. The proposed method was compared with two conventional methods in natural world environments. It was observed that error rates reduced by 62.2% and 69.4%, for flight altitudes between 10 and 20 m respectively. Moreover, a depth resolution of 1.6 mm and reduction of 44.4% and 63.0% in the error rates were achieved at an altitude of 4.1 m, and the execution time was 88 ms for images with a size of 5472 × 3468 pixels, which is sufficiently fast for real-time measurement.

Analysis of GPS/EGNOS Positioning Quality Using Different Ionospheric Models in UAV Navigation

Unmanned aerial vehicles (UAVs) have become very popular tools for geoinformation acquisition in recent years. They have also been applied in many other areas of life. Their navigation is highly dependent on global navigation satellite systems (GNSS). The European Geostationary Navigation Overlay Service (EGNOS) is intended to support GNSSs during positioning, mainly for aeronautical applications. The research presented in this paper concerns the analysis of the positioning quality of a modified GPS/EGNOS algorithm. The calculations focus on the source of ionospheric delay data as well as on the aspect of smoothing code observations with phase measurements. The modifications to the algorithm concerned the application of different ionospheric models for position calculation. Consideration was given to the EGNOS ionospheric model, the Klobuchar model applied to the GPS system, the Klobuchar model applied to the BeiDou system, and the NeQuick model applied to the Galileo system. The effect of removing ionospherical corrections from GPS/EGNOS positioning on the results of the determination of positioning quality was also analysed. The results showed that the original EGNOS ionospheric model maintains the best accuracy results and a better correlation between horizontal and vertical results than the other models examined. The additional use of phase-smoothing of code observations resulted in maximum horizontal errors of approximately 1.3 m and vertical errors of approximately 2.2 m. It should be noted that the results obtained have local characteristics related to the area of north-eastern Poland.

An Accurate and Robust Method for Absolute Pose Estimation with UAV Using RANSAC

In this paper, we proposed an accurate and robust method for absolute pose estimation with UAV (unmanned aerial vehicle) using RANSAC (random sample consensus). Because the artificial 3D control points with high accuracy are time-consuming and the small point set may lead low measuring accuracy, we designed a customized UAV to efficiently obtain mass 3D points. A light source was mounted on the UAV and used as a 3D point. The position of the 3D point was given by RTK (real-time kinematic) mounted on the UAV, and the position of the corresponding 2D point was given by feature extraction. The 2D-3D point correspondences exhibited some outliers because of the failure of feature extraction, the error of RTK, and wrong matches. Hence, RANSAC was used to remove the outliers and obtain the coarse pose. Then, we proposed a method to refine the coarse pose, whose procedure was formulated as the optimization of a cost function about the reprojection error based on the error transferring model and gradient descent to refine it. Before that, normalization was given for all the valid 2D-3D point correspondences to improve the estimation accuracy. In addition, we manufactured a prototype of a UAV with RTK and light source to obtain mass 2D-3D point correspondences for real images. Lastly, we provided a thorough test using synthetic data and real images, compared with several state-of-the-art perspective-n-point solvers. Experimental results showed that, even with a high outlier ratio, our proposed method had better performance in terms of numerical stability, noise sensitivity, and computational speed.

Comparison of Remote Sensing Methods for Plant Heights in Agricultural Fields Using Unmanned Aerial Vehicle-Based Structure From Motion

Remote sensing using unmanned aerial vehicles (UAVs) and structure from motion (SfM) is useful for the sustainable and cost-effective management of agricultural fields. Ground control points (GCPs) are typically used for the high-precision monitoring of plant height (PH). Additionally, a secondary UAV flight is necessary when off-season images are processed to obtain the ground altitude (GA). In this study, four variables, namely, camera angles, real-time kinematic (RTK), GCPs, and methods for GA, were compared with the predictive performance of maize PH. Linear regression models for PH prediction were validated using training data from different targets on different flights ("different-targets-and-different-flight" cross-validation). PH prediction using UAV-SfM at a camera angle of -60° with RTK, GCPs, and GA obtained from an off-season flight scored a high coefficient of determination and a low mean absolute error (MAE) for validation data (R ² _val = 0.766, MAE = 0.039 m in the vegetative stage; R ² _val = 0.803, MAE = 0.063 m in the reproductive stage). The low-cost case (LC) method, conducted at a camera angle of -60° without RTK, GCPs, or an extra off-season flight, achieved comparable predictive performance (R ² _val = 0.794, MAE = 0.036 m in the vegetative stage; R ² _val = 0.749, MAE = 0.072 m in the reproductive stage), suggesting that this method can achieve low-cost and high-precision PH monitoring.

Accuracy assessment of UAV-post-processing kinematic (PPK) and UAV-traditional (with ground control points) georeferencing methods

The use of unmanned aerial vehicles (UAV) in photogrammetric mapping/surveying facilities has increased recently due to the developments on photogrammetric instruments and algorithms that enhance high-quality final products (orthoimages, digital surface model-DSM, etc.) in fast, accurate, and economical way. The aim of this study was to assess the accuracy of a UAV-based post-processing kinematic (PPK) solution. To do that, two methods were implemented with PPK solution and georeferencing with ground control points (GCPs). According to the statistical results, root mean square error (RMSE) values obtained from the GCPs and PPK solutions in the horizontal component are 6.5 cm and 5.4 cm, respectively. The RMSE values in the vertical component (ellipsoidal heights) were obtained as 4.8 cm (GCPs) and 5.2 cm (PPK), respectively. The results show that UAV-PPK method can also be used to produce photogrammetric products where high accuracy (≤ 10 cm) is required without GCPs. In addition, the results obtained regarding the use of this method clearly show that it can be applied in many different fields such as agriculture, forestry, natural disasters, and geomatics.

UAV-Based Multitemporal Remote Sensing Surveys of Volcano Unstable Flanks: A Case Study from Stromboli

UAV-based photogrammetry is becoming increasingly popular even in application fields that, until recently, were deemed unsuitable for this technique. Depending on the characteristics of the investigated scenario, the generation of three-dimensional (3D) topographic models may in fact be affected by significant inaccuracies unless site-specific adaptations are implemented into the data collection and processing routines. In this paper, an ad hoc procedure to exploit high-resolution aerial photogrammetry for the multitemporal analysis of the unstable Sciara del Fuoco (SdF) slope at Stromboli Island (Italy) is presented. Use of the technique is inherently problematic because of the homogeneous aspect of the gray ash slope, which prevents a straightforward identification of match points in continuous frames. Moreover, due to site accessibility restrictions enforced by local authorities after the volcanic paroxysm in July 2019, Ground Control Points (GCPs) cannot be positioned to constrain georeferencing. Therefore, all 3D point clouds were georeferenced using GCPs acquired in a 2019 (pre-paroxysm) survey, together with stable Virtual Ground Control Points (VGCPs) belonging to a LiDAR survey carried out in 2012. Alignment refinement was then performed by means of an iterative algorithm based on the closest points. The procedure succeeded in correctly georeferencing six high-resolution point clouds acquired from April 2017 to July 2021, whose time-focused analysis made it possible to track several geomorphological structures associated with the continued volcanic activity. The procedure can be further extended to smaller-scale analyses such as the estimation of locally eroded/accumulated volumes and pave the way for rapid UAV-based georeferenced surveys in emergency conditions at the SdF.

Potential of Ultra-High-Resolution UAV Images with Centimeter GNSS Positioning for Plant Scale Crop Monitoring

To implement agricultural practices that are more respectful of the environment, precision agriculture methods for monitoring crop heterogeneity are becoming more and more spatially detailed. The objective of this study was to evaluate the potential of Ultra-High-Resolution UAV images with centimeter GNSS positioning for plant-scale monitoring. A Dji Phantom 4 RTK UAV with a 20 MPixel RGB camera was used, flying at an altitude of 25 m (0.7 cm resolution). This study was conducted on an experimental plot sown with maize. A centimeter-precision Trimble Geo7x GNSS receiver was used for the field measurements. After evaluating the precision of the UAV’s RTK antenna in static mode on the ground, the positions of 17 artificial targets and 70 maize plants were measured during a series of flights in different RTK modes. Agisoft Metashape software was used. The error in position of the UAV RTK antenna in static mode on the ground was less than one centimeter, in terms of both planimetry and elevation. The horizontal position error measured in flight on the 17 targets was less than 1.5 cm, while it was 2.9 cm in terms of elevation. Finally, according to the RTK modes, at least 81% of the corn plants were localized to within 5 cm of their position, and 95% to within 10 cm.

