A High-Precision Modeling and Error Analysis Method for Mountainous and Canyon Areas Based on TLS and UAV Photogrammetry

Three-dimensional digital technology is important in the maintenance and monitoring of cultural heritage sites. This study focuses on using a combination of terrestrial laser scanning and unmanned aerial vehicle (UAV) photogrammetry to establish a three-dimensional model and the associated digital documentation of the Magoksa Temple, Republic of Korea. Herein, terrestrial laser scanning and UAV photogrammetry was used to acquire the perpendicular geometry of the buildings and sites, where UAV photogrammetry yielded higher planar data acquisition rate in upper zones, such as the roof of a building, than terrestrial laser scanning. On comparing the two technologies’ accuracy based on their ground control points, laser scanning was observed to provide higher positional accuracy than photogrammetry. The overall discrepancy between the two technologies was found to be sufficient for the generation of convergent data. Thus, the terrestrial laser scanning and UAV photogrammetry data were aligned and merged post conversion into compatible extensions. A three-dimensional (3D) model, with planar and perpendicular geometries, based on the hybrid data-point cloud was developed. This study demonstrates the potential for using the integration of terrestrial laser scanning and UAV photogrammetry in 3D digital documentation and spatial analysis of cultural heritage sites.

Soil erosion is a decisive earth surface process strongly influencing the fertility of arable land. Several options exist to detect soil erosion at the scale of large field plots (here 600 m²), which comprise different advantages and disadvantages depending on the applied method. In this study, the benefits of unmanned aerial vehicle (UAV) photogrammetry and terrestrial laser scanning (TLS) are exploited to quantify soil surface changes. Beforehand data combination, TLS data is co-registered to the DEMs generated with UAV photogrammetry. TLS data is used to detect global as well as local errors in the DEMs calculated from UAV images. Additionally, TLS data is considered for vegetation filtering. Complimentary, DEMs from UAV photogrammetry are utilised to detect systematic TLS errors and to further filter TLS point clouds in regard to unfavourable scan geometry (i.e. incidence angle and footprint) on gentle hillslopes. In addition, surface roughness is integrated as an important parameter to evaluate TLS point reliability because of the increasing footprints and thus area of signal reflection with increasing distance to the scanning device. The developed fusion tool allows for the estimation of reliable data points from each data source, considering the data acquisition geometry and surface properties, to finally merge both data sets into a single soil surface model. Data fusion is performed for three different field campaigns at a Mediterranean field plot. Successive DEM evaluation reveals continuous decrease of soil surface roughness, reappearance of former wheel tracks and local soil particle relocation patterns.

Abstract. Soil erosion is a decisive earth surface process strongly influencing the fertility of arable land. Several options exist to detect soil erosion at the scale of large field plots (here 600 m²), which comprise different advantages and disadvantages depending on the applied method. In this study, the benefits of unmanned aerial vehicle (UAV) photogrammetry and terrestrial laser scanning (TLS) are exploited to quantify soil surface changes. Beforehand data combination, TLS data is co-registered to the DEMs generated with UAV photogrammetry. TLS data is used to detect global as well as local errors in the DEMs calculated from UAV images. Additionally, TLS data is considered for vegetation filtering. Complimentary, DEMs from UAV photogrammetry are utilised to detect systematic TLS errors and to further filter TLS point clouds in regard to unfavourable scan geometry (i.e. incidence angle and footprint) on gentle hillslopes. In addition, surface roughness is integrated as an important parameter to evaluate TLS point reliability because of the increasing footprints and thus area of signal reflection with increasing distance to the scanning device. The developed fusion tool allows for the estimation of reliable data points from each data source, considering the data acquisition geometry and surface properties, to finally merge both data sets into a single soil surface model. Data fusion is performed for three different field campaigns at a Mediterranean field plot. Successive DEM evaluation reveals continuous decrease of soil surface roughness, reappearance of former wheel tracks and local soil particle relocation patterns.

Abstract. Three-dimensional digital documentation is an important technique for the maintenance and monitoring of cultural heritage sites. This study focuses on the three-dimensional digital documentation of the Magoksa Temple, Republic of Korea, using a combination of terrestrial laser scanning and unmanned aerial vehicle (UAV) photogrammetry. Terrestrial laser scanning mostly acquired the vertical geometry of the buildings. In addition, the digital orthoimage produced by UAV photogrammetry had higher horizontal data acquisition rate than that produced by terrestrial laser scanning. Thus, the scanning and UAV photogrammetry were merged by matching 20 corresponding points and an absolute coordinate system was established using seven ground control points. The final, complete threedimensional shape had perfect horizontal and vertical geometries. This study demonstrates the potential of integrating terrestrial laser scanning and UAV photogrammetry for three-dimensional digital documentation. This new technique is expected to contribute to the three-dimensional digital documentation and spatial analysis of cultural heritage sites.

