Tree Height Derivation under Homogeneous Tree Pattern by Segmentation Algorithm

Suzanah Abdullah (1), Mohd Fadzil Abdul Rashid (2), Muhammad Ariffin Osoman (3), Khairul Nizam Tahar (4)
(1) Department of Built Environment Studies and Technologies, Faculty Architecture, Planning and Surveying, Universiti Teknologi MARA Perak Branch, 32610, Seri Iskandar, Perak, Malaysia
(2) Department of Built Environment Studies and Technologies, Faculty Architecture, Planning and Surveying, Universiti Teknologi MARA Perak Branch, 32610, Seri Iskandar, Perak, Malaysia
(3) Geoinfo Services Sdn Bhd, No 30 Jalan Bandar 2, Taman Melawati, Kuala Lumpur, Malaysia
(4) Centre of Studies for Surveying Science and Geomatics, Faculty of Architecture Planning and Surveying, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
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How to cite (IJASEIT) :
Abdullah, Suzanah, et al. “Tree Height Derivation under Homogeneous Tree Pattern by Segmentation Algorithm”. International Journal on Advanced Science, Engineering and Information Technology, vol. 12, no. 3, May 2022, pp. 1120-31, doi:10.18517/ijaseit.12.3.15547.
Tree height is an important element in plantation areas to determine the tree's maturity for harvesting. Airborne Laser Scanning (ALS) technology can give accurate elevation using point cloud data. However, this technology is quite expensive and unsuitable for small areas and low-budget projects. This research focuses on the UAV technology, exploring the appropriate methods of determining a tree height from the UAV’s photogrammetry images. This research aims to evaluate the tree height from the delineation of the tree crown. Three different algorithms have been used in this research to delineate tree crowns. The delineate tree crowns were extracted in vector format, and the crowns were used to calculate the tree height using the specific formula. The results of tree heights were assessed using Residual Mean Square Error (RMSE) to determine the accuracy of the outcome. It was found that the OBIA algorithm gives the best accuracy among these three algorithms. It is followed by WS algorithm and then IWS algorithm. The OBIA algorithm can give an accuracy of about 0.444m at 40m and 0.381m for 60 altitude. The accuracy for WS and OBIA stated that the 60m altitude gives the better accuracy compared to 40m altitude. However, the IWS gives the vice versa result. This research could help the planters to manage their plantations for the harvesting process.

M. Wang and J. Lin, "Retrieving individual tree heights from a point cloud generated with optical imagery from an unmanned aerial vehicle (UAV)," Canadian Journal of Forest Research, vol. 50, no. 10, 2020.

S. Krause, T.G. Sanders, J.P. Mund and K. Greve, “UAV-based photogrammetric tree height measurement for intensive forest monitoring,” Remote Sens., vol. 11: 758, 2019.

F. Giannettia, N. Puletti, P. Stefano, D. Travaglinia and G. Chiricia, “Assessment of UAV photogrammetric DTM-independent variables for modelling and mapping forest structural indices in mixed temperate forests,” Ecol. Indic., vol. 117: 106513, 2020.

H. Huang, S. He and C. Chen, “ Leaf abundance affects tree height estimation derived from UAV images,” Forests, vol. 10: 931, 2019.

C. Barnes, H. Balzter, K. Barrett, J. Eddy, S. Milner and J.C. Suí¡rez, "Individual Tree Crown Delineation from Airborne Laser Scanning for Diseased Larch Forest Stands," Remote Sensing, vol. 9, no. 3, pp. 1-20, 2017.

M. Vastaranta, M. Holopainen, M. Karjalainen, V. Kankare, J. Hyyppa and S. Kaasalainen, "TerraSAR-X Stereo Radargrammetry and Airborne Scanning LiDAR Height Metrics in Imputation of Forest Aboveground Biomass and Stem Volume," IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 2, pp. 1197-1204, 2014.