New Supplementary Photography Methods after the Anomalous of Ground Control Points in UAV Structure-from-Motion Photogrammetry

Recently, multirotor UAVs have been widely used in high-precision terrain mapping, cadastral surveys and other fields due to their low cost, flexibility, and high efficiency. Indirect georeferencing of ground control points (GCPs) is often required to obtain highly accurate topographic products such as orthoimages and digital surface models. However, in practical projects, GCPs are susceptible to anomalies caused by external factors (GCPs covered by foreign objects such as crops and cars, vandalism, etc.), resulting in a reduced availability of UAV images. The errors associated with the loss of GCPs are apparent. The widely used solution of using natural feature points as ground control points often fails to meet the high accuracy requirements. For the problem of control point anomalies, this paper innovatively presents two new methods of completing data fusion by supplementing photos via UAV at a later stage. In this study, 72 sets of experiments were set up, including three control experiments for analysis. Two parameters were used for accuracy assessment: Root Mean Square Error (RMSE) and Multiscale Model to Model Cloud Comparison (M3C2). The study shows that the two new methods can meet the reference accuracy requirements in horizontal direction and elevation direction (RMSEX = 70.40 mm, RMSEY = 53.90 mm, RMSEZ = 87.70 mm). In contrast, the natural feature points as ground control points showed poor accuracy, with RMSEX = 94.80 mm, RMSEY = 68.80 mm, and RMSEZ = 104.40 mm for the checkpoints. This research considers and solves the problems of anomalous GCPs in the photogrammetry project from a unique perspective of supplementary photography, and proposes two new methods that greatly expand the means of solving the problem. In UAV high-precision projects, they can be used as an effective means to ensure accuracy when the GCP is anomalous, which has significant potential for application promotion. Compared with previous methods, they can be applied in more scenarios and have higher compatibility and operability. These two methods can be widely applied in cadastral surveys, geomorphological surveys, heritage conservation, and other fields.

Assessment of RTK Quadcopter and Structure-from-Motion Photogrammetry for Fine-Scale Monitoring of Coastal Topographic Complexity

Advances in image-based remote sensing using unmanned aerial vehicles (UAV) and structure-from-motion (SfM) photogrammetry continue to improve our ability to monitor complex landforms over representative spatial and temporal scales. As with other water-worked environments, coastal sediments respond to shaping processes through the formation of multi-scale topographic roughness. Although this topographic complexity can be an important marker of hydrodynamic forces and sediment transport, it is seldom characterized in typical beach surveys due to environmental and technical constraints. In this study, we explore the feasibility of using SfM photogrammetry augmented with an RTK quadcopter for monitoring the coastal topographic complexity at the beach-scale in a macrotidal environment. The method had to respond to resolution and time constraints for a realistic representation of the topo-morphological features from submeter dimensions and survey completion in two hours around low tide to fully cover the intertidal zone. Different tests were performed at two coastal field sites with varied dimensions and morphologies to assess the photogrammetric performance and eventual means for optimization. Our results show that, with precise image positioning, the addition of a single ground control point (GCP) enabled a global precision (RMSE) equivalent to that of traditional GCP-based photogrammetry using numerous and well-distributed GCPs. The optimal model quality that minimized vertical bias and random errors was achieved from 5 GCPs, with a two-fold reduction in RMSE. The image resolution for tie point detection was found to be an important control on the measurement quality, with the best results obtained using images at their original scale. Using these findings enabled designing an efficient and effective workflow for monitoring coastal topographic complexity at a large scale.

A New Method for Absolute Pose Estimation with Unknown Focal Length and Radial Distortion

Estimating the absolute pose of a camera is one of the key steps for computer vision. In some cases, especially when using a wide-angle or zoom lens, the focal length and radial distortion also need to be considered. Therefore, in this paper, an efficient and robust method for a single solution is proposed to estimate the absolute pose for a camera with unknown focal length and radial distortion, using three 2D–3D point correspondences and known camera position. The problem is decomposed into two sub-problems, which makes the estimation simpler and more efficient. The first sub-problem is to estimate the focal length and radial distortion. An important geometric characteristic of radial distortion, that the orientation of the 2D image point with respect to the center of distortion (i.e., principal point in this paper) under radial distortion is unchanged, is used to solve this sub-problem. The focal length and up to four-order radial distortion can be determined with this geometric characteristic, and it can be applied to multiple distortion models. The values with no radial distortion are used as the initial values, which are close to the global optimal solutions. Then, the sub-problem can be efficiently and accurately solved with the initial values. The second sub-problem is to determine the absolute pose with geometric linear constraints. After estimating the focal length and radial distortion, the undistorted image can be obtained, and then the absolute pose can be efficiently determined from the point correspondences and known camera position using the undistorted image. Experimental results indicate this method’s accuracy and numerical stability for pose estimation with unknown focal length and radial distortion in synthetic data and real images.

A Comparative Analysis of Unmanned Aircraft Systems in Low Altitude Photogrammetric Surveys

Comparing photogrammetric performances of four user-grade unmanned aircraft systems (UAS) is the main aim of this paper. This study investigates what is the more suitable UAS for specific applications considering the required scale factor, such as for architectural, environmental and restoration purposes. Some photogrammetric surveys were conducted in a 5 ha area using a Phantom 4 Adv, Mavic 2 Pro, Mavic Air 2 and Mavic Mini 2. These unmanned aircrafts are commercial systems used mainly by private professionals. Some photogrammetric reconstructions were carried out by varying flight altitude and camera settings of the 4 UAS. Structure-from-motion (SfM) algorithms were applied to the images taken from the UASs. The surveys’ quality was analyzed by comparing the ground targets’ coordinates measured on the field with indirect georeferencing through global navigation satellite system (GNSS). Fifty targets were installed and arranged following a kind of regular grid. For each photogrammetric flight, the boundary conditions were maintained the same, as well as the flight trajectories and the ground control point distribution. Altimetric and planimetric residuals were reported and compared for each photogrammetric survey. Using a regular grid of ground targets, the result obtained from Phantom 4 is one order of magnitude better than the ones obtained from the other UASs. Mavic Mini 2 leads to an error average of about 5 cm. Remembering that the Mavic Mini 2 is an ultralight drone (it does not require a pilot’s license), it could significantly reduce costs compared to all the others.

Direct Georeferencing UAV-SfM in High-Relief Topography: Accuracy Assessment and Alternative Ground Control Strategies along Steep Inaccessible Rock Slopes

Steep rock slopes present key opportunities and challenges within Earth science applications. Due to partial or complete inaccessibility, high-precision surveys of these high-relief landscapes remain a challenge. Direct georeferencing (DG) of unoccupied aerial vehicles (UAVs) with advanced onboard GNSS receivers presents opportunities to generate high-resolution 3D datasets without ground-based access to the study area. However, recent research has revealed large vertical errors using DG that may prove problematic in near-vertical terrain. To address these concerns, we examined more than 75 photogrammetric UAV-datasets with various imaging angles (nadir, oblique, and combinations) and ground control scenarios, including DG, along a steep slope exposure. Results demonstrate that mean errors in DG scenarios are up to 0.12 m higher than datasets using integrated georeferencing with well-distributed GCPs. Inclusion of GCPs greatly reduced mean error values but had limited influence on precision (<0.01 m) for any given imaging strategy. Use of multiple image angles resulted in the highest precisions, regardless of georeferencing strategy. These findings have implications for applications requiring the highest precision and accuracy (e.g., geotechnical engineering, hazard mitigation and mapping, and geomorphic change detection), which should consider using ground control whenever possible. However, for applications less concerned with absolute accuracy, our results show that DG datasets provide strong internal consistency and relative accuracy that may be suitable for high precision measurements within a model, without use of ground control.

UAV and GPR Data Integration in Glacier Geometry Reconstruction: A Case Study from Irenebreen, Svalbard

Although measurements of thickness and internal structure of glaciers are substantial for the understanding of their evolution and response to climate change, detailed data about polythermal glaciers, are scarce. Here, we present the first ground-penetrating radar (GPR) measurement data of Irenebreen, and high-resolution DEM and orthomosaic, obtained from unmanned aerial vehicle (UAV) photogrammetry. A combination of GPR and UAV data allowed for the reconstruction of the glacier geometry including thermal structure. We compare different methods of GPR signal propagation speed determination and argue that a common midpoint method (CMP) should be used if possible. Our observations reveal that Irenebreen is a polythermal glacier with a basal temperate ice layer, the volume of which volume reaches only 12% of the total glacier volume. We also observe the intense GPR signal scattering in two small zones in the ablation area and suggest that intense water percolation occurs in these places creating local areas of temperate ice. This finding emphasizes the possible formation of localised temperate ice zones in polythermal glaciers due to the coincidence of several factors. Our study demonstrates that a combination of UAV photogrammetry and GPR can be successfully applied and should be used for the high-resolution reconstruction of 3D geometries of small glaciers.

