Performance and precision analysis of 3D surface modeling through UAVs: validation and comparison of different photogrammetric data processing software

Recent technological innovations have led to the available of lightweight Unmanned Aerial Vehicle (UAV) and Structure-from-Motion (SfM) photogrammetry that are successfully applied for 3D topographic surveys. However, application of UAV and SfM for complex topographic areas i.e. open-pit mine areas is still poorly understood. This paper aims to investigate and verify potential application of these techniques for generating Digital Surface Model (DSM) at open-pit coal mine area and assessing its accuracy. For this purpose, the Nui Beo open-pit coal mine located in northeast Vietnam is selected as a case study. Accordingly, a total of 206 photos were captured using DJI Phantom 3 Professional. In addition, 19 ground control points (GCPs) were established using Leica TS09 total station. The accuracy of DSM was assessed using root-mean-square error (RMSE) in X, Y, Z, XY, and XYZ components. The result showed that the DSM model has high accuracy, RMSE on the 12 calibrated GCPs for X, Y, Z, XY, XYZ is 1.1 cm, 1.9 cm, 0.8 cm, 2.2 cm, and 2.3 cm, respectively, whereas RMSE on the 7 checked GCPs is 1.8 cm, 2.4 cm, 3.2 cm, 3.0 cm, and 4.4 cm for X, Y, Z, XY, XYZ components, respectively. We concluded that small UAV and SfM are feasible and valid tools for 3D topographic mapping in complex terrains such as open-pit coal mine areas.

To accurately assess landslide susceptibility and model debris flow paths, acquisition of high-resolution elevation data is essential. These data enable precise topographical analysis, considering factors like slope and curvature. But most of the time such high-resolution data will have inaccuracies due to vegetation, especially in tropical region. This study proposes a comprehensive approach that integrates field data and Structure from Motion (SfM) technology to rectify such DEM inaccuracies caused by dense vegetation. The goal is to enhance the accuracy of landslide simulations and volumetric analysis for effective post-disaster management. The Western Ghats, running parallel to India's western coast, has experienced a surge in rainfall-induced landslides, resulting in significant casualties in recent years. Notably, the Pettimudi village landslide in 2020 caused 70 deaths. The unique geomorphological features of the Western Ghats, such as concave curvature, colluvium deposits, and evidence of paleo landslides, contribute to the area's susceptibility. The study emphasizes the need for detailed assessment and mitigation strategies. The proposed method aims to improve the post-event high-resolution DEM accuracy by integrating field-collected elevation values and utilizing Agisoft Metashape software with the SfM algorithm. The rectification process involves combining elevation differences measured during a field study with photogrammetric elevation data. The field-collected elevation differences are crucial for rectifying these points, and enhancing the Digital Elevation Model (DEM) accuracy. The inaccessible source region is improved using a SfM-created DEM from drone footage, resulting in a more accurate post-event DEM. Correcting the 1 m DEM using an SfM-generated DEM proves challenging but significantly improves detail and accuracy, especially over the landslide source area. The impact of the rectification on accuracy is assessed by comparing volumes calculated from the initial DEM and the newly corrected DEM. The difference in volumes of debris depletion and accumulation, computed using initial and corrected DEMs, highlights variations, with depleted volumes being significantly larger due to the extensive depth increase over the landslide source region. These volumes are then utilized in Rapid Mass Movement Simulation (RAMMS) to validate the rectification process and enhance landslide impact predictions. Debris flow simulations in RAMMS, utilizing the rectified 1 m DEM, show specific outcomes at the landslide source region and toe of the landslide. The study emphasizes the importance of integrating SfM technology with field data to improve DEM accuracy, acknowledging the significance of additional field data for further refinement. The potential adoption of highly precise aerial imagery from Unmanned Aerial Vehicle (UAV) surveys is suggested to further enhance the SfM DEM. Debris flow modeling with RAMMS serves as a vital step in validating the accuracy and reliability of the rectified elevation model. Keywords: Landslides, Western Ghats, Pettimudi, Digital elevation model (DEM), Structure-from-Motion (SfM) technology, Rapid Mass Movement Simulation (RAMMS)

