DOI QR코드

DOI QR Code

Calculating coniferous tree coverage using unmanned aerial vehicle photogrammetry

  • Ivosevic, Bojana (School of Applied Biosciences, College of Agriculture and Life Sciences, Kyungpook National University) ;
  • Han, Yong-Gu (School of Applied Biosciences, College of Agriculture and Life Sciences, Kyungpook National University) ;
  • Kwon, Ohseok (School of Applied Biosciences, College of Agriculture and Life Sciences, Kyungpook National University)
  • Received : 2016.11.28
  • Accepted : 2017.02.16
  • Published : 2017.03.31

Abstract

Unmanned aerial vehicles (UAVs) are a new and yet constantly developing part of forest inventory studies and vegetation-monitoring fields. Covering large areas, their extensive usage has saved time and money for researchers and conservationists to survey vegetation for various data analyses. Post-processing imaging software has improved the effectiveness of UAVs further by providing 3D models for accurate visualization of the data. We focus on determining the coniferous tree coverage to show the current advantages and disadvantages of the orthorectified 2D and 3D models obtained from the image photogrammetry software, Pix4Dmapper Pro-Non-Commercial. We also examine the methodology used for mapping the study site, additionally investigating the spread of coniferous trees. The collected images were transformed into 2D black and white binary pixel images to calculate the coverage area of coniferous trees in the study site using MATLAB. The research was able to conclude that the 3D model was effective in perceiving the tree composition in the designated site, while the orthorectified 2D map is appropriate for the clear differentiation of coniferous and deciduous trees. In its conclusion, the paper will also be able to show how UAVs could be improved for future usability.

Keywords

References

  1. Cramer, M. (2013). The UAV@ LGL BW project-a NMCA case study (pp. 9-13). Stuttgart, Germany: Proceedings of 54th Photogrammetric Week.
  2. Dalponte, M., Ene, L. V., Marconcini, M., Gobakken, T., & Naesset, E. (2015). Semisupervised SVM for individual tree crown species classification. ISPRS Journal of Photogrammetry and Remote Sensing, 110, 77-87. https://doi.org/10.1016/j.isprsjprs.2015.10.010
  3. Dji.com 2014. Phantom 2 Vision+. http://www.dji.com/product/phantom-2-visionplus/feature.
  4. Dji.com 2016. DJI-about us. http://www.dji.com/company.
  5. Droneflight 2016. General Drone/UAV FAQ's. http://shop.droneflight.co.uk/pages/general-drone-uav-faq-s.
  6. English.visitkorea.or.kr 2016. Sobaeksan National Park (Gyeongbuk Area). http://english.visitkorea.or.kr/enu/SI/SI_EN_3_1_1_1.jsp?cid=264153.
  7. Forance, K. M., Drakeley, C. J., William, T., Espino, F., & Cox, J. (2014). Mapping infectious disease landscapes: unmanned aerial vehicles and epidemiology. Trends in Parasitology, 30(11), 514-519. https://doi.org/10.1016/j.pt.2014.09.001
  8. Getzin, S., Wiegand, K., & Schoning, I. (2012). Assessing biodiversity in forests using very high-resolution images and unmanned aerial vehicles. Methods in Ecology and Evolution. British Ecological Society, 3, 397-404. https://doi.org/10.1111/j.2041-210X.2011.00158.x
  9. Govorcin, M., Pribicevic, B., & Dapo, A. (2014). Comparison and analysis of software solutions for creation of digital terrain model using unmanned aerial vehicles. In Photogrammetry and Remote Sensing. 14th International Multidisciplinary Scientific GeoConference SGEM.
  10. Introduction Pix4D Webinar 1: Introduction to Modern Photogrammetry and Optimal Flight Plans 2016. https://www.youtube.com/watch?v=NGdZ8O2cWks. Accessed 25 Apr 2016.
  11. Ivosevic, B., Han, Y.-G., Cho, Y., & Kwon, O. (2015). The use of conservation drones in ecology and wildlife research. Ecology and Environment, 38(1), 113-188. https://doi.org/10.5141/ecoenv.2015.012
  12. Koh LP. 2013. A drone's-eye view of conservation. http://www.ted.com/speakers/lian_pin_koh.
  13. Kung, O., Strecha, C., Beyeler, A., Zufferey, J. C., Floreano, D., Fua, P., & Gervaix, F. (2011). The accuracy of automatic photogrammetric techniques on ultra-light UAV imagery. UAV-g 2011-Unmanned aerial Vehicle in Geomatics. No. EPFLCONF-168806.
  14. Lisein, J., Pierrot-Deseilligny, M., Bonnet, S., & Lejeune, P. (2013). A photogrammetric workflow for the creation of a forest canopy height model from small unmanned aerial system imagery. Forests, 4, 922-944. https://doi.org/10.3390/f4040922
  15. Lopez-Fernandez, L., Laguela, S., Picon, I., & Gonzalez-Aguilera, D. (2015). Large scale automatic analysis and classification of roof surfaces for the installation of solar panels using a multi-sensor aerial platform. Remote Sensing, 7(9), 11226-11248. https://doi.org/10.3390/rs70911226
  16. Paneque-Galvez, J., McCall, M. C., Napoletano, B. M., Wich, S. A., & Koh, L. P. (2014). Small drones for community-based forest monitoring: an assessment of their feasibility and potential in tropical areas. Forests, 5(6), 1481-1507. https://doi.org/10.3390/f5061481
  17. Pix4D 2016a. Pix4D-Drone Mapping Software. https://pix4d.com/.
  18. Pix4D 2016b. Suppor-Pix4D. https://pix4d.com/support/.
  19. Puerto, D. A., Gila, D. M. M., García, J. G., & Ortega, J. G. (2015). Sorting olive batches for the milling process using image processing. Sensors, 15(7), 15738-15754. https://doi.org/10.3390/s150715738
  20. Puliti, S., Orka, H. O., Gobakken, T., & Naesset, E. (2015). Inventory of small forest areas using an unmanned aerial systems. Remote Sensing, 7(8), 9632-9654. https://doi.org/10.3390/rs70809632
  21. Vallet, J., Panissod, F., Strecha, C., & Tracol, M. (2011). Photogrammetric performance of an ultra light weight swinglet. "UAV". UAV-g. No. EPFL-CONF-169252.
  22. Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture, 13(6), 693-712. https://doi.org/10.1007/s11119-012-9274-5

Cited by

  1. Drone Monitoring of Breeding Waterbird Populations: The Case of the Glossy Ibis vol.2, pp.4, 2018, https://doi.org/10.3390/drones2040042
  2. Drone-Based Assessment of Canopy Cover for Analyzing Tree Mortality in an Oil Palm Agroforest vol.2, pp.None, 2019, https://doi.org/10.3389/ffgc.2019.00012
  3. 레이저스캐닝과 포토그래메트리 소프트웨어 기술을 이용한 조경 수목 3D모델링 재현 특성 비교 vol.24, pp.2, 2017, https://doi.org/10.6109/jkiice.2020.24.2.304
  4. AragoJ: A free, open‐source software to aid single camera photogrammetry studies vol.11, pp.5, 2020, https://doi.org/10.1111/2041-210x.13376
  5. UAV + BIM: Incorporation of Photogrammetric Techniques in Architectural Projects with Building Information Modeling Versus Classical Work Processes vol.12, pp.14, 2020, https://doi.org/10.3390/rs12142329