Terrestrial laser scanning point clouds as a data source for geometric analysis and inventory of hydraulic structures

The need for GCPs is increasingly more important with the increase in higher accuracy requirements and increase in user expectations. GCPs (Ground control points) are necessary for orientation and placement of photographs and 3D models in the spatial coordinate system, and they play a key role in co-registration of two point clouds. This paper deals with the assessment of the role of Ground Control Points in Co-registration of CRP and TLS point clouds by point-pair selection methodology and Automatic co-registration algorithm. In this work, the point cloud is generated from multiple overlapping sequences of images using Close-range Photogrammetry (CRP) and Terrestrial Laser Scanning (TLS) for a building over the planar surface. GCPs were collected by the total station to register the TLS and CRP point cloud. Overlapping photographs are processed in Agisoft PhotoScan software. TLS point cloud was generated from Riegl VZ 400 and GCPs were used to geo-reference it in Cloud Compare software. Various subsets of both point clouds are co-registered by the point-pair co-registration method and by Automatic point detection fine co-registration. Two subsets for each of CRP and TLS point cloud are considered in such a way that one is having some common overlap and other is having no common feature. GCPs registered point clouds integrate precisely as compared to that of the non-registered point cloud. The RMS error achieved in case of geo-referenced point cloud co-registration was 0.0091645433 m and in non-geo-referenced co-registration was found to be 0.03327466 m. In this study, it was also found that error is significantly higher in Automatic point detection method as compared to that of conventional point-pair selection co-registration. From this study, it is observed that higher accuracy of co-registration is achieved in the case of geo-referenced models by point-pair selection method. So, GCPs are the prerequisite for the effective and precise co-registration of 3D point clouds.

Airborne laser scanning (ALS) and terrestrial laser scanning (TLS) are two ways to obtain forest three-dimensional (3D) spatial information. Due to canopy occlusion and the features of different scanning methods, some of the forest point clouds acquired by a single scanning platform may be missing, resulting in an inaccurate estimation of forest structure parameters. Hence, the registration of ALS and TLS point clouds is an alternative for improving the estimation accuracy of forest structure parameters. Currently, forest point cloud registration is mainly conducted based on individual tree attributes (e.g., location, diameter at breast height, and tree height), but the registration is affected by individual tree segmentation and is inefficient. In this study, we proposed a method to automatically fuse ALS and TLS point clouds by using feature points of canopy gap shapes. First, the ALS and TLS canopy gap boundary vectors were extracted by the canopy point cloud density model, and the turning or feature points were obtained from the canopy gap vectors using the weighted effective area (WEA) algorithm. The feature points were then aligned, the transformation parameters were solved using the coherent point drift (CPD) algorithm, and the TLS point clouds were further aligned using the recovery transformation matrix and refined by utilizing the iterative closest point (ICP) algorithm. Finally, individual tree segmentations were performed to estimate tree parameters using the TLS and fusion point clouds, respectively. The results show that the proposed method achieved more accurate registration of ALS and TLS point clouds in four plots, with the average distance residuals of coarse and fine registration of 194.83 cm and 2.14 cm being much smaller compared with those from the widely used crown feature point-based method. Using the fused point cloud data led to more accurate estimates of tree height than using the TLS point cloud data alone. Thus, the proposed method has the potential to improve the registration of ALS and TLS point cloud data and the accuracy of tree height estimation.

A comparison between TLS and UAS LiDAR to represent eucalypt crown fuel characteristics

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 effective extraction of shape features is an important requirement for the accurate and efficient classification of terrestrial laser scanning (TLS) point clouds. However, the challenge of how to obtain robust and discriminative features from noisy and varying density TLS point clouds remains. This paper introduces a novel multiscale and hierarchical framework, which describes the classification of TLS point clouds of cluttered urban scenes. In this framework, we propose multiscale and hierarchical point clusters (MHPCs). In MHPCs, point clouds are first resampled into different scales. Then, the resampled data set of each scale is aggregated into several hierarchical point clusters, where the point cloud of all scales in each level is termed a point-cluster set. This representation not only accounts for the multiscale properties of point clouds but also well captures their hierarchical structures. Based on the MHPCs, novel features of point clusters are constructed by employing the latent Dirichlet allocation (LDA). An LDA model is trained according to a training set. The LDA model then extracts a set of latent topics, i.e., a feature of topics, for a point cluster. Finally, to apply the introduced features for point-cluster classification, we train an AdaBoost classifier in each point-cluster set and obtain the corresponding classifiers to separate the TLS point clouds with varying point density and data missing into semantic regions. Compared with other methods, our features achieve the best classification results for buildings, trees, people, and cars from TLS point clouds, particularly for small and moving objects, such as people and cars.