In recent years, low-cost unmanned aerial vehicles (UAVs) photogrammetry and terrestrial laser scanner (TLS) techniques have become very important non-contact measurement methods for obtaining topographic data about landslides. However, owing to the differences in the types of UAVs and whether the ground control points (GCPs) are set in the measurement, the obtained topographic data for landslides often have large precision differences. In this study, two types of UAVs (DJI Mavic Pro and DJI Phantom 4 RTK) with and without GCPs were used to survey a loess landslide. UAVs point clouds and digital surface model (DSM) data for the landslide were obtained. Based on this, we used the Geomorphic Change Detection software (GCD 7.0) and the Multiscale Model-To-Model Cloud Comparison (M3C2) algorithm in the Cloud Compare software for comparative analysis and accuracy evaluation of the different point clouds and DSM data obtained using the same and different UAVs. The experimental results show that the DJI Phantom 4 RTK obtained the highest accuracy landslide terrain data when the GCPs were set. In addition, we also used the Maptek I-Site 8,820 terrestrial laser scanner to obtain higher precision topographic point cloud data for the Beiguo landslide. However, owing to the terrain limitations, some of the point cloud data were missing in the blind area of the TLS measurement. To make up for the scanning defect of the TLS, we used the iterative closest point (ICP) algorithm in the Cloud Compare software to conduct data fusion between the point clouds obtained using the DJI Phantom 4 RTK with GCPs and the point clouds obtained using TLS. The results demonstrate that after the data fusion, the point clouds not only retained the high-precision characteristics of the original point clouds of the TLS, but also filled in the blind area of the TLS data. This study introduces a novel perspective and technical scheme for the precision evaluation of UAVs surveys and the fusion of point clouds data based on different sensors in geological hazard surveys.

In this study, we generated Digital Elevation Model (DEM) from Unmanned Aerial Vehicle (UAV) to confirm the suitability of UAV to the tidal flat study. The DEMs were generated from aerial triangulation method using rotary-wing UAV. For the accurate generation of mosaic images and DEM, the distorted images occurred by interior and exterior orientation were corrected using camera calibration. In addition, we set up a Ground Control Points (GCPs) in order to correct of the UAV position error. Therefore, the DEM was obtained with geometric error less than 30 cm. The height of generated DEM by UAV was compared with the levelled elevation by RTK-GPS and TanDEM-X radar satellite DEM. From this study, we could confirm that accurate DEM of the tidal flat can be generated using UAV and these detailed spatial information about tidal flat will be widely used for tidal flat management.

This study is about systematic approach to photogrammetric survey that is applicable in the extraction of elevation data for geophysical surveys in hilly terrains using Unmanned Aerial Vehicles (UAVs). The outcome will be to acquire high-quality geophysical data from areas where elevations vary by locating the best survey lines. The study area is located at the proposed construction site for the development of a water reservoir and related infrastructure in Kampus Pauh Putra, Universiti Malaysia Perlis. Seismic refraction surveys were carried out for the modelling of the subsurface for detailed site investigations. Study were carried out to identify the accuracy of the digital elevation model (DEM) produced from an UAV. At 100 m altitude (flying height), over 135 overlapping images were acquired using a DJI Phantom 3 quadcopter. All acquired images were processed for automatic 3D photo-reconstruction using Agisoft PhotoScan digital photogrammetric software, which was applied to all photogrammetric stages. The products generated included a 3D model, dense point cloud, mesh surface, digital orthophoto, and DEM. In validating the accuracy of the produced DEM, the coordinates of the selected ground control point (GCP) of the survey line in the imaging area were extracted from the generated DEM with the aid of Global Mapper software. These coordinates were compared with the GCPs obtained using a real-time kinematic global positioning system. The maximum percentage of difference between GCP’s and photogrammetry survey is 13.3 %. UAVs are suitable for acquiring elevation data for geophysical surveys which can save time and cost.

Unmanned aerial vehicle (UAV) photogrammetry is a technology used to create highly accurate maps, and the real-time kinematic (RTK) global navigation satellite system (GNSS) network has been implemented by several organizations to support their missions.Recent advancements in computer vision have driven progress in photogrammetry, alongside the development of more accessible UAVs equipped with GNSS technology.This paper evaluates the accuracy of UAV imagery and processing using ground control points (GCPs) with coordinates from the Thailand RTK GNSS network.The study area is located on the Sathing-Phra Peninsula in Songkhla Province, southern Thailand, covering approximately 174 hectares with a total of 660 images.The average ground sampling distance (GSD) was around 4.35 cm.The processed photogrammetry system used 8 GCPs via the GNSS RTK network, and results were compared with 25 checkpoints from a static survey.The UAV photogrammetry results showed a horizontal accuracy for orthomosaics and vertical accuracy for the digital surface model (DSM), with CE 95 values of 6.99 cm and 12.44 cm, respectively, as per the NSSDA standard.These results comply with the Thailand UAV Surveying for Engineering (TUSE) standards.According to the 2014 American Society for Photogrammetry and Remote Sensing (ASPRS) standards, horizontal accuracy class I and vertical accuracy class II should be applied.The map scale factor (MSF) standard was 1:160, with a contour interval of 20 cm.Additionally, the orthomosaic DSM can be imported into open-source programs for use in a web GIS online system, enabling local agencies to retrieve and utilize the data.