Topographic Analysis of Intertidal Polychaete Reefs (Sabellaria alveolata) at a Very High Spatial Resolution

In temperate coastal regions of Western Europe, the polychaete Sabellaria alveolata (Linné) builds large intertidal reefs of several hectares on soft-bottom substrates. These reefs are protected by the European Habitat Directive EEC/92/43 under the status of biogenic structures hosting a high biodiversity and providing ecological functions such as protection against coastal erosion. As an alternative to time-consuming field campaigns, a UAV-based Structure-from-Motion photogrammetric survey was carried out in October 2020 over Noirmoutier Island (France) where the second-largest known European reef is located in a tidal delta. A DJI Phantom 4 Multispectral UAV provided a topographic dataset at very high resolutions of 5 cm/pixel for the Digital Surface Model (DSM) and 2.63 cm/pixel for the multispectral orthomosaic images. The reef footprint was mapped using a combination of two topographic indices: the Topographic Openness Index and the Topographic Position Index. The reef structures covered an area of 8.15 ha, with 89% corresponding to the main reef composed of connected and continuous biogenic structures, 7.6% of large isolated structures (<60 m2), and 4.4% of small isolated reef clumps (<2 m2). To further describe the topographic complexity of the reef, the Geomorphon landform classification was used. The spatial distribution of tabular platforms considered as a healthy stage of the reef in contrast to a degraded stage was mapped with a proxy that consists in comparing the reef volume to a theoretical tabular-shaped reef volume. Epibionts colonizing the reef (macroalgae, mussels, and oysters) were also mapped by combining multispectral indices such as the Normalised Difference Vegetation Index and simple band ratios with topographic indices. A confusion matrix showed that macroalgae and mussels were satisfactorily identified but that oysters could not be detected by an automated procedure due to their spectral complexity. The topographic indices used in this work should now be further exploited to propose a health index for these large intertidal reefs.

Experimental Study on 3D Measurement Accuracy Detection of Low Altitude UAV for Repeated Observation of an Invariant Surface

Low-altitude unmanned aerial vehicle (UAV) photogrammetry combined with structure-from-motion (SFM) algorithms is the latest technological approach to imaging 3D stereo constructions. At present, derivative products have been widely used in landslide monitoring, landscape evolution, glacier movement, volume measurement, and landscape change detection. However, there is still a lack of research into the accuracy of 3D data positioning based on the structure-from-motion of unmanned aerial vehicle (UAV-SFM) technology, itself, which can affect the measurable effectiveness of the results in further applications of this technological approach. In this paper, validation work was carried out for the DJI Phantom 4 RTK UAV, for earth observation data related to 3D positioning accuracy. First, a test plot with a relatively stable surface was selected for repeated flight imaging observations. Specifically, three repeated flights were performed on the test plot to obtain three sorties of images; the structure from motion and multi-view stereo (SFM-MVS) key technology was used to process and construct a 3D scene model, and based on this model the digital surface model (DSM) and digital orthophoto map (DOM) data of the same plot with repeated observations were obtained. In order to check the level of 3D measurement accuracy of the UAV technology itself, a window selection-based method was used to sample the point cloud set data from the three-sortie repeat observation 3D model. The DSM and DOM data obtained from three repeated flights over the surface invariant test plots were used to calculate the repeat observation 3D point errors, taking into account the general methodology of redundant observation error analysis for topographic surveys. At the same time, to further analyze the limits of the UAV measurement technique, possible under equivalent observation conditions with the same processing environment, a difference model (DOD) was constructed for the DSM data from three sorties, to deepen the overall characterization of the differences between the DSMs obtained from repeated observations. The results of the experimental study concluded that both the analysis of the 3D point set measurements based on window sampling and the accuracy evaluation using the difference model were generally able to achieve a centimeter level of planimetric accuracy and vertical accuracy. In addition, the accuracy of the surface-stabilized hardened ground was better, overall, than the accuracy of the non-hardened ground. The results of this paper not only probe the measurement limits of this type of UAV, but also provide a quantitative reference for the accurate control and setting of an acquisition scheme of the UAV-based SfM-MVS method for geomorphological data acquisition and 3D reconstruction.

Studies on Three-Dimensional (3D) Accuracy Optimization and Repeatability of UAV in Complex Pit-Rim Landforms As Assisted by Oblique Imaging and RTK Positioning

Unmanned Aerial Vehicles (UAVs) are a novel technology for landform investigations, monitoring, as well as evolution analyses of long−term repeated observation. However, impacted by the sophisticated topographic environment, fluctuating terrain and incomplete field observations, significant differences have been found between 3D measurement accuracy and the Digital Surface Model (DSM). In this study, the DJI Phantom 4 RTK UAV was adopted to capture images of complex pit-rim landforms with significant elevation undulations. A repeated observation data acquisition scheme was proposed for a small amount of oblique-view imaging, while an ortho-view observation was conducted. Subsequently, the 3D scenes and DSMs were formed by employing Structure from Motion (SfM) and Multi-View Stereo (MVS) algorithms. Moreover, a comparison and 3D measurement accuracy analysis were conducted based on the internal and external precision by exploiting checkpoint and DSM of Difference (DoD) error analysis methods. As indicated by the results, the 3D scene plane for two imaging types could reach an accuracy of centimeters, whereas the elevation accuracy of the orthophoto dataset alone could only reach the decimeters (0.3049 m). However, only 6.30% of the total image number of oblique images was required to improve the elevation accuracy by one order of magnitude (0.0942 m). (2) An insignificant variation in internal accuracy was reported in oblique imaging-assisted datasets. In particular, SfM-MVS technology exhibited high reproducibility for repeated observations. By changing the number and position of oblique images, the external precision was able to increase effectively, the elevation error distribution was improved to become more concentrated and stable. Accordingly, a repeated observation method only including a few oblique images has been proposed and demonstrated in this study, which could optimize the elevation and improve the accuracy. The research results could provide practical and effective technology reference strategies for geomorphological surveys and repeated observation analyses in sophisticated mountain environments.

UAV Approach for Detecting Plastic Marine Debris on the Beach: A Case Study in the Po River Delta (Italy)

Anthropogenic marine debris (AMD) represent a global threat for aquatic environments. It is important to locate and monitor the distribution and presence of macroplastics along beaches to prevent degradation into microplastics (MP), which are potentially more harmful and more difficult to remove. UAV imaging represents a quick method for acquiring pictures with a ground spatial resolution of a few centimeters. In this work, we investigate strategies for AMD mapping on beaches with different ground resolutions and with elevation and multispectral data in support of RGB orthomosaics. Operators with varying levels of expertise and knowledge of the coastal environment map the AMD on four to five transects manually, using a range of photogrammetric tools. The initial survey was repeated after one year; in both surveys, beach litter was collected and further analyzed in the laboratory. Operators assign three levels of confidence when recognizing and describing AMD. Preliminary validation of results shows that items identified with high confidence were almost always classified properly. Approaching the detected items in terms of surface instead of a simple count increased the percentage of mapped litter significantly when compared to those collected. Multispectral data in near-infrared (NIR) wavelengths and digital surface models (DSMs) did not significantly improve the efficiency of manual mapping, even if vegetation features were removed using NDVI maps. In conclusion, this research shows that a good solution for performing beach AMD mapping can be represented by using RGB imagery with a spatial resolution of about 200 pix/m for detecting macroplastics and, in particular, focusing on the largest items. From the point of view of assessing and monitoring potential sources of MP, this approach is not only feasible but also quick, practical, and sustainable.