E.G. Parmehr and M. Amati, "Individual Tree Canopy Parameters Estimation Using UAV-Based Photogrammetric and LiDAR Point Clouds in an Urban Park," Remote Sensing. 2021, vol. 13, no. 11, 2062, 2021.

L. Cao, H. Liu, X. Fu, Z. Zhang, X. Shen and H. Ruan, “Comparison of UAV LiDAR and Digital Aerial Photogrammetry Point Clouds for Estimating Forest Structural Attributes in Subtropical Planted Forests,” Forests, vol. 10, 145, 2019

T. Swinfield, J.A. Lindsell, J.V. Williams, R.D. Harrison, Agustiono, Habibi, E. Gemita, C.B. Schí¶nlieb and D.A. Coomes, “Accurate Measurement of Tropical Forest Canopy Heights and Aboveground Carbon Using Structure From Motion,” Remote Sens., vol. 11, 928, 2019.

S. Ganz, Y. Kí¤ber and P. Adler, "Measuring Tree Height with Remote Sensing—A Comparison of Photogrammetric and LiDAR Data with Different Field Measurements," Forests, 2019, vol. 10, no. 8, 694, 2019.

L. Jurjević, X. Liang, M. Gašparović and I. Balenović, "Is field-measured tree height as reliable as believed - Part II, A comparison study of tree height estimates from conventional field measurement and low-cost close-range remote sensing in a deciduous forest, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 169, pp. 227-241, 2020.

M. Koeva, M. Muneza, C. Gevaert, M. Gerke, F. Nex, M. Koeva, ”¦ F. Nex, "Using UAVs for Map Creation and Updating. A Case Study in Rwanda," Survey Review, vol. 50, no. 361, pp. 312-325, 2018.

L. Huo and E. Lindberg, "Individual tree detection using template matching of multiple rasters derived from multispectral airborne laser scanning data," International Journal of Remote Sensing, vol. 41, no. 24, pp. 9525-9544, 2020.

A.C. Birdal, U. Avdan and T. Tí¼rk, "Estimating Tree Heights with Images from An Unmanned Aerial Vehicle," Geomatics, Natural Hazards and Risk, vol 8, no. 2, pp. 1144-1156, 2017.

L. Won-Jin and L. Chang-Wook, "Forest Canopy Height Estimation Using Multiplatform Remote Sensing Dataset," Journal of Sensors, vol. 2018, no. 9, 2018.

K.N. Tahar, M.A. Asmadin, S.A. Sulaiman, N. Khalid, A.N. Idris and M. H. Razali, "Individual Tree Crown Detection Using UAV Orthomosaic," Engineering, Technology & Applied Science Research, vol. 11, no. 2, pp. 7047-7053, 2021.

Y. Wang, M. Lehtomí¤ki, X. Liang, J. Pyí¶rí¤lí¤ and A. Kukko, "Is Field-Measured Tree Height As Reliable As Believed - A Comparison Study of Tree Height Estimates from Field Measurement , Airborne Laser Scanning and Terrestrial Laser Scanning In a Boreal Forest," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 147, no. 201, pp. 132-145, 2019.

K. Zhao, J.C. Suarez, M. Garcia, T. Hu, C. Wang and A. Londo, "Utility of Multitemporal LiDAR for Forest and Carbon Monitoring: Tree Growth, Biomass Dynamics, and Carbon Flux," Remote Sensing of Environment, vol. 204, 883-897, 2018.

X. Zhuo, T. Koch, F. Kurz, F. Fraundorfer and P. Reinartz, "Automatic UAV Image Geo-Registration by Matching UAV Images to Georeferenced Image Data," Remote Sensing, vol. 9, no. 4, pp. 1-25, 2017.

A.P. Colefax, P.A. Butcher and B.P. Kelaher, "The Potential for Unmanned Aerial Vehicles (UAVs) to Conduct Marine Fauna Surveys in Place of Manned Aircraft," ICES Journal of Marine Science, vol. 75, no. 1, pp. 1-8, 2018.

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