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.

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.

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.

Extracting moso bamboo parameters from single-source point cloud data has limitations. In this article, a new approach for extracting moso bamboo parameters using airborne laser scanning (ALS) and terrestrial laser scanning (TLS) point cloud data is proposed. Using the field-surveyed coordinates of plot corner points and the Iterative Closest Point (ICP) algorithm, the ALS and TLS point clouds were aligned. Considering the difference in point distribution of ALS, TLS, and the merged point cloud, individual bamboo plants were segmented from the ALS point cloud using the point cloud segmentation (PCS) algorithm, and individual bamboo plants were segmented from the TLS and the merged point cloud using the comparative shortest-path (CSP) method. The cylinder fitting method was used to estimate the diameter at breast height (DBH) of the segmented bamboo plants. The accuracy was calculated by comparing the bamboo parameter values extracted by the above methods with reference data in three sample plots. The comparison results showed that by using the merged data, the detection rate of moso bamboo plants could reach up to 97.30%; the R2 of the estimated bamboo height was increased to above 0.96, and the root mean square error (RMSE) decreased from 1.14 m at most to a range of 0.35-0.48 m, while the R2 of the DBH fit was increased to a range of 0.97-0.99, and the RMSE decreased from 0.004 m at most to a range of 0.001-0.003 m. The accuracy of moso bamboo parameter extraction was significantly improved by using the merged point cloud data.

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

A hierarchical methodology for urban facade parsing from TLS point clouds

Large-scale terrestrial laser scanning (TLS) point clouds are increasingly used for applications such as digital twins and cultural heritage documentation; however, removing unwanted human points captured during acquisition remains a largely manual and time-consuming process. This study proposes a geometry-aware framework for automatically removing human noise from TLS point clouds by projecting 2D instance segmentation masks (obtained using You Only Look Once (YOLO) v8 with an instance segmentation head) into 3D space and validating candidates through multi-stage geometric filtering. To suppress false positives induced by reprojection misalignment and planar background structures (e.g., walls and ground), we introduce projection-followed geometric validation (or "geometric gating") using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and principal component analysis (PCA)-based planarity analysis, followed by cluster-level plausibility checks. Experiments were conducted on two real-world outdoor TLS datasets-(i) Osaka Metropolitan University Sugimoto Campus (OMU) (82 scenes) and (ii) Jinaimachi historic district in Tondabayashi (JM) (68 scenes). The results demonstrate that the proposed method achieves high noise removal accuracy, obtaining precision/recall/intersection over union (IoU) of 0.9502/0.9014/0.8607 on OMU and 0.8912/0.9028/0.8132 on JM. Additional experiments on mobile mapping system (MMS) data from the Waymo Open Dataset demonstrate stable performance without parameter recalibration. Furthermore, quantitative and qualitative comparisons with representative time-series geometric dynamic object removal methods, including DUFOMap and BeautyMap, show that the proposed approach maintains competitive recall under a human-only ground-truth definition while reducing over-removal of static structures in TLS scenes, particularly when humans are observed in only one or a few scans due to limited revisit frequency. The end-to-end processing time with YOLOv8 was 935.62 s for 82 scenes (11.4 s/scene) on OMU and 571.58 s for 68 scenes (8.4 s/scene) on JM, supporting practical efficiency on high-resolution TLS imagery. Ablation studies further clarify the role of each stage and indicate stable performance under the observed reprojection errors. The annotated human point cloud dataset used in this study has been publicly released to facilitate reproducibility and further research on human noise removal in large-scale TLS scenes.

