Terrestrial Laser Scanning Data Research Articles

UNSUPERVISED STATISTICAL APPROACH FOR TREE-LEVEL SEPARATION OF FOLIAGE AND NON-LEAF COMPONENTS FROM POINT CLOUDS

Abstract. Accurately classifying foliage and non-leaf components in point clouds is essential for remote sensing forest applications. Existing methods rely on radiometric attributes or local geometric features, often requiring time-consuming manual labelling. In this paper, we propose a statistical approach using flexible thresholds determined based on tree-level geometric features. Selected features (local anisotropy, curvature, linearity, first principal component, verticality, and sphericity) have shown robustness in earlier studies. Threshold values are identified as points of inflection in the fitted distributions for each tree. Our method requires only two parameters and was tested with manually labelled Terrestrial Laser Scanning (TLS) data and non-labeled data from an oblique and above canopy setup (Permanent LiDAR scanner setup). We tested two boreal tree species, Scots Pine, and silver birch, with 28 trees in total (14 trees for each species) using two data sources. Compared to two alternative methods (namely, fixed thresholding and CANUPO), our approach consistently outperforms in terms of recall. We achieved an average overall accuracy of 85%, recall of 88.5%, precision of 83%, and f1 score of 85%. Visually assessing oblique and above canopy results, our algorithm effectively captures tree structures. Our statistical approach provides an effective solution for foliage and non-leaf separation, with processing times of less than five minutes for individual tree point clouds containing up to 2 million points and no need for extensive manual labelling or parameter adjustments.

MEASURING FOREST CANOPY WATER MASS IN THREE DIMENSIONS USING TERRESTRIAL LASER SCANNING

Abstract. Canopy water mass is an important plant characteristic that can indicate the water status of vegetation. However, the parameter remains under-investigated because measuring it requires defoliating the canopy. This study introduced a non-destructive approach to estimate canopy water mass using terrestrial laser scanning data. Tree 3D models were generated from dual-wavelength TLS data for six forest canopies, then the models were utilized in estimating the canopy LAI, total leaf area, and vertical profiles of canopy leaf area. The estimates were then coupled with canopy equivalent water thickness estimates and vertical profiles of canopy water mass were generated. The results revealed some over- and underestimation in the estimated LAI, but the obtained accuracy was considered sufficient as leaf-on point clouds were used to generate the 3D models. The vertical profiles of canopy water mass showed that the leaf area distribution within the canopy, and the canopy architecture were the main parameters affecting the water mass distribution within the canopy, with mid canopy layers having higher water mass than the other canopy layers. This study showed the potential of TLS to estimate canopy water mass, but controlled experiments that include defoliating canopies are still needed for a direct and accurate validation of the TLS estimates of canopy water mass.

FILAMENT EXTRACTION IN 3D PRINTING OF SHOTCRETE WALLS FROM TERRESTRIAL LASER SCANNER DATA

Abstract. This paper presents a method for filament extraction as a step in Shotcrete 3D printing (SC3DP) for quality control using Terrestrial Laser Scanning (TLS) after the printing process. The proposed approach involves comparing a Point Cloud (PC) generated from TLS data with the original design model using Cloud-to-Model (C2M) distance to obtain a coloured deviation map. This deviation map is then rasterized into an image with the same size as the object, with each pixel's colour representing the C2M distance. The method incorporates denoising techniques, such as bilateral filtering, and applies Canny edge detection to identify the filaments’ contour. Morphological operations are used to extract only the horizontal edges relevant to the planned printing path. To connect isolated edges, an algorithm based on the M-estimator sample consensus (MSAC) algorithm is employed to fit lines accurately. The proposed method achieves an average precision score of 76% in detecting printing filaments of a shotcrete wall. The results demonstrate a reliable quality control capability and the potential for early identification of manufacturing-related issues.

