scholarly journals Study of Subtropical Forestry Index Retrieval Using Terrestrial Laser Scanning and Hemispherical Photography

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Ting Yun ◽  
Weizheng Li ◽  
Yuan Sun ◽  
Lianfeng Xue
Keyword(s):  
Laser Scanning ◽  
Estimation Method ◽  
Terrestrial Laser Scanning ◽  
Tangent Plane ◽  
Transfer Model ◽  
Normal Vector ◽  
Plant Canopy ◽  
Hemispherical Photography ◽  
Area Index ◽  
Cloud Data

In order to retrieve gap fraction, leaf inclination angle, and leaf area index (LAI) of subtropical forestry canopy, here we acquired forestry detailed information by means of hemispherical photography, terrestrial laser scanning, and LAI-2200 plant canopy analyzer. Meanwhile, we presented a series of image processing and computer graphics algorithms that include image and point cloud data (PCD) segmentation methods for branch and leaf classification and PCD features, such as normal vector, tangent plane extraction, and hemispherical projection method for PCD coordinate transformation. In addition, various forestry mathematical models were proposed to deduce forestry canopy indexes based on the radiation transfer model of Beer-Lambert law. Through the comparison of the experimental results on many plot samples, the terrestrial laser scanner- (TLS-) based index estimation method obtains results similar to digital hemispherical photograph (HP) and LAI-2200 plant canopy analyzer taken of the same stands and used for validation. It indicates that the TLS-based algorithm is able to capture the variability in LAI of forest stands with a range of densities, and there is a high chance to enhance TLS as a calibration tool for other devices.

scholarly journals Retrieval of Aerodynamic Parameters in Rubber Tree Forests Based on the Computer Simulation Technique and Terrestrial Laser Scanning Data

2020 ◽  
Vol 12 (8) ◽  
pp. 1318
Author(s):  
Zhixian Huang ◽  
Xiao Huang ◽  
Jiangchuan Fan ◽  
Markus Eichhorn ◽  
Feng An ◽  
...  
Keyword(s):  
Machine Learning ◽  
Laser Scanning ◽  
Clustering Algorithm ◽  
Dynamic Pressure ◽  
Rubber Tree ◽  
Terrestrial Laser Scanning ◽  
Phenotypic Traits ◽  
Area Index ◽  
Cloud Data ◽  
Gap Fraction

Rubber trees along the southeast coast of China always suffer severe damage from hurricanes. Quantitative assessments of the capacity for wind resistance of various rubber tree clones are currently lacking. We focus on a vulnerability assessment of rubber trees of different clones under wind disturbance impacts by employing multidisciplinary approaches incorporating scanned points, aerodynamics, machine learning and computer graphics. Point cloud data from two typical rubber trees belonging to different clones (PR107 and CATAS 7-20-59) were collected using terrestrial laser scanning, and a connection chain of tree skeletons was constructed using a clustering algorithm of machine learning. The concept of foliage clumps based on the trunk and first-order branches was first proposed to optimize rubber tree plot 3D modelling for simulating the wind field and assessing the wind-related parameters. The results from the obtained phenotypic traits show that the variable leaf area index and included angle between the branches and trunk result in variations in the topological structure and gap fraction of tree crowns, respectively, which are the major influencing factors relevant to the rubber tree’s capacity to resist hurricane strikes. The aerodynamics analysis showed that the maximum dynamic pressure, wind velocity and turbulent intensity of the wind-related parameters in rubber tree plots of clone PR107 (300 Pa, 30 m/s and 15%) are larger than that in rubber tree plots of clone CATAS-7-20-59 (120 Pa, 18 m/s and 5%), which results in a higher probability of local strong cyclone occurrence and a higher vulnerability to hurricane damage.

scholarly journals Monitoring of the Production Process of Graded Concrete Component Using Terrestrial Laser Scanning

2021 ◽  
Vol 13 (9) ◽  
pp. 1622
Author(s):  
Yihui Yang ◽  
Laura Balangé ◽  
Oliver Gericke ◽  
Daniel Schmeer ◽  
Li Zhang ◽  
...  
Keyword(s):  
Production Process ◽  
Laser Scanning ◽  
Hollow Spheres ◽  
Terrestrial Laser Scanning ◽  
Region Growing ◽  
Casting Process ◽  
Concrete Surface ◽  
Cloud Data ◽  
Sphere Detection ◽  
Concrete Component

Accepting the ecological necessity of a drastic reduction of resource consumption and greenhouse gas emissions in the building industry, the Institute for Lightweight Structures and Conceptual Design (ILEK) at the University of Stuttgart is developing graded concrete components with integrated concrete hollow spheres. These components weigh a fraction of usual conventional components while exhibiting the same performance. Throughout the production process of a component, the positions of the hollow spheres and the level of the fresh concrete have to be monitored with high accuracy and in close to real-time, so that the quality and structural performance of the component can be guaranteed. In this contribution, effective solutions of multiple sphere detection and concrete surface modeling based on the technology of terrestrial laser scanning (TLS) during the casting process are proposed and realized by the Institute of Engineering Geodesy (IIGS). A complete monitoring concept is presented to acquire the point cloud data fast and with high-quality. The data processing method for multiple sphere segmentation based on the efficient combination of region growing and random sample consensus (RANSAC) exhibits great performance on computational efficiency and robustness. The feasibility and reliability of the proposed methods are verified and evaluated by an experiment monitoring the production of an exemplary graded concrete component. Some suggestions to improve the monitoring performance and relevant future work are given as well.

