Skip to main content

Advertisement

SpringerLink
Log in

Error estimation of trunk diameter and tree height measured with a backpack LiDAR system in Japanese plantation forests

  • Technical report
  • Published:
Landscape and Ecological Engineering Aims and scope Submit manuscript

Abstract

Trunk biomass in a forest is necessary information for good forest management. The trunk biomass is usually calculated by an allometric formula using trunk diameter at breast height (DBH) and tree height (TH) as non-destructive input values. To confirm the measurement errors of a backpack LiDAR system in forest inventory, DBH and TH values obtained by the LiDAR system were compared with conventional survey data from Japanese plantation forests. Japanese cedar (Cryptomeria japonica) and Japanese cypress (Chamaecyparis obtusa) form large plantation forests in Japan. The backpack LiDAR system was used in five plots of these species-dominated forests (20 m × 20 m for each plot). The tree detection rates were 100% in all plots. The relative RMSEs of the DBH and TH estimations ranged from 6.8 to 10.0% and 5.4–10.1%, respectively. The negative biases indicated underestimation of DBH and TH. The DBH and TH results of the present study were close to those obtained using the backpack LiDAR approach in previous studies. The paper demonstrated a fully automated solution for measuring DBH and TH using the backpack LiDAR system. The solution showed that the system has a distinct capability as an efficient and practical tool for forest inventory and management.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

References

  • Aruga K, Yanagihara E, Yamamoto T, Ishiguri F, Furusawa T, Akira K (2017) Application of portable terrestrial laser scanner to a secondary broad-leaved forest. Eur J for Eng 3:7–15

    Google Scholar 

  • Beland M, Parker G, Sparrow B et al (2019) On promoting the use of lidar systems in forest ecosystem research. For Ecol Manag 450:117484

    Article  Google Scholar 

  • Brolly G, Kiraly G (2009) Algorithms for stem mapping by means of terrestrial laser scanning. Acta Silv Et Lignaria Hungarica 5:119–130

    Google Scholar 

  • Cabo C, Ordóñez C, López-Sánchez CA, Armesto J (2018) Automatic dendrometry: Tree detection, tree height and diameter estimation using terrestrial laser scanning. Int J Appl Earth Obs Geoinf 69:164–174

    Google Scholar 

  • Clark NA, Wynne RH, Schmoldt DL (2000) A review of past research on dendrometers. For Sci 46:570–576

    Google Scholar 

  • Egusa T, To K, Shiraishi N (2020) Carbon stock in Japanese forests has been greatly underestimated. Sci Rep 10:1–9

    Article  Google Scholar 

  • Fleck S, Mölder I, Jacob M, Gebauer T, Jungkunst HF, Leuschner C (2011) Comparison of conventional eight-point crown projections with LIDAR-based virtual crown projections in a temperate old-growth forest. Ann for Sci 68:1173–1185

    Article  Google Scholar 

  • Hopkinson C, Chasmer L, Young-Pow C, Treitz P (2004) Assessing forest metrics with a ground-based scanning lidar. Can J for Res 34:573–583

    Article  Google Scholar 

  • Huang H, Li Z, Gong P et al (2011) Automated methods for measuring DBH and tree heights with a commercial scanning lidar. Photogramm Eng Remote Sens 77:219–227

    Article  Google Scholar 

  • Ishihara MI, Utsugi H, Tanouchi H, Aiba M et al (2015) Efficacy of generic allometric equations for estimating biomass: a test in Japanese natural forests. Ecol Appl 25:1433–1446

    Article  Google Scholar 

  • Kangas A, Maltamo M (2006) Forest inventory: methodology and applications. Springer Science & Business Media, Cham

    Book  Google Scholar 

  • Ko C, Lee S, Yim J, Kim D, Kang J (2021) Comparison of forest inventory methods at plot-level between a backpack personal laser scanning (BPLS) and conventional equipment in Jeju island. South Korea for 12:308

    Google Scholar 

  • Larjavaara M, Muller-Landau HC (2013) Measuring tree height: a quantitative comparison of two common field methods in a moist tropical forest. Methods Ecol Evol 4:793–801

    Article  Google Scholar 

  • Lefsky MA, Cohen WB, Parker GG, Harding DJ (2002) Lidar remote sensing for ecosystem studies: Lidar, an emerging remote sensing technology that directly measures the three-dimensional distribution of plant canopies, can accurately estimate vegetation structural attributes and should be of particular interest to forest, landscape, and global ecologists. Bioscience 52:19–30

    Article  Google Scholar 

  • Li W, Guo Q, Jakubowski MK, Kelly M (2012) A new method for segmenting individual trees from the lidar point cloud. Photogramm Eng Remote Sensing 78:75–84

    Article  Google Scholar 

  • Liang X, Hyyppä J (2013) Automatic stem mapping by merging several terrestrial laser scans at the feature and decision levels. Sensors 13:1614–1634

    Article  Google Scholar 

  • Liang X, Kukko A, Kaartinen H et al (2014) Possibilities of a personal laser scanning system for forest mapping and ecosystem services. Sensors 14:1228–1248

    Article  Google Scholar 

  • Liang X, Kankare V, Hyyppä J et al (2016) Terrestrial laser scanning in forest inventories. ISPRS J Photogramm Remote Sens 115:63–77

    Article  Google Scholar 

  • Lim K, Treitz P, Wulder M, St-Onge B, Flood M (2003) LiDAR remote sensing of forest structure. Prog Phys Geogr 27:88–106