Accuracy Assessment of UAV-Photogrammetric-Derived Products Using PPK and GCPs in Challenging Terrains: In Search of Optimized Rockfall Mapping

Unmanned aerial photogrammetric surveys are increasingly being used for mapping and studying natural hazards, such as rockfalls. Surveys using unmanned aerial vehicles (UAVs) can be performed in remote, hardly accessible, and dangerous areas, while the photogrammetric-derived products, with high spatial and temporal accuracy, can provide us with detailed information about phenomena under consideration. However, as photogrammetry commonly uses indirect georeferencing through bundle block adjustment (BBA) with ground control points (GCPs), data acquisition in the field is not only time-consuming and labor-intensive, but also extremely dangerous. Therefore, the main goal of this study was to investigate how accurate photogrammetric products can be produced by using BBA without GCPs and auxiliary data, namely using the coordinates X0, Y0 and Z0 of the camera perspective centers computed with PPK (Post-Processing Kinematic). To this end, orthomosaics and digital surface models (DSMs) were produced for three rockfall sites by using images acquired with a DJI Phantom 4 RTK and the two different BBA methods mentioned above (hereafter referred to as BBA_traditional and BBA_PPK). The accuracy of the products, in terms of the Root Mean Square Error (RMSE), was computed by using verification points (VPs). The accuracy of both BBA methods was also assessed. To test the differences between the georeferencing methods, two statistical test were used, namely a paired Student’s t-test, and a non-parametric Wilcoxon signed-rank. The results show that the accuracy of the BBA_PPK is inferior to that of BBA_traditional, with the total RMSE values for the three sites being 0.056, 0.066, and 0.305 m, respectively, compared to 0.019, 0.036 and 0.014 m obtained with BBA_traditional. The accuracies of the BBA methods are reflected in the accuracy of the orthomosaics, whose values for the BBA_PPK are 0.039, 0.043 and 0.157 m, respectively, against 0.029, 0.036 and 0.020 m obtained with the BBA_traditional. Concerning the DSM, those produced with the BBA_PPK method present accuracy values of 0.065, 0.072 and 0.261 m, respectively, against 0.038, 0.060 and 0.030 m obtained with the BBA_traditional. Even though that there are statistically significant differences between the georeferencing methods, one can state that the BBA_PPK presents a viable solution to map dangerous and exposed areas, such as rockfall transit and deposit areas, especially for applications at a regional level.

UAS-Based Plant Phenotyping for Research and Breeding Applications

Unmanned aircraft system (UAS) is a particularly powerful tool for plant phenotyping, due to reasonable cost of procurement and deployment, ease and flexibility for control and operation, ability to reconfigure sensor payloads to diversify sensing, and the ability to seamlessly fit into a larger connected phenotyping network. These advantages have expanded the use of UAS-based plant phenotyping approach in research and breeding applications. This paper reviews the state of the art in the deployment, collection, curation, storage, and analysis of data from UAS-based phenotyping platforms. We discuss pressing technical challenges, identify future trends in UAS-based phenotyping that the plant research community should be aware of, and pinpoint key plant science and agronomic questions that can be resolved with the next generation of UAS-based imaging modalities and associated data analysis pipelines. This review provides a broad account of the state of the art in UAS-based phenotyping to reduce the barrier to entry to plant science practitioners interested in deploying this imaging modality for phenotyping in plant breeding and research areas.

Geometric Accuracy of 3D Reality Mesh Utilization for BIM-Based Earthwork Quantity Estimation Workflows

Current surveying techniques are typically applied to survey the as-is condition of buildings, brownfield sites and infrastructure prior to design. However, within the past decade, these techniques evolved significantly, and their applications can be enhanced by adopting unmanned aerial vehicles (UAVs) for data acquisition, up-to-date software for creating 3D reality mesh, which in turn opens new possibilities for much more efficient construction site surveying and constant updating and process management. In this study the workflows of three UAV-based photogrammetry techniques: Real Time Kinematic (RTK), Post-Processing Kinematic (PPK) and Global Positioning System (GPS) based on control points were analyzed, described, and compared to conventional surveying method with Global Navigation Satellite System (GNSS) receiver. Tests were performed under realistic conditions in 36 ha quarry in Lithuania. The results of the relationship between ground sample distance (GSD) and the comparison of volume measurements under each technique, including conventional method were analyzed. The deviation of data collected on field vs. generated in reality mesh, including ground control points (GCPs) and check points (CHPs) with different configurations, was investigated. The research provides observations on each workflow in the terms of efficiency and reliability for earthwork quantity estimations and explains processing schemes with advanced commercial software tools.

Evaluating the Performance of sUAS Photogrammetry with PPK Positioning for Infrastructure Mapping

Traditional acquisition methods for generating digital surface models (DSMs) of infrastructure are either low resolution and slow (total station-based methods) or expensive (LiDAR). By contrast, photogrammetric methods have recently received attention due to their ability to generate dense 3D models quickly for low cost. However, existing frameworks often utilize many manually measured control points, require a permanent RTK/PPK reference station, or yield a reconstruction accuracy too poor to be useful in many applications. In addition, the causes of inaccuracy in photogrammetric imagery are complex and sometimes not well understood. In this study, a small unmanned aerial system (sUAS) was used to rapidly image a relatively even, 1 ha ground surface. Model accuracy was investigated to determine the importance of ground control point (GCP) count and differential GNSS base station type. Results generally showed the best performance for tests using five or more GCPs or when a Continuously Operating Reference Station (CORS) was used, with vertical root mean square errors of 0.026 and 0.027 m in these cases. However, accuracy outputs generally met comparable published results in the literature, demonstrating the viability of analyses relying solely on a temporary local base with a one hour dwell time and no GCPs.

Debris Flow and Rockslide Analysis with Advanced Photogrammetry Techniques Based on High-Resolution RPAS Data. Ponte Formazza Case Study (NW Alps)

The use of a Remotely Piloted Aircraft System (RPAS) for the characterization and monitoring of landslides has been widely improved in the last decade. In particular, the use of this system is particularly effective for the study of areas prone to geohazards. Zones affected by landslides, such as rock slides and debris flows, are often quite critical in terms of accessibility due to unstable blocs that can strongly limit the direct access to the studied area. In this paper, we present the case study of Ponte Formazza in NW Italian Alps. In June 2019, a massive and complex debris flow re-mobilized about 300,000 m3 of a rockslide deposit that occurred in 2009. In this particular environment, we tested traditional, direct and mixed photogrammetric approaches using various configurations of Ground Control Points (GCPs) of the photogrammetric block and by calculating the relative errors. The minimum configuration of GCPs was established to reduce in situ measurements without degrading the accuracy of the cartographic products. The images of three RPAS campaigns (2017, 2018 and 2019), processed with a Structure from Motion (SfM) technique, allowed us to obtain very high-resolution orthophoto and digital surface models (DSMs) before and after the 2019 event. A few GCPs, geolocated with a Global Navigation Satellite System (GNSS), improved the orthophoto and DSM quality (Root Mean Squared Error RMSE 5 cm) even in the areas far from the drone deployment. The availability of high-resolution models has been fundamental for the identification of the volume changes. Furthermore, the 3D view supported and completed the geomorphological mapping of affected areas, particularly in the areas where the field survey is dangerous. The use of ancillary meteorological data and Sentinel-2 satellite images allows for a better definition of the kinematics and the predisposal and triggering factors of the 2019 debris flow.

High-Resolution Monitoring of Tidal Systems Using UAV: A Case Study on Poplar Island, MD (USA)

Tidal processes regulating sediment accretion rates and vegetated platform erosion in tidal systems strongly affect salt marsh evolution. A balance between erosion and deposition in a restored salt marsh is crucial for analyzing restoration strategies to be adopted within a natural context. Marsh morphology is also coupled with tidal mudflats and channel networks and this makes micro-tidal systems crucial for a detailed assessment of restoration interventions. Here, we present a methodological approach for monitoring channel morphodynamics and vegetation variations over a time frame of six years in a low tidal energy salt marsh of the Paul S. Sarbanes Ecosystem Restoration Project at Poplar Island (Maryland, USA). The project is a restoration site where sediment dredged from the shipping channels in the upper Chesapeake Bay is used to restore a tidal marsh habitat in mid-Chesapeake Bay. Aerial surveys with an Unmanned Aerial Vehicle (UAV) have been performed for the high-resolution mapping of a small tidal system. Flight missions were planned to obtain a Ground Sample Distance (GSD) of 2 cm. Structure-from-Motion (SfM) and Multi-View-Stereo (MVS) algorithms have been used to reconstruct the 3D geometry of the site. The mapping of channel morphology and an elevation assessment on the mudflat were performed using orthomosaics, Digital Terrain Models (DTMs) and GNSS survey. The results highlight that the workflow adopted in this pilot work is suitable to assess the geomorphological evolution over time in a micro-tidal system. However, issues were encountered for salt marsh due to the presence of dense vegetation. The UAV-based photogrammetry approach with GNSS RTK ground surveys can hence be replicated in similar sites all over the world to evaluate restoration interventions and to develop new strategies for a better management of existing shorelines.

