DOI QR코드

DOI QR Code

UAV를 이용한 농경지 분광특성 및 식생지수 분석

Analysis of Cropland Spectral Properties and Vegetation Index Using UAV

  • 이근상 (전주비전대학교 지적토목학과) ;
  • 최연웅 (조선이공대학교 토목건설과)
  • LEE, Geun-Sang (Dept. of Cadastre & Civil Engineering, Vision College of Jeonju) ;
  • CHOI, Yun-Woong (Dept. of Civil Construction Engineering, Chosun College of Science & Technology)
  • 투고 : 2019.11.27
  • 심사 : 2019.12.18
  • 발행 : 2019.12.31

초록

원격탐사 기술은 플랫폼 개발, 탐사면적 및 탐사기능 등 양적 및 질적 향상의 관점에서 지속적으로 발전되어왔으며 비용절감 및 현장자료보완의 방법으로 유용하게 활용되고 있다. 최근에는 농업분야에서의 활용사례와 관련연구가 증가하는 추세에 있으며 농경지의 상태를 탐지하고 정량화하여 농경지 및 농업환경에 대한 관리방안 수립 및 정책지원이 가능하기 때문에 농작물 생육이상 판별, 시계열 정보에 의한 작황 추정 등 다양한 분야에서 연구되고 있다. 본 연구에서는 다중분광센서를 장착한 UAV를 이용하여 간척지 농경지에 대한 식생지수를 분석하고자 하였다. 한편, UAV를 이용하여 취득한 다중분광영상 자료로부터 산정된 식생지수의 정확도를 평가하기 위해서 현지 조사를 실시하였다. 현지조사에 의한 식생지수와 UAV 다중분광영상으로부터 산정된 식생지수간의 상관성을 평가함으로써 가장 적절한 식생지수를 도출하였으며 대상지역 전체에 대한 식생지수 분석에 활용하고자 하였다.

Remote sensing technology has been continuously developed both quantitatively and qualitatively, including platform development, exploration area, and exploration functions. Recently, the use cases and related researches in the agricultural field are increasing. Also, since it is possible to detect and quantify the condition of cropland and establish management plans and policy support for cropland and agricultural environment, it is being studied in various fields such as crop growth abnormality determination and crop estimation based on time series information. The purpose of this study was to analyze the vegetation index for agricultural land reclamation area using a UAV equipped with a multi-spectral sensor. In addition, field surveys were conducted to evaluate the accuracy of vegetation indices calculated from multispectral image data obtained using UAV. The most appropriate vegetation index was derived by evaluating the correlation between vegetation index calculated by field survey and vegetation index calculated from UAV multispectral image, and was used to analyze vegetation index of the entire area.