Abstract. This study evaluates the potential that lies in the photogrammetric processing of aerial images captured by unmanned aerial vehicles. UAV-Systems have gained increasing attraction during the last years. Miniaturization of electronic components often results in a reduction of quality. Especially the accuracy of the GPS/IMU navigation unit and the camera are of the utmost importance for photogrammetric evaluation of aerial images. To determine the accuracy of digital elevation models (DEMs), an experimental setup was chosen similar to the situation of data acquisition during a field campaign. A quarry was chosen to perform the experiment, because of the presence of different geomorphologic units, such as vertical walls, piles of debris, vegetation and even areas. In the experimental test field, 1042 ground control points (GCPs) were placed, used as input data for the photogrammetric processing and as high accuracy reference data for evaluating the DEMs. Further, an airborne LiDAR dataset covering the whole quarry and additional 2000 reference points, measured by total station, were used as ground truth data. The aerial images were taken using a MAVinci Sirius I – UAV equipped with a Canon 300D as imaging system. The influence of the number of GCPs on the accuracy of the indirect sensor orientation and the absolute deviation’s dependency on different parameters of the modelled DEMs was subject of the investigation. Nevertheless, the only significant factor concerning the DEMs accuracy that could be isolated was the flying height of the UAV.

For monitoring purposes and in the context of geomorphological research, Unmanned Aerial Vehicles (UAV) appear to be a promising solution to provide multi-temporal Digital Surface Models (DSMs) and orthophotographs. There are a variety of photogrammetric software tools available for UAV-based data. The objective of this study is to investigate the level of accuracy that can be achieved using two of these software tools: Agisoft PhotoScan® Pro and an open-source alternative, IGN© MicMac®, in sub-optimal survey conditions (rugged terrain, with a large variety of morphological features covering a range of roughness sizes, poor GPS reception). A set of UAV images has been taken by a hexacopter drone above the Rivière des Remparts, a river on Reunion Island. This site was chosen for its challenging survey conditions: the topography of the study area (i) involved constraints on the flight plan; (ii) implied errors on some GPS measurements; (iii) prevented an optimal distribution of the Ground Control Points (GCPs) and; (iv) was very complex to reconstruct. Several image processing tests are performed with different scenarios in order to analyze the sensitivity of each software package to different parameters (image quality, numbers of GCPs, etc.). When computing the horizontal and vertical errors within a control region on a set of ground reference targets, both methods provide rather similar results. A precision up to 3–4 cm is achievable with these software packages. The DSM quality is also assessed over the entire study area comparing PhotoScan DSM and MicMac DSM with a Terrestrial Laser Scanner (TLS) point cloud. PhotoScan and MicMac DSM are also compared at the scale of particular features. Both software packages provide satisfying results: PhotoScan is more straightforward to use but its source code is not open; MicMac is recommended for experimented users as it is more flexible.

