A COMPARISON OF UAV AND TLS DATA FOR SOIL ROUGHNESS ASSESSMENT

Soil roughness represents fine-scale surface geometry which figures in many geophysical models. While static photogrammetric techniques (terrestrial images and laser scanning) have been recently proposed as a new source for deriving roughness heights, there is still need to overcome acquisition scale and viewing geometry issues. By contrast to the static techniques, images taken from unmanned aerial vehicles (UAV) can maintain near-nadir looking geometry over scales of several agricultural fields. This paper presents a pilot study on high-resolution, soil roughness reconstruction and assessment from UAV images over an agricultural plot. As a reference method, terrestrial laser scanning (TLS) was applied on a 10 m x 1.5 m subplot. The UAV images were self-calibrated and oriented within a bundle adjustment, and processed further up to a dense-matched digital surface model (DSM). The analysis of the UAV- and TLS-DSMs were performed in the spatial domain based on the surface autocorrelation function and the correlation length, and in the frequency domain based on the roughness spectrum and the surface fractal dimension (spectral slope). The TLS- and UAV-DSM differences were found to be under ±1 cm, while the UAV DSM showed a systematic pattern below this scale, which was explained by weakly tied sub-blocks of the bundle block. The results also confirmed that the existing TLS methods leads to roughness assessment up to 5 mm resolution. However, for our UAV data, this was not possible to achieve, though it was shown that for spatial scales of 12 cm and larger, both methods appear to be usable. Additionally, this paper suggests a method to propagate measurement errors to the correlation length.

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.

This paper presents a practical framework for the integration of unmanned aerial vehicle (UAV) based photogrammetry and terrestrial laser scanning (TLS) with application to open-pit mine areas, which includes UAV image and TLS point cloud acquisition, image and cloud point processing and integration, object-oriented classification and three-dimensional (3D) mapping and monitoring of open-pit mine areas. The proposed framework was tested in three open-pit mine areas in southwestern China. (1) With respect to extracting the conjugate points of the stereo pair of UAV images and those points between TLS point clouds and UAV images, some feature points were first extracted by the scale-invariant feature transform (SIFT) operator and the outliers were identified and therefore eliminated by the RANdom SAmple Consensus (RANSAC) approach; (2) With respect to improving the accuracy of geo-positioning based on UAV imagery, the ground control points (GCPs) surveyed from global positioning systems (GPS) and the feature points extracted from TLS were integrated in the bundle adjustment, and three scenarios were designed and compared; (3) With respect to monitoring and mapping the mine areas for land reclamation, an object-based image analysis approach was used for the classification of the accuracy improved UAV ortho-image. The experimental results show that by introduction of TLS derived point clouds as GCPs, the accuracy of geo-positioning based on UAV imagery can be improved. At the same time, the accuracy of geo-positioning based on GCPs form the TLS derived point clouds is close to that based on GCPs from the GPS survey. The results also show that the TLS derived point clouds can be used as GCPs in areas such as in mountainous or high-risk environments where it is difficult to conduct a GPS survey. The proposed framework achieved a decimeter-level accuracy for the generated digital surface model (DSM) and digital orthophoto map (DOM), and an overall accuracy of 90.67% for classification of the land covers in the open-pit mine.

The 3D reconstruction of historical and cultural heritage monuments is a procedure recommended by the UNESCO World Heritage Institution since 1985. It is crucial when conserving monuments and creating digital twins. Current 3D reconstruction techniques using digital images and terrestrial laser scanning (TLS) data are considered as cost-effective and efficient methods for the production of high-quality digital 3D models. In the presented study, laser scanning and close-range photogrammetry techniques and images taken by a low-cost unmanned aerial vehicle (UAV) were applied to quickly and completely acquire the point cloud and texture of a historic church in Poland. The aim of this study was to evaluate two options for integrating TLS and UAV data, using ground control points (GCP) measured by two independent techniques: tachymetry and laser scanning. The study shows that the 3D model created based on ground control points acquired by the laser scanning technique has a mean square error RMSEXYZ = 2.5 cm on the check points. The result obtained is not much larger than the second variant of data integration, for which RMSEXYZ = 1.7 cm. Thus, the TLS method was positively evaluated as a GCP measurement technique for the integration of UAV and TLS data and the creation of cartometric 3D models of religious buildings.

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%.

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.

