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Vegetation Monitoring using Unmanned Aerial System based Visible, Near Infrared and Thermal Images

UAS 기반, 가시, 근적외 및 열적외 영상을 활용한 식생조사

  • Lee, Yong-Chang (Department of Urban Construction Engineering, College of Urban Science, Incheon National University)
  • 이용창 (인천대학교 도시과학대학 도시공학과)
  • Received : 2018.05.03
  • Accepted : 2018.06.27
  • Published : 2018.06.30

Abstract

In recent years, application of UAV(Unmanned Aerial Vehicle) to seed sowing and pest control has been actively carried out in the field of agriculture. In this study, UAS(Unmanned Aerial System) is constructed by combining image sensor of various wavelength band and SfM((Structure from Motion) based image analysis technique in UAV. Utilization of UAS based vegetation survey was investigated and the applicability of precision farming was examined. For this purposes, a UAS consisting of a combination of a VIS_RGB(Visible Red, Green, and Blue) image sensor, a modified BG_NIR(Blue Green_Near Infrared Red) image sensor, and a TIR(Thermal Infrared Red) sensor with a wide bandwidth of $7.5{\mu}m$ to $13.5{\mu}m$ was constructed for a low cost UAV. In addition, a total of ten vegetation indices were selected to investigate the chlorophyll, nitrogen and water contents of plants with visible, near infrared, and infrared wavelength's image sensors. The images of each wavelength band for the test area were analyzed and the correlation between the distribution of vegetation index and the vegetation index were compared with status of the previously surveyed vegetation and ground cover. The ability to perform vegetation state detection using images obtained by mounting multiple image sensors on low cost UAV was investigated. As the utility of UAS equipped with VIS_RGB, BG_NIR and TIR image sensors on the low cost UAV has proven to be more economical and efficient than previous vegetation survey methods that depend on satellites and aerial images, is expected to be used in areas such as precision agriculture, water and forest research.

최근 영농분야에서 종자파종, 병충해 방제 등에 무인항공기(UAV ; Unmanned Aerial Vehicle)를 활용한 응용이 활발히 진행되고 있다. 본 연구는 UAV에 다양한 파장대의 영상센서를 탑재하고 SfM(Structure from Motion) 영상해석기법과 연계한'고해상 저고도 원격탐측시스템(UAS ; Unmanned Aerial System)'를 구성, UAS 기반 식생조사의 효용성을 고찰하여 정밀영농의 활용성을 검토하였다. 이를 위해 저가 UAV에 가시 컬러(VIS_RGB ; Visible Red, Green, and Blue) 영상센서, 수정된 BG_NIR(Blue Green_Near Infrared Red) 근적외 영상 센서, $7.5{\sim}13.5{\mu}m$ 분광대역의 열적외 영상(TIR ; Thermal Infrared Red)센서를 조합 연계한 UAS를 구성하였다. 또한, 가시 근적외 및 열적외 파장대를 기본요소로 광합성에 따른 식물의 엽록소, 질소 및 수분 함유량 등을 검토할 수 있는 총 10종의 식생지수를 선정, 식생상태 검출에 활용하였다. 시험대상지에 대한 각 파장대역의 영상을 획득하고 사전에 조사된 지상 피복현황을 기준으로 각 식생지수의 분포도 및 식생지수 간 상관성(결정계수 R2) 등을 비교 고찰하여 무인항공기를 활용한 가시 컬러, 근 적외 및 열 적외 영상에 의한 식생상태의 검측 수행능력을 검토하였다. 저가 무인항공기에 VIS_RGB, BG_NIR 및 TIR 영상 센서를 탑재, 식생조사의 효용성을 종합적으로 검토한 결과, 인공위성과 항공영상에 의존한 과거의 식생조사방식 대비, 영상해상도, 경제성 및 운용성 면에서 UAV기반 고해상 저고도 원격탐측시스템(UAS)의 효용성을 입증할 수 있었으므로 정밀농업, 수계 및 산림조사 등의 분야에 그 활용이 기대된다.

