Finite Element Analysis based on A Parametric Model by Approximating Point Clouds

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.

Accurate measurement of vegetation density metrics including plant, wood and leaf area indices (PAI, WAI and LAI) is key to monitoring and modelling carbon storage and uptake in forests. Traditional passive sensor approaches, such as Digital Hemispherical Photography (DHP), cannot separate leaf and wood material, nor individual trees, and require many assumptions in processing. Terrestrial Laser Scanning (TLS) data offer new opportunities to improve understanding of tree and canopy structure. Multiple methods have been developed to derive PAI and LAI from TLS data, but there is little consensus on the best approach, nor are methods benchmarked as standard. Using TLS data collected in 33 plots containing 2472 trees of five species in Mediterranean forests, we compare three TLS methods (LiDAR Pulse, 2D Intensity Image and Voxel-Based) to derive PAI and compare with co-located DHP. We then separate leaf and wood in individual tree point clouds to calculate wood to total plant area (α), a metric to correct for non-photosynthetic material in LAI estimates. We use individual tree TLS point clouds to estimate how α varies with species, tree height and stand density. We find the LiDAR Pulse method agrees most closely with DHP, but is limited to single scan data so cannot determine individual tree α. The Voxel-Based method shows promise for ecological studies as it can be applied to individual tree point clouds. Using the Voxel-Based method, we show that species explain some variation in α, however, height and density were stronger predictors. Our findings highlight the value of TLS data to improve fundamental understanding of tree form and function, but also the importance of rigorous testing of TLS data processing methods at a time when new approaches are being rapidly developed. New algorithms need to be compared against traditional methods, and existing algorithms, using common reference data. Whilst promising, our results show that metrics derived from TLS data are not yet reliably calibrated and validated to the extent they are ready to replace traditional approaches for large scale monitoring of PAI and LAI.

Accurate measurement of vegetation density metrics including plant, wood and leaf area indices (PAI, WAI and LAI) is key to monitoring and modelling carbon storage and uptake in forests. Traditional passive sensor approaches, such as Digital Hemispherical Photography (DHP), cannot separate leaf and wood material, nor individual trees, and require many assumptions in processing. Terrestrial Laser Scanning (TLS) data offer new opportunities to improve understanding of tree and canopy structure. Multiple methods have been developed to derive PAI and LAI from TLS data, but there is little consensus on the best approach, nor are methods benchmarked as standard. Using TLS data collected in 33 plots containing 2472 trees of five species in Mediterranean forests, we compare three TLS methods (LiDAR Pulse, 2D Intensity Image and Voxel-Based) to derive PAI and compare with co-located DHP. We then separate leaf and wood in individual tree point clouds to calculate wood to total plant area (α), a metric to correct for non-photosynthetic material in LAI estimates. We use individual tree TLS point clouds to estimate how α varies with species, tree height and stand density. We find the LiDAR Pulse method agrees most closely with DHP, but is limited to single scan data so cannot determine individual tree α. The Voxel-Based method shows promise for ecological studies as it can be applied to individual tree point clouds. Using the Voxel-Based method, we show that species explain some variation in α, however, height and density were stronger predictors. Our findings highlight the value of TLS data to improve fundamental understanding of tree form and function, but also the importance of rigorous testing of TLS data processing methods at a time when new approaches are being rapidly developed. New algorithms need to be compared against traditional methods, and existing algorithms, using common reference data. Whilst promising, our results show that metrics derived from TLS data are not yet reliably calibrated and validated to the extent they are ready to replace traditional approaches for large scale monitoring of PAI and LAI.

With the increasing use of terrestrial laser scanning (TLS) technology in the field of forest ecology, a large number of studies have been carried out on the separation of wood and leaves based on TLS point cloud data. However, most wood–leaf separation methods adopt the point‐wise classification strategy, which is not efficient for processing large‐volume TLS datasets acquired at the forest plot level. In this study, we proposed a segment‐wise classification strategy to improve the efficiency of the wood–leaf separation from large‐volume TLS point cloud datasets collected at the forest plot. The proposed method first decomposes the point cloud into three parts based on the threshold values of its local curvature. Then, the first two parts with lower local curvatures were segmented respectively by a connected component labelling algorithm. Finally, the segmented point clouds were classified into wood or leaf segments according to the segment‐wise geometric features of each segment. We tested our method on both needleleaf and broadleaf forest plots in temperate and tropical forests. We also compared our method with two other state‐of‐the‐art wood–leaf separation methods, that is, the CANUPO and LeWoS. The results showed that our method was more than 10 times faster than the compared methods while maintaining comparable and even higher accuracy. Our study demonstrates that the segment‐wise classification strategy applies to the large‐volume TLS datasets and can greatly improve the efficiency of the classification. The proposed method is simple, fast and universally applicable to the TLS data from various tree species and forest types at the plot level, which may facilitate the adoption of TLS technology by forest ecologists in their studies.