The accurate estimation of tree attributes is essential for sustainable forest management. Terrestrial Laser Scanning (TLS) is a viable remote sensing technology suitable for estimating under canopy structure. However, TLS measurements generally underestimate tree height in taller trees, which leads to the underestimation of other tree attributes (e.g., stem volume). The integration of information derived from TLS and Unmanned Aerial Vehicle (UAV) photogrammetry could potentially improve tree height estimation. This study investigated the applicability of integrating TLS and UAV photogrammetry to estimate individual tree attributes in managed coniferous forests of Japan. Diameter at breast height (DBH), tree height, and stem volume were estimated by (1) TLS data only, (2) integrating TLS and UAV data with TLS tree locations, and (3) integrating TLS and UAV data with treetop detections of the tree canopy. The TLS data only approach achieved high accuracy for DBH estimations with a root mean squared error (RMSE) of 2.36 cm (RMSE% 5.6%); however, tree height was greatly underestimated, with an RMSE of 8.87 m (28.9%) and a bias of −8.39 m. Integrating TLS and UAV photogrammetric data improved tree height estimation accuracy for both the TLS tree location (RMSE of 1.89 m and a bias of −0.46 m) and the treetop detection (RMSE of 1.77 m and a bias of 0.36 m) approaches. Integrating TLS and UAV photogrammetric data also improved the accuracy of the stem volume estimations with RMSEs of 0.21 m3 (10.8%) and 0.21 m3 (10.5%) for the TLS tree location and treetop detection approaches, respectively. Although the tree height of suppressed trees tended to be overestimated by TLS and UAV photogrammetric data integration, a good performance was obtained for dominant trees. The results of this study indicate that the integration of TLS and UAV photogrammetry is beneficial for the accurate estimation of tree attributes in coniferous forests.

Abstract. Laser scanning or photogrammetry are useful individual techniques for digital documentation of cultural heritage sites. However, these techniques are of limited usage if cultural heritage such as the Great Wall is in harsh geographical conditions. The Great Wall is usually built on the ridge with cliffs on both sides, so it is very difficult to construct scaffolding. Therefore, the three-dimensional (3D) data obtained from the traditional 3D laser scanning is not complete. As UAV cannot enter the enemy tower, the 3D structure data inside the enemy tower with unmanned aerial vehicle (UAV) photogrammetry is missing. In order to explore effective methods to completely collect the 3D data of cultural heritage under harsh geographical environment, this study focuses on establishing a 3D model and the associated digital documentation for the No.15 enemy tower of the New Guangwu Great Wall using a combination of terrestrial laser scanning and UAV photogrammetry. This paper proposes an integrated data collection method and reduces the layout of image control points using RTK-UAV technology, which improved work efficiency and reduced work risks as well. In this paper, the internal structure data of the Great Wall enemy tower was collected by laser scanning, the external structure data was collected by UAV photogrammetry, and data fusion was based on ICP algorithm. Finally, we obtained the complete and high quality 3D digital documentation of the Great Wall enemy tower, the data can be displayed digitally and help heritage experts complete the Great Wall's restoration. This study demonstrates the potential of integrating terrestrial laser scanning and UAV photogrammetry in 3D digital documentation of cultural heritage sites.

Kim, S.; Park, S.; Han, J.; Son, S.; Lee, S.; Han, K.; Kim, J., and Kim, J., 2019. Feasibility of UAV photogrammetry for coastal monitoring: A case study in Imlang Beach, South Korea. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 386-392. Coconut Creek (Florida), ISSN 0749-0208.This study assessed the potential of unmanned aerial vehicle (UAV) photogrammetry to accurately monitor coastal zone features, such as vertical profiles, and to detect shoreline. In total, 245 images with a ground spatial distance (GSD) of 1.59 cm were captured using a Zenmuse X7 camera mounted on an Inspire 2 UAV at Imlang Beach, Busan, Korea; 40 ground control points (GCPs) for UAV photogrammetry and 21 stations for terrestrial laser scanning (TLS) were surveyed using a network real-time kinematic (RTK) approach. The root mean square error (RMSE) values in the X, Y, and Z directions were 0.015, 0.017, and 0.040 m, respectively, based on bundle adjustment of 24 GCPs and 16 checkpoints. The root sum of squares error (RSSE) was 0.046 m. To assess the accuracy of the vertical profiles obtained for Imlang Beach, digital elevation models (DEMs) of six cross-shore profiles were constructed and compared based on UAV photogrammetry and TLS surveying. Vertical accuracy assessment showed an average difference in height between the models of 0.02 m and an RMSE of 0.04 m. A well-established object-based image segmentation approach was applied with standard parameters (size 100, shape 0.5, and compactness 0.5) to extract the shoreline from orthomosaic images of Imlang Beach. The results suggest that UAV photogrammetry has the capacity to achieve accurate and continuous coastal monitoring.