The fully automatic registration of 3D terrestrial laser scanning (TLS) point clouds, which is the first step in the usage of point clouds, is a highly challenging task in Light Detection and Ranging (LiDAR) remote sensing applications. Here, an automatic algorithm for pairwise coarse registration of TLS point clouds using 3D line features is proposed. First, the 3D line sets were extracted from the original pair of point clouds respectively, and two arbitrary lines from a point cloud were used to construct a 2-line base; Then, a pair of conjugate 2-line bases were identified from the source and target 3D line sets at a time, based on which a 3D rotation matrix together with its corresponding overlap between the pairwise 3D line sets was calculated; Third, a series of 3D rotation matrixes together with their overlaps were obtained using the traversing strategy to identify conjugate 2-line base pairs, and the 3D rotation matrix with the highest overlap was outputted as the final 3D rotation matrix, which was further used to compute the corresponding lines set from the pairwise 3D line sets; Next, based on the set of common perpendicular midpoints between the line correspondences, the 3D translation vector was computed; Finally, the 3D transformation matrix between pairwise point clouds was obtained by combining the 3D rotation matrix and the 3D translation vector. The proposed method was tested on three different TLS datasets, with experimental results demonstrating that the proposed algorithm could perform well on registering pairwise TLS point clouds. The rotation and translation errors of aligning the nine experimental pairwise point clouds were all less than 0.50° and 0.55m, respectively. This registration framework was also shown to be superior to state-of-the-art methods in terms of registration accuracy.

Gaining insight into the wood quality of standing timber could facilitate more precise utilization of wood material, thereby promoting a more sustainable use of forest resources. In this study, we utilized convolutional neural network–based object detectors to segment individual branch whorls and sawlog sections from images derived from terrestrial laser scanning (TLS) point clouds. TLS was employed to capture the point clouds of 479 Norway spruce sample trees (Picea abies (L.) H. Karst.) from 14 stands in southeastern Finland. Subsequently, the trees were harvested and the sawlogs measured with X-ray at an industrial sawmill. The convolutional neural network–based branch whorl detector was trained with 2D images of the stem sections of trees in the TLS point clouds from which the branch whorls were manually annotated. The sawlog section detector was trained with 2D TLS images of whole trees in which sawlogs were automatically annotated, using the sawmill measurements. Comparing the detections of the whorl detector with those of the X-ray measurements yielded a root-mean-squared error of 7.73 (64.41%) for the whorl count. Additionally, we conducted further comparison of the detections against a dataset in which the whorls were manually measured from the TLS images, resulting in a root-mean-squared error of 3.99 (20.60%). The detections made by the sawlog detector in the TLS images of whole trees were utilized to calculate the predicted log length and volume, which were then compared with the sawmill measurements of the reference logs. In this comparison, the root-mean-squared error of log length was 0.73 m (15.18%), and that of volume was 0.10 m3 (36.62%). The results indicate that the whorl detector can be utilized for extracting branching features of standing timber that can serve as predictors of the internal wood quality. However, directly depicting the internal knot structure with external branch whorl detections poses a challenge. Additionally, while the sawlog detector demonstrated moderate performance in sawlog segmentation, the accuracies of the predicted log length and volume were relatively weak. Nevertheless, we anticipate that deep learning–based approaches can enhance the autonomous characterization of standing timber, e.g. when laser scanners in harvesters become more commonplace.

Over the last two decades, terrestrial light detection and ranging (LiDAR), also known as terrestrial laser scanning (TLS) has become a valuable tool in assessing the woody structure of trees, in a method that is accurate, non-destructive, and replicable. This technique provides the ability to scan an area, and utilizes specialized software to create highly detailed 3D point cloud representations of its surroundings. Although the original usage of LiDAR was for precision survey applications, researchers have begun to apply LiDAR to forest research. Tree metrics can be extracted from TLS tree point clouds, and in combination with structure modelling, can be used to extract tree volume, aboveground biomass (AGB), growth, species, and to understand ecological questions such as tree mechanics, branching architecture, and surface area. TLS can provide a robust and rapid assessment of tree characteristics. These characteristics will improve current global efforts to measure forest carbon emissions, understand their uncertainties, and provide new insight into tropical forest ecology. Thus, the main objective of this PhD is to explore the use of 3D models from terrestrial laser scanning point clouds to estimate biomass and architecture of tropical trees. TLS-derived biomass and TLS-derived architecture can potentially be used to generate significant quality data for a better understanding of ecological challenges in tropical forests.