Ground Control Points Research Articles

High‐Accuracy Geological Deformation Monitoring Through Unmanned Aerial Vehicle Image Registration Without Control Points

ABSTRACTUnmanned aerial vehicles (UAVs) have gained popularity in geological deformation monitoring due to their flexibility, cost‐effectiveness, and ease of image acquisition. However, accurate co‐registration of multi‐phase UAV images, essential for effective monitoring, requires numerous ground control points (GCPs), challenging in deformation‐prone areas. This study introduces a UAV‐based monitoring method that eliminates the need for GCPs. Initially, two‐phase images are integrated for joint aerial triangulation. The first‐phase image and tie points undergo bundle adjustment refinement. Control points are identified as tie points with minimal deviation and used to refine the second‐phase image. Three‐dimensional (3D) models are generated from these refined adjustments and differential analysis between the models reveals geological deformation. Experimental results show that this method automatically identifies stable areas and effectively captures both the location and extent of geological deformation, achieving a relative change accuracy of 0.06 m, which is twice the average ground resolution. Compared to traditional aerial triangulation methods without control points, this approach offers more than a tenfold increase in accuracy. The accuracy is on par with methods that utilize multiple control points, indicating the promising potential of this approach for geological deformation monitoring.

Empirical Evaluation and Simulation of the Impact of Global Navigation Satellite System Solutions on Uncrewed Aircraft System–Structure from Motion for Shoreline Mapping and Charting

Uncrewed aircraft systems (UASs) and structure-from-motion/multi-view stereo (SfM/MVS) photogrammetry are efficient methods for mapping terrain at local geographic scales. Traditionally, indirect georeferencing using ground control points (GCPs) is used to georeference the UAS image locations before further processing in SfM software. However, this is a tedious practice and unsuitable for surveying remote or inaccessible areas. Direct georeferencing is a plausible alternative that requires no GCPs. It relies on global navigation satellite system (GNSS) technology to georeference the UAS image locations. This research combined field experiments and simulation to investigate GNSS-based post-processed kinematic (PPK) as a means to eliminate or reduce reliance on GCPs for shoreline mapping and charting. The study also conducted a brief comparison of real-time network (RTN) and precise point positioning (PPP) performances for the same purpose. Ancillary experiments evaluated the effects of PPK base station distance and GNSS sample rate on the accuracy of derived 3D point clouds and digital elevation models (DEMs). Vertical root mean square errors (RMSEz), scaled to the 95% confidence interval using an assumption of normally-distributed errors, were desired to be within 0.5 m to satisfy National Oceanic and Atmospheric Administration (NOAA) requirements for nautical charting. Simulations used a Monte Carlo approach and empirical tests to examine the influence of GNSS performance on the quality of derived 3D point clouds. RTN and PPK results consistently yielded RMSEz values within 10 cm, thus satisfying NOAA requirements for nautical charting. PPP did not meet the accuracy requirements but showed promising results that prompt further investigation. PPK experiments using higher GNSS sample rates did not always provide the best accuracies. GNSS performance and model accuracies were enhanced when using base stations located within 30 km of the survey site. Results without using GCPs observed a direct relationship between point cloud accuracy and GNSS performance, with R2 values reaching up to 0.97.

Is it possible to generate accurate 3D point clouds with UAS-LIDAR and UAS-RGB photogrammetry without GCPs? A case study on a beach and rocky cliff