A three-dimensional road point cloud is not only useful for civil engineers (road rehabilitation, road condition assessment) but can also be useful for vehicle engineers (autonomous vehicle driving scenario, vehicle dynamics simulation). Currently, there are several scanning techniques can be used to obtain these point clouds, such as terrestrial laser scanning (TLS), mobile laser scanning (MLS), airborne laser scanning (ALS), unmanned aerial vehicle (UAV) photogrammetry or UAV laser scanning. This paper discusses the investigation of four road surface scanning techniques by comparing their point clouds and the derived products. The comparison was performed for a section of a road with 1136 m length and 4 m width, the TLS survey provided the reference data. Aspects of point cloud evaluation included geometric accuracy, density, and the parameters of plane-fitting. CRG models were created from all studied point clouds to compare the difference between the final products to be used by the automotive industry. The results show that the MLS and the UAV photogrammetry generated the most accurate point cloud, while UAV laser scanning accuracy was the lowest. Similarly, the CRG models comparison showed that there was no significant difference between MLS and TLS models, and the UAV photogrammetry gave a smoother variation relative to the reference surface. Whereas the largest differences were noted for the CRG model derived from the UAV laser scanning models.

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.

The surface plant infrastructure (SPI) of underground coal mines is one of important sets of underground mines as it includes essential objects, such as office buildings, structures and equipment used to load, receive, sort or process minerals; receive and discharge waste rocks; provide ventilation for tunnels and energy for mining operations. The measurement and collection of spatial data of SPI are important to ensure the safe and effective management and operation of mining activities in underground mines. A rapid development in geospatial technologies has facilitated the acquisition of geospatial data in the mining industry. Unmanned Aerial Vehicle (UAV) photogrammetry and Terrestrial Laser Scanning (TLS) are two of the typical geospatial technologies, which have made significant contributions to the field of geospatial data collection. While UAV photogrammetry allows to create dense point clouds with centimeter - level accuracy in a short time and large areas, TLS technology can produce dense point clouds with millimeter - level accuracy. However, the latter is time - consuming and expensive while performing on a large area. The integration of UAV and TLS data can be seen as a reasonable solution to gain the advantages of both and avoid the disadvantages of each technology. This paper presents the results of an integrated study of point cloud data generated by UAV and TLS for the plant infrastructure of the underground coal mine. Featuring structures in the study area include mineshaft tower, office and factory buildings. The results show that the UAV and TLS integrated point cloud data has millimeter - level accuracy for important objects such as mineshaft towers, while ancillary structures in the study area have centimeter - level accuracy.

With the growing emphasis on sustainability and resource efficiency within the architectural, engineering, and construction (AEC) sectors, Unmanned Aerial Vehicles (UAVs) and Terrestrial Laser Scanner (TLS) have emerged as indispensable tools for the monitoring and inspection of building structures by using 3D modelling. This research is dedicated to assessing the quality and accuracy obtained from 3D modelling for a building and its structural components between UAV photogrammetry and TLS techniques. The investigation involved nadir and oblique flight missions for UAV data acquisition around the target structure, utilising six (6) Ground Control Points (GCPs), while TLS data collection employed direct georeferencing via the traversing method. The results revealed that TLS yielded superior surface reconstruction quality owing to its denser point cloud density, whereas UAV data met the requirements of numerous applications, offering a convenient and economically viable data acquisition solution. Regarding accuracy, a minimal disparity was observed for building objects discernible from both instruments, achieving centimetre-level accuracy. These findings not only highlighted the potential of UAVs and TLS in optimising 3D modelling processes but also offered practical insights for professionals engaged in urban planning, architectural design, and structural analysis endeavours.

