Estimations of Forest Stand Parameters in Open Forest Stand Using Point Cloud Data from Terrestrial Laser Scanning, Unmanned Aerial Vehicle and Aerial LiDAR Data

Adil Enis Arslan; Muhittin İnan; Mehmet Furkan Çelik; Esra Erten

doi:10.33904/ejfe.1174123

EN

Estimations of Forest Stand Parameters in Open Forest Stand Using Point Cloud Data from Terrestrial Laser Scanning, Unmanned Aerial Vehicle and Aerial LiDAR Data

Abstract

Two of the very basic forestry parameters, the Breast Height Diameter (DBH) and Tree Height (TH) are very effective when characterizing forest stands and individual trees. The traditional measurement process of these parameters takes a lot of time and consumes human power. On the other hand, 3D Point Cloud (PC) quickly provides a very detailed view of forestry parameters, because of the development of computer processing power and digital storage in recent years. PC data sources for forestry applications include Airborne LiDAR Systems (ALS), Terrestrial Laser Scanning (TLS) and most recently the Unmanned Air Vehicle (UAV). In this study, the PC datasets from these sources were used to study the feasibility of the DBH and TH values of a d development stage (i.e. DBH > 52 cm in mature stage) oak stand. The DBH and TH estimates are compared with the onsite measurements, which are considered to be fundamental truths, to their performance due to overall error statistics, as well as the cost of calculation and the difficulties in data collection. The results show that the computer data obtained by TLS has the best average square error (0.22 cm for DBH and 0,051 m for TH) compared to other computer data. The size of Pearson correlation between TLS-based and on-site-based measurements has reached 0.97 and 0.99 for DBH, respectively.

Keywords

3D remote sensing , TLS , ALS , UAV , Tree Height , diameter at breast height , forest tree height

References

Anderson, J., Martin, M., Dubayah, ML., Dubayah, R., Hofton, M., Hyde, P., Peterson, B., Blair, J., Knox, R. 2006. The use of waveform LiDAR to measure northern temperate mixed conifer and deciduous forest structure in New Hampshire. Remote Sensing of Environment, 105:248-261. https://doi.org/10.1016/j.rse.2006.07.001
Arslan, A.E., Erten, E., Inan, M. 2021. A comparative study for obtaining effective Leaf Area Index from single Terrestrial Laser Scans by removal of wood material. Measurement, 178: 109262. https://doi.org/10.1016/j.measurement.2021.109262
Arslan, A.E., Erten, E., Inan, M. 2016. Application of Geodetic Projections to Terrestrial Laser Scanning in Leaf Area Index Calculation in Proceedings of the 2016 24th Signal Processing and Communication Application Conference (SIU), pp. 957-960. https://doi.org/10.1109/SIU.2016.7495900
Cabo, C., Ordonez, C., Lopez-Sanchez, C.A., Armesto, J. 2018. Automatic dendrometry: Tree detection, tree height and diameter estimation using terrestrial laser scanning. International Journal of Applied Earth Observation and Geoinformation, 69: 164-174. https://doi.org/10.1016/j.jag.2018.01.011
Demir, N. 2018. Using UAVs for detection of trees from digital surface models. Journal of Forestry Research, 29: 813-821. https://doi.org/10.1007/s11676-017-0473-9
Dobrowolska, D., Kuberski, P., Sterenczak, K. 2022. Canopy gap characteristics and regeneration patterns in the Bialowieza forest based on remote sensing data and field measurements. Forest Ecology and Management, 511: 120123. https://doi.org/10.1016/j.foreco.2022.120123
Drake JB, Dubayah RO, Clark DB, Knox RG, Blair JB, Hofton MA, Chazdon RL, Weishampel JF, Prince S (2002). Estimation of tropical forest structural characteristics, using large-footprint LiDAR. Remote Sensing of Environment, 79:305-319. https://doi.org/10.1016/S0034-4257(01)00281-4
Firoz, A, Laxmi, G, Abdul, Q. 2017. Natural Resource Mapping Using Landsat and Lidar towards Identifying Digital Elevation, Digital Surface and Canopy Height Models. Int. J. Environ. Sci. Nat. Res., 2(1): 555-580. https://doi.org/10.19080/IJESNR.2017.02.555580
Guerra-Hernandez, J., Diaz-Varela, R.A., Avarez-Gonzalez, J.G., Rodriguez-Gonzalez, P.M. 2021. Assessing a novel modelling approach with high resolution UAV imagery for monitoring health status in priority riparian forests. Forest Ecosystems, 8:61. https://doi.org/10.1186/s40663021-00342-8
Gülci, N., Akay, A.E., Erdaş, O., Wing, M.G., Sessions, J. 2015. Planning optimum logging operations through precision forestry approaches. Eur J For Eng. 1:56–60.

