STUDY OF AIRCRAFT EXTERIOR GEOMETRY BY PHOTOGRAMMETRY AND LASER SCANNING USING A SELECTED EXAMPLE

Quantifying tropical forest structure through terrestrial and UAV laser scanning fusion in Australian rainforests

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.

Information on a crop’s three-dimensional (3D) structure is important for plant phenotyping and precision agriculture (PA). Currently, light detection and ranging (LiDAR) has been proven to be the most effective tool for crop 3D characterization in constrained, e.g., indoor environments, using terrestrial laser scanners (TLSs). In recent years, affordable laser scanners onboard unmanned aerial systems (UASs) have been available for commercial applications. UAS laser scanners (ULSs) have recently been introduced, and their operational procedures are not well investigated particularly in an agricultural context for multi-temporal point clouds. To acquire seamless quality point clouds, ULS operational parameter assessment, e.g., flight altitude, pulse repetition rate (PRR), and the number of return laser echoes, becomes a non-trivial concern. This article therefore aims to investigate DJI Zenmuse L1 operational practices in an agricultural context using traditional point density, and multi-temporal canopy height modeling (CHM) techniques, in comparison with more advanced simulated full waveform (WF) analysis. Several pre-designed ULS flights were conducted over an experimental research site in Fargo, North Dakota, USA, on three dates. The flight altitudes varied from 50 m to 60 m above ground level (AGL) along with scanning modes, e.g., repetitive/non-repetitive, frequency modes 160/250 kHz, return echo modes (1n), (2n), and (3n), were assessed over diverse crop environments, e.g., dry corn, green corn, sunflower, soybean, and sugar beet, near to harvest yet with changing phenological stages. Our results showed that the return echo mode (2n) captures the canopy height better than the (1n) and (3n) modes, whereas (1n) provides the highest canopy penetration at 250 kHz compared with 160 kHz. Overall, the multi-temporal CHM heights were well correlated with the in situ height measurements with an R2 (0.99–1.00) and root mean square error (RMSE) of (0.04–0.09) m. Among all the crops, the multi-temporal CHM of the soybeans showed the lowest height correlation with the R2 (0.59–0.75) and RMSE (0.05–0.07) m. We showed that the weaker height correlation for the soybeans occurred due to the selective height underestimation of short crops influenced by crop phonologies. The results explained that the return echo mode, PRR, flight altitude, and multi-temporal CHM analysis were unable to completely decipher the ULS operational practices and phenological impact on acquired point clouds. For the first time in an agricultural context, we investigated and showed that crop phenology has a meaningful impact on acquired multi-temporal ULS point clouds compared with ULS operational practices revealed by WF analyses. Nonetheless, the present study established a state-of-the-art benchmark framework for ULS operational parameter optimization and 3D crop characterization using ULS multi-temporal simulated WF datasets.

Automated markerless registration of point clouds from TLS and structured light scanner for heritage documentation

By simulating laser scanning of dynamic tree scenes, we investigate how tree movement during point cloud acquisition affects the accuracy of a range of tree metrics.Terrestrial laser scanning (TLS) has proven to be an effective surveying method for forestry and ecology, producing highly detailed 3D point clouds of trees. From these point clouds, a variety of metrics can be derived, such as tree and crown dimensions, stem diameter and taper, foliage parameters, and woody volume. In this way, TLS supports traditional forest inventory and monitoring, and provides valuable in-situ data for the calibration of remote sensing approaches.Typically, TLS point clouds are acquired from multiple scan positions to increase coverage and minimise occlusion. Scans from these positions are then co-registered and merged into a single point cloud. If wind is blowing during data acquisition and branches and leaves are moving, the merged point clouds may show multiple or blurred representations of branches and leaves. This is likely to affect the quality of the tree information derived from the point clouds. Although this problem is well known, few studies have systematically investigated the effect of vegetation movement during the scanning process on the derived tree metrics.The aim of this work is to quantify the errors induced by vegetation movement during TLS acquisition on a variety of metrics. We also investigate the extent to which point cloud filtering methods and the omission of 'problematic' scan positions can improve metric accuracies.To enable a systematic and controlled investigation, we use virtual laser scanning (VLS) with the open-source laser scanning simulator HELIOS++ [1, 2]. We first generate synthetic 3D tree models using procedural modelling [3, 4]. These tree models are then animated in different scenarios by simulating different wind conditions. For each wind scenario, the trees are virtually scanned from multiple positions, each scan being performed at a randomly sampled frame of the animation. From the simulated multi-scan TLS point clouds, we estimate several point cloud metrics, both with and without prior point cloud filtering. We compare the metrics with metrics derived from the reference meshes or point clouds.Performing such an analysis in a simulation environment has several major strengths: a) we can isolate the wind effects from other errors such as co-registration errors, b) we can define arbitrary custom wind scenarios and do not need to carry out real wind measurements, and c) reference data is available in the form of the input 3D tree models and base simulations without wind.We demonstrate how VLS can be used to investigate wind effects, which are a common source of error and uncertainty in TLS of vegetation. This not only allows us to develop strategies to account for these effects, but also informs us of the importance of modelling these effects when using VLS in other contexts, such as algorithm development or machine learning.REFERENCES[1] HELIOS++: https://github.com/3dgeo-heidelberg/helios[2] Winiwarter, L., et al. (2022): DOI: https://doi.org/10.1016/j.rse.2021.112772[3] Sapling Tree Gen: https://docs.blender.org/manual/en/latest/addons/add_curve/sapling.html[4] Weber, J. & Penn, J. (1995): DOI: https://doi.org/10.1145/218380.218427

