Comparison of unmanned aerial vehicle (UAV) RGB imagery and point clouds in crown area estimation of individual trees within pine (Pinus eldarica) man-made forests

Subject Areas : Agriculture, rangeland, watershed and forestry

Ali Hosingholizade ¹ , Yousef Erfanifard ^{2 *} , Seyed Kazem Alavipanah ³ , Homan Latifi ⁴ , Yaser Jouybari Moghaddam ⁵

1 - Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran
2 - Dept. of Remote sensing and GIS, Faculty of Geography, University of Tehran, Iran
3 - Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran
4 - Assistant Professor, Faculty of Surveying Engineering, Khajeh Nasir Tusi University of Technology, Tehran, Iran.
5 - Department of Surveying Engineering, Faculty of Engineering, Bojnurd University, Bojnurd, Iran

Received: 2022-02-14 Accepted : 2022-03-06 Published : 2023-05-22

Keywords: Marker-Controlled Watershed algorithm, Crown height model, Bojnurd Pardisan Park, Segmentation,

Abstract :

Application of unmanned aerial vehicles (UAVs) is widespread in measurement of quantitative characteristics of single trees such as crown area. However, the efficiency of different types of data collected by UAVs are less compared. Therefore, the aim of this study was comparison of UAV RGB imagery and point clouds in crown area estimation of individual pine trees within a man-made forest in Pardisan Park, Northern Khorasan province, Iran. Both datatypes were analyzed by similar segmentation method (Multiresolution segmentation on the RGB images and Marker-Controlled Watershed algorithm in the point clouds) to estimate crown area of 324 sample pine trees. The results showed that the crown area measured on the point clouds had higher accuracy and precision compared to RGB imager (Spearman correlation of 0.95 and 0.81, coefficient of determination of 0.97 and 0.59, p-value of 0.97 and 0.001, respectively). Additionally, crown area of pine trees with large crown (> 18 m2) delineated on the point clouds showed the highest accuracy in comparison to trees with small and medium crowns. In general, it was concluded that segmentation of UAV point clouds was more efficient than RGB imagery segmentation in quantification of crown area of pine trees within the study area.

References:

1. Costa L, Nunes L. Ampatzidis Y. 2020. A new visible band index (vNDVI) for estimating NDVI values on RGB images utilizing genetic algorithms. Computers and Electronics in Agriculture, 172:105334. https://doi.org/10.1016/j.compag.2020.105334.
2. Dong X, Zhang Z, Yu R, Tian Q, Zhu X. 2020. Extraction of information about individual trees from high-spatial-resolution UAV-acquired images of an orchard. Remote Sensing, 12(1):133. https://doi.org/10.3390/rs12010133.
3. Fankhauser K.E, Strigul N.S, Gatziolis D. 2018. Augmentation of traditional forest inventory and airborne laser scanning with unmanned aerial systems and photogrammetry for forest monitoring. Remote Sensing, 10(10):1562. https://doi.org/10.3390/rs10101562.
4. Gorgoso-Varela J.J, Ponce R.A, Rodríguez-Puerta F. 2021. Modeling Diameter Distributions with Six Probability Density Functions in Pinus halepensis Mill. Plantations Using Low-Density Airborne Laser Scanning Data in Aragón (Northeast Spain). Remote Sensing, 13(12):2307. https://doi.org/10.3390/rs13122307.
5. Grznárová A, Mokroš M, Surový P, Slavík M, Pondelík M, Merganič J. 2019. THE CROWN DIAMETER ESTIMATION FROM FIXED WING TYPE OF UAV IMAGERY. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 15:337. https://pdfs.semanticscholar.org/a9d3/749d5d316b609984de1dd6b5b84a972a3236.pdf.
6. Gu J, Grybas H, Congalton R.G. 2020. Individual tree crown delineation from UAS imagery based on region growing and growth space considerations. Remote Sensing, 12(15):2363. https://doi.org/10.3390/rs12152363.
7. Gülci S, Akay A.E, Gülci N, Taş İ. 2021. An assessment of conventional and drone-based measurements for tree attributes in timber volume estimation: A case study on stone pine plantation. Ecological Informatics, 63:101303. https://doi.org/10.1016/j.ecoinf.2021.101303.
8. Huang H, Li X, Chen C. 2018. Individual tree crown detection and delineation from very-high-resolution UAV images based on bias field and marker-controlled watershed segmentation algorithms. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(7):2253-2262. DOI: 10.1109/JSTARS.2018.2830410.
9. Iizuka K, Yonehara T, Itoh M. Kosugi Y. 2018. Estimating tree height and diameter at breast height (DBH) from digital surface models and orthophotos obtained with an unmanned aerial system for a Japanese cypress (Chamaecyparis obtusa) forest. Remote Sensing, 10(1):13. https://doi.org/10.3390/rs10010013.
10. Iqbal I.A, Osborn J, Stone C, Lucieer A. 2021. A Comparison of ALS and Dense Photogrammetric Point Clouds for Individual Tree Detection in Radiata Pine Plantations. Remote Sensing, 13(17):3536. https://doi.org/10.3390/rs13173536.
11. Kim S.R, Kwak D.A, Olee W.K, Son Y, Bae S.W, Kim C, Yoo S. 2010. Estimation of carbon storage based on individual tree detection in Pinus densiflora stands using a fusion of aerial photography and LiDAR data. Science China Life Sciences, 53(7): 885-897. doi:10.1007/s11427-010-4017-1.
12. Lim Y.S, La P.H, Park J.S, Lee M.H, Pyeon M.W, Kim J.I. 2015. Calculation of tree height and canopy crown from drone images using segmentation. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 33(6):605-614. https://doi.org/10.7848/ksgpc.2015.33.6.605.
13. McMahon C.A. 2019. Remote sensing pipeline for tree segmentation and classification in a mixed softwood and hardwood system. PeerJ, 6:5837. https://doi.org/10.7717/peerj.5837.
14. Menendez-Miguelez M, Ruiz-Peinado R, Del Río M, Calama R. 2021. Improving tree biomass models through crown ratio patterns and incomplete data sources. European Journal of Forest Research, 140(3):675-689. https://doi:10.1007/s10342-021-01354-3
15. Moe K.T, Owari T, Furuya N, Hiroshima T. 2020. Comparing individual tree height information derived from field surveys, LiDAR and UAV-DAP for high-value timber species in Northern Japan. Forests, 11(2):223. https://doi.org/10.3390/f11020223.
16. Safonova A, Hamad Y, Dmitriev E, Georgiev G, Trenkin V, Georgieva M, Dimitrov S, Iliev M. 2021. Individual Tree Crown Delineation for the Species Classification and Assessment of Vital Status of Forest Stands from UAV Images. Drones, 5(3):77. https://doi.org/10.3390/drones5030077.
17. Scholz J, De Meyer A, Marques A.S, Pinho T.M, Boaventura-Cunha J, Van Orshoven J, Rosset C, Künzi J, Kaarle J, Nummila K. 2018. Digital technologies for forest supply chain optimization: existing solutions and future trends. Environmental management, 62(6):1108-1133. doi:10.1007/s00267-018-1095-5.
18. Surový P, Almeida Ribeiro N, Panagiotidis D. 2018. Estimation of positions and heights from UAV-sensed imagery in tree plantations in agrosilvopastoral systems. International Journal of Remote Sensing, 39(14):4786-4800. https://doi.org/10.1080/01431161.2018.1434329.
19. Torres-Sánchez J, de Castro A.I, Pena J.M, Jimenez-Brenes F.M, Arquero O, Lovera M, Lopez-Granados F . 2018. Mapping the 3D structure of almond trees using UAV acquired photogrammetric point clouds and object-based image analysis. Biosystems engineering, 176:172-184. https://doi.org/10.1016/j.biosystemseng.2018.10.018.
20. Tu Y.H, Phinn S, Johansen K, Robson A, Wu D. 2020. Optimising drone flight planning for measuring horticultural tree crop structure. ISPRS Journal of Photogrammetry and Remote Sensing, 160:83-96. https://doi.org/10.1016/j.isprsjprs.2019.12.006.
21. Weinstein B.G, Marconi S, Bohlman S, Zare A, White E. 2019. Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks. Remote Sensing, 11(11): 1309. https://doi.org/10.3390/rs11111309.
22. Wu B, Wu S, Li Y, Wu J, Huang Y, Chen Z, Yu B. 2020. Automatic building rooftop extraction using a digital surface model derived from aerial stereo images. Journal of Spatial Science,1-20. https://doi.org/10.1080/14498596.2020.1720836.
23. Wulder M, Niemann K.O, Goodenough D.G. 2000. Local maximum filtering for the extraction of tree locations and basal area from high spatial resolution imagery. Remote Sensing of environment, 73(1):103-114. https://doi.org/10.1016/S0034-4257(00)00101-2.
24. Yilmaz V, Güngör O. 2019. Estimating crown diameters in urban forests with Unmanned Aerial System-based photogrammetric point clouds. International Journal of Remote Sensing, 40(2):468-505. https://doi.org/10.1080/01431161.2018.1562255.
25. Yim J.S, Kim Y.H, Kim S.H, Jeong J.H, Shin, M.Y. 2011. Comparison of the k-nearest neighbor technique with geographical calibration for estimating forest growing stock volume. Canadian journal of forest research, 41(1):73-82. https://doi.org/10.1139/X10-132.
26. Zheng J, Fu H, Li W, Wu W, Yu L, Yuan S, Ta, W.Y.W, Pang T.K, Kanniah K.D. 2021. Growing status observation for oil palm trees using Unmanned Aerial Vehicle (UAV) images. ISPRS Journal of Photogrammetry and Remote Sensing, 173:95-121. https://doi.org/10.1016/j.isprsjprs.2021.01.008.
27. Zimudzi E, Sanders I, Rollings N, Omlin C.W. 2021. Remote sensing of mangroves using unmanned aerial vehicles: Current state and future directions. Journal of Spatial Science, 66(2):195-212. https://doi.org/10.1080/14498596.2019.1627252.
28. Zobeyri M.2019. Forest Survey, University of Tehran Press, Fifth Edition.(in persiaan)