Hauglin, M., Rahlf, J., Schumacher, J., Astrup, R., Breidenbach, J. 2021. Large scale mapping of forest attributes using heterogeneous sets of airborne laser scanning and National Forest Inventory data. Forest Ecosystems, 8: 65. https://doi.org/10.1186/s40663-021-00338-4
Hyde, P., Dubayah, R., Peterson, B., Blair, J., Hofton, M., Hunsaker, C., Knox, R., Walker, W. 2005. Mapping forest structure for wildlife habitat analysis using waveform LiDAR: validation of montane ecosystems. Remote Sensing of Environment, 96: 427-437. https://doi.org/10.1016/j.rse.2005.03.005
Hyyppa, E., Hyyppa, J., Hakala, T., Kukko, A., Wulder, M.A., White, J.C., Pyorala, J., Yu, X., Wang, Y., Virtanen, J.P. 2020. Under-canopy UAV laser scanning for accurate forest field measurements. ISPRS Journal of Photogrammetry and Remote Sensing. 164: 41-60. https://doi.org/10.1016/j.isprsjprs.2020.03.021
Ige, P.O., Akinyemi, G.O., Smith, A.S. 2013. Nonlinear growth functions for modeling tree height-diameter relationships for Gmelina arborea (Roxb.) in south-west Nigeria. Forest Science and Technology, 9: 20-24, https://doi.org/10.1080/21580103.2013.773662
Inan, M., Bilici, E., Akay, A.E. 2017. Using Airborne Lidar Data for Assessment of Forest Fire Fuel Load Potential. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-4/W4, 255-258. https://doi.org/10.5194/isprs-annals-IV-4-W4-255-2017
Jensen, J.L.R., Mathews, A.J. 2016. Assessment of Image-Based Point Cloud Products to Generate a Bare Earth Surface and Estimate Canopy Heights in a Woodland Ecosystem. Remote Sensing, 8. https://doi.org/10.3390/rs8010050
Koc-San, D., Selim, S., Aslan, N., San, B.T. 2018. Automatic citrus tree extraction from UAV images and digital surface models using circular Hough transform. Computers and Electronics in Agriculture, 150: 289-301. https://doi.org/10.1016/jcompag.2018.05.001
Koren, M., Mokros, M., Bucha, T. 2017. Accuracy of tree diameter estimation from terrestrial laser scanning by circle-fitting methods. International Journal of Applied Earth Observation and Geoinformation, 63: 122-128. https://doi.org/10.1016/jjag.2017.07.015
Kovácsová, P., Antalová, M. 2010. Precision forestry–definition and technologies. Šumarski List,134(11-12): 603-610.
Lefsky MA, Cohen WB, Harding DJ, Parker GG, Acker SA, Gower ST (2002). LiDAR remote sensing of above-ground biomass in three biomes. Global Ecology & Biogeography 11:393-399. https://doi.org/10.1046/j.1466-822x.2002.00303.x
Liang, X., Wang, Y., Pyorala, J., Lehtomaki, M., Yu, X., Kaartinen, H., Kukko, A., Honkavaara, E., Issao ui, A.E.I., Nevalainen, O. 2019. Forest in situ observations using unmanned aerial vehicle as an alternative of terrestrial measurements. Forest Ecosystems. 6:20. https://doi.org/10.1186/s40663-019-0173-3
Li, L., Mu, X., Soma, M., Wan, P., Qi, J., Hu, R., Zhang, W., Tong, Y., Yan, G. 2021. An Iterative-Mode Scan Design of Terrestrial Laser Scanning in Forests for Minimizing Occlusion Effects. IEEE Transactions on Geoscience and Remote Sensing, 59:3547-3566. https://doi.org/10.1109/TGRS.2020.3018643
Lizuka, K., Kosugi, Y., Noguchi, S., Iwagami, S. 2022. Toward a comprehensive model for estimating diameter at breast height of Japanese cypress (Chamaecyparis obtusa) using crown size derived from unmanned aerial systems. Computers and Electronics in Agriculture, 192: 106579. https://doi.org/10.1016/j.compag.2021.106579