• Combining 3D simulation with UAV images can improve LCC inversion accuracy. • The inversion accuracy based on 3D model and UAV images rivals data-driven methods. • The 3D-UAV method reduces shadow, soil, and canopy effects on LCC inversion. • 3D model can effectively alleviate saturation under high LCC conditions. Leaf chlorophyll content (LCC) is a crucial parameter reflecting vegetation’s photosynthetic activity. Many LCC inversion algorithms based on satellite and unmanned aerial vehicle (UAV) data have been developed in recent decades. The one-dimensional radiative transfer model, like PROSAIL (1D model), has been a classic tool for LCC inversion. In recent years, three-dimensional radiative transfer models (3D model) have been developed rapidly. However, studies on 3D models for LCC inversion are limited, and their impact on inversion accuracy across different sensor resolutions remains unclear. This study focuses on winter wheat and integrates the DART, Adel-Wheat, and PROSPECT models to construct the 3D-model-derived look-up table (LUT). The 3D-model-based LUT and 1D-model-based LUT were applied to Sentinel-2 (S2) and UAV data to retrieve LCC. Validation results demonstrate that the 3D-model-based algorithm significantly improves LCC inversion accuracy for both S2 and UAV images. For UAV data, the root mean square error (RMSE) decreases from 9.90 μg/cm 2 to 7.97 μg/cm 2 , and the coefficient of determination (R 2 ) improves from 0.70 to 0.79. For S2 data, the RMSE decreases from 12.40 μg/cm 2 to 8.68 μg/cm 2 , while R 2 increases from 0.66 to 0.85. Additionally, overestimation at low LAI levels and underestimation at high LCC levels are effectively reduced. The high accuracy achieved under varying LAI and LCC conditions allows the 3D model to capture temporal trends throughout the growing season better. The 3D-model-based LCC inversion algorithm can better utilize the high spatial resolution advantages, thereby playing a significant role in vegetation physiological monitoring and crop phenotyping.

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.

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.

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. Crop monitoring is crucial for precision agriculture, providing insights for optimizing yield and managing resources effectively. This study explores the fusion of Unmanned Aerial Vehicle (UAV) and Sentinel-2 (S2) satellite imagery for monitoring the crop by analyzing vegetation indices and canopy height information from the temporal dataset. Brovey Transform (BT) and Principal Component Analysis (PCA) fusion techniques are used to fuse the UAV and satellite images, aiming to leverage the high spatial resolution of UAV imagery with the broader spectral range of S2 data. Five key vegetation indices, including NDVI, GNDVI, SAVI, EVI, and LAI, were calculated from UAV, S2, and fused imagery in various temporal dates. Canopy height was derived from UAV data, and statistical analyses, including coefficient of determination (R2), Pearson correlation coefficient, and Root Mean Square Error (RMSE), were performed to assess relationships between canopy height and vegetation indices across the fused images and UAV and S2 images. Results indicate that fused imagery significantly enhances crop health metrics' accuracy and spatial relevance, with high R2 values and strong correlations between vegetation indices of fused images and UAV images, suggesting enhanced predictive power in monitoring crop health. Our findings highlight the advantages of fusing UAV and S2 imagery for comprehensive crop condition assessment, demonstrating that fused images provide a robust tool for monitoring crop vigor and stress levels. This approach offers valuable support for timely, data-driven decisions in crop management practices.

Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest

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.

Terrestrial laser scanning (TLS) and unmanned aerial vehicles (UAVs) equipped with digital cameras have attracted much attention from the forestry community as potential tools for forest inventories and forest monitoring. This research fills a knowledge gap about the viability and dissimilarities of using these technologies for measuring the top of canopy structure in tropical forests. In an empirical study with data acquired in a Guyanese tropical forest, we assessed the differences between top of canopy models (TCMs) derived from TLS measurements and from UAV imagery, processed using structure from motion. Firstly, canopy gaps lead to differences in TCMs derived from TLS and UAVs. UAV TCMs overestimate canopy height in gap areas and often fail to represent smaller gaps altogether. Secondly, it was demonstrated that forest change caused by logging can be detected by both TLS and UAV TCMs, although it is better depicted by the TLS. Thirdly, this research shows that both TLS and UAV TCMs are sensitive to the small variations in sensor positions during data collection. TCMs rendered from UAV data acquired over the same area at different moments are more similar (RMSE 0.11–0.63 m for tree height, and 0.14–3.05 m for gap areas) than those rendered from TLS data (RMSE 0.21–1.21 m for trees, and 1.02–2.48 m for gaps). This study provides support for a more informed decision for choosing between TLS and UAV TCMs to assess top of canopy in a tropical forest by advancing our understanding on: (i) how these technologies capture the top of the canopy, (ii) why their ability to reproduce the same model varies over repeated surveying sessions and (iii) general considerations such as the area coverage, costs, fieldwork time and processing requirements needed.