Improving the ecological benefits evaluation on urban street trees: Development of a living vegetation volume quantifying framework with multi-source data

The rate of urbanization is increasing, and solutions to the pollution issue are desperately needed. Urban street trees are an essential factor in improving the environmental quality of cities. Quantifying precise ecological benefits of individual trees is important for monitoring urban ecology. However, since street tree surveys are time-consuming and expensive, it is challenging to assess the ecological benefits of street trees. Besides, there is still a need for more investigation into the advantages of multi-source data for ecological benefits estimates. Effective living vegetation volume (eLVV) as an urban green space indicator is beneficial for providing the vertical structure of street trees and offering a possibility for estimating the ecological benefits. The objective of this study is to evaluate the accurate ecological benefits of street trees by calculating eLVV. We developed and validated a framework for evaluating the ecological benefits of urban vegetation using point cloud and image processing techniques. Terrestrial laser scanning and unmanned aerial vehicle data were used to predict indicators at the single tree scale and road scale. Models based on game theory were applied to score all the indicators and assess the ecological benefits. The results showed that: (1) The proposed framework demonstrated that multi-source data allowed for the exact indicators of ecological benefits. (2) The eLVV of the street trees on two roads was less on the north side than on the south side, reflecting the difference in the ecology of the street trees in terms of orientation. This paper explores the advantages of multi-source data in measuring detailed ecological benefits, which support the digital management of street trees and the sustainability of regional ecology.

The evaluation of hand-crafted and learned-based features in Terrestrial Laser Scanning-Structure-from-Motion (TLS-SfM) indoor point cloud registration: the case study of cultural heritage objects and public interiors

Modern technologies are commonly used to inventory different architectural or industrial objects (especially cultural heritage objects and sites) to generate architectural documentation or 3D models. The Terrestrial Laser Scanning (TLS) method is one of the standard technologies researchers investigate for accurate data acquisition and processing required for architectural documentation. The processing of TLS data to generate high-resolution architectural documentation is a multi-stage process that begins with point cloud registration. In this step, it is a common practice to identify corresponding points manually, semi-manually or automatically. There are several challenges for the TLS point cloud processing in the data registration process: correct spatial distribution, marking of control points, automation, and robustness analysis. This is particularly important when large, complex heritage sites are investigated, where it is impossible to distribute marked control points. On the other hand, when orientating multi-temporal data, there is also the problem of corresponding reference points. For this reason, it is necessary to use automatic tie-point detection methods. Therefore, this article aims to evaluate the quality and completeness of the TLS registration process using 2D raster data in the form of spherical images and Affine Hand-crafted and Learned-based detectors in the multi-stage TLS point cloud registration as test data; point clouds were used for the historic 17th-century cellars of the Royal Castle in Warsaw without decorative structures, two baroque rooms in the King John III Palace Museum in Wilanów with decorative elements, ornaments and materials on the walls and flat frescoes, and two modern test fields, narrow office, and empty shopping mall. The extended Structure-from-Motion was used to determine the tie points for the complete TLS registration and reliability analysis. The evaluation of detectors demonstrates that for the test sites exhibiting rich textures and numerous ornaments, a combination of AFAST, ASURF, ASIFT, SuperGlue and LoFTR can be effectively employed. For the point cloud registration of less textured buildings, it is advisable to use AFAST/ASIFT. The robust method for point cloud registration exhibits comparable outcomes to the conventional target-based and Iterative Closest Points methods.

TREE DIGITISATION FROM POINT CLOUDS WITH UNREAL ENGINE

Abstract. Trees are fundamental parts of urban areas and green urbanism. Although much effort is being put into the digitisation of urban areas, trees present great complexity and are usually replaced by predefined models. On the one hand, trees are elements composed of trunk, branches, and leaves, each with a completely different structure and geometry. On the other hand, the tree parts are closely related to each species. Therefore, in order to obtain a realistic digital urban environment, in 3D models such as CityGML or Metaverse, it is necessary that the trees correspond faithfully to reality. The aim of this work is to propose a method to digitise trees from Mobile Laser Scanning and Terrestrial Laser Scanning data. The process takes advantage of the differentiation between trunks and leaves for their segmentation by point cloud geometric features. Unreal Engine is then used to digitise each part. Trunk and branches are geometrically preserved. For dense canopy trees, predefined leaves according to the species are imported and the alpha shape of the crown is filled. For non-dense canopy trees, the canopy is imported and modified to fit the branches. The method was tested on four real case studies. The results show realistic trees, with correct trunk and foliage segmentation, but highly dependent on the life/canopy repositories. Unreal Engine was a very complete and useful tool for the digitisation of trees generating realistic textures and lighting options.