Abstract. Tourists and hikers visiting glaciers all year round face hazards such as sudden terminus collapses, typical of such a dynamically evolving environment. In this study, we analyzed the potential of different survey techniques to analyze hazards of the Forni Glacier, an important geosite located in Stelvio Park (Italian Alps). We carried out surveys in the 2016 ablation season and compared point clouds generated from an unmanned aerial vehicle (UAV) survey, close-range photogrammetry and terrestrial laser scanning (TLS). To investigate the evolution of glacier hazards and evaluate the glacier thinning rate, we also used UAV data collected in 2014 and a digital elevation model (DEM) created from an aerial photogrammetric survey of 2007. We found that the integration between terrestrial and UAV photogrammetry is ideal for mapping hazards related to the glacier collapse, while TLS is affected by occlusions and is logistically complex in glacial terrain. Photogrammetric techniques can therefore replace TLS for glacier studies and UAV-based DEMs hold potential for becoming a standard tool in the investigation of glacier thickness changes. Based on our data sets, an increase in the size of collapses was found over the study period, and the glacier thinning rates went from 4.55 ± 0.24 m a−1 between 2007 and 2014 to 5.20 ± 1.11 m a−1 between 2014 and 2016.

Airborne and terrestrial laser scanning and close-range photogrammetry are frequently used for very high-resolution mapping of land surface. These techniques require a good strategy of mapping to provide full visibility of all areas otherwise the resulting data will contain areas with no data (data shadows). Especially, deglaciated rugged alpine terrain with abundant large boulders, vertical rock faces and polished roche-moutones surfaces complicated by poor accessibility for terrestrial mapping are still a challenge. In this paper, we present a novel methodological approach based on a combined use of terrestrial laser scanning (TLS) and close-range photogrammetry from an unmanned aerial vehicle (UAV) for generating a high-resolution point cloud and digital elevation model (DEM) of a complex alpine terrain. The approach is demonstrated using a small study area in the upper part of a deglaciated valley in the Tatry Mountains, Slovakia. The more accurate TLS point cloud was supplemented by the UAV point cloud in areas with insufficient TLS data coverage. The accuracy of the iterative closest point adjustment of the UAV and TLS point clouds was in the order of several centimeters but standard deviation of the mutual orientation of TLS scans was in the order of millimeters. The generated high-resolution DEM was compared to SRTM DEM, TanDEM-X and national DMR3 DEM products confirming an excellent applicability in a wide range of geomorphologic applications.

Determination of the coefficient of proportionality between horizontal displacement and tilt change using UAV photogrammetry

Abstract. Baseline Surveys Ltd is a company which specialises in the supply of accurate geospatial data, such as cadastral, topographic and engineering survey data to commercial and government bodies. Baseline Surveys Ltd invested in aerial drone photogrammetric technology and had a requirement to establish the spatial accuracy of the geographic data derived from our unmanned aerial vehicle (UAV) photogrammetry before marketing our new aerial mapping service. Having supplied the construction industry with survey data for over 20 years, we felt that is was crucial for our clients to clearly understand the accuracy of our photogrammetry so they can safely make informed spatial decisions, within the known accuracy limitations of our data. This information would also inform us on how and where UAV photogrammetry can be utilised. What we wanted to find out was the actual accuracy that can be reliably achieved using a UAV to collect data under field conditions throughout a 2 Ha site. We flew a UAV over the test area in a "lawnmower track" pattern with an 80% front and 80% side overlap; we placed 45 ground markers as check points and surveyed them in using network Real Time Kinematic Global Positioning System (RTK GPS). We specifically designed the ground markers to meet our accuracy needs. We established 10 separate ground markers as control points and inputted these into our photo modelling software, Agisoft PhotoScan. The remaining GPS coordinated check point data were added later in ArcMap to the completed orthomosaic and digital elevation model so we could accurately compare the UAV photogrammetry XYZ data with the RTK GPS XYZ data at highly reliable common points. The accuracy we achieved throughout the 45 check points was 95% reliably within 41 mm horizontally and 68 mm vertically and with an 11.7 mm ground sample distance taken from a flight altitude above ground level of 90 m.The area covered by one image was 70.2 m × 46.4 m, which equals 0.325 Ha. This finding has shown that XYZ data derived from UAV photogrammetry has a similar practical accuracy to RTK GPS, which is commonly used for cadastral, topographic and engineering survey work. This means that UAV photogrammetry can, for the most part, replace GPS surveying as the main method of data capture for engineering projects, boundary mapping and topographical surveying. Aerial Photogrammetry, in conjunction with RTK GPS, can now be used for projects with a 1:200 map scale accuracy requirement.