Design and Implementation of System for Estimating Diameter at Breast Height and Tree Height using LiDAR point cloud data (LiDAR 점군 데이터를 이용한 흉고직경과 수고를 예측하는 시스템의 설계와 구현)

Jong-Su Yim (임종수); Dong-Hyeon Kim (김동현); Chiung Ko (고치웅); 김동근; Hyung-Ju Cho (조형주)

doi:10.9708/jksci.2023.28.02.099

Design and Implementation of System for Estimating Diameter at Breast Height and Tree Height using LiDAR point cloud data

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2023, 28(2), pp.99~110
DOI : 10.9708/jksci.2023.28.02.099
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : January 26, 2023
Accepted : February 15, 2023
Published : February 28, 2023

Jong-Su Yim ¹, Dong-Hyeon Kim ², Chiung Ko ¹, 김동근 ², Hyung-Ju Cho ²

¹국립산림과학원
²경북대학교

Accredited

ABSTRACT

In this paper, we propose a system termed ForestLi that can accurately estimate the diameter at breast height (DBH) and tree height using LiDAR point cloud data. The ForestLi system processes LiDAR point cloud data through the following steps: downsampling, outlier removal, ground segmentation, ground height normalization, stem extraction, individual tree segmentation, and DBH and tree height measurement. A commercial system, such as LiDAR360, for processing LiDAR point cloud data requires the user to directly correct errors in lower vegetation and individual tree segmentation. In contrast, the ForestLi system can automatically remove LiDAR point cloud data that correspond to lower vegetation in order to improve the accuracy of estimating DBH and tree height. This enables the ForestLi system to reduce the total processing time as well as enhance the accuracy of accuracy of measuring DBH and tree height compared to the LiDAR360 system. We performed an empirical study to confirm that the ForestLi system outperforms the LiDAR360 system in terms of the total processing time and accuracy of measuring DBH and tree height.

KEYWORDS

LiDAR point cloud data, Diameter at breast height, Tree height, Terrestrial LiDAR, Software

Citation status

* References for papers published after 2023 are currently being built.

[journal] 김동현 / 2021 / Development of a Smartphone Application for the Measurement of Tree Height and Diameter at Breast Height / 한국산림과학회지 / 한국산림과학회 110(1) : 72~81

[journal] ChiUng Ko / 2022 / The Application of Terrestrial Light Detection and Ranging to Forest Resource Inventories for Timber Yield and Carbon Sink Estimation / Forests / MDPI AG 13(12) : 2087~

[journal] 김경민 / 2014 / Comparison of Three Kinds of Methods on Estimation of Forest Carbon Stocks Distribution Using National Forest Inventory DB and Forest Type Map / 한국지리정보학회지 / 한국지리정보학회 17(4) : 69~85

[journal] Chiung Ko / 2021 / Comparison of Forest Inventory Methods at Plot-Level between a Backpack Personal Laser Scanning (BPLS) and Conventional Equipment in Jeju Island, South Korea / Forests / MDPI AG 12(3) : 308~

[journal] B. Andrew / 2019 / Extracting Individual Trees from LiDAR Point Clouds using treeseg / Methods in Ecology and Evolution 10(3) : 291~445

[journal] T. J. Pingel / 2013 / An Improved Simple Morphological Filter for the Terrain Classification of Airborne LiDAR Data / ISPRS Journal of Photogrammetry and Remote Sensing 77 : 21~30

[journal] Maciej Lisiewicz / 2022 / Correcting the Results of CHM-Based Individual Tree Detection Algorithms to Improve Their Accuracy and Reliability / Remote Sensing / MDPI AG 14(8) : 1822~

[journal] 고치웅 / 2021 / Analysis of Optimal Pathways for Terrestrial LiDAR Scanning for the Establishment of Digital Inventory of Forest Resources / 대한원격탐사학회지 / 대한원격탐사학회 37(2) : 245~256

[journal] Lingfeng Liao / 2022 / A Supervoxel-Based Random Forest Method for Robust and Effective Airborne LiDAR Point Cloud Classification / Remote Sensing / MDPI AG 14(6) : 1516~

[journal] Roberto Ferrara / 2018 / An automated approach for wood-leaf separation from terrestrial LIDAR point clouds using the density based clustering algorithm DBSCAN / Agricultural and Forest Meteorology / Elsevier BV 262(15) : 434~444

[journal] 김두용 / 2016 / Extracting Individual Number and Height of Tree using Airborne LiDAR Data / 지적과 국토정보 / 한국국토정보공사 46(1) : 87~100

[journal] Rim Douss / 2022 / Extraction of individual trees based on Canopy Height Model to monitor the state of the forest / Trees, Forests and People / Elsevier BV 8 : 100257~

[journal] Fangyi Wang / 2022 / Estimating tree Diameter at Breast Height (DBH) using an iPad Pro LiDAR sensor / Remote Sensing Letters / Informa UK Limited 13(6) : 568~578

[journal] D. Wang / 2020 / LeWoS: A Universal Leaf-Wood Classification Method to Facilitate the 3D Modeling of Large Tropical Trees using Terrestrial LiDAR 11(3) : 376~389

[journal] 오윤석 / 2005 / Development of LiDAR and SBES data Merging Program for Calculation of Water Volume / Spatial Information Research / 대한공간정보학회 13(2) : 157~166

[journal] 장안진 / 2008 / Study of Biomass Estimation in Forest by Aerial Photograph and LiDAR Data / 한국지리정보학회지 / 한국지리정보학회 11(3) : 166~173

[journal] Sean Krisanski / 2021 / Forest Structural Complexity Tool—An Open Source, Fully-Automated Tool for Measuring Forest Point Clouds / Remote Sensing / MDPI AG 13(22) : 4677~

[journal] 우충식 / 2007 / Automatic Extraction of Individual Tree Height in Mountainous Forest Using Airborne Lidar Data / 한국산림과학회지 / 한국산림과학회 96(3) : 251~258

[journal] 심형섭 / 2023 / A Study on Implementation of a Disaster Crisis Alert System based on National Disaster Management System / 한국컴퓨터정보학회논문지 / 한국컴퓨터정보학회 28(1) : 55~63

This paper was written with support from the National Research Foundation of Korea.

KJCKorea
Journal Central

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Design and Implementation of System for Estimating Diameter at Breast Height and Tree Height using LiDAR point cloud data

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Design and Implementation of System for Estimating Diameter at Breast Height and Tree Height using LiDAR point cloud data

ABSTRACT

KEYWORDS

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REFERENCES (19) * References for papers published after 2023 are currently being built.

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* References for papers published after 2023 are currently being built.