SCAN-TO-BIM method and analysis of measurement accuracy on the example of a historic church

Building information modeling (BIM) data for existing buildings based on scans and point clouds acquired from terrestrial laser scanning (TLS) is the basis of the Scan-to-BIM methodology and is becoming common practice. However, work on the accuracy of the resulting model is still desired. The article discusses the possibility of developing a BIM model of a historical building, based on data obtained by terrestrial laser scanning. The subject of the study was the church in Posada Rybotycka (Poland). The mapping reliability studies included PointCab and ReCap point cloud processing, 3D modeling of the object using Revit software, and analysis of the accuracy of distance measurements made by TLS with data obtained from measurements made with traditional methods: total station and laser rangefinder. Based on the conducted research, the possibility of using the BIM with TLS data in the process of reconstructing the geometry of a historic building was evaluated. The results of the study showed that the convergence of the 3D model geometry with the actual course of the structure depends on the development methodology, i.e. the accuracy of 3D data acquisition, the registration process, the filtering procedure, or the parametric structural modeling method used.

FUSION OF DIRECT GEOREFERENCED AERIAL DRONE WITH TERRESTRIAL LASER SCANNER DATA THE CASE OF THE ROMAN BATHS OF AMATHUS, CYPRUS

Abstract. The fusion of geomatic techniques with different accuracy and resolution has been used in recent years to applications of geometrical documentation for several archaeological sites. In this research article, the case of the Roman Baths of Amathus in Limassol, Cyprus was investigated.Aerial photogrammetry and Terrestrial Laser Scanning (TLS) techniques were used to produce the final product. The photogrammetric data were based on Post-Processed Kinematic (PPK) imagery while the TLS georeference was based on targets.The main research objective of this paper is to examine the reliability of PPK photogrammetric data without the use of Ground Control Points (GCPs) and how well they can be integrated with TLS data. Also, the two acquisition techniques were compared and indicated that 0.005 m Ground Sampling Distance (GSD) resolution aerial images and 0.0061/10 m resolution scans can be qualitatively fused.The research paper will present the entire methodology up to the generation of the final 3D photorealistic product.

Volume Calculation of Sidoarjo Mud Using TLS Data and Numerical Methods

Sidoarjo Mud is one of the phenomena of mud volcanoes located in the northern part of Java Island. A mud volcano is a phenomenon where mud material is released from the earth due to pressure (eruption). The mud continuously flows and accumulates around the central embankments. This phenomenon has caused the embankments to break several times due to the uncontainable mud volume. Observing the volume of Sidoarjo Mud is expected to help mitigate this problem. The Sidoarjo Mud volume was calculated using point cloud data obtained from Terrestrial Laser Scanner (TLS). Numerical methods such as the Trapezoidal and Simpson rules were employed to determine the mud volume. These numerical methods involve approximating the volume of irregularly shaped earthwork surfaces using mathematical techniques. The numerical method approximates the area under a trapezoid or curve representing the cross-section of the earthwork. In this study, kriging interpolation was conducted with a grid size of five meters. The relative difference between the numerical methods was found to be within acceptable limits, not exceeding 2.78% based on the American Society for Testing and Materials (ASTM) 2002 and not exceeding 2% based on ASTM 1998 as an industry acceptance error. Considering the relative difference results and the institutional report, the Trapezoidal method with a 5-meter grid size was considered the accurate volume calculation method. Based on the processed data, the total volume of Sidoarjo Mud within the embankment was 32,793,917.171 m3 in February 2022, 32,889,012.793 m3 in April 2022, and 33,390,051.407 m3 in June 2022. The volume calculation results show a difference of 95,095.622 m3 between February-April and a difference of 501,038.613 m3 between April-June. The mud flow rate for the February-April period was calculated as 1,872.114 m3/day, while for the April-June period was determined as 9,278.492 m3/day.