ContextRecently, Unoccupied Aerial Systems (UAS) with photographic or Light Detection and Ranging (LIDAR) sensors have incorporated on-board survey-grade Global Navigation Satellite Systems that allow the direct georeferencing of the resulting datasets without Ground Control Points either in Real-Time (RTK) or Post-Processing Kinematic (PPK) modes. These approaches can be useful in hard-to-reach or hazardous areas. However, the resulting 3D models have not been widely tested, as previous studies tend to evaluate only a few points and conclude that systematic errors can be found.ObjectivesWe test the absolute positional accuracy of point clouds produced using UAS with direct-georeferencing systems.MethodsWe test the accuracy and characteristics of point clouds produced using a UAS-LIDAR (with PPK) and a UAS-RGB (Structure-from-Motion or SfM photogrammetry with RTK and PPK) in a challenging environment: a coastline with a composite beach and cliff. The resulting models of each processing were tested using as a benchmark a point cloud surveyed simultaneously by a Terrestrial Laser Scanner.ResultsThe UAS-LIDAR produced the most accurate point cloud, with homogeneous cover and no noise. The systematic bias previously observed in the UAS-RGB RTK approaches are minimized using oblique images. The accuracy observed across the different surveyed landforms varied significantly.ConclusionsThe UAS-LIDAR and UAS-RGB with PPK produced unbiased point clouds, being the latter the most cost-effective method. For the other direct georeferencing systems/approaches, the acquisition of GCP or the co-registration of the resulting point cloud is still necessary.

Evaluating the Effectiveness of ICESat-2 ATL03 Data for Georeferencing Satellite Images

Abstract. ICESat-2 laser points, as a kind of public geographic data, have high ranging accuracy and have great potential in improving the geometric positioning accuracies of satellite images. This paper aims to evaluate the effectiveness of ICESat-2 ATL03 data for georeferencing satellite image. Our study combines ATL03 laser points with an image basemap processed using open-source data positioning to construct digital control photo (DCP), facilitating the rapid transformation of laser points into laser control points for image georeferencing. Through a comprehensive analysis involving comparison with traditional georeferencing methods using ground control points (GCPs), our findings indicate a significant enhancement in accuracy and efficiency in georeferencing, showcasing the potential of ICESat-2 data in improving the quality and reliability of remote sensing images. Experiments using the extracted laser points and GeoEye-1 images to construct DCP for georeferencing IKONOS images in Hobart, Australia demonstrate that the proposed method improved the horizontal accuracy from 2.33 meters to 0.97 meters and the vertical accuracy from 3.44 meters to 0.59 meters, compared to the georeferencing method without GCPs.

Machine learning-based optimization of photogrammetric JRC accuracy

To improve the accuracy of photogrammetric joint roughness coefficient (JRC) estimation, this study proposes two optimization models based on ground sample distance (GSD), point density, and the root mean square error (RMSE) of checkpoints. First, an algorithm that automatically generates spatial positions for equipment based on the convergence strategy was developed, using principles of Structure from Motion and Multi-View Stereo (SfM-MVS) and the shooting parameter selection algorithm (SPSA). Second, a portable positioning plate containing ground control points and checkpoints was designed based on optical principles, and a moving camera capture strategy guided by SPSA was proposed. Combining SPSA, portable positioning plate, and moving camera capture strategy, a photogrammetric experiment for small-scale rock samples in the field was conducted, collecting 48 datasets with different shooting parameters. Subsequently, a dataset incorporating GSD, point density, RMSE, and three JRC estimation metrics was established, revealing their correlations and sensitivities. Using seven machine learning algorithms, optimization models for photogrammetric JRC accuracy were developed, with Linear Multidimensional Regression and Gaussian Process Regression models improving JRC accuracy by an average of 85.73%. Finally, the applicability and limitations of the newly proposed method were further discussed.

Impacts of GCP Distributions on UAV-PPK Photogrammetry at Sermeq Avannarleq Glacier, Greenland

