Integrating UAV and TLS Approaches for Environmental Management: A Case Study of a Waste Stockpile Area

Purpose. The research aims to address the challenges posed by the integration of diverse methods, focusing on data collection techniques and level of detail (LoD) considerations, which facilitates the creation of detailed 3D models. The CityGML standard is employed for its ability to represent complex urban features, adapted here for mining environments. Methodology. Combining Unmanned Aerial Vehicle (UAV) and Terrestrial Laser Scanning (TLS) technologies to collect data for open-pit and underground coal mines. These data are processed to generate point clouds, which are then used to create 3D models of mining structures using Sketchup and REVIT. Finally, these models are converted into the CityGML standard using FME SAFE software. Findings. Through the use of Unmanned Aerial Vehicle and Terrestrial Laser Scanning technologies, precise point cloud data for open-pit and underground structures are acquired. CityGML serves as a suitable framework for digital mine representation, offering standardized data organization and exchange. The proposed methodology optimizes data collection and processing procedures, ensuring accuracy and efficiency in model creation. Notably, the study introduces a nuanced approach to LoD selection, considering the complexity and specific requirements of different mining structures. Originality. The article innovatively combines UAV and TLS technologies with the CityGML standard to create comprehensive 3D GIS models for coal mines operating with both open-pit and underground methods, addressing the unique challenges of modeling diverse mining structures and terrain features. Practical value. The practical value of the article lies in its provision of a systematic approach using UAV and TLS technologies, coupled with the CityGML standard, to create accurate 3D GIS models for coal mines employing both open-pit and underground methods. This methodology enhances mine management efficiency, resource estimation accuracy, and safety assessment capabilities.

Abstract. A small unmanned aerial vehicle (UAV) with survey-grade GNSS positioning is used to produce a point cloud for topographic mapping and 3D reconstruction. The objective of this study is to assess the accuracy of a UAV imagery-derived point cloud by comparing a point cloud generated by terrestrial laser scanning (TLS). Imagery was collected over a 320 m by 320 m area with undulating terrain, containing 80 ground control points. A SenseFly eBee Plus fixed-wing platform with PPK positioning with a 10.6 mm focal length and a 20 MP digital camera was used to fly the area. Pix4Dmapper, a computer vision based commercial software, was used to process a photogrammetric block, constrained by 5 GCPs while obtaining cm-level RMSE based on the remaining 75 checkpoints. Based on results of automatic aerial triangulation, a point cloud and digital surface model (DSM) (2.5 cm/pixel) are generated and their accuracy assessed. A bias less than 1 pixel was observed in elevations from the UAV DSM at the checkpoints. 31 registered TLS scans made up a point cloud of the same area with an observed horizontal root mean square error (RMSE) of 0.006m, and negligible vertical RMSE. Comparisons were made between fitted planes of extracted roof features of 2 buildings and centreline profile comparison of a road in both UAV and TLS point clouds. Comparisons showed an average +8 cm bias with UAV point cloud computing too high in two features. No bias was observed in the roof features of the southernmost building.

Purpose. The main objective of this paper is to assess the quality of the 3D model of industrial buildings generated from Unmanned Aerial Vehicle (UAV) imagery datasets, including nadir (N), oblique (O), and Nadir and Oblique (N+O) UAV datasets. Methodology. The quality of a 3D model is defined by the accuracy and density of point clouds created from UAV images. For this purpose, the UAV was deployed to acquire images with both O and N flight modes over an industrial mining area containing a mine shaft tower, factory housing and office buildings. The quality assessment was conducted for the 3D point cloud model of three main objects such as roofs, facades, and ground surfaces using CheckPoints (CPs) and terrestrial laser scanning (TLS) point clouds as the reference datasets. The Root Mean Square Errors (RMSE) were calculated using CP coordinates, and cloud to cloud distances were computed using TLS point clouds, which were used for the accuracy assessment. Findings. The results showed that the point cloud model generated by the N flight mode was the most accurate but least dense, whereas that of the O mode was the least accurate but most detailed level in comparison with the others. Also, the combination of O and N datasets takes advantages of individual mode as the point clouds accuracy is higher than that of case O, and its density is much higher than that of case N. Therefore, it is optimal to build exceptional accurate and dense point clouds of buildings. Originality. The paper provides a comparative analysis in quality of point cloud of roofs and facades generated from UAV photogrammetry for mining industrial buildings. Practical value. Findings of the study can be used as references for both UAV survey practices and applications of UAV point cloud. The paper provides useful information for making UAV flight planning, or which UAV points should be integrated into TLS points to have the best point cloud.