Combination of Terrestrial Laser Scanning and Unmanned Aerial Vehicle Photogrammetry for Heritage Building Information Modeling: A Case Study of Tarsus St. Paul Church

Cultural heritage building information modeling (HBIM) is an emerging process allowing us to reconstruct built heritage virtually. The data of a digitally documented cultural heritage building offers significant advantages as it is accessible and modifiable by all professionals involved in the same or different projects. The most important factor affecting the accuracy and precision of the HBIM model is the ability to collect complete and accurate information about the physical structure. Combining terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV) photogrammetry point clouds is one of the most efficient ways to capture accurate digital data on the building. This study provides the foundation for creating an HBIM model for cultural heritage the coupling of spatial data with TLS and UAV. This paper aims to generate synergy between TLS and UAV point cloud data and ensure that the spatial database contains sufficient data to model historical objects with HBIM tendencies.

Features of inventory of green plantings by automated terrestrial laser scanning methods

The aim of this work is to investigate the process of obtaining necessary information about the metric parameters of small-area arrays, linearly arranged and individual green plantings on predominantly urbanized territories, and to apply the results of data processing in the compilation of topographic and special maps from the corresponding scanning materials. Methodology. For this purpose, terrestrial laser scanning methods, dynamic laser scanning as a data source for tree-level mapping of the territory, and as an information base for filling in the respective cadastres are subject to research. The possibilities of using data from these methods to obtain information about green plantings using modern software tools have been explored. Based on terrestrial laser scanning data performed in accordance with the requirements of regulatory spatial reference documents, data processing of terrestrial laser scanning was carried out using automated methods, namely the Terrasolid software suite. The need for more than 40% coverage of the tree trunk with a point cloud obtained from laser scanning to eliminate possible errors in determining the relevant parameters due to the heterogeneity of the structure of different tree trunks has been confirmed. Preliminary processing of scanning materials was carried out using FARO Scene 2020 software. Scientific novelty and practical significance. An experiment was conducted to analyze the creation of both a plan-altitude and an information base regarding green plantings on selected objects within the Zakarpattia region. The process of collecting data on green plantings was improved by using terrestrial laser scanning and partial GNSS measurements, instead of traditional topographic-geodetic methods. A table containing information on green planting data has been created for the studied objects' territory. Automated methods were used to gather this information, including details about their location in the adopted coordinate system and the trunk diameter at a height of 1.3 meters.

Classification of tree forms in aerial LiDAR point clouds using CNN for 3D tree modelling

ABSTRACT Three-dimensional models of trees that correspond to the real-world forms of the trees on the ground are used in urban planning, solar power estimation, and other disciplines. Previous studies have focused on generating 3D tree models from high-density point cloud data such as Terrestrial Laser Scanning (TLS) data, which is expensive and limited to small spatial extents. However, there has been limited exploration of inexpensive solutions to model trees over large spatial extents. The goal of this study is to use widely available discrete return Airborne Laser Scanning (ALS) data along with field-captured tree photographs and Google Street View (GSV) images to develop 3D equivalents of trees over larger spatial extents. To this end, we designed a process to assign representative 3D models for individual trees in discrete return ALS point clouds. This study demonstrates the use of a Convolutional Neural Network (CNN) model and 3D models generated with Structure from Motion (SfM) for the realistic modelling of deciduous non-overlapping trees from discrete return ALS data. We classified and labelled the crown shapes of deciduous trees in a study area into four classes based on GSV images of trees. We delineated and segmented non-overlapping deciduous trees from ALS data and reduced them to 2D images using voxel point counts. Next, we trained a CNN architecture to match the 2D images to the corresponding classes observed from GSV images. For each class, we created a representative 3D tree model using field-captured circumnavigational photos of trees and SfM. To demonstrate 3D visualization using the 3D tree models, we created a 3D visualization of the trees surrounding a parking lot. The trained CNN model from this study can be used to classify non-overlapping deciduous trees from discrete return ALS data and subsequently visualize near-realistic 3D tree models of trees.

