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

Year 2022, Volume: 8 Issue: 2, 46 - 54, 30.12.2022

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

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

Cited By: 1

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

Supporting Institution

Scientific Research Projects Coordination of Istanbul Technical University

Project Number

Project 38370

Thanks

The authors would like to thank the Marmara Forestry Research Institute for their assistance in the fieldwork.

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.