Keywords

References

  1. Agapiou A, Hadjimitsis DG, Alexakis DD. 2012. Evaluation of Broadband and Narrowband Vegetation Indices for the Identification of Archaeological Crop Marks, Remote Sensing, 4: 892-3919.
  2. Barati S, Rayegani B, Saati M, Sharifi A, Nasri M. 2011. Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas, The Egyptian Journal of Remote Sensing and Space Sciences, 14:49-56. https://doi.org/10.1016/j.ejrs.2011.06.001
  3. Campbell JE, Michael S. 2012. Chapter 6. Data characteristics and visualization, http://2012books.lardbucket.org/
  4. Candiago S, Remondino F, Giglio MD, Dubbini M, Gattelli M. 2015. Evaluating multispectral images and vegetation indices for precision farming applications from UAV images, Remote Sensing, 7:4026-4047. https://doi.org/10.3390/rs70404026
  5. Cohen Y, Alchanatis V, Meron M, Saranga Y, Tsipris J. 2005. Estimation of leaf water potential by thermal imagery and spatial analysis, Journal of Experimental Botany, Vol. 56, No. 417, 1843-1852. https://doi.org/10.1093/jxb/eri174
  6. DJI. 2018. www.dji.com
  7. Gitelson AA, Kaufman YJ, Merzylak MN. 1996. Use of a green channel remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58:289-298. https://doi.org/10.1016/S0034-4257(96)00072-7
  8. Harris. 2018. http://harrisgeospatial.com/docs/BroadbandGreenness.html
  9. Huete AR. 1988. A soil-adjusted vegetation index(SAVI), Remote Sensing of Environment, 25:295-309. https://doi.org/10.1016/0034-4257(88)90106-X
  10. Hunt ER, Doraiswamy PC, McMurtrey JE, Daughtry C.S.T, Perryb EM, Akhmedov B. 2013. A visible band index for remote sensing leaf chlorophyll content at the canopy scale, International Journal of Applied Earth Observation and Geoinformation, 21:103-112. https://doi.org/10.1016/j.jag.2012.07.020
  11. Jackson RD, Huete AR. 1991. Interpreting vegetation indices, Preventive Veterinary Medicine, 11:185-200. https://doi.org/10.1016/S0167-5877(05)80004-2
  12. Rouse JW, Haas RH, Schell JA, Deering DW. 1973. Monitoring vegetation systems in the great plains with ERTS. Third ERTS Symposium, NASA, Paper A20: 309-317.
  13. Jiang Z, Huete AR, Kim YW, Didan K. 2007. 2-band Enhanced Vegetation Index without a blue band and its application to AVHRR data, Remote Sensing and Modeling of Ecosystems for Sustainability IV, edited by Wei Gao, Susan L. Ustin, Proc. of SPIE, Vol. 6679, 667905.
  14. Kriegler FJ, Malila WA, Nalepka RF, Richardson W. 1969. Preprocessing transformations and their effects on multispectral recognition, in: Proceedings of the Sixth International Symposium on Remote Sensing of Environment, University of Michigan, Ann Arbor, MI, p. 97-131
  15. Lee GS. 2017. The analysis of evergreen tree area using UAV-based vegetation index, Journal of cadastre & land informatiX, 47:15-26.
  16. Lee YC. 2017. Validation on the utilization of small-scale unmanned aerial system(sUAS) for topographic volume calculations, Journal of cadastre & land informatiX, 47:111-126.
  17. LDP LCC. 2018. www.maxmax.com/index.php
  18. Louhaichi M, Borman M, Johnson D. 2001. Spatially located platform and aerial photography for documentation of grazing impacts on wheat." Geocarto International, 16, 1:65-70. https://doi.org/10.1080/10106040108542184
  19. Maxmax. 2018. www.maxmax.com
  20. McFeeters SK. 1996. The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features. International Journal of Remote Sensing, 17:1425-1432. https://doi.org/10.1080/01431169608948714
  21. NGII. 2018. Guidelines for the Use of Unmanned Aerial Vehicles in Public Surveys, www.ngii.go.kr
  22. Park S, Nolan A, Ryu D, Fuentes S, Hernandez E, Chung H, Connell MO. 2015. Estimation of crop water stress in a nectarine orchard using high-resolution imagery from unmanned aerial vehicle (UAV), 21st International Congress on Modelling and Simulation, Gold Coast, Australia, www.mssanz.org.au/modsim2015
  23. Pix4D. 2018. Pix4Dmapper user manual, www.pix4d.com
  24. Rasmussen J, Ntakos G, Nielsen J, Svensgaard J, Poulsen RN, Christensen S. 2016. Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots ?, European Journal of Agronomy, 74:75-92. https://doi.org/10.1016/j.eja.2015.11.026
  25. Rondeaux G, Steven M, Baret F. 1996. Optimization of soil-adjusted vegetation indices, Remote Sens. Environ. 55:95-107. https://doi.org/10.1016/0034-4257(95)00186-7
  26. Sankarana S, Khotb LR, Espinoza CZ, Jarolmasjed S, Sathuvallic VR, Vandemarkd GJ, Miklas PN, Carter AH. Pumphrey MO, Knowlesg NR, Pavek MJ. 2015. Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review, European Journal of Agronomy, 70:112-123. https://doi.org/10.1016/j.eja.2015.07.004
  27. Tom M, Paul H. 2017. Comparing RGB-Based Vegetation Indices With NDVI For Agricultural Drone Imagery, RGB Vegetation Indices, Agribotix, LLC, Agribotix.com.
  28. Tucker CJ. 1979. Red and photographic infrared linear combinations for monitoring vegetation, Remote Sensing. Environ. 8:127-150. https://doi.org/10.1016/0034-4257(79)90013-0
  29. Turner D, Lucieer A, Watson C. 2011. Development of an Unmanned Aerial Vehicle (UAV) for hyper resolution vineyard mapping based on visible, multispectral, and thermal imagery, Proceedings of 34th International Symposium on Remote Sensing of Environment.

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