Derivation, Validation, and Sensitivity Analysis of Terrestrial Laser Scanning-Based Leaf Area Index

2016 ◽  
Vol 42 (6) ◽  
pp. 719-729 ◽  
Author(s):  
Yumei Li ◽  
Qinghua Guo ◽  
Shengli Tao ◽  
Guang Zheng ◽  
Kaiguang Zhao ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Area Index

scholarly journals Development of Image Processing for Crack Detection on Concrete Structures through Terrestrial Laser Scanning Associated with the Octree Structure

2018 ◽  
Vol 8 (12) ◽  
pp. 2373 ◽  
Author(s):  
Soojin Cho ◽  
Seunghee Park ◽  
Gichun Cha ◽  
Taekeun Oh
Keyword(s):  
Image Processing ◽  
Laser Scanning ◽  
Crack Detection ◽  
Surface Condition ◽  
Terrestrial Laser Scanning ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Morphological Operations ◽  
Cloud Data ◽  
Remote Sensing Technique

Terrestrial laser scanning (TLS) provides a rapid remote sensing technique to model 3D objects but can also be used to assess the surface condition of structures. In this study, an effective image processing technique is proposed for crack detection on images extracted from the octree structure of TLS data. To efficiently utilize TLS for the surface condition assessment of large structures, a process was constructed to compress the original scanned data based on the octree structure. The point cloud data obtained by TLS was converted into voxel data, and further converted into an octree data structure, which significantly reduced the data size but minimized the loss of resolution to detect cracks on the surface. The compressed data was then used to detect cracks on the surface using a combination of image processing algorithms. The crack detection procedure involved the following main steps: (1) classification of an image into three categories (i.e., background, structural joints and sediments, and surface) using K-means clustering according to color similarity, (2) deletion of non-crack parts on the surface using improved subtraction combined with median filtering and K-means clustering results, (3) detection of major crack objects on the surface based on Otsu’s binarization method, and (4) highlighting crack objects by morphological operations. The proposed technique was validated on a spillway wall of a concrete dam structure in South Korea. The scanned data was compressed up to 50% of the original scanned data, while showing good performance in detecting cracks with various shapes.

scholarly journals Intelligent crack extraction based on terrestrial laser scanning measurement

2020 ◽  
Vol 53 (3-4) ◽  
pp. 416-426 ◽  
Author(s):  
Hao Yang ◽  
Xiangyang Xu
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Cloud Data ◽  
Time Cost ◽  
Visual Examination ◽  
Average Width ◽  
Cloud Data ◽  
Novel Method

The hazards of cracks, which could badly decrease reliability and safety of structures, are receiving increasing attention with the popularity of tunnel constructions. Traditional crack inspection relies on visual examination, which is time-, cost- and labor-intensive. Therefore, how to identify and measure cracks intelligently is significantly essential. The paper focuses on the Canny method to extract cracks of tunnel structures by the intensity value of reflectivity. We propose and investigate a novel method which combines dilation and the Canny algorithm to identify and extract the cracks automatically and intelligently based on the point cloud data of terrestrial laser scanning measurement. In order for measurement of cracks, the projection of summed edge pixels is adopted, where a synthesis is carried out on the detection results with all sampling parameters. Based on the synthesized image, vertical crack presents two sharp peaks where the space of the peaks indicates the average width of the crack, as well as its position. The advantage of the method is that it does not require determination of Canny detector parameters. The deviation between manual measurement and Canny detection is 2.92%.

scholarly journals Determining leaf area index and leafy tree roughness using terrestrial laser scanning

2010 ◽  
Vol 46 (6) ◽  
Author(s):  
A. S. Antonarakis ◽  
K. S. Richards ◽  
J. Brasington ◽  
E. Muller
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Area Index

Research on the Point Cloud Data Processing Method of 3D Terrestrial Laser Scanning in Existing Railway Track

2015 ◽  
Vol 744-746 ◽  
pp. 1298-1302 ◽  
Author(s):  
Feng Han ◽  
Xiao Feng Duan
Keyword(s):  
Laser Scanning ◽  
Structural Characteristics ◽  
Data Extraction ◽  
Three Dimensional ◽  
Terrestrial Laser Scanning ◽  
Railway Track ◽  
Cloud Data ◽  
Railway Line ◽  
Data Processing Method ◽  
Static Detection

Characterized with efficient, accurate and non-contact measurement, and the fast and three-dimensional visualization features, using 3D terrestrial laser scanning technology in track static detection has attracted widespread attention. Based on the structural characteristics of the railway line, use Geomagic software and Cyclone software in the pre-processing stage, remove the noise and redundancy, package the data after registration, get the initial line model finally. In the data extraction stage, combined with professional needs, respectively research the data extraction of track pitch and direction, and the bed section, from line, plane, and body. Which have provided a good research idea for using 3D terrestrial laser scanning technology in track static detection, acceptance, and some other aspects.

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