Photogrammetry Using UAV-Mounted GNSS RTK: Georeferencing Strategies without GCPs

Georeferencing using ground control points (GCPs) is the most common strategy in photogrammetry modeling using unmanned aerial vehicle (UAV)-acquired imagery. With the increased availability of UAVs with onboard global navigation satellite system–real-time kinematic (GNSS RTK), georeferencing without GCPs is becoming a promising alternative. However, systematic elevation error remains a problem with this technique. We aimed to analyze the reasons for this systematic error and propose strategies for its elimination. Multiple flights differing in the flight altitude and image acquisition axis were performed at two real-world sites. A flight height of 100 m with a vertical (nadiral) image acquisition axis was considered primary, supplemented with flight altitudes of 75 m and 125 m with a vertical image acquisition axis and two flights at 100 m with oblique image acquisition axes (30° and 15°). Each of these flights was performed twice to produce a full double grid. Models were reconstructed from individual flights and their combinations. The elevation error from individual flights or even combinations yielded systematic elevation errors of up to several decimeters. This error was linearly dependent on the deviation of the focal length from the reference value. A combination of two flights at the same altitude (with nadiral and oblique image acquisition) was capable of reducing the systematic elevation error to less than 0.03 m. This study is the first to demonstrate the linear dependence between the systematic elevation error of the models based only on the onboard GNSS RTK data and the deviation in the determined internal orientation parameters (focal length). In addition, we have shown that a combination of two flights with different image acquisition axes can eliminate this systematic error even in real-world conditions and that georeferencing without GCPs is, therefore, a feasible alternative to the use of GCPs.

Shipborne Mobile Photogrammetry for 3D Mapping and Landslide Detection of the Water-Level Fluctuation Zone in the Three Gorges Reservoir Area, China

The water-level fluctuation zone (WLFZ) of the Three Gorges Reservoir is a serious landslide-prone area. However, current remote sensing methods for landslide mapping and detection in the WLFZ are insufficient because of difficulties in data acquisition and lack of facade information. We proposed a novel shipborne mobile photogrammetry approach for 3D mapping and landslide detection in the WLFZ for the first time, containing a self-designed shipborne hardware platform and a data acquisition and processing workflow. To evaluate the accuracy and usability of the resultant 3D models in the WLFZ, four bundle block adjustment (BBA) control configurations were developed and adopted. In the four configurations, the raw Global Navigation Satellite System (GNSS) data, the raw GNSS data and fixed camera height, the GCPs extracted from aerial photogrammetric products, and the mobile Light Detection and Ranging (LiDAR) point cloud were used. A comprehensive accuracy assessment of the 3D models was conducted, and the comparative results indicated the BBA with GCPs extracted from the aerial photogrammetric products was the most practical configuration (RMSE 2.00 m in plane, RMSE 0.46 m in height), while the BBA with the mobile LiDAR point cloud as a control provided the highest georeferencing accuracy (RMSE 0.59 m in plane, RMSE 0.40 m in height). Subsequently, the landslide detection ability of the proposed approach was compared with multisource remote sensing images through visual interpretation, which showed that the proposed approach provided the highest landslide detection rate and unique advantages in small landslide detection as well as in steep terrains due to the more detailed features of landslides provided by the shipborne 3D models. The approach is an effective and flexible supplement to traditional remote sensing methods.

Three-Dimensional Inundation Mapping Using UAV Image Segmentation and Digital Surface Model

Flood occurrence is increasing due to the expansion of urbanization and extreme weather like hurricanes; hence, research on methods of inundation monitoring and mapping has increased to reduce the severe impacts of flood disasters. This research studies and compares two methods for inundation depth estimation using UAV images and topographic data. The methods consist of three main stages: (1) extracting flooded areas and create 2D inundation polygons using deep learning; (2) reconstructing 3D water surface using the polygons and topographic data; and (3) deriving a water depth map using the 3D reconstructed water surface and a pre-flood DEM. The two methods are different at reconstructing the 3D water surface (stage 2). The first method uses structure from motion (SfM) for creating a point cloud of the area from overlapping UAV images, and the water polygons resulted from stage 1 is applied for water point cloud classification. While the second method reconstructs the water surface by intersecting the water polygons and a pre-flood DEM created using the pre-flood LiDAR data. We evaluate the proposed methods for inundation depth mapping over the Town of Princeville during a flooding event during Hurricane Matthew. The methods are compared and validated using the USGS gauge water level data acquired during the flood event. The RMSEs for water depth using the SfM method and integrated method based on deep learning and DEM were 0.34m and 0.26m, respectively.

Monitoring the Efficacy of Crested Floatingheart (Nymphoides cristata) Management with Object-Based Image Analysis of UAS Imagery

This study investigates the use of unmanned aerial systems (UAS) mapping for monitoring the efficacy of invasive aquatic vegetation (AV) management on a floating-leaved AV species, Nymphoides cristata (CFH). The study site consists of 48 treatment plots (TPs). Based on six unique flights over two days at three different flight altitudes while using both a multispectral and RGB sensor, accuracy assessment of the final object-based image analysis (OBIA)-derived classified images yielded overall accuracies ranging from 89.6% to 95.4%. The multispectral sensor was significantly more accurate than the RGB sensor at measuring CFH areal coverage within each TP only with the highest multispectral, spatial resolution (2.7 cm/pix at 40 m altitude). When measuring response in the AV community area between the day of treatment and two weeks after treatment, there was no significant difference between the temporal area change from the reference datasets and the area changes derived from either the RGB or multispectral sensor. Thus, water resource managers need to weigh small gains in accuracy from using multispectral sensors against other operational considerations such as the additional processing time due to increased file sizes, higher financial costs for equipment procurements, and longer flight durations in the field when operating multispectral sensors.

High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation

Plant height (PH) is an essential trait in the screening of most crops. While in crops such as wheat, medium stature helps reduce lodging, tall plants are preferred to increase total above-ground biomass. PH is an easy trait to measure manually, although it can be labor-intense depending on the number of plots. There is an increasing demand for alternative approaches to estimate PH in a higher throughput mode. Crop surface models (CSMs) derived from dense point clouds generated via aerial imagery could be used to estimate PH. This study evaluates PH estimation at different phenological stages using plot-level information from aerial imaging-derived 3D CSM in wheat inbred lines during two consecutive years. Multi-temporal and high spatial resolution images were collected by fixed-wing (P l a t F W ) and multi-rotor (P l a t M R ) unmanned aerial vehicle (UAV) platforms over two wheat populations (50 and 150 lines). The PH was measured and compared at four growth stages (GS) using ground-truth measurements (PHground) and UAV-based estimates (PHaerial). The CSMs generated from the aerial imagery were validated using ground control points (GCPs) as fixed reference targets at different heights. The results show that PH estimations using P l a t F W were consistent with those obtained from P l a t M R , showing some slight differences due to image processing settings. The GCPs heights derived from CSM showed a high correlation and low error compared to their actual heights (R 2 ≥ 0.90, RMSE ≤ 4 cm). The coefficient of determination (R 2) between PHground and PHaerial at different GS ranged from 0.35 to 0.88, and the root mean square error (RMSE) from 0.39 to 4.02 cm for both platforms. In general, similar and higher heritability was obtained using PHaerial across different GS and years and ranged according to the variability, and environmental error of the PHground observed (0.06-0.97). Finally, we also observed high Spearman rank correlations (0.47-0.91) and R 2 (0.63-0.95) of PHaerial adjusted and predicted values against PHground values. This study provides an example of the use of UAV-based high-resolution RGB imagery to obtain time-series estimates of PH, scalable to tens-of-thousands of plots, and thus suitable to be applied in plant wheat breeding trials.

Unmanned Aerial Systems-Aided Post-Flood Peak Discharge Estimation in Ephemeral Streams

The spatial and temporal scale of flash flood occurrence provides limited opportunities for observations and measurements using conventional monitoring networks, turning the focus to event-based, post-disaster studies. Post-flood surveys exploit field evidence to make indirect discharge estimations, aiming to improve our understanding of hydrological response dynamics under extreme meteorological forcing. However, discharge estimations are associated with demanding fieldwork aiming to record in small timeframes delicate data and data prone-to-be-lost and achieve the desired accuracy in measurements to minimize various uncertainties of the process. In this work, we explore the potential of unmanned aerial systems (UAS) technology, in combination with the Structure for Motion (SfM) and optical granulometry techniques in peak discharge estimations. We compare the results of the UAS-aided discharge estimations to estimates derived from differential Global Navigation Satellite System (d-GNSS) surveys and hydrologic modelling. The application in the catchment of the Soures torrent in Greece, after a catastrophic flood, shows that the UAS-aided method determined peak discharge with accuracy, providing very similar values compared to the ones estimated by the established traditional approach. The technique proved to be particularly effective, providing flexibility in terms of resources and timing, although there are certain limitations to its applicability, related mostly to the optical granulometry as well as the condition of the channel. The application highlighted important advantages and certain weaknesses of these emerging tools in indirect discharge estimations, which we discuss in detail.