    Article  Google Scholar 

  • Lu J, Wang H, Qin S et al (2020) Estimation of aboveground biomass of Robinia pseudoacacia forest in the Yellow River Delta based on UAV and Backpack LiDAR point clouds. Int J Appl Earth Obs Geoinf 86:102014

    Google Scholar 

  • Maas HG, Bienert A, Scheller S, Keane E (2008) Automatic forest inventory parameter determination from terrestrial laser scanner data. Int J Remote Sens 29:1579–1593

    Article  Google Scholar 

  • MacDicken KG (2015) Global forest resources assessment 2015: what, why and how? For Ecol Manag 352:3–8

    Article  Google Scholar 

  • Marselis SM, Yebra M, Jovanovic T, van Dijk AIJM (2016) Deriving comprehensive forest structure information from mobile laser scanning observations using automated point cloud classification. Environ Model Softw 82:142–151

    Article  Google Scholar 

  • Miura N, Jones SD (2010) Characterizing forest ecological structure using pulse types and heights of airborne laser scanning. Remote Sens Environ 114:1069–1076

    Article  Google Scholar 

  • Moran LA, Williams RA (2002) Field note—comparison of three dendrometers in measuring diameter at breast height field note. N J Appl for 19:28–33

    Google Scholar 

  • Næsset E, Bjerknes K-O (2001) Estimating tree heights and number of stems in young forest stands using airborne laser scanner data. Remote Sens Environ 78:328–340

    Article  Google Scholar 

  • Nagasaka K, Böcher M, Krott M (2016) Science-policy interaction: the case of the forest and forestry revitalisation plan in Japan. Land Use Policy 58:145–151

    Article  Google Scholar 

  • Ninan KN, Inoue M (2013) Valuing forest ecosystem services: what we know and what we don’t. Ecol Econ 93:137–149

    Article  Google Scholar 

  • Olofsson K, Holmgren J, Olsson H (2014) Tree stem and height measurements using terrestrial laser scanning and the RANSAC algorithm. Remote Sens 6:4323–4344

    Article  Google Scholar 

  • Pueschel P, Newnham G, Rock G, Udelhoven T, Werner W, Hill J (2013) The influence of scan mode and circle fitting on tree stem detection, stem diameter and volume extraction from terrestrial laser scans. ISPRS J Photogramm Remote Sens 77:44–56

    Article  Google Scholar 

  • Tao S, Wu F, Guo Q et al (2015) Segmenting tree crowns from terrestrial and mobile LiDAR data by exploring ecological theories. ISPRS J Photogramm Remote Sens 110:66–76

    Article  Google Scholar 

  • Watt PJ, Donoghue DNM (2005) Measuring forest structure with terrestrial laser scanning. Int J Remote Sens 26:1437–1446

    Article  Google Scholar 

  • Wulder MA, White JC, Andrew ME, Seitz NE, Coops NC (2009) Forest fragmentation, structure, and age characteristics as a legacy of forest management. For Ecol Manag 258:1938–1949

    Article  Google Scholar 

  • Wulder MA, White JC, Nelson RF et al (2012) Lidar sampling for large-area forest characterization: a review. Remote Sens Environ 121:196–209

    Article  Google Scholar 

  • Xie Y, Zhang J, Chen X, Pang S, Zeng H, Shen Z (2020) Accuracy assessment and error analysis for diameter at breast height measurement of trees obtained using a novel backpack LiDAR system. For Ecosyst 7:1–11

    Article  Google Scholar 

  • Yu X, Hyyppä J, Vastaranta M, Holopainen M, Viitala R (2011) Predicting individual tree attributes from airborne laser point clouds based on the random forests technique. ISPRS J Photogramm Remote Sens 66:28–37

    Article  Google Scholar 

  • Yu Z, You W, Agathokleous E, Zhou G, Liu S (2021) Forest management required for consistent carbon sink in China’s forest plantations. For Ecosyst 8:1–9

    Article  Google Scholar 

  • Zhu J, Dai E, Zheng D, Wang S (2021) Using a simple model to determine the best management regimes for plantations at the stand level: a case study of Moshao forest farm in the red-soil hilly region of southern China. Forests 12:1358

    Article  Google Scholar 

Download references

Acknowledgements

This work was partly supported by the Consortium of Universities & Local Communities in Shizuoka, Collaborative Research Grant. The authors thank the staff of Fuji City Office for providing them with the forest information and facilitating the research. The authors also thank Naohiro Ukai and Hiroko Muramatsu (Kaiteki Kukan FC Co. Ltd.) for their assistance.

Author information

Authors and Affiliations

  1. National Institute of Technology, Numazu College, 3600 Ooka, Numazu, 410-8501, Japan

    Bido Tsuchiya & Shizuo Suzuki

  2. Kaiteki Kukan FC Co. Ltd., 7-2-27 Hontoori Aoiku, Shizuoka, 420-0064, Japan

    Hirotaka Mochizuki

  3. Shizuoka Professional University of Agriculture, 678-1 Tomigaoka, Iwata, 438-8577, Japan

    Takeshi Hoshikawa

Authors
  1. Bido Tsuchiya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hirotaka Mochizuki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Takeshi Hoshikawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shizuo Suzuki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shizuo Suzuki.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tsuchiya, B., Mochizuki, H., Hoshikawa, T. et al. Error estimation of trunk diameter and tree height measured with a backpack LiDAR system in Japanese plantation forests. Landscape Ecol Eng 19, 169–177 (2023). https://doi.org/10.1007/s11355-022-00530-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11355-022-00530-w

Keywords

Search

Navigation