Real-Time/Post-Processing Kinematic (RTK/PPK) technology has been widely applied in Unmanned Aerial Vehicle (UAV) photogrammetry in glaciological research. Considering that ground control points (GCPs) cannot be set on glaciers, evaluating the impacts of one-sided distribution is essential. In this study, 8571 images were captured at Sermeq Avannarleq glacier in western Greenland from 4 August 2021 to the 6th, covering approximately 85 km2, with the furthest distance being 13.22 km away from the coastline. Benefited by the meandering coastline, 11 roving stations roughly uniformly distributed on bare rock were surveyed with the RTK technique. PPK-geotagged images were processed in Agisoft Metashape Professional to derive the DSMs, utilizing eight different configurations of GCP distributions that gradually extended longitudinally (along the glacier flow direction) to the upper part of the glacier. The accuracy of DSMs was evaluated by referring to the validation points (VPs) that were not employed in the Bundle Block Adjustment (BBA). The results indicated that the RMSE values of the easting, northing, and height of the reconstruction model georeferenced by only PPK geotagging (no GCPs applied) were 0.038 m, 0.031 m, and 0.146 m, respectively. Applying four GCPs located at one side of the region but with both longitudinal and lateral distribution improved the RMSE values in easting, northing, and vertical to 0.037 m, 0.031 m, and 0.081 m, respectively, and these values were stable even when distributing four GCPs evenly or when increasing the number of GCPs to eleven. Moreover, the cross-validation with ICESat-2 and ArcticDEM performed only at an off-glacier region also suggested that vertical accuracy shows significant improvements for every configuration of GCPs compared to the reconstruction model optimized only by PPK, but such improvements were not obvious if the number of GCPs exceeded four. Moreover, no elevation ramps appeared in the UAV DSM, even for the GCP configuration with only two GCPs distributed at the terminus. Therefore, combining PPK with only a few GCPs but distributing in both directions of the surveying region can offer a viable solution for obtaining glacier DSMs at the coastline with decimeter-level accuracy.

On-Orbit Geometric Calibration and Accuracy Validation of the Jilin1-KF01B Wide-Field Camera

On-orbit geometric calibration is key to improving the geometric positioning accuracy of high-resolution optical remote sensing satellite data. Grouped calibration with geometric consistency (GCGC) is proposed in this paper for the Jilin1-KF01B satellite, which is the world’s first satellite capable of providing 150-km swath width and 0.5-m resolution data. To ensure the geometric accuracy of high-resolution image data, the GCGC method conducts grouped calibration of the time delay integration charge-coupled device (TDI CCD). Each group independently calibrates the exterior orientation elements to address the multi-time synchronization issues between imaging processing system (IPS). An additional inter-chip geometric positioning consistency constraint is used to enhance geometric positioning consistency in the overlapping areas between adjacent CCDs. By combining image simulation techniques associated with spectral bands, the calibrated panchromatic data are used to generate simulated multispectral reference band image as control data, thereby enhancing the geometric alignment consistency between panchromatic and multispectral data. Experimental results show that the average seamless stitching accuracy of the basic products after calibration is better than 0.6 pixels, the positioning accuracy without ground control points(GCPs) is better than 20 m, the band-to-band registration accuracy is better than 0.3 pixels, the average geometric alignment consistency between panchromatic and multispectral data are better than 0.25 multispectral pixels, the geometric accuracy with GCPs is better than 2.1 m, and the geometric alignment consistency accuracy of multi-temporal data are better than 2 m. The GCGC method significantly improves the quality of image data from the Jilin1-KF01B satellite and provide important references and practical experience for the geometric calibration of other large-swath high-resolution remote sensing satellites.

A Virtual Reality Tool for Accuracy Assessment of 3D Models in an Immersive Virtual Environment

Abstract. Accurate validation and assessment techniques are essential for ensuring the reliability of spatial reconstructions derived from photogrammetry, enabling well-informed decision-making across diverse domains. This study presents a Virtual Reality (VR) based accuracy assessment tool tailored for evaluating the accuracy and quality of 3D models generated by Unmanned Aerial Vehicles (UAVs). Leveraging the Unity game engine platform, our workflow entails three key steps: aligning real-world coordinates with an arbitrary Unity coordinate system, transforming the positions of Ground Control Points (GCPs) from field survey to the arbitrary system using a reference GCP, and marking observed points on the 3D models. Absolute and Relative Root Mean Square Errors (RMSE), Mean Errors (ME), and Standard Deviation of errors (SD) are computed within the virtual environment via the game object transform properties. The error distributions around each GCP are visually depicted using Unity game engine components for enhanced interaction and comprehension. The efficacy of the tool is validated through experimentation on four 3D models generated from varying camera angles during UAV data capture. The tool provides the opportunity to directly interact with the 3D models and visualize the errors, which is quite distinct from traditional methods. Using the developed tool, results were obtained to indicate that configurations employing camera angles of 60° + 75º exhibit notable performance in terms of relative and absolute accuracy.