Many Digital Elevation Model (DEM)-based methods have been proposed to enable automated gully mapping. However, the accuracy of a gully map derived from a DEM is inevitably affected by the DEM resolution. Related literature evaluated the accuracy of DEMs generated from stereo imagery of different satellite sensors; however, limited work has been done on quantitative analysis concerning the capability and accuracy of satellite DEMs in Gully erosion mapping. Therefore, this study was centered to map gully erosion and to investigate the accuracy of measured heights of gully erosion using field acquired data (RTK GNSS) and Global Satellite DEMs obtained from ALOS World 3D 30m and 30m ASTER GDEM. The Etim Umana Gully Erosion site in Uyo Local Government Area of Akwa Ibom State, Nigeria was used as the test site for this study. These datasets were referenced to orthometric height, therefore were converted to ellipsoidal height using the Earth Gravitational Model (EGM) 2008. Digital Elevation Model of the field data were generated using various kriging method (simple, ordinary, universal) of which the ordinary method assumed the best with the least Root-Mean-Square Error(RMSE) of 1.326199. Ground Control Points (GCP) (32) values were used to validate the accuracy of the field acquired data and obtained a RMSE of 1.125463 and a Mean Absolute Error (MAE) of 0.8. The spot height values obtained from field were used to validate the satellite DEMs by calculating their RMSE from the difference between the measured values and the predicted values. The ALOS had a RMSE of 10.89154 when compared to the ASTER which had the least RMSE of 10.71999 which indicates the propagation of error. The Mean-Absolute-Error was calculated recoding MAE of 8.539147 for ALOS and ASTER 9.346781 and a Standard deviation of 9.176663 for the ALOS and ASTER 10.53712. ALOS had a mean error of -8.5174 and ASTER 7.706339. The result from this research indicates that they are similar levels of error propagation between the ALOS and ASTER and this can further be ascertained by carrying out validation on different terrain surfaces. These results indicate that it can be map to scale with contours based on the specifications of the National Mapping Accuracy Standards (NMAS)

Unmanned aerial vehicles (UAVs) are being increasingly used to provide high resolution imagery which, through photogrammetric method Structure-from-Motion (SfM), can be used for high resolution topographic reconstruction. PPK (Post-Processing Kinematic) positioning solution offers the potential of accurate image georeferencing without ground control points (GCPs). By integrating consumer-grade cameras and an onboard RTK/PPK GNSS receiver, we managed to have a low-cost and effective approach involving topographic change-detection. Here, we evaluated the positional accuracy and reproducibility of DSMs generated from PPK-SfM workflow with two camera setups by conducting multiple UAV surveys over a test field. Our results showed that the PPK solution has the same accuracy (mean: ca. 0.01 m, RMSE: ca. 0.03 m) as the more traditional georeferencing method based on ground control points. Furthermore, our results indicated that camera properties (i.e. focal length, resolution, sensor quality) have a large impact on the accuracy: a DSLR camera (Canon EOS), with 0.63 cm px-1 GSD, had 0.011 m planimetric error and 0.012 m altimetric error; while a cheap and light Action camera (GoPro) with 3.11 cm px-1 GSD had 0.019 m planimetric error and 0.024 m altimetric error at the same flight height. The repeatability of DSM construction was assessed by a DoD (DEMs of Difference) analysis. The LoD95% and LoDmin were ca. ±0.08 m and ca. ±0.04 m for the DSLR camera and ca. ±0.16 m and ca. ±0.08 m for the action camera (for a flight altitude of 45 m). The level of detection substantially improved when reducing the UAV flight altitude. We found that tie point density greatly controlled the error of the topography reconstruction for the DSLR camera. By exploiting the relation between error and tie point density, we demonstrated that a spatially explicit DoD threshold, according to the local surface condition, can greatly improve surface change detection. Volume estimation was conducted over the plowing area and obtained comparable results from DSLR and action camera datasets. Overall, the application of a PPK-UAV workflow provides a high-precision and high-efficiency solution in surveying and geomorphological applications.

The forest inventory technique by applying remote sensing technology has become a new breakthrough in technological developments in forest inventory activities. Unmanned Aerial Vehicle (UAV) imagery with camera sensor is one of the inventory tools that produce data with high spatial resolution. The level of spatial resolution of the image is strongly influenced by the flying height of the UAV for a certain camera’s focus. In addition, flight height also affects the acquisition time and accuracy of inventory results, although there is still little research on this matter. The study aims to (a)evaluate the effect of various flying heights on the accuracy of tree height measurements through UAV imagery for every stand age class, (b).estimate the trees diameter and canopy cover for every stand age class. Stand height was estimated using Digital Surface Models (DSM), Digital Terrain Models (DTM) and Orthophoto. DSM and DTM were built by converting orthophoto to pointclouds using the PIX4Dmapper based on Structure From Motion (SFM) on the photogrammetric method to reconstruct topography automatically. Meanwhile, the tree cover canopy was estimated using the All Return Canopy Index (ARCI) formula. The results show that the flight height of 100 meters produces a stronger correlation than the flying height of 80 meters and 120 meters in estimating tree height, based on the high coefficient of determination (R2) and the low root mean square error (RMSE) value. In addition, tree canopy estimation analysis using ARCI has a maximum difference of 9.8% with orthophoto visual delineation. Key words: canopy height model (CHM), digital surface models (DSM), digital terrain models (DTM), forest inventory, UAV image