High-Quality UAV-Based Orthophotos for Cadastral Mapping: Guidance for Optimal Flight Configurations

During the past years, unmanned aerial vehicles (UAVs) gained importance as a tool to quickly collect high-resolution imagery as base data for cadastral mapping. However, the fact that UAV-derived geospatial information supports decision-making processes involving people’s land rights ultimately raises questions about data quality and accuracy. In this vein, this paper investigates different flight configurations to give guidance for efficient and reliable UAV data acquisition. Imagery from six study areas across Europe and Africa provide the basis for an integrated quality assessment including three main aspects: (1) the impact of land cover on the number of tie-points as an indication on how well bundle block adjustment can be performed, (2) the impact of the number of ground control points (GCPs) on the final geometric accuracy, and (3) the impact of different flight plans on the extractability of cadastral features. The results suggest that scene context, flight configuration, and GCP setup significantly impact the final data quality and subsequent automatic delineation of visual cadastral boundaries. Moreover, even though the root mean square error of checkpoint residuals as a commonly accepted error measure is within a range of few centimeters in all datasets, this study reveals large discrepancies of the accuracy and the completeness of automatically detected cadastral features for orthophotos generated from different flight plans. With its unique combination of methods and integration of various study sites, the results and recommendations presented in this paper can help land professionals and bottom-up initiatives alike to optimize existing and future UAV data collection workflows.

Photogrammetric 3D Model via Smartphone GNSS Sensor: Workflow, Error Estimate, and Best Practices

Geotagged smartphone photos can be employed to build digital terrain models using structure from motion-multiview stereo (SfM-MVS) photogrammetry. Accelerometer, magnetometer, and gyroscope sensors integrated within consumer-grade smartphones can be used to record the orientation of images, which can be combined with location information provided by inbuilt global navigation satellite system (GNSS) sensors to geo-register the SfM-MVS model. The accuracy of these sensors is, however, highly variable. In this work, we use a 200 m-wide natural rocky cliff as a test case to evaluate the impact of consumer-grade smartphone GNSS sensor accuracy on the registration of SfM-MVS models. We built a high-resolution 3D model of the cliff, using an unmanned aerial vehicle (UAV) for image acquisition and ground control points (GCPs) located using a differential GNSS survey for georeferencing. This 3D model provides the benchmark against which terrestrial SfM-MVS photogrammetry models, built using smartphone images and registered using built-in accelerometer/gyroscope and GNSS sensors, are compared. Results show that satisfactory post-processing registrations of the smartphone models can be attained, requiring: (1) wide acquisition areas (scaling with GNSS error) and (2) the progressive removal of misaligned images, via an iterative process of model building and error estimation.

Color and Laser Data as a Complementary Approach for Heritage Documentation

Heritage recording has received much attention and benefits from recent developments in the field of range and imaging sensors. While these methods have often been viewed as two different methodologies, data integration can achieve different products, which are not always found in a single technique. Data integration in this paper can be divided into two levels: laser scanner data aided by photogrammetry and photogrammetry aided by scanner data. At the first level, superior radiometric information, mobility and accessibility of imagery can be actively used to add texture information and allow for new possibilities in terms of data interpretation and completeness of complex site documentation. In the second level, true orthophoto is generated based on laser data, the results are rectified images with a uniform scale representing all objects at their planimetric position. The proposed approaches enable flexible data fusion and allow images to be taken at an optimum time and position for radiometric information. Data fusion usually involves serious distortions in the form of a double mapping of occluded objects that affect the product quality. In order to enhance the efficiency of visibility analysis in complex structures, a proposed visibility algorithm is implemented into the developed methods of texture mapping and true orthophoto generation. The algorithm filters occluded areas based on a patch processing using a grid square unit set around the projected vertices. The depth of the mapped triangular vertices within the patch neighborhood is calculated to assign the visible one. In this contribution, experimental results from different historical sites in Jordan are presented as a validation of the proposed algorithms. Algorithms show satisfactory performance in terms of completeness and correctness of occlusion detection and spectral information mapping. The results indicate that hybrid methods could be used efficiently in the representation of heritage structures.

Discrete and Distributed Error Assessment of UAS-SfM Point Clouds of Roadways

Perishable surveying, mapping, and post-disaster damage data typically require efficient and rapid field collection techniques. Such datasets permit highly detailed site investigation and characterization of civil infrastructure systems. One of the more common methods to collect, preserve, and reconstruct three-dimensional scenes digitally, is the use of an unpiloted aerial system (UAS), commonly known as a drone. Onboard photographic payloads permit scene reconstruction via structure-from-motion (SfM); however, such approaches often require direct site access and survey points for accurate and verified results, which may limit its efficiency. In this paper, the impact of the number and distribution of ground control points within a UAS SfM point cloud is evaluated in terms of error. This study is primarily motivated by the need to understand how the accuracy would vary if site access is not possible or limited. In this paper, the focus is on two remote sensing case studies, including a 0.75 by 0.50-km region of interest that contains a bridge structure, paved and gravel roadways, vegetation with a moderate elevation range of 24 m, and a low-volume gravel road of 1.0 km in length with a modest elevation range of 9 m, which represent two different site geometries. While other studies have focused primarily on the accuracy at discrete locations via checkpoints, this study examines the distributed errors throughout the region of interest via complementary light detection and ranging (lidar) datasets collected at the same time. Moreover, the international roughness index (IRI), a professional roadway surface standard, is quantified to demonstrate the impact of errors on roadway quality parameters. Via quantification and comparison of the differences, guidance is provided on the optimal number of ground control points required for a time-efficient remote UAS survey.

UAS Imagery-Based Mapping of Coarse Wood Debris in a Natural Deciduous Forest in Central Germany (Hainich National Park)

Dead wood such as coarse dead wood debris (CWD) is an important component in natural forests since it increases the diversity of plants, fungi, and animals. It serves as habitat, provides nutrients and is conducive to forest regeneration, ecosystem stabilization and soil protection. In commercially operated forests, dead wood is often unwanted as it can act as an originator of calamities. Accordingly, efficient CWD monitoring approaches are needed. However, due to the small size of CWD objects satellite data-based approaches cannot be used to gather the needed information and conventional ground-based methods are expensive. Unmanned aerial systems (UAS) are becoming increasingly important in the forestry sector since structural and spectral features of forest stands can be extracted from the high geometric resolution data they produce. As such, they have great potential in supporting regular forest monitoring and inventory. Consequently, the potential of UAS imagery to map CWD is investigated in this study. The study area is located in the center of the Hainich National Park (HNP) in the federal state of Thuringia, Germany. The HNP features natural and unmanaged forest comprising deciduous tree species such as Fagus sylvatica (beech), Fraxinus excelsior (ash), Acer pseudoplatanus (sycamore maple), and Carpinus betulus (hornbeam). The flight campaign was controlled from the Hainich eddy covariance flux tower located at the Eastern edge of the test site. Red-green-blue (RGB) image data were captured in March 2019 during leaf-off conditions using off-the-shelf hardware. Agisoft Metashape Pro was used for the delineation of a three-dimensional (3D) point cloud, which formed the basis for creating a canopy-free RGB orthomosaic and mapping CWD. As heavily decomposed CWD hardly stands out from the ground due to its low height, it might not be detectable by means of 3D geometric information. For this reason, solely RGB data were used for the classification of CWD. The mapping task was accomplished using a line extraction approach developed within the object-based image analysis (OBIA) software eCognition. The achieved CWD detection accuracy can compete with results of studies utilizing high-density airborne light detection and ranging (LiDAR)-based point clouds. Out of 180 CWD objects, 135 objects were successfully delineated while 76 false alarms occurred. Although the developed OBIA approach only utilizes spectral information, it is important to understand that the 3D information extracted from our UAS data is a key requirement for successful CWD mapping as it provides the foundation for the canopy-free orthomosaic created in an earlier step. We conclude that UAS imagery is an alternative to laser data in particular if rapid update and quick response is required. We conclude that UAS imagery is an alternative to laser data for CWD mapping, especially when a rapid response and quick reaction, e.g., after a storm event, is required.