Thermal Infrared Orthophoto Geometry Correction Using RGB Orthophoto for Unmanned Aerial Vehicle

The geometric correction of thermal infrared (TIR) orthophotos generated by unmanned aerial vehicles (UAVs) presents significant challenges due to low resolution and the difficulty of identifying ground control points (GCPs). This study addresses the limitations of real-time kinematic (RTK) UAV data acquisition, such as network instability and the inability to detect GCPs in TIR images, by proposing a method that utilizes RGB orthophotos as a reference for geometric correction. The accelerated-KAZE (AKAZE) method was applied to extract feature points between RGB and TIR orthophotos, integrating binary descriptors and absolute coordinate-based matching techniques. Geometric correction results demonstrated a significant improvement in regions with stable and changing environmental conditions. Invariant regions exhibited an accuracy of 0.7~2 px (0.01~0.04), while areas with temporal and spatial changes saw corrections within 5~7 px (0.10~0.14 m). This method reduces reliance on GCP measurements and provides an effective supplementary technique for cases where GCP detection is limited or unavailable. Additionally, this approach enhances time and economic efficiency, offering a reliable alternative for precise orthophoto generation across various sensor data.

Analysis of land use land cover change dynamics in Habru District, Amhara Region, Ethiopia

This study examines spatiotemporal changes in Land Use Land Cover (LULC) patterns within Habru District, Amhara Region, Ethiopia, between 1985 and 2021. Employing Landsat imagery and a supervised classification approach, we mapped six LULC classes: water body, settlement area, cultivated land, bare land, shrub land, and forest land. Ground control points (GCP) were obtained through field observations to ensured image accuracy. Complementing the remote sensing analysis, focus group discussions (FGDs) and key informant interviews were conducted to understand local perspectives on LULC changes. The analysis revealed significant LULC transformations over the study period. Between 1985 and 2021, there was a significant increase in cultivated land and settlement areas, which expanded from 12.27 % to 0.23 %–42.12 % and 2.58 % of the total area, respectively. Conversely, shrub and forest lands saw a marked reduction during the same period, declining from 71.52 % to 12.88 %–45.35 % and 7.06 % of the total area, respectively. Water body area showed a minor increase, while bare land exhibited negligible change. Agricultural land and settlement area expansion, population growth, land tenure insecurity, economic challenge, and climatic change were identified as key drivers of observed LULC changes. Our findings underscore the urgency of implementing multifaceted interventions to address these drivers and promote sustainable resource use practices. Such actions are critical for safeguarding Habru District's natural resources and ensuring the long-term viability of ecosystem services.

An Image Compensation-Based Range–Doppler Model for SAR High-Precision Positioning

The range–Doppler (R–D) model is extensively employed for the geometric processing of synthetic aperture radar (SAR) images. Refining the sensor motion state and imaging parameters is the most common method for achieving high-precision geometric processing using the R–D model, comprising a process that involves numerous parameters and complex computations. In order to reduce the specialization and complexity of parameter optimization in the classic R–D model, we introduced a novel approach called ICRD (image compensation-based range–Doppler) to improve the positioning accuracy of the R–D model, implementing a low-order polynomial to compensate for the original imaging errors without altering the initial positioning parameters. We also designed low-order polynomial compensation models with different parameters. The models were evaluated on various SAR images from different platforms and bands, including spaceborne TerraSAR-X and Gaofen3-C images, manned airborne SAR-X images, and unmanned aerial vehicle-mounted miniSAR-Ku images. Furthermore, image positioning experiments involving the use of different polynomial compensation models and various numbers and distributions of ground control points (GCPs) were conducted. The experimental results demonstrate that geometric processing accuracy comparable to that of the classical rigorous positioning method can be achieved, even when applying only an affine transformation model to the images. Compared to classical refinement models, however, the proposed image-compensated R–D model is much simpler and easy to implement. Thus, this study provides a convenient, robust, and widely applicable method for the geometric-positioning processing of SAR images, offering a potential approach for the joint-positioning processing of multi-source SAR images.