The availability of high-resolution Digital Surface Models of coastal environments is of increasing interest for scientists involved in the study of the coastal system processes. Among the range of terrestrial and aerial methods available to produce such a dataset, this study tests the utility of the Structure from Motion (SfM) approach to low-altitude aerial imageries collected by Unmanned Aerial Vehicle (UAV). The SfM image-based approach was selected whilst searching for a rapid, inexpensive, and highly automated method, able to produce 3D information from unstructured aerial images. In particular, it was used to generate a dense point cloud and successively a high-resolution Digital Surface Models (DSM) of a beach dune system in Marina di Ravenna (Italy). The quality of the elevation dataset produced by the UAV-SfM was initially evaluated by comparison with point cloud generated by a Terrestrial Laser Scanning (TLS) surveys. Such a comparison served to highlight an average difference in the vertical values of 0.05 m (RMS = 0.19 m). However, although the points cloud comparison is the best approach to investigate the absolute or relative correspondence between UAV and TLS methods, the assessment of geomorphic features is usually based on multi-temporal surfaces analysis, where an interpolation process is required. DSMs were therefore generated from UAV and TLS points clouds and vertical absolute accuracies assessed by comparison with a Global Navigation Satellite System (GNSS) survey. The vertical comparison of UAV and TLS DSMs with respect to GNSS measurements pointed out an average distance at cm-level (RMS = 0.011 m). The successive point by point direct comparison between UAV and TLS elevations show a very small average distance, 0.015 m, with RMS = 0.220 m. Larger values are encountered in areas where sudden changes in topography are present. The UAV-based approach was demonstrated to be a straightforward one and accuracy of the vertical dataset was comparable with results obtained by TLS technology.

The accuracy of 3D indoor reconstructed models is critical in various applications such as indoor navigation, virtual reality (VR), and building information modelling (BIM). This research study aims to evaluate the accuracy of Unmanned Aerial Vehicles (UAV) and Terrestrial Laser Scanning (TLS) for 3D indoor modelling in large-scale building environments. To achieve this, several evaluations were made towards the number of point clouds, estimated costs and accuracy of the 3D indoor reconstructed model generated from dense point clouds acquired by Unmanned Aerial Vehicle (UAV) and Terrestrial Laser Scanner (TLS). A small indoor classroom was selected for this study approximately 100m2. In UAV data acquisition, three (3) flight missions were set up at the front, left and right views. Meanwhile, five (5) scanning stations were placed on-site for the TLS method. Due to various different flight mission views in the UAV dataset, the number of point clouds was quite higher compared to the TLS method. However, a better-quality visualization of the TLS model has been obtained as opposed to the UAV 3D model. For the required time to generate a 3D model, it showed that UAV processing time was more consuming lots of time than the TLS method, especially when georeferencing the overlapping photographs. In terms of accuracy, the RMSE value from TLS was better than UAV at 0.003m compared to UAV at 0.021m. Overall, this study provides insights into the accuracy and suitability of UAV and TLS for 3D indoor modelling in large-scale building environments. The results can inform decision-making processes in various industries such as architecture, engineering, and construction, where accurate and reliable 3D models are crucial for design, planning, and management purposes.

Both Unmanned Aerial Vehicles (UAVs) and Terrestrial Laser Scanners (TLS) are important techniques for surveying and mapping. UAV equipment is commonly used to collect 2D or 3D data acquisition. Meanwhile, TLS equipment is used for obtaining only 3D data acquisition. However, if both are integrated, they were able to produce more accurate data. Multi-sensor data fusion helps overcome the limitations of a single sensor and enables a complete 3D model for the structure and better object classification. This study focuses on studying the combination of UAV and TLS technologies to collect, process data, and create the complete point cloud between two point clouds of the high building in Ha Long city, Quang Ninh province to establish a 3D model at LoD 3 detail level, with high accuracy. FARO FOCUS3D X130 and DJI Phantom 4 RTK equipments were used to acquire the data in the field. The aerial and ground data were processed using FARO SCENE 2019 and Agisoft PhotoScan software, respectively. The data integration process is done by converting both point clouds into the same coordinate system and then by aligning the same points of both points clouds in Cloud Compare. The result of this study is a 3D model at LoD 3 detail level of the high building based on the point cloud accuracy in centimeter level. The combined use of UAV and TLS technologies has proven to be possible to create a highly accurate 3D model, at the 1:500 scale of urban areas according to current standards.

Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle (UAV) Laser Scanning (ULS) are both emerging technologies for rapidly capturing detailed 3D data of structures and environments. This study provides a comparative analysis between these two scanning techniques in terms of the accuracy and differences of the resulting 3D point clouds. A case study was conducted where TLS data was collected from ground-level scan positions while ULS data was captured through automated flights around the facility exterior. The point clouds from each platform were evaluated based on point density, geometric accuracy assessments, and ability to capture fine details. The TLS scans produced a highly accurate and detailed point cloud which was used as a benchmark in this study. The UAV scans exhibited less accuracy when compared to static TLS. However, the UAV was better able to capture hard-to-reach areas and provide a more complete model of the study site exterior. This research provides quantitative and qualitative comparisons between these scanning platforms to help determine the best approach based on requirements. The results will help professionals select the optimal scanning technology for generating the Digital Elevation Models (DEMs) depending on the application and accuracy requirements of the targeted DEMs.

3D modelling of buildings is an important task for getting geometrical knowledge of the building as well as for planning and monitoring purposes. The use of low-cost UAV (Unmanned Aerial Vehicle) derived point cloud is becoming a popular way for 3D model creation. To enhance the accuracy of such models generally researchers employ GCP’s(Ground Control Points), based on GNSS survey to georeference the point cloud. But using GCP’s has limitations like object’s accessibility & also it is time consuming. Another way to increase the accuracy of such point clouds is merging them with TLS(Terrestrial Laser Scanner) point cloud which is georeferenced using a geodetic GNSS. The research here is trying to achieve the same by fusing point clouds derived from low-cost UAV (Phantom 4 pro v.2) and TLS. This is achieved in 3 steps a) Metric based comparison b) C2C(Cloud to Cloud) & M3C2 (Multi scale Model to Model Cloud Comparison) algorithm-based comparison c) running linear regression for selected points on both the point clouds. An RMS(Root Mean Square) of 0.125 is achieved for Phantom pro metric-based comparison. The C2C & M3C2 algorithm-based comparison shows that most points in the facade of the building have almost zero error i.e., the point clouds are totally identical in these areas. Also, linear regression comparison gives very high R-squared value affirming the fact that point clouds obtained from UAV highly correlate to that obtained from TLS. Thus, based on above 3 comparison methods, it can be concluded that a UAV point cloud could be fused with a single view GNSS referenced TLS point cloud to obtain a complete 3D visualization of building.

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.

Abstract. In general, poor road conditions, specifically road cracks constitute a public nuisance, causing troublesome to road users, severe damage to vehicles and accidents. Hence, it is essential to detect the road crack earlier as an early-stage preventive for maintenance, but traditional inspection method used in Malaysia to physically collect the road information is extremely time-consuming, hazardous and labour-intensive. Nowadays, new technologies have improved the measuring performance. This leaves a gap in comparing two modern technologies in road cracks mapping. This study aims to explore the quality assessment between the unmanned aerial vehicle (UAV) and terrestrial laser scanning (TLS) methods in road cracks mapping. Two study areas inside the campus of Universiti Teknologi Malaysia (UTM), Johor, were selected. The ground control points (GCPs) and check points (CPs) at the study areas were georeferenced by global navigation satellite system (GNSS) data with MyRTKNet and ISKnet connections. Low altitude aerial imageries were collected using DJI Phantom 4 UAV, while Topcon GLS-2000 was employed to acquire the dense data of the cracked road. GNSS and manual inspection methods were performed to evaluate the results. In terms of mapping and measurement, both TLS and UAV methods produce almost similar results with average RMSE differences ranging from ±0.003 m to ±0.030 m and ±0.042 m to ±0.224 m respectively. This revealed that TLS is more accurate than UAV in mapping and measuring work. Differences in DTM quality across these approaches is below two (2) cm. Based on this study, TLS is more reliable than UAV. However, UAV offers advantages based on several considerations such as cost, time, safety, and accessibility. In summary, findings from this study shed some light to the authorities on the feasibility of UAV and TLS methods in road cracks mapping.