Mobile laser scanning (MLS) systems are often used to efficiently acquire reference data covering a large-scale scene. The terrestrial laser scanner (TLS) can easily collect high point density data of local scene. Localization of static TLS scans in mobile mapping point clouds can afford detailed geographic information for many specific tasks especially in autonomous driving and robotics. However, large-scale MLS reference data often have a huge amount of data and many similar scene data; significant differences may exist between MLS and TLS data. To overcome these challenges, this paper presents a novel deep neural network-based localization method in urban environment, divided by place recognition and pose refinement. Firstly, simple, reliable primitives, cylinder-like features were extracted to describe the global features of a local urban scene. Then, a probabilistic framework is applied to estimate a similarity between TLS and MLS data, under a stable decision-making strategy. Based on the results of a place recognition, we design a patch-based convolution neural network (CNN) (point-based CNN is used as kernel) for pose refinement. The input data unit is the batch consisting of several patches. One patch goes through three main blocks: feature extraction block (FEB), the patch correspondence search block and the pose estimation block. Finally, a global refinement was proposed to tune the predicted transformation parameters to realize localization. The research aim is to find the most similar scene of MLS reference data compared with the local TLS scan, and accurately estimate the transformation matrix between them. To evaluate the performance, comprehensive experiments were carried out. The experiments demonstrate that the proposed method has good performance in terms of efficiency, i.e., the runtime of processing a million points is 5 s, robustness, i.e., the success rate of place recognition is 100% in the experiments, accuracy, i.e., the mean rotation and translation error is (0.24 deg, 0.88 m) and (0.03 deg, 0.06 m) on TU Delft campus and Shanghai urban datasets, respectively, and outperformed some commonly used methods (e.g., iterative closest point (ICP), coherent point drift (CPD), random sample consensus (RANSAC)-based method).

Nowadays, 3D city models are used by the increasing number of applications. Most applications require not only geometric information but also semantic information. As a standard and tool for 3D city model, CityGML, provides a method for storing and managing both geometric and semantic information. Moreover, it also provides the multi-scale representation of 3D building model for efficient visualization. In CityGML, building models are represented in five LODs (Level of Detail), start from LOD0, LOD1, LOD2, LOD3, and LOD4. Each level has different accuracy and detail requirement for visualization. Usually, for obtaining multi-LOD of 3D building model, several data sources are integrated. For example, LiDAR data is used for generating LOD0, LOD1, and LOD2 as close-range photogrammetry data is used for generating more detailed model in LOD3 and LOD4. However, using additional data sources is increasing cost and time consuming. Since the development of TLS (Terrestrial Laser Scanner), data collection for detailed model can be conducted in a relative short time compared to photogrammetry. Point cloud generated from TLS can be used for generating the multi-LOD of building model. This paper gives an overview about the representation of 3D building model in CityGML and also method for generating multi-LOD of building from TLS data. An experiment was conducted using TLS. Following the standard in CityGML, point clouds from TLS were processed resulting 3D model of building in different level of details. Afterward, models in different LOD were converted into XML schema to be used in CityGML. From the experiment, final result shows that TLS can be used for generating 3D models of building in LOD1, LOD2, and LOD3.

Abstract. Monitoring of engineering structures is important for ensuring safety of operation. Traditional surveying methods have proven to be reliable; however, the advent of new point cloud technologies such as terrestrial laser scanning (TLS) and small unmanned aerial systems (sUAS) have provided an unprecedented wealth of data. Furthermore, simultaneous localization and mapping (SLAM) is now able to facilitate the collection of registered point clouds on the fly. SLAM is most successful when applied to indoor environments where the algorithm can identify primitives (points, planes, lines) for registration, but it can be problematic in outdoor settings where there is absence of constructed features. This work includes the collection of SLAM-based LiDAR data along a levee for the purpose of inspection and monitoring. Due to the outdoor setting and absence of man-made features, the resulting point cloud was considerably distorted due to erroneous drift in sensor orientation. A correction algorithm is proposed that relies on reference TLS point cloud data to remove drift distortions identified in the SLAM LiDAR. Results indicate an alignment between the corrected SLAM LiDAR and TLS data of around ±10cm, which is sufficient for general inspection and multi-epoch monitoring of levees. The algorithm is based on common points identified in the TLS and SLAM data and necessitates that the SLAM LiDAR is collected in individual, one-way lines to allow correction of distortions as a function of distance from the starting point. This approach increases the efficiency of LiDAR-based levee monitoring by reducing the time required to survey the levees.