Quantifying variation in the physical size of footwear test impressions

The analysis of forensic footwear evidence often requires the preparation of test impressions created under controlled laboratory conditions. When these test impressions are compared to questioned impressions, (dis)agreement in physical size is an important attribute that must be evaluated and documented. Integral to this comparison is an understanding of the variation that may exist between replicate test impressions, and test impressions created using different methods. The aim of this study was to empirically characterize the variation that exists within and between test impressions prepared using a static benchtop and a dynamic walking method, as well as explore the potential influence of the wearer’s foot size when using the walking method. To examine this variation, twenty-three participants were recruited to prepare test impressions of two different shoe makes and models in four different manufacturer’s sizes. Five replicate benchtop impressions per make/model/size and three replicate walking impressions per participant/make/model/size were created, resulting in approximately 550 test samples, to which an additional 150 quality control copies were blindly added, resulting in almost 700 processed test impressions. Using reproducible and reliable ground control points, the physical size of toe-to-heel length and medial-to-lateral ball of the toe width measurements were collected and compared. For the shoe make/models examined in this study, a systematic bias was observed between benchtop and walking impressions, such that benchtop impressions were almost always longer and narrower than walking impressions, and that within the walking method, physical size differences vary with foot/shoe size mismatch. Of the experimental shoes and groups examined in this study, the variation in toe-to-heel length measurements between benchtop and walking impressions was greatest when the shoe was two sizes smaller than the foot, resulting in a maximum physical size difference of 4.18 mm for a Nike® Downshifter 11 outsole. Similarly, when comparing the variation in toe-to-heel length measurements between walking impressions created using a correctly-fitted shoe versus a shoe that was two sizes smaller than the wearer’s foot, a maximum physical size difference of 3.25 mm was observed. Based on these findings, best practice suggests that footwear analysts document the foot size of the wearer preparing walking test impressions, refrain from using benchtop impressions to form opinions about physical size consistency/differences, and to be cognizant that a large mismatch between foot/shoe size when creating walking impressions can lead to length differences greater than 3.0 mm.

Quality Assessment of Multiple UAV-SfM DEMs Derived for Impact Assessment of a Co-Seismic Avalanche in the Himalayas

Combined with the structure from motion (SfM) technique, unmanned aerial vehicles (UAVs) are powerful tools for generating high-resolution digital elevation models (DEMs) for application in hazard assessments. During our field observations in October 2015 at Langtang Village, which was destroyed by the Gorkha earthquake in April 2015, three different UAVs with mounted cameras were operated to evaluate the volume of the avalanche deposit covering the village. This study evaluated the performance of DEMs created from the different cameras on board those UAVs. Multiple DEMs for the different cameras, including Sony-α7R (PA7), Ricoh-GR (XGR), and Canon-IXUS (EIX), were created using SfM software. All DEMs were compared with a base DEM created from differential global positioning system survey data, which was obtained simultaneously with the UAV campaigns. The results show that the elevation difference of PA7-, XGR-, and EIX-DEMs are within ±0.14 m; the standard deviations of elevation difference range from 0.33 to 0.40 m. Although there were slightly larger differences in elevation on the southwest-to-west sides of the XGR- and EIX-DEMs, which can be attributed mainly to the flight paths and ground control point network, our DEMs are still of high enough quality to be used in hazard assessments.

Rectifying Discrepancies Between GB-SAR Images and Terrain Model Caused by Measurement Deviations in Open-Pit Mines

Ground-based Interferometric Synthetic Aperture Radar (GB-InSAR) is a valuable technique for monitoring deformation of landslides. GB-InSAR can construct high-resolution 2D images in a range-doppler plane. However, it is difficult for geo-engineers who are unfamiliar with radar monitoring geometry to interpret the results. Geometric mapping method has been applied to the co-registration of terrain models and radar images for the deformation zonation, but the mismatching correction with GB-InSAR and terrain model is not fully investigated by existing researches. To address this issue, this paper proposes a method exploiting an optimum linear transformation based on ground control points (GCPs). The proposed method was evaluated on simulated data and a field campaign in an open pit mine. The deformation mapping result of the method was verified by a collapse event. The method proposed in this paper has an RMSE value ranging from 0.502 to 0.720[Formula: see text]m (@700[Formula: see text]m average monitoring range) when the average density of the terrain point cloud is 0.5[Formula: see text]m. Although this method cannot accurately evaluate the antenna footprint vector deflection parameters, it can meet the needs of coarse matching calibration in relatively flat slope sub-target areas of open-pit mines for engineering applications.