Boosting the Timeliness of UAV Large Scale Mapping. Direct Georeferencing Approaches: Operational Strategies and Best Practices

The use of unmanned aerial vehicles (UAVs) is nowadays a standard approach in several application fields. Researches connected with these systems cover several topics and the evolution of these platforms and their applications are rapidly growing. Despite the high level of automatization reached nowadays, there is still a phase of the overall UAVs’ photogrammetric pipeline that requires a high effort in terms of time and resources (i.e., the georeferencing phase). However, thanks to the availability of survey-grade GNSS (Global Navigation Satellite System) receivers embedded in the aerial platforms, it is possible to also enhance this phase of the processing by adopting direct georeferencing approaches (i.e., without using any ground control point and exploiting real time kinematic (RTK) positioning). This work investigates the possibilities offered by a multirotor commercial system equipped with a RTK-enabled GNSS receiver, focusing on the accuracy of the georeferencing phase. Several tests were performed in an ad-hoc case study exploiting different georeferencing solutions and assessing the 3D positional accuracies, thanks to a network of control points. The best approaches to be adopted in the field according to accuracy requirements of the final map products were identified and operational guidelines proposed accordingly.

Quality Assessment of Photogrammetric Models for Façade and Building Reconstruction Using DJI Phantom 4 RTK

Aerial photogrammetry by Unmanned Aerial Vehicles (UAVs) is a widespread method to perform mapping tasks with high-resolution to reconstruct three-dimensional (3D) building and façade models. However, the survey of Ground Control Points (GCPs) represents a time-consuming task, while the use of Real-Time Kinematic (RTK) drones allows for one to collect camera locations with an accuracy of a few centimeters. DJI Phantom 4 RTK (DJI-P4RTK) combines this with the possibility to acquire oblique images in stationary conditions and it currently represents a versatile drone widely used from professional users together with commercial Structure-from-Motion software, such as Agisoft Metashape. In this work, we analyze the architectural application of this drone to the photogrammetric modeling of a building with particular regard to metric survey specifications for cultural heritage for 1:20, 1:50, 1:100, and 1:200 scales. In particular, we designed an accuracy assessment test signalizing 109 points, surveying them with total station and adjusting the measurements through a network approach in order to achieve millimeter-level accuracy. Image datasets with a designed Ground Sample Distance (GSD) of 2 mm were acquired in Network RTK (NRTK) and RTK modes in manual piloting and processed both as single façades (S–F) and as an overall block (4–F). Subsequently, we compared the results of photogrammetric models generated in Agisoft Metashape to the Signalized Point (SP) coordinates. The results highlight the importance of processing an overall photogrammetric block, especially whenever part of camera locations exhibited a poorer accuracy due to multipath effects. No significant differences were found between the results of network real-time kinematic (NRTK) and real-time kinematic (RTK) datasets. Horizontal residuals were generally comparable to GNSS accuracy in NRTK/RTK mode, while vertical residuals were found to be affected by an offset of about 5 cm. We introduced an external GCP or used one SP per façade as GCP, assuming a poorer camera location accuracy at the same time, in order to fix this issue and comply with metric survey specifications for the widest architectural scale range. Finally, both S–F and 4–F projects satisfied the metric survey requirements of a scale of 1:50 in at least one of the approaches tested.

Ground Control Point Distribution for Accurate Kilometre-Scale Topographic Mapping Using an RTK-GNSS Unmanned Aerial Vehicle and SfM Photogrammetry

Unmanned Aerial Vehicles (UAVs) have revolutionised the availability of high resolution topographic data in many disciplines due to their relatively low-cost and ease of deployment. Consumer-grade Real Time Kinematic Global Navigation Satellite System (RTK-GNSS) equipped UAVs offer potential to reduce or eliminate ground control points (GCPs) from SfM photogrammetry surveys, removing time-consuming target deployment. Despite this, the removal of ground control can substantially reduce the georeferencing accuracy of SfM photogrammetry outputs. Here, a DJI Phantom 4 RTK UAV is deployed to survey a 2 × 0.5 km reach of the braided River Feshie, Scotland that has local channel-bar relief of c.1 m and median grain size c.60 mm. Five rectangular adjacent blocks were flown, with images collected at 20° from the nadir across a double grid, with strips flown in opposing directions to achieve locally convergent imagery geometry. Check point errors for seven scenarios with varying configurations of GCPs were tested. Results show that, contrary to some published Direct Georeferencing UAV investigations, GCPs are not essential for accurate kilometre-scale topographic modelling. Using no GCPs, 3300 independent spatially-distributed RTK-GNSS surveyed check points have mean z-axis error −0.010 m (RMSE = 0.066 m). Using 5 GCPs gave 0.016 m (RMSE = 0.072 m). Our check point results do not show vertical systematic errors, such as doming, using either 0 or 5 GCPs. However, acquiring spatially distributed independent check points to check for systematic errors is recommended. Our results imply that an RTK-GNSS UAV can produce acceptable errors with no ground control, alongside spatially distributed independent check points, demonstrating that the technique is versatile for rapid kilometre-scale topographic survey in a range of geomorphic environments.

Determining the Optimal Number of Ground Control Points for Varying Study Sites through Accuracy Evaluation of Unmanned Aerial System-Based 3D Point Clouds and Digital Surface Models

The rapid development of drone technologies, such as unmanned aerial systems (UASs) and unmanned aerial vehicles (UAVs), has led to the widespread application of three-dimensional (3D) point clouds and digital surface models (DSMs). Due to the number of UAS technology applications across many fields, studies on the verification of the accuracy of image processing results have increased. In previous studies, the optimal number of ground control points (GCPs) was determined for a specific area of a study site by increasing or decreasing the amount of GCPs. However, these studies were mainly conducted in a single study site, and the results were not compared with those from various study sites. In this study, to determine the optimal number of GCPs for modeling multiple areas, the accuracy of 3D point clouds and DSMs were analyzed in three study sites with different areas according to the number of GCPs. The results showed that the optimal number of GCPs was 12 for small and medium sites (7 and 39 ha) and 18 for the large sites (342 ha) based on the overall accuracy. If these results are used for UAV image processing in the future, accurate modeling will be possible with minimal effort in GCPs.

UAS-Based Archaeological Remote Sensing: Review, Meta-Analysis and State-of-the-Art

Over the last decade, we have witnessed momentous technological developments in unmanned aircraft systems (UAS) and in lightweight sensors operating at various wavelengths, at and beyond the visible spectrum, which can be integrated with unmanned aerial platforms. These innovations have made feasible close-range and high-resolution remote sensing for numerous archaeological applications, including documentation, prospection, and monitoring bridging the gap between satellite, high-altitude airborne, and terrestrial sensing of historical sites and landscapes. In this article, we track the progress made so far, by systematically reviewing the literature relevant to the combined use of UAS platforms with visible, infrared, multi-spectral, hyper-spectral, laser, and radar sensors to reveal archaeological features otherwise invisible to archaeologists with applied non-destructive techniques. We review, specific applications and their global distribution, as well as commonly used platforms, sensors, and data-processing workflows. Furthermore, we identify the contemporary state-of-the-art and discuss the challenges that have already been overcome, and those that have not, to propose suggestions for future research.

UAV Photogrammetry Accuracy Assessment for Corridor Mapping Based on the Number and Distribution of Ground Control Points

Unmanned aerial vehicle (UAV) photogrammetry has recently emerged as a popular solution to obtain certain products necessary in linear projects, such as orthoimages or digital surface models. This is mainly due to its ability to provide these topographic products in a fast and economical way. In order to guarantee a certain degree of accuracy, it is important to know how many ground control points (GCPs) are necessary and how to distribute them across the study site. The purpose of this work consists of determining the number of GCPs and how to distribute them in a way that yields higher accuracy for a corridor-shaped study area. To do so, several photogrammetric projects have been carried out in which the number of GCPs used in the bundle adjustment and their distribution vary. The different projects were assessed taking into account two different parameters: the root mean square error (RMSE) and the Multiscale Model to Model Cloud Comparison (M3C2). From the different configurations tested, the projects using 9 and 11 GCPs (4.3 and 5.2 GCPs km−1, respectively) distributed alternatively on both sides of the road in an offset or zigzagging pattern, with a pair of GCPs at each end of the road, yielded optimal results regarding fieldwork cost, compared to projects using similar or more GCPs placed according to other distributions.