Forest structural parameters are key indicators for forest growth assessment, and play a critical role in forest resources monitoring and ecosystem management. Terrestrial laser scanning (TLS) can obtain three-dimensional (3D) forest structures with ultra-high precision without destruction, whereas some shortcomings such as non-portability and cost-consuming can limit the quick and broad acquisition of forest structure. Structure from motion (SfM) and backpack laser scanning (BLS) technology have the advantages of low-cost and high-portability while obtaining 3D structure information of forests. In this study, the high-overlapped images and the BLS point cloud, combined with the point cloud registration and individual tree segmentation to extract the forest structural parameters and compared with the TLS for assessing the accuracy and efficiency of low-cost SfM and portable BLS point clouds. Three plots with different forest structural complexity (coniferous, broadleaf and mixed plot) in the northern subtropical forests were selected. Firstly, portable photography camera, BLS and TLS were used to acquire 3D SfM and LiDAR point clouds, and spatial co-registration of different-sourced point cloud datasets were carried out based on the understory markers. Secondly, the point clouds of individual tree trunk and crown were segmented by the comparative shortest-path algorithm (CSP), and then the height and position of individual tree were extracted based on the tree crown point cloud. Thirdly, the trunk diameter at different heights were calculated by point cloud slices using the density-based spatial clustering of applications with noise (DBSCAN) algorithm, and combined with the stem curve of individual tree which was constructed using four Taper equations to estimate the individual tree volume. Finally, the extraction accuracy of forest structural parameters based on SfM and BLS point clouds were verified and comprehensively compared with field-measured and TLS data. The results showed that: (1) the individual tree segmentation based on SfM and BLS point clouds all performed quite well, among which the segmentation accuracy (F) of SfM point cloud was 0.80 and the BLS point cloud was 0.85; and (2) the accuracy of DBH and tree height extraction based on the SfM and BLS point clouds in comparison with the field-measured data were relatively high. The root mean square error (RMSE) of DBH and tree height extraction based on SfM point cloud were 2.15 cm and 4.08 m, and the RMSE of DBH and tree height extraction based on BLS point cloud were 2.06 cm and 1.63 m. This study shows that with the adopted image capture method, terrestrial SfM photogrammetry can be applied quite well in extracting DBH.

Structural complexity of trees is related to various ecological processes and ecosystem services. To support management for complexity, there is a need to assess the level of structural complexity objectively. The fractal-based box dimension (Db) provides a holistic measure of the structural complexity of individual trees. This study aimed to compare the structural complexity of Scots pine (Pinus sylvestris L.) trees assessed with Db that was generated with point cloud data from terrestrial laser scanning (TLS) and aerial imagery acquired with an unmanned aerial vehicle (UAV). UAV imagery was converted into point clouds with structure from motion (SfM) and dense matching techniques. TLS and UAV measured Db-values were found to differ from each other significantly (TLS: 1.51 ± 0.11, UAV: 1.59 ± 0.15). UAV measured Db-values were 5% higher, and the range was wider (TLS: 0.81–1.81, UAV: 0.23–1.88). The divergence between TLS and UAV measurements was found to be explained by the differences in the number and distribution of the points and the differences in the estimated tree heights and number of boxes in the Db-method. The average point density was 15 times higher with TLS than with UAV (TLS: 494,000, UAV 32,000 points/tree), and TLS received more points below the midpoint of tree heights (65% below, 35% above), while UAV did the opposite (22% below, 78% above). Compared to the field measurements, UAV underestimated tree heights more than TLS (TLS: 34 cm, UAV: 54 cm), resulting in more boxes of Db-method being needed (4–64%, depending on the box size). Forest structure (two thinning intensities, three thinning types, and a control group) significantly affected the variation of both TLS and UAV measured Db-values. Still, the divergence between the two approaches remained in all treatments. However, TLS and UAV measured Db-values were consistent, and the correlation between them was 75%.

