SENSITIVITY ANALYSIS OF UAV-PHOTOGRAMMETRY FOR CREATING DIGITAL ELEVATION MODELS (DEM)

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)

The planimetric and altimetric accuracies of the Digital Elevation Model (DEM) obtained from Unmanned Aerial Vehicle (UAV) depend on some photogrammetric parameters such as flight height, flight speed, and/or ground sample distance. In the last few years, the study of computer vision algorithms has increased focusing on their importance on the photogrammetric reconstruction process for land surface mapping. According to this, the main goal of this paper was to evaluate the performance of different photogrammetric processing software, such as Open Drone Map (ODM), Agisoft PhotoScan, and Pix4D, on DEM accuracy. For this purpose, a DJI Phantom 4 Pro drone was used for the acquisition of 600 images in a difficult topography area (630 ha) with 27 Ground Control Points (GCP) previously established. The photogrammetric products were generated, and a statistical analysis was carried out for the comparison of the DEMs. The results of the Root Mean Square Error (RMSE) show that the planimetric and altimetric accuracy of Agisoft PhotoScan (RMSExy = 0.514 m; RMSEz = 0.162 m) is greater than those obtained using Pix4D and ODM. Unsatisfactory results were obtained with ODM since deformations and high planimetric and altimetric errors were identified in the orthomosaic and DEM, which was related to inefficient key point detection and an incomplete mosaic construction process. Agisoft PhotoScan modeled the anthropogenic objects as part of the land surface, while Pix4d smoothed the terrain, eliminating these anthropogenic objects and not considering them as part of the ground surface.

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.

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.

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.

This research conducts a comparative analysis of the vertical accuracies of satellite-derived Digital Elevation Models (DEMs) in the Akure South Local Government Area, Ondo State, Nigeria. The DEMs assessed were obtained from three open-source satellite missions: Sentinel-1A, ALOS PALSAR, and TanDEM-X. The reference Ground Control Points (GCPs) used in the evaluations comprised 133 GCPs of 2nd and 3rd-order accuracy acquired from the Office of the Surveyor General, Ondo State, and through field surveys using the South Galaxy-1 Differential GPS. These obtained GCPs provided extensive coverage of the study area. The evaluation was undertaken based on the land cover, which was established by dividing the study area into open (built-up and bare land) and vegetation-covered (agricultural and forested) areas. The vertical accuracies of the satellite-based DEM collected within the study area were then compared and analyzed. The results revealed that Sentinel-1A and TanDEM-X were the most suitable in both open and vegetation-covered parts of the study area, with Sentinel-1A exhibiting the best performance. The effect of vegetation cover was mostly felt by ALOS PALSAR. Sentinel-1A and TanDEM-X displayed commendable vertical accuracies, deemed highly suitable for applications in topographic mapping, as shown by an RMS of less than 2m. This research recommends the open-source satellite DEMs Sentinel-1A and TanDEM-X for planning and engineering applications because they offer the highest accuracy among their counterparts within Akure and its environs.

This study aims to assess the accuracy of digital elevation model (DEM) created with utilization of handheld Global Positioning System (GPS) and comparing with Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), version 2. It is known that the quality of the DEM is affected by both of accuracy of elevation at each pixel (absolute accuracy) and accuracy of presented morphology (relative accuracy). The University of Baghdad, Al Jadriya campus was selected as a study area to create and analysis the resulting DEM. Additionally, Geographic Information System (GIS) was used to visualize, analyses and interpolate GPS track points (elevation data) of the study area. In this research, three additional DEMs were created using 60%, 30% and 15% of the all GPS track points to deduce the effect of the number of the included points on the accuracy of the resulting DEM. The study findings show a high resolution for the resulting DEM less than 5m when taking into consideration all GPS tracking points that observed in this research. Moreover, the resulting DEM has relative accuracy better than absolute accuracy and reaches to around 2m. By comparing with ground control points (reference points), the quality of handheld GPS DEM shows considerable improvement better than ASTER GDEM. Thus, this study indicates to improve the accuracy level of handheld GPS DEM by about 40% with increasing the observed number of GPS track points to twice.
 

