Journal of People, Plants, and Environment (인간식물환경학회지)

Society For People, Plants, And Environment (인간식물환경학회)
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A Study on Obtaining Tree Data from Green Spaces in Parks Using Unmanned Aerial Vehicle Images: Focusing on Mureung Park in Chuncheon

  • Lee, Do-Hyung (Department of Energy & Environment Research, Korea Research Institute on Climate Change) ;
  • Kil, Sung-Ho (Department of Ecological Landscape Architecture Design, Kangwon National University) ;
  • Lee, Su-Been (Department of Landscape Architecture, Kangwon National University)
  • Received : 2021.03.05
  • Accepted : 2021.06.22
  • Published : 2021.08.31
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Abstract

Background and objective: The purpose of study is to analyze the three-dimensional (3D) structure by creating a 3D model for green spaces in a park using unmanned aerial vehicle (UAV) images. Methods: After producing a digital surface model (DSM) and a digital terrain model (DTM) using UAV images taken in Mureung Park in Chuncheon-si, we generated a digital tree height model (DHM). In addition, we used the mean shift algorithm to test the classification accuracy, and obtain accurate tree height and volume measures through field survey. Results: Most of the tree species planted in Mureung Park were Pinus koraiensis, followed by Pinus densiflora, and Zelkova serrata, and most of the shrubs planted were Rhododendron yedoense, followed by Buxus microphylla, and Spiraea prunifolia. The average height of trees measured at the site was 7.8 m, and the average height estimated by the model was 7.5 m, showing a difference of about 0.3 m. As a result of the t-test, there was no significant difference between height values of the field survey data and the model. The estimated green coverage and volume of the study site using the UAV were 5,019 m2 and 14,897 m3, respectively, and the green coverage and volume measured through the field survey were 6,339 m2 and 17,167 m3. It was analyzed that the green coverage showed a difference of about 21% and the volume showed a difference of about 13%. Conclusion: The UAV equipped with RTK (Real-Time Kinematic) and GNSS (Global Navigation Satellite System) modules used in this study could collect information on tree height, green coverage, and volume with relatively high accuracy within a short period of time. This could serve as an alternative to overcome the limitations of time and cost in previous field surveys using remote sensing techniques.

Keywords

References

  1. Bang, D.S., D.G. Lee, S.R. Yang, and H.J. Lee. 2018. Study on the Tree Height Using Unmanned Aerial Photogrammetry Method. J. Korean Assoc. Geogr. Inf. Stud. 21(3): 35-47. https://doi.org/10.11108/kagis.2018.21.3.035
  2. Husch, B., C.I. Miller, and T.W. Beers. 1982. Forest Mensuration (3th ed.). New York, USA: John Wiley & Sons Publishing.
  3. Hwang, S.L. 2012. Estimation of forest terrain and tree height using airborne LIDAR data. Master's Thesis, University of Seoul, Seoul, Korea.
  4. Iizuka, K., T. Yonehara, M. Itoh, and Y. Kosugi. 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 Sens. (Basel) 10(1): 13. https://doi.org/10.3390/rs10010013
  5. Kim, D.S. 2017. The construction of precise DTM using UAV images. Master's Thesis, Incheon National University, Incheon, Korea.
  6. Kim, M.S. 2019. A study on riparian vegetation survey method using UAV. Master's Thesis, Gachon University, Seongnam-si, Korea.
  7. Kim, S.H. 2014. A study on estimation of the forest carbon stocks and the NDVI analysis using satellite image in Chungcheongnam-do, South Korea. Master's Thesis, Kongju National University, Kongju, Korea.
  8. Kwak, N.J., Y.G. Kim, and D.J. Kwon. 2006. An edge preserving color image segmentation using mean shift algorithm and region merging method. J. Korea Contents Assoc. 6(9): 19-27.
  9. Lee, G.S., S.G. Kim, and Y.W. Choi. 2015. A comparative study of image classification method to detect water body based on UAS. J. Korean Assoc. Geogr. Inf. Stud. 18(3): 113-127. https://doi.org/10.11108/kagis.2015.18.3.113
  10. Lee, J.O and S.M. Sung. 2018. Assessment of Positioning Accuracy of UAV Photogrammetry based on RTK-GPS. J. Korea Acad. Ind. Coop. Soc. 19(4): 63-68. https://doi.org/10.5762/KAIS.2018.19.4.63
  11. Lim, Y.S. 2017. A study on the extraction of tree structure characteristics by drone images. Master's Thesis, Konkuk University, Seoul, Korea.
  12. Moe, K.T., T. Owari, N. Furuya, and T. Hiroshima. 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
  13. Park, H.K. 2017. 3D measurement method based on point cloud and solid model for urban single trees. Korean J. Remote Sens. 33(6): 1139-1149. https://doi.org/10.7780/kjrs.2017.33.6.2.8
  14. Park, S.M. 2004. Segmentation and road change detection of urban area satellite image using mean shift. Master's Thesis, Jeonbuk National University, Jeonju, Korea.
  15. Perko, R., H. Raggam, J. Deutscher, K. Gutjahr, and M. Schardt. 2011. Forest assessment using high resolution SAR data in X-Band. Remote Sens. (Basel). 3(4): 792-815. https://doi.org/10.3390/rs3040792
  16. Rue, J., J.h. Hwang, J.h. Lee, H.I. Chung, K.I. Lee, Y.Y. Choi, Y.Y. Zhu, M.J. Sung, R.I. Jang, H.C. Sung, S.W. Jeon, and J.Y. Kang. 2017. Analysis of Changes in Forest According to Urban Expansion Pattern and Morphological Features-Focused on Seoul and Daegu. Korean J. Remote Sens. 33(5_3): 835-854. https://doi.org/10.7780/kjrs.2017.33.5.3.7
  17. Seo, H.J and B.W. Jun. 2015. An Analysis of temporal and spatial variation in Urban Green Areas - Focused on Daegu. The Korean Geographlcal Society. Proceeding of Research, Conference of Geogr. (pp. 92-93). Seoul, Korea.
  18. Thorne, M.S., Q.D. Skinner, M.A. Smith, J.D. Rodgers, W.A. Laycock, and S.A. Cerekci. 2002. Evaluation of a technique for measuring canopy volume of shrubs. J. Range Manage. 55(3): 235-241. https://doi.org/110.2307/4003129
  19. Warfield, A.D and J.X. Leon. 2019. Estimating Mangrove Forest Volume Using Terrestrial Laser Scanning and UAV-Derived Structure-from-Motion. drones. 3(2): 32. https://doi.org/10.3390/drones3020032
  20. Woo, H.S., S.W. Cho, G.H. Jung, and J.W. Park. 2019. Precision Forestry Using Remote Sensing Techniques: Opportunities and Limitations of Remote Sensing Application in Forestry. Korean J. Remote Sens. 35(6_2): 1067-1082. https://doi.org/10.7780/kjrs.2019.35.6.2.4
  21. PIX4D. 2011. Pix4Dmapper-Manual-DSM, Orthomosaic and Index. Retrieved from http://www.pix4d.com
  22. XAG. 2007. Remote Sensing Drone. Retrieved from http://www.xa.com