Most conventional Leaf Area Index (LAI) retrieval methods using Terrestrial Laser Scanning (TLS) data are based on Beer’s law and are severely affected by the effects of leaf occlusion and aggregation. Moreover, the correction of LAI using the Clumping Index (CI) relies on assumptions and is generally not robust. This paper exploits the high spatial resolution and penetration capability of TLS to explore the physical meaning of point cloud data sampling and then model the leaf cluster envelope by the Alpha-shape algorithm. Subsequently, canopy LAI is obtained by counting the surface area of the envelope of each leaf cluster within the canopy and combining it with the projected area of the canopy. The entire process is physically based and introduces a new LAI inversion approach based on TLS. We tested the approach by simulating the TLS data of 25 synthetic trees with different leaf areas and morphologies to evaluate its robustness. Four strategies were adopted for parameter selection in the envelope modeling step to automate the process of finding the optimal envelope radius and improve the inversion accuracy of LAI. In comparison with the traditional LAI retrieval method based on Beer’s law (RMSE% is 47.3%), we found that the method proposed in this letter has a higher inversion accuracy with a minimum RMSE% of 27.7%. Our method also is significantly more robust for high LAI scenes and performs well in scenes with high occlusion and aggregation.

We present the point cloud slicing (PCS) algorithm, to post process point cloud data (PCD) from terrestrial laser scanning (TLS). We then test this tool for forest inventory application in urban heterogeneous forests. The methodology was based on a voxel data structure derived from TLS PCD. We retrieved biophysical tree parameters including diameter at breast height (DBH), tree height, basal area, and volume. Our results showed that TLS-based metrics explained 91.17% (RMSE = 9.1739 cm, p < 0.001) of the variation in DBH at individual tree level. Though the scanner generated a high-density PCD, only 57.27% (RMSE = 0.7543 m, p < 0.001) accuracy was achieved for predicting tree heights in these very heterogeneous stands. Furthermore, we developed a voxel-based TLS volume estimation method. Our results showed that PCD generated from TLS single location scans only captures 18% of the total tree volume due to an occlusion effect; yet there are significant relationships between the TLS data and field measured parameters for DBH and height, giving promise to the utility of a side scanning approach. Using our method, a terrestrial LiDAR-based inventory, also applicable to mobile- or vehicle-based laser scanning (MLS or VLS), was produced for future calibration of Aerial Laser Scanning (ALS) data and urban forest canopy assessments.

Terrestrial laser scanning (TLS) represents the gold standard in remote quantification of woody vegetation structure and volume, but is costly and time consuming to acquire. TLS data is typically collected at spatial scales of 1 ha or smaller, which limits its suitability for representing heterogeneous landscapes, and for training and validating satellite-based models which are needed for larger area monitoring. Advances in unoccupied aerial vehicle laser scanning (UAV-LS) sensors have recently narrowed the gap in quality between what TLS delivers and what can be acquired over larger areas from UAV platforms. We tested how well new nadir-forward–backward (NFB) UAV-LS technology can capture the structure of individual trees in a tropical savanna setting with a diversity of tree sizes and growth forms. UAV-LS data was acquired with a RIEGL VUX-120 LiDAR sensor mounted on a Acecore NOA hexacopter. Reference data was obtained with a RIEGL VZ-2000i TLS scanner using a multi-scan approach. Point clouds were segmented into individual trees and volumetrically reconstructed with RayCloudTools (RCT). We found no statistical difference between UAV-LS and TLS derived estimates of tree height, canopy cover, diameter, and wood volume. Mean tree height and DBH derived from UAV-LS were within 3% of the TLS estimate, and there was less than 1% deviation in stand wood volume. Our findings ease the advancements on the detailed monitoring of open forests, potentially achieving large-scale mapping and multi-temporal investigations. The open structure of savanna systems is well suited to UAV-LS sensing, but more research is needed across diverse ecosystems to understand the generality of these findings in landscapes with greater canopy closure or complex understorey conditions.

A critical component of the forest ecosystem, the understorey supports the vast majority of wildlife habitat and total ecosystem floristic diversity. Remote sensing data have been developed to provide information at different scales for surveys of forest ecosystems, but obtaining information about the understorey remains a challenge. As rapid and efficient tools for forest structure attribute estimation, Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS) have attracted much attention. We examine the relationship between ALS and TLS data and detect changes in the forest understorey caused by forest-tending events in the study area. We conducted trials in five plots within a young Khasi pine (Pinus kesiya Royle ex Gord.) plantation in Yunnan province, China, before and after forest tending. We collected bi-temporal ALS data in this area and TLS data from 10 scanning stations. Canopy height profiles were retrieved from ALS and TLS data, and understorey material volume retrieved from filled TLS voxels volume agreed well with the understorey point clouds percentile distribution (PD) obtained from ALS data. The PD value for the understorey from ALS multiplied by the percentage of ALS return points in the overstorey had a stronger correlation (R2 = 0.90) with the TLS-derived understorey material volume than did the ALS PD value for the understorey only (R2 = 0.71). Taking the effect of the overstorey into consideration will improve evaluations of forest understorey parameters from ALS data. This study confirmed the potential of TLS as a validation tool to assess the accuracy of forest understorey material volume estimation at large scales using ALS data.