A 3D measurement, such as a terrestrial laser scanning, is applied for an advanced infrastructure management and Building Information Modeling (BIM). Although a terrestrial laser scanning can acquire massive point cloud data for the BIM, 3D measurement using high precision LiDAR is affected by slab-vending with active loading, such as vehicle movements on a bridge. Thus, stripy noises occur in acquired point clouds. Therefore, we proposed three methodologies, such as multiple data subtraction, plane estimation with Least Median of Squares (LMeds), and noise pattern estimation, to remove the stripy noises for precise 3D bridge modeling. Our three methodologies were verified using terrestrial LiDAR data taken under a road bridge. Through our experiments, we confirmed that our algorithms can automatically cancel the vending affect of bridge in laser scanning works.

The demand for high-definition surveys within cultural heritage-related projects represents one of the main factors that promoted the use of laser scanning technology and photogrammetry. By measuring millions of points within relatively short time periods, terrestrial laser scanners (TLS) allows to researchers to derive complete and very detailed three-dimensional (3D) models of real objects from acquired point clouds. These features drew in recent years the interest of surveyors, engineers, architects, and archaeologists towards the laser scanning technique as an invaluable surveying tool for 3D modeling of sites and artifacts of cultural heritage. A wide variety of objects, such as small pieces of pottery, statues, buildings, and large areas of archaeological sites, have been scanned and modeled for various purposes like preservation, reconstruction, study, and museum exhibitions. However, the use of TLS systems for stability control is still a research field not much investigated. In the view of in-depth investigation on this topic, a 3-years project has been established to evaluate the use of multiple surveying techniques for the stability control of a complex historical structure. To this aim, TLS, total station (TS) and photogrammetry are being employed for stability control monitoring with finite element model (FEM) analysis applied to an historical building, Teatro Olimpico (Olympic Theatre), in Vicenza, Italy. The main goal of this work is to analyze and verify the stability over time of this kind of structure by applying FEM analysis to a highly detailed 3D model of the theater. To date, three consecutive surveys of the theater have been carried out with consumer digital-reflex camera Nikon D200, a Leica Laser Scanner (HDS 3000) and a Leica Total Station (TCR 705). The first survey comprised approximately 250 pictures to derive a global and complete 3D model of theater with an inexpensive measuring and modeling photogrammetry software (Photomodeler). In the second study, the historical structure was fully surveyed with a TLS, the Leica HDS 3000, to produce a complete 3D model. A set of scans of complex elements, such as the wood trompe l'oeil onstage scenery, statues, and fine trim, were acquired in this stage. This article presents the results from the repeated surveys and highlights the issues and difficulties related to the laser scanning and photogrammetry of an unusual and complex geometry such as the one provided by the Olympic Theater in Vicenza.

SLAM-aided forest plot mapping combining terrestrial and mobile laser scanning

Abstract. Three-dimensional (3D) models of historical buildings are created for documentation and virtual realization of them. Laser scanning and photogrammetry are extensively used to perform for these aims. The selection of the method that will be used in threedimensional modelling study depends on the scale and shape of the object, and also applicability of the method. Laser scanners are high cost instruments. However, the cameras are low cost instruments. The off-the-shelf cameras are used for taking the photogrammetric images. The camera is imaging the object details by carrying on hand while the laser scanner makes ground based measurement. Laser scanner collect high density spatial data in a short time from the measurement area. On the other hand, image based 3D (IB3D) measurement uses images to create 3D point cloud data. The image matching and the creation of the point cloud can be done automatically. Historical buildings include more complex details. Thus, all details cannot be measured by terrestrial laser scanner (TLS) due to the blocking the details with each others. Especially, the artefacts which have complex shapes cannot be measured in full details. They cause occlusion on the point cloud model. However it is possible to record photogrammetric images and creation IB3D point cloud for these areas. Thus the occlusion free 3D model is created by the integration of point clouds originated from the TLS and photogrammetric images. In this study, usability of laser scanning in conjunction with image based modelling for creation occlusion free three-dimensional point cloud model of historical building was evaluated. The IB3D point cloud was created in the areas that could not been measured by TLS. Then laser scanning and IB3D point clouds were integrated in the common coordinate system. The registration point clouds were performed with the iterative closest point (ICP) and georeferencing methods. Accuracy of the registration was evaluated by convergency and its standard deviations for the ICP and residuals on the control points for the georeferencing method.

