Modeling the Geometry of Tree Trunks Using LiDAR Data

Article


Tarsha Kurdi, Fayez, Gharineiat, Zahra, Lewandowicz, Elzbieta and Shan, Jie. 2024. "Modeling the Geometry of Tree Trunks Using LiDAR Data." Forests. 15 (2). https://doi.org/10.3390/f15020368
Article Title

Modeling the Geometry of Tree Trunks Using LiDAR Data

ERA Journal ID210472
Article CategoryArticle
AuthorsTarsha Kurdi, Fayez, Gharineiat, Zahra, Lewandowicz, Elzbieta and Shan, Jie
Journal TitleForests
Journal Citation15 (2)
Article Number368
Number of Pages20
Year2024
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN1999-4907
Digital Object Identifier (DOI)https://doi.org/10.3390/f15020368
Web Address (URL)https://www.mdpi.com/1999-4907/15/2/368
AbstractThe effective development of digital twins of real-world objects requires sophisticated data collection techniques and algorithms for the automated modeling of individual objects. In City Information Modeling (CIM) systems, individual buildings can be modeled automatically at the second Level of Detail or LOD2. Similarly, for Tree Information Modeling (TIM) and building Forest Digital Twins (FDT), automated solutions for the 3D modeling of individual trees at different levels of detail are required. The existing algorithms support the automated modeling of trees by generating models of the canopy and the lower part of the trunk. Our argument for this work is that the structure of tree trunk and branches is as important as canopy shape. As such, the aim of the research is to develop an algorithm for automatically modeling tree trunks based on data from point clouds obtained through laser scanning. Aiming to generate 3D models of tree trunks, the suggested approach starts with extracting the trunk point cloud, which is then segmented into single stems. Subsets of point clouds, representing individual branches, are measured using Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS). Trunks and branches are generated by fitting cylinders to the layered subsets of the point cloud. The individual stems are modeled by a structure of slices. The accuracy of the model is calculated by determining the fitness of cylinders to the point cloud. Despite the huge variation in trunk geometric forms, the proposed modeling approach can gain an accuracy of better than 4 cm in the constructed tree trunk models. As the developed tree models are represented in a matrix format, the solution enables automatic comparisons of tree elements over time, which is necessary for monitoring changes in forest stands. Due to the existence of large variations in tree trunk geometry, the performance of the proposed modeling approach deserves further investigation on its generality to other types of trees in multiple areas.
KeywordsCityGML; digital twins; tree model; tree trunk; trunk geometry; LiDAR; OGC
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020401399. Geomatic engineering not elsewhere classified
Byline AffiliationsSchool of Surveying and Built Environment
University of Warmia and Mazury, Poland
Purdue University, United States
Permalink -

https://research.usq.edu.au/item/z5vx0/modeling-the-geometry-of-tree-trunks-using-lidar-data

Download files


Published Version
forests-15-00368.pdf
License: CC BY 4.0
File access level: Anyone

  • 6
    total views
  • 2
    total downloads
  • 1
    views this month
  • 1
    downloads this month