This article introduces a mathematical model for photogrammetric processing of linear array stereo images acquired by high-resolution satellite imaging systems such as IKONOS. The experimental result of the generation of simulated IKONOS stereo images based on photogrammetric principles, IKONOS imaging geometry and a set of georeferenced aerial images is presented. An accuracy analysis of ground points derived from the simulated IKONOS stereo images is performed. The impact of the number of GCPs (ground control points), distribution of GCPs, and image measurement errors on the ground point accuracy is investigated. It is concluded that an accuracy of ground coordinates from 2 m to 3 m is attainable with GCPs, and 5 m to 12 m without GCPs. Two data sets of HRSC (high resolution stereo camera) and MOMS (modular opto-electronic multispectral stereo-scanner)-2P are also utilized to test the model and system. The presented data processing method is a key to the generation of mapping products such as digital terrain models (DEM) and digitial shorelines from high-resolution satellite images.

Introduction: A digital elevation model (DEM) allows for the analysis of specific features on the earth’s surface in three dimensions. The engineering DEM is useful to evaluate resources and design management strategies. Objective: To evaluate the technical-operational feasibility of generating DEMs from total station (TS) topographic surveys, GPS RTK and aerial photogrammetry using an unmanned aerial vehicle (UAV). Methodology: A 20x20 m grid was traced in a plot without vegetation (1.4 ha) located in Montecillo, Estado de México, and topographic surveys were carried out with three methods, from which DEMs were generated for graphic and statistical evaluation and by tracing contour lines. Results: The estimated statistical errors were 0.15, 0.15 and 0.02 m, for TS vs. UAV, GPS RTK vs. UAV and TS vs. GPS RTK, respectively. Study limitations: The instruments used and the geographical conditions of central Mexico may be a reason for variation when extrapolating the results with other devices. Originality: A methodology is provided to generate DEMs accurately. The results allow the user to make reasoned choices based on the equipment available. Conclusion: The DEMs generated with TS and GPS RTK data have a smaller error than the one obtained from UAVs. The use of UAV helps in the representation of the terrain, since it generates a dense cloud of points that strengthens the procedure for topographic surveys.

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)

Aim. Determination of the elements of external spatial orientation of the surveying systems at the moment of image acquisition is the fundamental task in photogrammetry. Principally, this problem is solving in two ways. The first way is direct positioning and measuring of directions of camera optical axis in the geodetic space with the help of GNSS/INS equipment. The second way is the analytical solution of the problem using a set of reference information (often such information is a set of ground control points whose geodetic positions are known with sufficient accuracy and which are reliably recognised on aerial images of the photogrammetric block). The authors consider the task of providing reference and control information using the second approach, which has a number of advantages in terms of reliability and accuracy of determining the unknown image exterior orientation parameters. It is proposed to obtain additional images of ground control points by the method of their auxiliary aerial photography using an unmanned aerial vehicle (UAV) on a larger scale compared to the scale of the images of the photogrammetric block. The aim of the presented work is the implementation of the method of creating reference points and experimental confirmation of its effectiveness for photogrammetric processing. Methods and results. For the entire realization of the potential of the analytical way to determine the elements of external orientation of images, it is necessary to have a certain number of ground control points (GCP) and to keep the defined scheme of their location on the photogrammetric block. As the main source of input data authors use UAV aerial images of the terrain, which are obtained separately from the block of aerial survey, and have a better geometric resolution and which clearly depict the control reference points. Application of such auxiliary images gives the possibility of automated transferring of the position of ground control point into images of the main photogrammetric block. In our interpretation, these images of ground control points and their surroundings on the ground are called "control reference images". The basis of the work is to develop a method for obtaining the auxiliary control reference images and transferring of position of GCP depicted on them into aerial or space images of terrain by means of computer stereo matching. To achieve this goal, we have developed a processing method for the creation of control reference images of aerial image or a series of auxiliary multi-scale aerial images obtained by a drone from different heights above the reference point. The operator identifies and measures the GCP once on the auxiliary aerial image of the highest resolution. Then there is an automatic stereo matching of the control reference image in the whole series of auxiliary images in succession with a decrease in the resolution and, ultimately, directly with the aerial images of photogrammetric block. On this stage there are no recognition/cursor targeting by the human operator, and therefore there are no discrepancies, errors or mistakes related to it. In addition, if to apply fairly large size of control reference images, the proposed method can be used on a low-texture terrain, and therefore deal in many cases without the physical marking of points measured by GNSS method. And this is a way to simplify and reduce the cost of photogrammetric technology. The action of the developed method has been verified experimentally to provide the control reference information of the block of archival aerial images of the low-texture terrain. The results of the experimental approbation of the proposed method give grounds to assert that the method makes it possible to perform geodetic reference of photogrammetric projects more efficiently due to the refusal of the physical marking of the area before aerial survey. The proposed method can also be used to obtain the information for checking the quality of photogrammetric survey for provision of check points. The authors argue that the use of additional equipment - UAV of semi-professional class to obtain control reference images is economically feasible. Scientific novelty and practical relevance. The results of approbation of the "control reference image" method with obtaining stereo pairs of aerial images with vertical placement of the base are presented for the first time. There was implemented the study of the properties of such stereo pairs of aerial images to obtain images of reference points. The effectiveness of including reference images in the main block of the digital aerial triangulation network created on UAV’s images is shown.