Structure from Motion of Multi-Angle RPAS Imagery Complements Larger-Scale Airborne Lidar Data for Cost-Effective Snow Monitoring in Mountain Forests

Snowmelt from mountain forests is critically important for water resources and hydropower generation. More than 75% of surface water supply originates as snowmelt in mountainous regions, such as the western U.S. Remote sensing has the potential to measure snowpack in these areas accurately. In this research, we combine light detection and ranging (lidar) from crewed aircraft (currently, the most reliable way of measuring snow depth in mountain forests) and structure from motion (SfM) remotely piloted aircraft systems (RPAS) for cost-effective multi-temporal monitoring of snowpack in mountain forests. In sparsely forested areas, both technologies give similar snow depth maps, with a comparable agreement with ground-based snow depth observations (RMSE ~10 cm). In densely forested areas, airborne lidar is better able to represent snow depth than RPAS-SfM (RMSE ~10 cm vs ~10–20 cm). In addition, we find the relationship between RPAS-SfM and previous lidar snow depth data can be used to estimate snow depth conditions outside of relatively small RPAS-SfM monitoring plots, with RMSE’s between these observed and estimated snow depths on the order of 10–15 cm for the larger lidar coverages. This suggests that when a single airborne lidar snow survey exists, RPAS-SfM may provide useful multi-temporal snow monitoring that can estimate basin-scale snowpack, at a much lower cost than multiple airborne lidar surveys. Doing so requires a pre-existing mid-winter or peak-snowpack airborne lidar snow survey, and subsequent well-designed paired SfM and field snow surveys that accurately capture substantial snow depth variability.

Multiplatform-SfM and TLS Data Fusion for Monitoring Agricultural Terraces in Complex Topographic and Landcover Conditions

Agricultural terraced landscapes, which are important historical heritage sites (e.g., UNESCO or Globally Important Agricultural Heritage Systems (GIAHS) sites) are under threat from increased soil degradation due to climate change and land abandonment. Remote sensing can assist in the assessment and monitoring of such cultural ecosystem services. However, due to the limitations imposed by rugged topography and the occurrence of vegetation, the application of a single high-resolution topography (HRT) technique is challenging in these particular agricultural environments. Therefore, data fusion of HRT techniques (terrestrial laser scanning (TLS) and aerial/terrestrial structure from motion (SfM)) was tested for the first time in this context (terraces), to the best of our knowledge, to overcome specific detection problems such as the complex topographic and landcover conditions of the terrace systems. SfM–TLS data fusion methodology was trialed in order to produce very high-resolution digital terrain models (DTMs) of two agricultural terrace areas, both characterized by the presence of vegetation that covers parts of the subvertical surfaces, complex morphology, and inaccessible areas. In the unreachable areas, it was necessary to find effective solutions to carry out HRT surveys; therefore, we tested the direct georeferencing (DG) method, exploiting onboard multifrequency GNSS receivers for unmanned aerial vehicles (UAVs) and postprocessing kinematic (PPK) data. The results showed that the fusion of data based on different methods and acquisition platforms is required to obtain accurate DTMs that reflect the real surface roughness of terrace systems without gaps in data. Moreover, in inaccessible or hazardous terrains, a combination of direct and indirect georeferencing was a useful solution to reduce the substantial inconvenience and cost of ground control point (GCP) placement. We show that in order to obtain a precise data fusion in these complex conditions, it is essential to utilize a complete and specific workflow. This workflow must incorporate all data merging issues and landcover condition problems, encompassing the survey planning step, the coregistration process, and the error analysis of the outputs. The high-resolution DTMs realized can provide a starting point for land degradation process assessment of these agriculture environments and supplies useful information to stakeholders for better management and protection of such important heritage landscapes.

RTK GNSS-Assisted Terrestrial SfM Photogrammetry without GCP: Application to Coastal Morphodynamics Monitoring

The present article describes a new and efficient method of Real Time Kinematic (RTK) Global Navigation Satellite System (GNSS) assisted terrestrial Structure-from-Motion (SfM) photogrammetry without the need for Ground Control Points (GCPs). The system only requires a simple frame that mechanically connects a RTK GNSS antenna to the camera. The system is low cost, easy to transport, and offers high autonomy. Furthermore, not requiring GCPs enables saving time during the in situ acquisition and during data processing. The method is tested for coastal cliff monitoring, using both a Reflex camera and a Smartphone camera. The quality of the reconstructions is assessed by comparison to a synchronous Terrestrial Laser Scanner (TLS) acquisition. The results are highly satisfying with a mean error of 0.3 cm and a standard deviation of 4.7 cm obtained with the Nikon D800 Reflex camera and, respectively, a mean error of 0.2 cm and a standard deviation of 3.8 cm obtained with the Huawei Y5 Smartphone camera. This method will be particularly interesting when simplicity, portability, and autonomy are desirable. In the future, it would be transposable to participatory science programs, while using an open RTK GNSS network.

Reliability and Uncertainties of the Analysis of an Unstable Rock Slope Performed on RPAS Digital Outcrop Models: The Case of the Gallivaggio Landslide (Western Alps, Italy)

A stability investigation based on Digital Outcrop Models (DOMs) acquired in emergency conditions by photogrammetric surveys based on Remote Piloted Aerial System (RPAS) was conducted on an unstable rock slope near Gallivaggio (Western Alps, Italy). The predicted mechanism of failure and volume of the unstable portion of the slope were successively verified on the DOMs acquired after the rockfall that effectively collapsed the May 29th, 2018. The comparison of the pre- and post-landslide 3D models shows that the estimated mode of failure was substantially correct. At the same time, the predicted volume of rock involved in the landslide was overestimated by around 10%. To verify if this error was due to the limited accuracy of the models georeferenced in emergency considering only the Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU)-information of RPAS, several Ground Control Points (GCPs) were acquired after the failure. The analyses indicate that the instrumental error in the volume calculation due to the direct-georeferencing method is only of the 1.7%. In contrast, the significant part is due to the geological uncertainty in the reconstruction of the real irregular geometry of the invisible part of the failure surface. The results, however, confirm the satisfying relative accuracy of the direct-georeferenced DOMs, compatible with most geological and geoengineering purposes.

Evaluation of the Georeferencing Accuracy of a Photogrammetric Model Using a Quadrocopter with Onboard GNSS RTK

Using a GNSS RTK (Global Navigation Satellite System Real Time Kinematic) -equipped unmanned aerial vehicle (UAV) could greatly simplify the construction of highly accurate digital models through SfM (Structure from Motion) photogrammetry, possibly even avoiding the need for ground control points (GCPs). As previous studies on this topic were mostly performed using fixed-wing UAVs, this study aimed to investigate the results achievable by a quadrocopter (DJI Phantom 4 RTK). Three image acquisition flights were performed for two sites of a different character (urban and rural) along with three calculation variants for each flight: georeferencing using ground-surveyed GCPs only, onboard GNSS RTK only, and a combination thereof. The combined and GNSS RTK methods provided the best results (at the expected level of accuracy of 1–2 GSD (Ground Sample Distance)) for both the vertical and horizontal components. The horizontal positioning was also accurate when georeferencing directly based on the onboard GNSS RTK; the vertical component, however, can be (especially where the terrain is difficult for SfM evaluation) burdened with relatively high systematic errors. This problem was caused by the incorrect identification of the interior orientation parameters calculated, as is customary for non-metric cameras, together with bundle adjustment. This problem could be resolved by using a small number of GCPs (at least one) or quality camera pre-calibration.

Coastal Mapping Using DJI Phantom 4 RTK in Post-Processing Kinematic Mode

Topographic and geomorphological surveys of coastal areas usually require the aerial mapping of long and narrow sections of littoral. The georeferencing of photogrammetric models is generally based on the signalization and survey of Ground Control Points (GCPs), which are very time-consuming tasks. Direct georeferencing with high camera location accuracy due to on-board multi-frequency GNSS receivers can limit the need for GCPs. Recently, DJI has made available the Phantom 4 Real-Time Kinematic (RTK) (DJI-P4RTK), which combines the versatility and the ease of use of previous DJI Phantom models with the advantages of a multi-frequency on-board GNSS receiver. In this paper, we investigated the accuracy of both photogrammetric models and Digital Terrain Models (DTMs) generated in Agisoft Metashape from two different image datasets (nadiral and oblique) acquired by a DJI-P4RTK. Camera locations were computed with the Post-Processing Kinematic (PPK) of the Receiver Independent Exchange Format (RINEX) file recorded by the aircraft during flight missions. A Continuously Operating Reference Station (CORS) located at a 15 km distance from the site was used for this task. The results highlighted that the oblique dataset produced very similar results, with GCPs (3D RMSE = 0.025 m) and without (3D RMSE = 0.028 m), while the nadiral dataset was affected more by the position and number of the GCPs (3D RMSE from 0.034 to 0.075 m). The introduction of a few oblique images into the nadiral dataset without any GCP improved the vertical accuracy of the model (Up RMSE from 0.052 to 0.025 m) and can represent a solution to speed up the image acquisition of nadiral datasets for PPK with the DJI-P4RTK and no GCPs. Moreover, the results of this research are compared to those obtained in RTK mode for the same datasets. The novelty of this research is the combination of a multitude of aspects regarding the DJI Phantom 4 RTK aircraft and the subsequent data processing strategies for assessing the quality of photogrammetric models, DTMs, and cross-section profiles.