Accurate and precise topographic information from Digital Elevation Models (DEM) is of fundamental requirement for many geoscience and engineering applications. With a goal of providing accurate global topographic products, German Aerospace Centre (DLR) and Airbus Defence and Space developed X-band TanDEM-X mission. The TanDEM-X Global DEM is generated with a pixel spacing of 12×12 m from interferometric data acquired in strip map mode with a resolution of 3.3 m [1]. In this study, the vertical accuracy of TanDEM-X Global DEM is assessed by comparing it with various elevation datasets. The elevation datasets include DGPS measurements, SRTM 30m, and 90 m DEM, CartoSat DEM, LiDAR data, photogrammetry data, intermediate and fused DEMs’ derived from TanDEM-X CoSSC [2], ICESat elevations and the newly released TanDEM-X 90 m DEM. The study areas include Bihar (flat terrain), Mumbai (moderate and urban terrain), Godavari (moderate and hilly), Koyna (hilly and vegetated), Manali (rugged Himalayan) and Kartarniaghat Wild Life Sanctuary-KWLS (flat and forest) with different terrain conditions. Widely used statistical parameters such as mean, standard deviation (SD) and root mean square error (RMSE) are used for vertical accuracy assessment [3]. The minimum and maximum differences between elevations are also noted, which are useful in exploiting quality of DEM under evaluation. The accuracy of the TanDEM-X Global DEM is found to be less than 1.6 m, 4 m, 5 m, 13 m, 6 m for plain, moderate relief, hilly, Himalayan, vegetated terrains respectively. In vegetated areas (KWLS), we observed an 8 m difference in tree height estimation using TanDEM-X Global DEM and GPS measurement. Elevation and difference profiles were drawn to understand the vertical accuracy of these DEMs’. In all cases, the elevations of TanDEM-X Global DEM closely matched with heights derived from DGPS, ICESAT, LiDAR and photogrammetry data.

Interferometric synthetic aperture radar (InSAR) processing, if lacking high quality ground control points (GCPs), may produce large errors in the final DEM. Some kind of alignment or registration can reduce these errors. This paper evaluates the accuracy of InSAR processed digital elevation models (DEM), against a high resolution DEM. InSAR DEMs were aligned and least-squares registered with USGS, 1/3 arc second NED. The accuracy evaluations after registration show InSAR DEMs alignment and registration can eliminate DEM errors caused by lack of accurate GCPs. In some cases, this process improves the accuracy of the InSAR DEM even more efficiently than including GCPs into InSAR processing.

The accuracy of digital elevation models (DEMs) plays an important role in many terrain-related applications, particular in high northern latitudes where there is uncertainty in DEMs. Using the interferometric synthetic aperture radar techniques, this study examined how different RADARSAT-2 beam modes can be used to generate DEMs with high accuracy. Using a conventional interferometry method, the Spotlight DEM shows the highest accuracy among all studied DEM products, with the root-mean-square error (RMSE) ranging from 13.9 to 17.4 m, followed by the F0W3 DEM and U26W2 DEM. The error sources in DEM generation due to uncertainty in perpendicular baseline and atmospheric delay are likely more important than the random phase noise caused by volume scattering and environmental changes during synthetic aperture radar (SAR) acquisitions. The small baselines subset (SBAS) method did not significantly improve DEM quality due to the limitation of the number of SAR images in this study. The integration of both Spotlight conventional DEMs and SBAS DEM considerably improved results yielding high-quality DEMs for the study area, with an RMSE of 9.7 m. Further studies are necessary to quantitatively evaluate the effects of surface motion as well as the orbital and atmospheric errors on the DEM accuracy. The Slave River Delta in the Northwest Territories of Canada was used as a test case.

One of the first essential steps in the analysis of satellite imagery is the orthorectification of the images. Orthorectification without ground control points (GCPs) using only the ephemeris and attitude data provided by the satellite operator provides an absolute accuracy of about 20 m to 1 km (depending on the satellite), which can be improved by measuring precise GCPs. In this paper, a method to obtain GCPs from an existing digital elevation model (DEM) is described and assessed. Since at least the SRTM DEM is available worldwide, DEMs could serve as a valuable additional source for the generation of GCPs. Furthermore, several planned and ongoing missions will increase the availability and accuracy of DEMs or stereo imagery respectively, e.g. ALOS, Tandem-X, etc.