The warming climate, biodiversity loss, and escalating natural disturbances emphasize the need for sustainable forest management, which relies on understanding tree growth and competition. Laser scanning has opened new possibilities for measuring these processes. This thesis aims to develop approaches to evaluate stem and crown growth and competition using laser scanning point clouds, exploring their utility in assessing and quantifying competition dynamics and growth patterns in forest stands. Study I developed approaches for assessing stem and crown competition using terrestrial laser scanning (TLS) point clouds and investigated the effect of different thinning treatments on competition in Scots pine (Pinus sylvestris L.)-dominated forests. The results indicated that TLS-derived competition decreased across different thinning methods compared to the control plots for both moderate and intensive thinning. Thinning from below showed the greatest reduction in competition, followed by thinning from above and systematic thinning. Study I demonstrates that TLS provides an advanced solution for assessing tree crown characteristics and growing space, highlighting a novel approach to understanding competition between trees. Study II investigated the use of bi-temporal TLS and low-altitude airborne laser scanning (ALS), individually and in combination, to assess the relationship between tree stem volume growth (ΔV) and crown structure, including its change (ΔC), over a 7-year monitoring period. The results showed a strong correlation between ΔV and crown metrics (top height, projection area, and perimeter) for Scots pine. For Norway spruce, ΔV weakly correlated with 3D crown area (CA3D), volume (CV), and its change (ΔCV). Birch ΔV showed weak to moderate correlations with CA2D, crown perimeter, and ΔCV. Random Forest (RF) analyses revealed that changes in crown structure were important for explaining ΔV variations for Norway spruce and birch, while top height (CHmax) was the key metric for Scots pine. In conclusion, Study II showed that multisensor laser scanning data can serve to evaluate the relationships between ΔV and tree crown structure. Study III examined the utility of TLS and low-altitude ALS data in describing the competitive stress of individual trees using two approaches. The object-based approach quantified competition by identifying and characterizing neighboring trees, while the point cloud-based approach evaluated competition through point cloud structures representing competitive vegetation around a target tree. The results showed that object-based competition indices (CIs) correlated more strongly with in situ-based CIs compared to point cloud-based CIs and were more consistent between TLS and ALS. Overall, Study III demonstrated that TLS is effective for small-scale competition assessments, while low-altitude ALS has similar potential for describing competition on a large scale. This thesis demonstrates the capability of the developed laser scanning-based approaches to assess stem and crown growth and competition. It shows how TLS and ALS enhance our understanding of tree growth and their responses to neighboring trees, helping identify processes driving changes in forest dynamics. These findings offer concrete steps toward more precise and efficient forest management, although further refinement of the methodologies is needed to optimize their use across varying forest ecosystems.

Abstract. Slow moving deep-seated gravitational slope deformations are threatening infrastructure and economic wellbeing in mountainous areas. Accelerating landslides may end up in a catastrophic slope failure in terms of rapid rock avalanches. Continuous landslide monitoring enables the identification of critical acceleration thresholds, which are required in natural hazard management. Among many existing monitoring methods, laser scanning is a cost effective method providing 3D data for deriving three dimensional and areawide displacement vectors at certain morphological structures travelling on top of the landslide. Comparing displacements between selected observation periods allows the spatial interpretation of landslide acceleration or deceleration. This contribution presents five laser scanning datasets of the active Reissenschuh landslide (Tyrol, Austria) acquired by airborne laser scanning (ALS), terrestrial laser scanning (TLS) and Unmanned aerial vehicle Laser Scanning (ULS) sensors. Three observation periods with acquisition dates between 2008 and 2018 are used to derive area-wide displacement vectors. To ensure a most suitable displacement derivation between ALS, TLS and ULS platforms, an analysis investigating point cloud features within varying search radii is carried out, in order to identify a neighbourhood where common surfaces are represented platform independent or differences between the platforms are minimized. Consequent displacement vector estimation is done by ICP-Matching using morphological structures within the high resolution TLS and ULS point cloud. Displacements from the lower resolution ALS point cloud and TLS point cloud were determined using a modified version of the well-known image correlation (IMCORR) method working with point cloud derived shaded relief images combined with digital terrain models (DTM). The interplatform compatible analyses of the multi-temporal laser scanning data allows to quantify the area-wide displacement patterns of the landslide. Furthermore, changes of these displacement patterns over time are assessed area-wide. Spatially varying areas of landslide acceleration and deceleration in the order of ±15 cm a−1 between 2008 and 2017 and an area wide acceleration of up to 20 cm a−1 between 2016 and 2018 are identified. Continuing the existing time series with future ULS acquisitions may enable a more complete and detailed displacement monitoring using entirely represented objects within the point clouds.