Laser scanner and UAV digital photogrammetry as support tools for cosmic-ray muon radiography applications: an archaeological case study from Italy

The use of light detection and ranging technologies, i.e. terrestrial laser scanner (TLS), airborne laser scanner (ALS) and mobile laser scanner (MLS), together with the unmanned aerial vehicles digital photogrammetry (UAV-DP) and satellite data are proving to be fundamental tools to carry out reliable muographic measurement campaigns. The main purpose of this paper is to propose a workflow to correctly plan and exploit these types of data for muon radiography aims. To this end, a real case study is presented: searching for hidden tombs in the Etruscan necropolis of Palazzone (Umbria, Italy). A high-resolution digital elevation model (DEM) and three-dimensional models of the ground surface/sub-surface of the study area were created by merging data obtained using different survey methods to achieve the most accurate three-dimensional environment. Indeed, the simulated muon flux transmission used to infer relative transmission values, and the estimated density distribution, depends on the reliability of the three-dimensional reconstructed ground surface model. The aim of this study is to provide knowledge on the use of TLS and UAV-DP data and GPS-acquired points within the transmission-based muography process and how these data could improve or worsen the muon imaging results. Moreover, this study confirmed that muography applications require a multidisciplinary approach.

Assessing Forest Traversability for Autonomous Mobile Systems Using Close-Range Airborne Laser Scanning

Advances in sensor technology and computing performance has brought us into an era of digital forestry where a forest environment can be digitally replicated. At the same time, an increasing interest in the use of unmanned vehicles and other autonomous mobile systems (AMSs) in forest mapping and operations has emerged. However, a forest is an unstructured and rather complex environment for AMSs to operate in, and usually some kind of a priori information of traversability is required. The aim of this study was to assess forest traversability for AMSs using high-density airborne laser scanning (ALS) point clouds. It was assumed that such point clouds acquired from a helicopter flying at a low altitude can be used to characterise vegetation obstacles affecting forest traversability. A voxel-based vegetation occupancy analysis was carried out with the aim to detect open space to define traversable three-dimensional space. The experimental setup included seven sample plots (32×32 m) representing diverse boreal forest structures. Terrestrial laser scanning (TLS) was used for obtaining reference for vegetation occupancy. Comparison between ALS and TLS revealed an overall accuracy of 0.85–0.94 with a recall of 0.78–0.91 and a precision of 0.62–0.74 for ALS-based voxel classification for vegetation occupancy depending on forest structure. This implies that up to 91% of voxels assigned a classification »occupied« based on the TLS could be correctly classified using the ALS, while up to 74% of voxels assigned a classification »occupied« using the ALS were occupied based on the TLS. Density of low vegetation accounted for 83% of the variation in accuracy and precision. The feasibility of vegetation occupancy information to be used by an AMS for navigation was also demonstrated. It was assumed that the ALS data convey as sufficient information of AMS path planning as does the TLS data. The experiments showed that out of 1393 randomly generated paths based on empty space detected by the ALS, 72% were considered feasible when validated with the TLS data. The success rate in path planning varied from 0.54 to 0.92 between the sample plots and was seemingly affected by vegetation density that accounted for 53% of variation in success rate. Altogether, the demonstrated possibility to predefine forest traversability using remote sensing will support the use of AMSs in forestry.

An Automated Pipeline for Extracting Forest Structural Parameters by Integrating UAV and Ground-Based LiDAR Point Clouds

In recent times, airborne and terrestrial laser scanning have been utilized to collect point cloud data for forest resource surveys, aiding in the estimation of tree and stand attributes over hectare-scale plots. In this study, an automated approach was devised to estimate the diameter at breast height (DBH) and tree height across the entire sample area, utilizing information acquired from terrestrial laser scanning (TLS) and airborne laser scanning (ULS). Centered around a meticulously managed artificial forest in Northern China, where Mongolian oak and Chinese Scots pine are the predominant species, both TLS and ULS operations were conducted concurrently on each plot. Subsequent to data collection, a detailed processing of the point cloud data was carried out, introducing an innovative algorithm to facilitate the matching of individual tree point clouds from ULS and TLS sources. To enhance the accuracy of DBH estimation, a weighted regression correction equation based on TLS data was introduced. The estimations obtained for the Chinese Scots pine plots showed a correlation of R2 = 0.789 and a root mean square error (RMSE) of 3.2 cm, while for the Mongolian oak plots, an improved correlation of R2 = 0.761 and a RMSE of 3.1 cm was observed between predicted and measured values. This research significantly augments the potential for non-destructive estimations of tree structural parameters on a hectare scale by integrating TLS and ULS technologies. The advancements hold paramount importance in the domain of large-scale forest surveys, particularly in the calibration and validation of aboveground biomass (AGB) estimations.