Ground Control Point Chip-based Kompsat-3A Rational Polynomial Coefficient Bias Compensation Using Both Intensity- and Edge-based Matching Methods

Ground Control Point Chip-based Kompsat-3A Rational Polynomial Coefficient Bias Compensation Using Both Intensity- and Edge-based Matching Methods

Herbage biomass predictions from UAV data using a derived digital terrain model and machine learning

AbstractMore than 70% of Switzerland's agricultural area is covered by grasslands that often exhibit highly diverse species compositions and heterogeneous growth patterns. An essential requirement for efficient and effective pasture management is the regular estimation of herbage biomass. While various methods exist for estimating herbage biomass, they are often time‐consuming and may not accurately capture the variability within pastures. This highlights the need for more efficient, accurate estimation techniques. To help improve herbage biomass estimation, we present estiGrass3D+, a Random Forest model. This model predicts pasture biomass using a digital terrain model (DTM) derived from a digital surface model (DSM) for sward height modelling, along with vegetation indices and agronomic variables from UAV images only. The model was successfully evaluated with independent test data from different sites on the Swiss central plateau, including both grazed and mown areas. Model performance on an independent validation dataset achieved a NRMSE of 20.3%, while the training dataset had an NRMSE of 21.5%. These consistent results confirm that estiGrass3D+ is both transferable and applicable to unseen data while maintaining accuracy and reliability across different datasets. The wide applicability of our method demonstrates its practicality for predicting herbage biomass under different pasture management scenarios. Additionally, our method of deriving a DTM directly from a DSM simplifies the measurement of grass sward height by UAVs, eliminating the need for prior ground control point (GCP) marking and subsequent aligning, enhancing the efficiency of herbage biomass estimation.

Accuracy assessment of UAV-based documentation of archaeological site: Kültepe-Kaneš

High quality 2D/3D metric documentation of archaeological sites is used in many different applications such as archaeological-heritage management, digital archaeological excavation planning, orthophotographic documentation, and ancient Digital Surfaces Models (DSM) research. Structure-from-Motion based photogrammetry (SfM) is an indirect geodesic measurement technique based on multiple view image processing using high-level automation techniques. Using SfM to generate the high accuracy 2D/3D data needed for digital documentation of archaeological cultural heritage is relatively practical, economical, and fast. Factors such as the number of ground control points and their distribution to the study area, the robustness of the camera system model parameters, and the analytical model of the photogrammetric triangulation method used affect the accuracy of the data produced using SfM-photogrammetry. Kültepe-Kaneš, the center of the ancient Kingdom of Kaneš, which is located in central-Anatolia, was an Assyrian Trade Colony active between ca. 2000 BC and 1700 BC. The site, which was the center of the Old Assyrian trade colonies in Anatolia and one of the most important sites in ancient history, has been under excavation for the last 75 years. In this paper, high accuracy digital documentation of the Kültepe-Kaneš archaeological excavation area was made using different SfM methods. Statistical analysis revealed no significant difference in accuracy between Real-Time Kinematic (RTK) and Post-Process Kinematic (PPK) methods for the study area. This suggests that either PPK, RTK, or aerial triangulation with Ground Control Points (GCPs) can be employed to generate orthophotos and DSMs for archaeological sites with relatively flat surface areas, as long as a sufficient number of uniformly distributed GCPs are used.