Luo, H., Wang, C., Wen, C., Chen, Z., Zai, D., Yu, Y., Li, J. 2018. Semantic Labeling of Mobile LiDAR Point Clouds via Active Learning aid Higher Order MRF. IEEE Transactions on Geoscience and Remote Sensing, 56: 3631-3644. https://doi.org/10.1109/TGRS2018.2802935
Mielcarek, M., Sterenczak, K., Khosravipour, A. 2018. Testing and evaluating different LiDAR-derived canopy height model generation methods for tree height estimation. International Journal of Applied Earth Observation and Geoinformation, 71: 132-143. https://doi.org/10.1016/j.jag.2018.05.002
Patricio, M.S., Dias, C.R., Nunes, L. 2022. Mixed-effects generalized height-diameter model: A tool for forestry management of young sweet chestnut stands. Forest Ecology and Management, 514, 120209. https://doi.org/10.1016/j.foreco.2022.1.20209
Paris, C., Bruzzone, L. 2019. A Growth-Model-Driven Technique for Tree Stem Diameter Estimation by Using Airborne LiDAR Data. IEEE Transactions on Geoscience and Remote Sensing, 57: 76-92. https://doi.org/10.1109/TGRS.2018.2852364
Philip, M.S, Measuring trees and forests. CABI, 1994
Popescu, S. 2007. Estimating biomass of individual pine trees using air-borne LIDAR. Biomass and Bioenergy. 31:(9)646-655. https://doi.org/10.1016/j.biombioe.2007.06.022
Reitberger J, Krzystek P, Stilla U (2008). Analysis of full waveform LIDAR data for the classification of deciduous and coniferous trees. International Journal of Remote Sensing, 29: 1407-1431. https://doi.org/10.1080/01431160701736448
Serengil, Y. 2020. Climate Change and Carbon Management, United Nations Development Program Report.
Shimizu, K., Nishizono, T., Kitahara, F., Fukumoto, K., Saito, H. 2022. Integrating terrestrial laser scanning and unmanned aerial vehicle photogrammetry to estimate individual tree attributes in managed coniferous forests in Japan. International Journal of Applied Earth Observation and Geoinformation, 106: 102658. https://doi.org/10.1016/j.jag.2021.102658
Su, Y., Guo, Q., Jin, S., Guan, H., Sun, X., Ma, Q., Hu, T., Wang, R., Li, Y. 2021. The Development and Evaluation of a Backpack LiDAR System for Accurate and Efficient Forest Inventory. IEEE Geoscience and Remote Sensing Letters, 18:1660-1664. https://doi.org/10.1109/LGRS.2020.3005166
Spurr, S. Forest Inventory, Ronald Press Company, 1952
Tan, K., Zhang, W., Dong, Z., Cheng, X., Cheng, X. 2021. Leaf and Wood Separation for Individual Trees Using the Intensity and Density Data of Terrestrial Laser Scanners. IEEE Transactions on Geoscience and Remote Sensing, 59:7038-7050. https://doi.org/10.1109/TGRS.2020.3032167
White, J.C., Coops, N.C., Wulder, M.A., Vastaranta, M., Hilker, T., Tompalski, P. 2016. Remote Sensing Technologies for Enhancing Forest Inventories: A Review. Canadian Journal of Remote Sensing, 42: 619-641. https://doi.org/10.1080/07038992.2016. 1207484.

Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Authors

Adil Enis Arslan
0000-0003-2080-0278
Türkiye

Muhittin İnan ^*
0000-0001-8179-9499
Türkiye

Mehmet Furkan Çelik
0000-0001-7948-536X
Türkiye

Esra Erten
0000-0002-4208-7170
Türkiye

Publication Date

December 30, 2022

Submission Date

September 12, 2022

Acceptance Date

September 25, 2022

Published in Issue

Year 2022 Volume: 8 Number: 2

DOI

https://doi.org/10.33904/ejfe.1174123

IZ

https://izlik.org/JA45TN62MR

APA

Arslan, A. E., İnan, M., Çelik, M. F., & Erten, E. (2022). Estimations of Forest Stand Parameters in Open Forest Stand Using Point Cloud Data from Terrestrial Laser Scanning, Unmanned Aerial Vehicle and Aerial LiDAR Data. European Journal of Forest Engineering, 8(2), 46-54. https://doi.org/10.33904/ejfe.1174123