Volume Estimation of Stem Segments Based on a Tetrahedron Model Using Terrestrial Laser Scanning Data

Stem volume is a very important parameter in forestry inventory and carbon storage. The stem volume estimated by most existing methods deviates from its true value because the irregularity of the stem is usually overlooked. In this study, we propose a stem segment volume estimation based on the tetrahedron model using TLS data. First, the initial stem segment surface model, including the lower, upper, and outer triangular surface models, was gradually reconstructed. Next, the outer surface model was subdivided based on the edge subdivision. Then, a closed triangular surface model without self-intersection was obtained. Afterward, a tetrahedron model of the stem segment was generated using TetGen software (Version 1.6.0) for the triangular surface model. Finally, the stem segment volume was calculated by summing the volumes of all the tetrahedrons in the tetrahedron model. An experiment with 76 stem segments from different tree species with different parameters showed that the reconstructed stem segment surface model effectively reflected the geometrical features of the stem segment surface. Compared to the volume based on the simulated sectional measurement, the MAPE of the volume based on the tetrahedron model was 2.12%. The results demonstrated the validity of the presented method for stem surface reconstruction and stem volume estimation, and the volume based on the tetrahedron model was closer to the true value than that based on the sectional measurement.

TLS2trees: A scalable tree segmentation pipeline forTLSdata

AbstractAbove‐ground biomass (AGB) is an important metric used to quantify the mass of carbon stored in terrestrial ecosystems. For forests, this is routinely estimated at the plot scale (typically 1 ha) using inventory measurements and allometry. In recent years, terrestrial laser scanning (TLS) has appeared as a disruptive technology that can generate a more accurate assessment of tree and plot scale AGB; however, operationalising TLS methods has had to overcome a number of challenges. One such challenge is the segmentation of individual trees from plot level point clouds that are required to estimate woody volume, this is often done manually (e.g. with interactive point cloud editing software) and can be very time consuming.Here we presentTLS2trees, an automated processing pipeline and set of Python command line tools that aims to redress this processing bottleneck.TLS2treesconsists of existing and new methods and is specifically designed to be horizontally scalable. The processing pipeline is demonstrated on 7.5 ha of TLS data captured across 10 plots of seven forest types; from open savanna to dense tropical rainforest.A total of 10,557 trees are segmented withTLS2trees: these are compared to 1281 manually segmented trees. Results indicate thatTLS2treesperforms well, particularly for larger trees (i.e. the cohort of largest trees that comprise 50% of total plot volume), where plot‐wise tree volume bias is ±0.4 m3and %RMSE is 60%. Segmentation performance decreases for smaller trees, for example where DBH ≤10 cm; a number of reasons are suggested including performance of semantic segmentation step.The volume and scale of TLS data captured in forest plots is increasing. It is suggested that to fully utilise this data for activities such as monitoring, reporting and verification or as reference data for satellite missions an automated processing pipeline, such asTLS2trees, is required. To facilitate improvements toTLS2trees, as well as modification for other laser scanning modes (e.g. mobile and UAV laser scanning),TLS2treesis a free and open‐source software.