Abstract. Different approaches and tools are required in Cultural Heritage Documentation to deal with the complexity of monuments and sites. The documentation process has strongly changed in the last few years, always driven by technology. Accurate documentation is closely relied to advances of technology (imaging sensors, high speed scanning, automation in recording and processing data) for the purposes of conservation works, management, appraisal, assessment of the structural condition, archiving, publication and research (Patias et al., 2008). We want to focus in this paper on the recording aspects of cultural heritage documentation, especially the generation of geometric and photorealistic 3D models for accurate reconstruction and visualization purposes. The selected approaches are based on the combination of photogrammetric dense matching and Terrestrial Laser Scanning (TLS) techniques. Both techniques have pros and cons and recent advances have changed the way of the recording approach. The choice of the best workflow relies on the site configuration, the performances of the sensors, and criteria as geometry, accuracy, resolution, georeferencing, texture, and of course processing time. TLS techniques (time of flight or phase shift systems) are widely used for recording large and complex objects and sites. Point cloud generation from images by dense stereo or multi-view matching can be used as an alternative or as a complementary method to TLS. Compared to TLS, the photogrammetric solution is a low cost one, as the acquisition system is limited to a high-performance digital camera and a few accessories only. Indeed, the stereo or multi-view matching process offers a cheap, flexible and accurate solution to get 3D point clouds. Moreover, the captured images might also be used for models texturing. Several software packages are available, whether web-based, open source or commercial. The main advantage of this photogrammetric or computer vision based technology is to get at the same time a point cloud (the resolution depends on the size of the pixel on the object), and therefore an accurate meshed object with its texture. After matching and processing steps, we can use the resulting data in much the same way as a TLS point cloud, but in addition with radiometric information for textures. The discussion in this paper reviews recording and important processing steps as geo-referencing and data merging, the essential assessment of the results, and examples of deliverables from projects of the Photogrammetry and Geomatics Group (INSA Strasbourg, France).

Interest in measuring forest biomass and carbon stock has increased as a result of the United Nations Framework Convention on Climate Change, and sustainable planning of forest resources is therefore essential. Biomass and carbon stock estimates are based on the large area estimates of growing stock volume provided by national forest inventories (NFIs). The estimates for growing stock volume based on the NFIs depend on stem volume estimates of individual trees. Data collection for formulating stem volume and biomass models is challenging, because the amount of data required is considerable, and the fact that the detailed destructive measurements required to provide these data are laborious. Due to natural diversity, sample size for developing allometric models should be rather large. Terrestrial laser scanning (TLS) has proved to be an efficient tool for collecting information on tree stems. Therefore, we investigated how TLS data for deriving stem volume information from single trees should be collected. The broader context of the study was to determine the feasibility of replacing destructive and laborious field measurements, which have been needed for development of empirical stem volume models, with TLS. The aim of the study was to investigate the effect of the TLS data captured at various distance (i.e. corresponding 25%, 50%, 75% and 100% of tree height) on the accuracy of the stem volume derived. In addition, we examined how multiple TLS point cloud data acquired at various distances improved the results. Analysis was carried out with two ways when multiple point clouds were used: individual tree attributes were derived from separate point clouds and the volume was estimated based on these separate values (multiple-scan A), and point clouds were georeferenced as a combined point cloud from which the stem volume was estimated (multiple-scan B). This permitted us to deal with the practical aspects of TLS data collection and data processing for development of stem volume equations in boreal forests. The results indicated that a scanning distance of approximately 25% of tree height would be optimal for stem volume estimation with TLS if a single scan was utilized in boreal forest conditions studied here and scanning resolution employed. Larger distances increased the uncertainty, especially when the scanning distance was greater than approximately 50% of tree height, because the number of successfully measured diameters from the TLS point cloud was not sufficient for estimating the stem volume. When two TLS point clouds were utilized, the accuracy of stem volume estimates was improved: RMSE decreased from 12.4% to 6.8%. When two point clouds were processed separately (i.e. tree attributes were derived from separate point clouds and then combined) more accurate results were obtained; smaller RMSE and relative error were achieved compared to processing point clouds together (i.e. tree attributes were derived from a combined point cloud). TLS data collection and processing for the optimal setup in this study required only one sixth of time that was necessary to obtain the field reference. These results helped to further our knowledge on TLS in estimating stem volume in boreal forests studied here and brought us one step closer in providing best practices how a phase-shift TLS can be utilized in collecting data when developing stem volume models.

Planning for terrestrial laser scanning in construction: A review

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.