In recent years, three-dimensional (3D) models are being built in many fields including mining. These products are often used to develop a database of smart mines which in terms can be used in the management of production in underground coal mines. Unmanned aerial vehicle (UAV) and terrestrial laser scanning (TLS) technologies are known as the two main technologies that quickly and accurately collect 3D point cloud (PC) data. This article presents the integration of a 3D point cloud produced from UAV photos and TLS to build a detailed 3D model for the ground plant at the level of +35 m in the Nui Beo underground coal mine. To collect data, a DJI Phantom 4 Advanced drone was used to take photos in three modes: a shot angle of 900, a 3D grid with a 450 angle, and a circular flight orbit with 450 and 600 shooting angles. A Faro Focus3D X130 laser scanner was used for scanning the mine shaft’s tower to fill the missing point cloud of the UAV. The PC established by both methods was evaluated for accuracy based on the control points measured by a Leica TS09 total station, which was merged by the Iterative Closest Point (ICP) algorithm. The integrated PC met the accuracy requirement of establishing a 3D model of the study area with the level of detail 3 in the CityGML standard.

The rapid development of the construction world in Indonesia has led to an increase in supporting technology that is more effective and efficient. The Building Information Model (BIM) technology that begins with the creation of an as-built 3D model, this model describes the existing condition of the building. The Terrestrial Laser Scanner (TLS) method can provide a point cloud with a decent point density, but there are still areas of the building that aren't covered, such as the roof. To be more complete and detailed, additional data is needed using an Unmanned Aerial Vehicle (UAV). The results of the combination of TLS and UAV complement each other so that the results of the point cloud can form more detailed buildings. BIM may be built by combining these two data sets, allowing for the three-dimensional depiction of assets in buildings. The registration results for TLS point cloud data have a fairly good value where the overlap value is 44.9% (minimum 30%), balance is 41.2% (minimum 20%), points < 6mm is 98.9% (minimum 90%). The measurement results using the UAV have an RMSE GCP value of 0.266m and an RMSE ICP of 0.455m. Merging the results of TLS and UAV measurements is done using 3DReshaper software with four align points. The final result of making the BIM model is obtained level of detail (LOD) 3 where room models such as columns, floors, stairs, and walls are well depicted, while asset models such as furniture are also depicted although they are still simple objects.

Calibration and validation of aboveground biomass (AGB) (AGB) products retrieved from satellite-borne sensors require accurate AGB estimates across hectare scales (1 to 100ha). Recent studies recommend making use of non-destructive terrestrial laser scanning (TLS) based techniques for individual tree AGB estimation that provide unbiased AGB predictors. However, applying these techniques across large sites and landscapes remains logistically challenging. Unoccupied aerial vehicle laser scanning (UAV-LS) has the potential to address this through the collection of high density point clouds across many hectares, but estimation of individual tree AGB based on these data has been challenging so far, especially in dense tropical canopies. In this study, we investigated how TLS and UAV-LS can be used for this purpose by testing different modelling strategies with data availability and modelling framework requirements. The study included data from four forested sites across three biomes: temperate, wet tropical, and tropical savanna. At each site, coincident TLS and UAV-LS campaigns were conducted. Diameter at breast height (DBH) and tree height were estimated from TLS point clouds. Individual tree AGB was estimated for ≥170 trees per site based on TLS tree point clouds and quantitative structure modelling (QSM), and treated as the best available, non-destructive estimate of AGB in the absence of direct, destructive measurements. Individual trees were automatically segmented from the UAV-LS point clouds using a shortest-path algorithm on the full 3D point cloud. Predictions were evaluated in terms of individual tree root mean square error (RMSE) and population bias, the latter being the absolute difference between total tree sample population TLS QSM estimated AGB and predicted AGB. The application of global allometric scaling models (ASM) at local scale and across data modalities, i.e., field-inventory and light detection and ranging LiDAR metrics, resulted in individual tree prediction errors in the range of reported studies, but relatively high population bias. The use of adjustment factors should be considered to translate between data modalities. When calibrating local models, DBH was confirmed as a strong predictor of AGB, and useful when scaling AGB estimates with field inventories. The combination of UAV-LS derived tree metrics with non-parametric modelling generally produced high individual tree RMSE, but very low population bias of ≤5% across sites starting from 55 training samples. UAV-LS has the potential to scale AGB estimates across hectares with reduced fieldwork time. Overall, this study contributes to the exploitation of TLS and UAV-LS for hectare scale, non-destructive AGB estimation relevant for the calibration and validation of space-borne missions targeting AGB estimation.