Stem Detection from Terrestrial Laser Scanning Data with Features Selected via Stem-Based Evaluation

Terrestrial laser scanning (TLS) is an effective tool for extracting stem distribution, providing essential information for forest inventory and ecological studies while also assisting forest managers in monitoring and controlling forest stand density. A feature-based method is commonly integrated into the pipelines of stem detection, facilitating the transition from stem point to stem instance, but most studies focus on feature effectiveness from the point level, neglecting the relationship between stem point extraction and stem detection. In this paper, a feature-based method is proposed to identify stems from TLS data, with features selected from stem levels. Firstly, we propose a series of voxel-based features considering the stem characteristics under the forest. Then, based on the evaluation of some commonly used and proposed features, a stem-based feature selection method is proposed to select a suitable feature combination for stem detection by constructing and evaluating different combinations. Experiments are carried out on three plots with different terrain slopes and tree characteristics, each having a sample plot size of about 8000 m2. The results show that the voxel-based features can supplement the basic features, which improve the average accuracy of stem point extraction and stem detection by 9.5% and 1.2%, respectively. The feature set obtained by the proposed feature selection method achieves a better balance between accuracy and feature number compared with the point-based feature selection method and the features used in previous studies. Moreover, the accuracies of the proposed stem detection methods are also comparable to the three methods evaluated in the international TLS benchmarking project.

Deformation of High Rise Cooling Tower through Projection of Coordinates Resulted from Terrestrial Laser Scanner Observations onto a Vertical Plane

The appearance and development of new construction materials, technology and accurate geodetic instruments have led to the necessity of their inevitable use in the health monitoring, maintenance, and restoration of civil structures and special structures such as high-rise cooling towers. This paper presents an accurate practical and analytical method for determining the structural deformation and axis inclination of high rise cooling towers using terrestrial laser scanner (TLS) observations through the projection of tower surface points coordinates on a vertical plane. Four cooling towers in El-Mahla El-Kubra city, Egypt are observed and studied. Two of them with height 56 m, and the others were 36 m height. The studied four towers have different cross-section diameters along the tower height. Each tower has a cone shape with a curved wall. The equation of the tower wall is determined using TLS observations and least squares adjustment techniques. The equations of cone projection with a curved wall are derived and presented in this paper. From TLS observations, the radii and accuracy of each 2 m tower height are determined with center coordinates, and then the inclination of the tower axis is calculated and drawn. From the results of TLS observations, data processing, and analysis using the suggested techniques, it is deduced that there is a deformation in tower walls with small values. The specified technique for observations collection and TLS data analysis through projection on a vertical plane is significant and valuable for determining the structural deformation of circular high rise buildings and towers. From the results, there are obvious deformation values in some cooling towers, so maintenance work must be included. The towers also must be checked and monitored several times at brief intervals.

Improving the application of UAV-LiDAR for erosion monitoring through accounting for uncertainty in DEM of difference

Successful control of erosion requires effective monitoring and an in-depth understanding of erosion processes over large areas. Data from repeated unmanned aerial vehicles (UAV) light detection and ranging (LiDAR) acquisitions have been one important option to derive erosion-related information across large areas for example by calculating a digital elevation model of difference (DoD). However, the lack of an efficient and practical DoD uncertainty derivation method constrains the use of repeated UAV- LiDAR acquisitions for erosion monitoring, particularly for erosion that induces relatively small changes (i.e. field scale erosion). This study employed repeated UAV-LiDAR acquisitions to promote the understanding of DoD uncertainties and integrate the gained understanding in an erosion monitoring approach. The uncertainty of DoD was derived based on the impacting factors (point density and topography) using the fuzzy inference system (FIS), with the rules established according to the empirical relationships between DoD uncertainty and impacting factors for one sloping field. The proposed method was then validated on another sloping field by using terrestrial laser scanning (TLS) data. Results showed that the absolute value of DoD uncertainty (DoDua) linearly increased with local roughness and decreased exponentially with increasing point density. The established DoD uncertainty derivation method was found to be able to account for over 96% of raw DoD uncertainty. A validation with TLS results showed that our method slightly underestimated erosion and deposition volume, respectively, (-0.29 m3 and 0.74 m3 compared to −0.94 m3 and 1.34 m3 measured by TLS) but the trends were comparable. The distribution of deposition (volume and area) in different parts of the validation slope was well captured by our method, while the distribution of detected erosion was less satisfactory. This may be explained by the fact that our method did not well capture subtle erosion, suggesting a direction for future improvement.