Building Information Modelling (BIM) is a globally adapted methodology by government organisations and builders who conceive the integration of the organisation, planning, development and the digital construction model into a single project. In the case of a heritage building, the Historic Building Information Modelling (HBIM) approach is able to cover the comprehensive restoration of the building. In contrast to BIM applied to new buildings, HBIM can address different models which represent either periods of historical interpretation, restoration phases or records of heritage assets over time. Great efforts are currently being made to automatically reconstitute the geometry of cultural heritage elements from data acquisition techniques such as Terrestrial Laser Scanning (TLS) or Structure From Motion (SfM) into BIM (Scan-to-BIM). Hence, this work advances on the parametric modelling from remote sensing point cloud data, which is carried out under the Rhino+Grasshopper-ArchiCAD combination. This workflow enables the automatic conversion of TLS and SFM point cloud data into textured 3D meshes and thus BIM objects to be included in the HBIM project. The accuracy assessment of this workflow yields a standard deviation value of 68.28 pixels, which is lower than other author’s precision but suffices for the automatic HBIM of the case study in this research.

Abstract. Terrestrial laser scanning (TLS) systems have been established as a leading tool for the acquisition of high density three-dimensional point clouds from physical objects. The collected point clouds by these systems can be utilized for a wide spectrum of object extraction, modelling, and monitoring applications. Pole-like features are among the most important objects that can be extracted from TLS data especially those acquired in urban areas and industrial sites. However, these features cannot be completely extracted and modelled using a single TLS scan due to significant local point density variations and occlusions caused by the other objects. Therefore, multiple TLS scans from different perspectives should be integrated through a registration procedure to provide a complete coverage of the pole-like features in a scene. To date, different segmentation approaches have been proposed for the extraction of pole-like features from either single or multiple-registered TLS scans. These approaches do not consider the internal characteristics of a TLS point cloud (local point density variations and noise level in data) and usually suffer from computational inefficiency. To overcome these problems, two recently-developed PCA-based parameter-domain and spatial-domain approaches for the segmentation of pole-like features are introduced, in this paper. Moreover, the performance of the proposed segmentation approaches for the extraction of pole-like features from a single or multiple-registered TLS scans is investigated in this paper. The alignment of the utilized TLS scans is implemented using an Iterative Closest Projected Point (ICPP) registration procedure. Qualitative and quantitative evaluation of the extracted pole-like features from single and multiple-registered TLS scans, using both of the proposed segmentation approaches, is conducted to verify the extraction of more complete pole-like features using multipleregistered TLS scans.

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. Nowadays, Terrestrial Laser Scanning (TLS) technology is gaining popularity in monitoring and predicting the movement of landslide due to the capability of high-speed data capture without requiring a direct contact with the monitored surface. It offers very high density of point cloud data in high resolution and also can be an effective tool in detecting the surface movement of the landslide area. The aim of this research is to determine the optimal level of scanning resolution for landslide monitoring using TLS. The Topcon Geodetic Laser Scanner (GLS) 2000 was used in this research to obtain the three dimensional (3D) point cloud data of the landslide area. Four types of resolution were used during scanning operation which were consist of very high, high, medium and low resolutions. After done with the data collection, the point clouds datasets were undergone the process of registration and filtering using ScanMaster software. After that, the registered point clouds datasets were analyzed using CloudCompare software. Based on the results obtained, the accuracy of TLS point cloud data between picking point manually and computed automatically by ScanMaster software shows the maximum Root Mean Square (RMS) value of coordinate differences were 0.013 m in very high resolution, 0.017 m in high resolution, 0.031 m in medium resolution and 0.052 m in low resolution respectively. Meanwhile, the accuracy of TLS point cloud data between picking point manually and total station data using intersection method shows the maximum RMS values of coordinate differences were 0.013 m in very high resolution, 0.018 m in high resolution, 0.033 m in medium resolution and 0.054 m in low resolution respectively. Hence, it can be concluded that the high or very high resolution is needed for landslide monitoring using Topcon GLS-2000 which can provide more accurate data in slope result, while the low and medium resolutions is not suitable for landslide monitoring due to the accuracy of TLS point cloud data that will decreased when the resolution value is increased.