Export as

Related outputs

Three-Dimensional Modeling and Visualization of Single Tree LiDAR Point Cloud Using Matrixial Form
Tarsha Kurdi, Fayez, Lewandowicz, Elżbieta, Shan, Jie and Gharineiat, Zahra. 2024. "Three-Dimensional Modeling and Visualization of Single Tree LiDAR Point Cloud Using Matrixial Form." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 17, pp. 3010-3022. https://doi.org/10.1109/JSTARS.2024.3349549
Efficiency of Terrestrial Laser Scanning in Survey Works: Assessment, Modelling, and Monitoring
Tarsha Kurdi, Fayez, Reed, Paul, Gharineiat, Zahra and Awrangjeb, Mohammad. 2023. "Efficiency of Terrestrial Laser Scanning in Survey Works: Assessment, Modelling, and Monitoring." International Journal of Environmental Sciences and Natural Resources. 32 (2). https://doi.org/10.19080/IJESNR.2023.32.556334
3D LoD2 and LoD3 Modelling of Rotating Surface Building of Ornamental Towers starting from LiDAR Data
Lewandowicz, Elzbieta, Tarsha Kurdi, Fayez and Gharineiat, Zahra. 2023. "3D LoD2 and LoD3 Modelling of Rotating Surface Building of Ornamental Towers starting from LiDAR Data." Oniga, Valeria-Ersilia (ed.) Prime Archives in Remote Sensing. India. Vide Leaf.
Torsional behavior of GFRP-reinforced concrete pontoon decks with and without an edge cutout
Manalo, Allan, Yang, Xian, Alajarmeh, Omar, Benmokrane, Brahim, Gharineiat, Zahra, Ebrahimzadeh, Shahrad, Sorbello, Charles-Dean and Weerakoon, Senarath. 2023. "Torsional behavior of GFRP-reinforced concrete pontoon decks with and without an edge cutout." Marine Structures. 88. https://doi.org/10.1016/j.marstruc.2022.103345
Modeling Multi-Rotunda Buildings at LoD3 Level from LiDAR Data
Tarsha Kurdi, Fayez, Lewandowicz, Elzbieta, Gharineiat, Zahra and Shan, Jie. 2023. "Modeling Multi-Rotunda Buildings at LoD3 Level from LiDAR Data." Remote Sensing. 15 (13). https://doi.org/10.3390/rs15133324
Contribution of Geometric Feature Analysis for Deep Learning Classification Algorithms of Urban LiDAR Data
Tarsha Kurdi, Fayez, Amakhchan, Wijdan, Gharineiat, Zahra, Boulaassal, Hakim and Kharki, Omar El. 2023. "Contribution of Geometric Feature Analysis for Deep Learning Classification Algorithms of Urban LiDAR Data." Sensors. 23 (17). https://doi.org/10.3390/s23177360
Machine learning-based segmentation of aerial LiDAR point cloud data on building roof
Dey, Emon Kumar, Awrangjeb, Mohammad, Tarsha Kurdi, Fayez and Stantic, Bela. 2023. "Machine learning-based segmentation of aerial LiDAR point cloud data on building roof." European Journal of Remote Sensing. 56 (1). https://doi.org/10.1080/22797254.2023.2210745
Construction and Deconstruction: Materials and substances from 'waste'
Burey, Paulomi, Feldman, Jessica, Seligmann, Hannah, Song, Eric, Flynn, Matthew, Helwig, Andreas, Gharineiat, Zahra, Seneviratne, Dinuki, Whiteside, Eliza, Shelley, Tristan, Priesler, Nils, Manalo, Allan, Mirzaghorbanali, Ali, Nourizadeh, Hadi, Roberts, Michae, Nicol, Rose, Redmond, Petrea, Lynch, Mark, Dearnaley, John, ..., Germon, Geoff. 2023. "Construction and Deconstruction: Materials and substances from 'waste'." Chemistry in Australia. (June-August 2023), pp. 16-21.
CNN Based Image Classification of Malicious UAVs
Brown, Jason, Gharineiat, Zahra and Raj, Nawin. 2023. "CNN Based Image Classification of Malicious UAVs." Applied Sciences. 13 (1), pp. 1-13. https://doi.org/10.3390/app13010240
Crack Detection in Concrete Structures Using Deep Learning
Golding, Vaughn Peter, Gharineiat, Zahra, Munawar, Hafiz Suliman and Ullah, Fahim. 2022. "Crack Detection in Concrete Structures Using Deep Learning." Sustainability. 14 (13), pp. 1-25. https://doi.org/10.3390/su14138117
A Framework for Burnt Area Mapping and Evacuation Problem Using Aerial Imagery Analysis
Munawar, Hafiz Suliman, Gharineiat, Zahra, Akram, Junaid and Khan, Sara Imran. 2022. "A Framework for Burnt Area Mapping and Evacuation Problem Using Aerial Imagery Analysis." Fire. 5 (4), pp. 1-15. https://doi.org/10.3390/fire5040122
3D LoD2 and LoD3 Modeling of Buildings with Ornamental Towers and Turrets Based on LiDAR Data
Lewandowicz, Elzbieta, Tarsha Kurdi, Fayez and Gharineiat, Zahra. 2022. "3D LoD2 and LoD3 Modeling of Buildings with Ornamental Towers and Turrets Based on LiDAR Data." Remote Sensing. 14 (19), pp. 1-17. https://doi.org/10.3390/rs14194687
Review of Automatic Processing of Topography and Surface Feature Identification LiDAR Data Using Machine Learning Techniques
Gharineiat, Zahra, Tarsha Kurdi, Fayez and Campbell, Glenn. 2022. "Review of Automatic Processing of Topography and Surface Feature Identification LiDAR Data Using Machine Learning Techniques." Remote Sensing. 14 (19), pp. 1-24. https://doi.org/10.3390/rs14194685
Assessment and Prediction of Sea Level Trend in the South Pacific Region
Raj, Nawin, Gharineiat, Zahra, Ahmed, Abul Abrar Masrur and Stepanyants, Yury. 2022. "Assessment and Prediction of Sea Level Trend in the South Pacific Region." Remote Sensing. 14 (4), pp. 1-25. https://doi.org/10.3390/rs14040986