Assessment of Ground Control Points Positional Shift and Management Practices: Evidences from Assosa Town, Western Ethiopia

This study assesses the positional shift of existing control points and management practices found in and around Assosa town. The static GPS measurement technique was used as an independent source and a point wise method were used to assess the positional shift and accuracy level evaluation. Due to unavailability of local CORS station, two first order GCPs were used as a reference during static GPS measurement. After measurement was conducted depending on the baseline length between control point marks, the STC desktop software and online CSRS-PPP post processing technique was used. Thus, the positional accuracy of selected GCPs of STC Post processing technique has a mean error of 0.324 m and whereas the GCPs of CSRS-PPP has a mean error of 0.571 m. Horizontal distance error obtained with the STC differential technique is ranging from 0 to 0.83 m which is inconsistent relative to the range 0.437 m to 0.815 m obtained from online CSRS-PPP solution. The positional accuracy of GCPs was also assessed using horizontal distance error of both processed techniques. The positional accuracy of GCPs at 95% confidence level is between -0.117 m and 0.765 m with STC and between 0.395 m and 0.747 m in online CSRS-PPP solution. Based on the result, the accuracy level of existing GCPs found in Assosa town, does not meet the FGDC accuracy. This was mainly associated with perception of communities on ground control point management activities. The study revealed almost low communities practice and control point management responsiveness. Finally, study suggested that there should be participatory guidance to establish and manage the ground control points.

A New Precise Point Positioning with Ambiguity Resolution (PPP-AR) Approach for Ground Control Point Positioning for Photogrammetric Generation with Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) are now widely preferred systems that are capable of rapid mapping and generating topographic models with relatively high positional accuracy. Since the integrated GNSS receivers of UAVs do not allow for sufficiently accurate outcomes either horizontally or vertically, a conventional method is to use ground control points (GCPs) to perform bundle block adjustment (BBA) of the outcomes. Since the number of GCPs to be installed limits the process in UAV operations, there is an important research question whether the precise point positioning (PPP) method can be an alternative when the real-time kinematic (RTK), network RTK, and post-process kinematic (PPK) techniques cannot be used to measure GCPs. This study introduces a novel approach using precise point positioning with ambiguity resolution (PPP-AR) for ground control point (GCP) positioning in UAV photogrammetry. For this purpose, the results are evaluated by comparing the horizontal and vertical coordinates obtained from the 24 h GNSS sessions of six calibration pillars in the field and the horizontal length differences obtained by electronic distance measurement (EDM). Bartlett’s test is applied to statistically determine the accuracy of the results. The results indicate that the coordinates obtained from a two-hour PPP-AR session show no significant difference from those acquired in a 30 min session, demonstrating PPP-AR to be a viable alternative for GCP positioning. Therefore, the PPP technique can be used for the BBA of GCPs to be established for UAVs in large-scale map generation. However, the number of GCPs to be selected should be four or more, which should be homogeneously distributed over the study area.

Effects of Analog Modeling Materials on Topographic Photogrammetry (SfM) Reconstructions

AbstractAccurate topographic data are essential for quantitative structural analysis, both in natural settings and in the laboratory. The selection of modeling materials (with appropriate rheological properties) is known to be fundamental for the success of scaled physical analog experiments. However, the optical properties of analog materials and their impact on the reliability and precision of high‐resolution topographic reconstructions have not (to our knowledge) previously been assessed. Here we evaluate the effects of material composition, color, and grain size on Structure‐from‐Motion (SfM) photogrammetry reconstruction efficacy for deformed and undeformed model configurations in the laboratory. Image collections for photogrammetry are acquired from multiple camera positions with a handheld digital camera, and reconstructions are registered using ground control points in a local coordinate system. Static experiments show that low reflectivity granular materials (e.g., silica sand, volcanic ash, pumice, and Al2O3) yield relatively reliable photogrammetry data for a wide range of grain sizes (44–2,400 μm) but larger grain sizes (≥250 μm) provide more robust results. Reflective materials (e.g., glass beads, wet clay) yield less reliable point‐clouds but the addition of low‐reflectivity granular materials (e.g., Al2O3 grains) on the surface of wet clay improves reconstruction results, with higher grain densities typically yielding lower point‐cloud residuals. SfM‐photogrammetry reconstruction of deformed clay analog models tends to improve at higher extension magnitudes because of fault and associated texture development on model surfaces. We anticipate that our results will help practitioners to improve the precision and reliability of photogrammetric data acquired in the analog modeling laboratory.