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.

The past few years have seen the raise of unmanned aerial vehicles (UAV) in geosciences for generating highly accurate digital elevation models (DEM) at low costs, which promises to be an interesting alternative to satellite data for small river basins. The reliability of UAV-derived topography as input to hydraulic modelling is still under investigation: here, we analyse potentialities and highlight challenges of employing UAV-derived topography in hydraulic modelling in a tropical environment, where weather conditions and remoteness of the study area might affect the quality of the retrieved data. We focused on a stretch of the Limpopo River in Mozambique, where detailed ground survey and airborne data were available. First, we tested and compared topographic data derived by UAV (25 cm), RTK-GPS (50 cm DEM), LiDAR (1 m DEM) and SRTM (30 m DEM); then, we used each DEM as input data to a hydraulic model and compared the performance of each DEM-based model against the LiDAR based model, currently used as benchmark by practitioners in the area. Despite the challenges experienced during the field campaign—and described here—, the degree of accuracy in terrain modelling produced errors in water depth calculations within the tolerances adopted in this typology of studies and comparable in magnitude to the ones obtained from high-precision topography models. This suggests that UAV is a promising source of geometric data even in natural environments with extreme weather conditions.

The volcanic Trindade Island is a remote Brazilian offshore territory in the South Atlantic, located ca. 1.140 kilometers east of the southeast coast of Brazil. The island’s permanent exposure to geological hazards requires assessment. However, the lack of erosion and landslides temporal data impedes predictive geohazard analyses. Therefore, we compiled pre-existing data from nautical charts and surveyed the surface terrain on Trindade Island to generate Digital Terrain Models (DTMs) and comparative accuracy analyses. The DTM based on pre-existing data shows the lowest accuracy (root mean square error - RMSE: 12.3 m) yet is adequate for regional studies. In contrast, the DTM developed from real-time kinematic global navigation satellite systems (RTK-GNSS) has the highest vertical accuracy (RMSE: 0.48 m), but spatial variability of ground elements was underestimated and limited to meter-sized (and larger) elements. The DTM obtained using the unmanned aerial vehicle (UAV) with ground control points (GCP), on the other hand, presented lower accuracy (RMSE: 2.37 m) than the RTK-GNSS model but still allowed observation of centimetric (and larger) ground features. For geohazard assessment on Trindade Island, models that allow fine-scale studies are needed. A UAV with GCP provides such standards and proved to be the most viable option in remote and complex sites as well. Hence, this study, the first to allow multi-temporal analysis of geohazard assessment on Trindade Island, offers a viable solution for similar analyses in other remote locations.

Unmanned aerial vehicle (UAV)-based snow depth is mapped as the difference between snow-on and snow-off digital surface models (DSMs), which are derived using the structure from motion (SfM) technique with ground control points (GCPs). In this study, we evaluated the impacts of the quality and deployment of GCPs on the accuracy of snow depth estimates. For 15 GCPs in our study area, we surveyed each of their coordinates using an ordinary global positioning system (GPS) and a differential GPS, producing two sets of GCP measurements (hereinafter, the low-accuracy and high-accuracy sets). The two sets of GCP measurements were then incorporated into SfM processing of UAV images by following two deployment strategies to create snow-off and snow-on DSMs and then to retrieve snow depth. In Strategy A, the same GCP measurements in each set were used to create both the snow-on and snow-off DSMs. In Strategy B, each set of GCP measurements was divided into two sub-groups, one sub-group for creating snow-on DSMs and the other sub-group for snow-off DSMs. The accuracy of snow depth estimates was evaluated in comparison to concurrent in-situ snow depth measurements. The results showed that Strategy A, using both the low-accuracy and high-accuracy sets, generated accurate snow depth estimates, while in Strategy B, only the high-accuracy set could generate reliable snow depth estimates. The results demonstrated that the deployment of GCPs had a significant influence on UAV-based SfM snow depth retrieval. When accurate GCP measurements cannot be guaranteed (e.g., in mountainous regions), Strategy A is the optimal option for producing reliable snow depth estimates. When highly accurate GCP measurements are available (e.g., collected by differential GPS in open space), both deployment strategies can produce accurate snow depth estimates.