Comparative Approach of Unmanned Aerial Vehicle Restrictions in Controlled Airspaces
McTegg, Stephen, Kurdi, Fayez, Simmons, Shane and Gharineiat, Zahra. 2022. "Comparative Approach of Unmanned Aerial Vehicle Restrictions in Controlled Airspaces." Remote Sensing. 14 (4), pp. 1-30. https://doi.org/10.3390/rs14040822
Automatic Filtering of Lidar Building Point Cloud in Case of Trees Associated to Building Roof
Tarsha Kurdi, Fayez, Gharineiat, Zahra, Campbell, Glenn, Awrangjeb, Mohammad and Dey, Emon Kumar. 2022. "Automatic Filtering of Lidar Building Point Cloud in Case of Trees Associated to Building Roof." Remote Sensing. 14 (2), pp. 1-23. https://doi.org/10.3390/rs14020430
Full series algorithm of automatic building extraction and modelling from LiDAR data
Tarsha Kurdi, Fayez, Gharineiat, Zahra, Campbell, Glenn, Dey, Emon Kumar and Awrangjeb, Mohammad. 2021. "Full series algorithm of automatic building extraction and modelling from LiDAR data." Zhou, Jun, Salvado, Olivier, Sohel, Ferdous, Borges, Paulo and Wang, Shilin (ed.) 2021 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2021). Gold Coast, Australia 29 Nov - 01 Dec 2021 United States.
Anthocyanin retention in Queen Garnet plums during processing and bottling
Pahl, Jessica, Burey, Polly, Lynch, Mark, Helwig, Andreas and Gharineiat, Zahra. 2022. Anthocyanin retention in Queen Garnet plums during processing and bottling. Australia. Fight Food Waste Cooperative Research Centre.
Pressure beneath the foot for older adults using an improved approach
Al-Daffaie, Kadhem, Chong, Albert K. and Gharineiat, Zahra. 2019. "Pressure beneath the foot for older adults using an improved approach." 9th IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE 2019). Kota Kinabalu, Malaysia 27 - 28 Apr 2019 United States. https://doi.org/10.1109/ISCAIE.2019.8743653
Evaluation of multivariate adaptive regression splines and artificial neural network for prediction of mean sea level trend around northern Australian coastlines
Raj, Nawin and Gharineiat, zahra. 2021. "Evaluation of multivariate adaptive regression splines and artificial neural network for prediction of mean sea level trend around northern Australian coastlines." Mathematics. 9 (21), pp. 1-20. https://doi.org/10.3390/math9212696
Random forest machine learning technique for automatic vegetation detection and modelling in LiDAR data
Tarsha Kurdi, Fayez, Amakhchan, Wijdan and Gharineiat, Zahra. 2021. "Random forest machine learning technique for automatic vegetation detection and modelling in LiDAR data." International Journal of Environmental Sciences and Natural Resources. 28 (2). https://doi.org/10.19080/IJESNR.2021.28.556234
Plantar Pressure Characteristics in Obese Individuals: A Proposed Methodology
Al-Daffaie, Kadhem, Chong, Albert K. and Gharineiat, Zahra. 2019. "Plantar Pressure Characteristics in Obese Individuals: A Proposed Methodology." 2019 3rd International Conference on Imaging, Signal Processing and Communication (ICISPC). Singapore 27 - 29 Jul 2019 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/ICISPC.2019.8935691
Spectral Analysis of Satellite Altimeters and Tide Gauges Data around the Northern Australian Coast
Gharineiat, Zahra and Deng, Xiaoli. 2020. "Spectral Analysis of Satellite Altimeters and Tide Gauges Data around the Northern Australian Coast." Remote Sensing. 12 (1), pp. 1-15. https://doi.org/10.3390/rs12010161
Description and assessment of regional sea-level trends and variability from altimetry and tide gauges at the northern Australian coast
Gharineiat, Zahra and Deng, Xiaoli. 2018. "Description and assessment of regional sea-level trends and variability from altimetry and tide gauges at the northern Australian coast." Advances in Space Research. 61 (10), pp. 2540-2554. https://doi.org/10.1016/j.asr.2018.02.038
Coastal altimetry for sea level changes in Northern Australian coastal oceans
Gharineiat, Zahra. 2017. Coastal altimetry for sea level changes in Northern Australian coastal oceans. PhD Thesis Doctor of Philosophy. University of Newcastle.
Observing and modelling the high water level from satellite radar altimetry during tropical cyclones
Deng, Xiaoli, Gharineiat, Zahra, Andersen, Ole B. and Stewart, Mark G.. 2016. "Observing and modelling the high water level from satellite radar altimetry during tropical cyclones." Rizos, Chris and Willis, Pascal (ed.) 2013 IAG Scientific Assembly. Postdam, Germany 01 - 06 Sep 2013 Switzerland. https://doi.org/10.1007/1345_2015_108
Application of the multi adaptive regression splines to integrate sea level data from altimetry and tide gauges for monitoring extreme sea level events
Gharineiat, Zahra and Deng, Xiaoli. 2015. "Application of the multi adaptive regression splines to integrate sea level data from altimetry and tide gauges for monitoring extreme sea level events." Marine Geodesy. 38 (3), pp. 261-276. https://doi.org/10.1080/01490419.2015.1036183