키워드

참고문헌

  1. Birth, G.S. and G.R. McVey. 1968. Measuring the Color of Growing Turf with a Reflectance Spectrophotometer. American Society of Agronomy, 60:640-643. https://doi.org/10.2134/agronj1968.00021962006000060016x
  2. Booth, D.T., S.E. Cox, T. Meikle and H.R. Zuuring. 2008. Ground-cover measurements: assessing correlation among aerial and ground-based methods. Environmental Management 42(6):1091-1100. https://doi.org/10.1007/s00267-008-9110-x
  3. Broge, N.H. and J.V. Mortensen. 2002. Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data. Remote Sensing of Environment 81(1):45-57. https://doi.org/10.1016/S0034-4257(01)00332-7
  4. Candiago, S., F. Remondino, M.D. Giglio, M. Dubbini, and M. Gattelli. 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. Choi, Y.W., J.H. You and G.S. Cho. 2015. Accuracy analysis of UAV data processing using DPW. Journal of the Korean Society for Geospatial Information Science 23(4):3-10 https://doi.org/10.7319/kogsis.2015.23.4.003
  6. Dieter H., Z. Werner, S. Gunter, and S. Peter. 2005. Monitoring of gas pipelines - a civil UAV application. Aircraft Engineering and Aerospace Technology 77:352-360. https://doi.org/10.1108/00022660510617077
  7. Feng, Q., J. Liu, and J. Gong. 2015. UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis. Remote Sensing 7:1074-1094. https://doi.org/10.3390/rs70101074
  8. Flynn, K.F. and S.C. Chapra. 2014. Remote Sensing of submerged aquatic vegetation in a shallow Non-turbid river using an unmanned aerial vehicle. Remote Sensing 6:12815-12836. https://doi.org/10.3390/rs61212815
  9. Gitelson, A., Y.J. Kaufman and M.N. Merzlyak. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment 58(3):289-298. https://doi.org/10.1016/S0034-4257(96)00072-7
  10. Hardisky, M. A., V. Klemas and R.M. Smart. 1983. The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies. Photogrammetric Engineering & Remote Sensing 49:77-83.
  11. Hassan, F.M., H.S. Lim and M.Z. Mat Jafri. 2011. CropCam UAV for Land Use/Land Cover mapping over Penang Island, Malaysia, Pertanika Journal of Science & Technology, 19:69-76.
  12. Herwitz, S.R., L.F. John, S.E. Dunagan, R.G. Higgins, D.V. Sullivan, J. Zheng, B.M. Lobitz, J.G. Leung, B.A. Gallmeyer, M. Aoyagi, R.E. Slye and J.A. Brass. 2004. Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support, Computers and Electronics in Agriculture 44:49-61. https://doi.org/10.1016/j.compag.2004.02.006
  13. Hornbuckle, J., J. Brinkhoff, C. Ballester and S. North. 2016, Using Satellite And Drones For Water And Nitrogen Management Decision Making, CRDC.
  14. Hong, S.Y., J.N. Hur, J.B. Ahn, J.M. Lee, B.K. Min, C.K. Lee, Y.H. Kim, K.D. Lee, S.H. Kim, G.Y. Kim, and K.M. Shim. 2012. Estimating rice yield using MODIS NDVI and meteorological data in Korea. Korean Journal of Remote Sensing 28(5):509-520 https://doi.org/10.7780/kjrs.2012.28.5.4
  15. Huete, A.R. 1988. A soil-adjusted vegetation index(SAVI). Remote Sensing of Environment 25(3):259-309.
  16. Huete, A.R. and H.Q. Liu. 1994. An error and sensitivity analysis of the atmosphericand soil-correcting variants of the NDVI for the MODIS-EOS. IEEE Transactions on Geoscience and Remote Sensing 32(4):897-905. https://doi.org/10.1109/36.298018
  17. Hunt, E.R., J.H. Everitt, J.C. Ritchie, M.S. Moran, D.T. Booth, G.L. Anderson, P.E. Clark and M.S. Seyfried. 2003. Applications and research using remote sensing for rangeland management. Photogrammetric Engineering & Remote Sensing 69(6):675-693. https://doi.org/10.14358/PERS.69.6.675
  18. Im, S.H., S.I. Hassan, L.M. Dang, K.B. Min and H. Moon. 2018. Analysis of Fusarium Wilt Based on Normalized Difference Vegetation Index for Radish Field Images from Unmanned Aerial Vehicle. The Transactions of the Korean Institute of Electrical Engineers 67(10):1353-1357 https://doi.org/10.5370/KIEE.2018.67.10.1353
  19. Jeong, S.H., Y.W. Choi and G.S. Cho. 2018. Basic Data Investigation Method of Crop Insurance using Spatial Information Based on UAV. Journal of the Korean Society for Geospatial Information Science 26(3):61-68 https://doi.org/10.7319/kogsis.2018.26.3.061
  20. Jung, K.S., Y.S. Kim, and S.R. Oh. 2015. Technical Development of Flood Damage Estimation using UAV. Water for future, 48(1):51-59
  21. Kim, D.I., Y.S. Song, G. Kim, and C.W. Kim. 2014. A study on the application of UAV for korean land monitoring. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography 32(1):29-38 https://doi.org/10.7848/ksgpc.2014.32.1.29
  22. Kim, K.Y. and S.I. Na. 2019. Vegetation Map Service System Using UAV Imagery and Sample Field Data on Major Cultivation Regions. Journal of the Korean Society for Geospatial Information Science 27(1):33-41 https://doi.org/10.7319/kogsis.2019.27.1.033
  23. Laliberte, A.S., A. Rango, J.E. Herrick, E.L. Fredrickson and L. Burkett. 2007. An object-based image analysis approach for determining fractional cover of senescent and green vegetation with digital plot photography. Journal of Arid Environments 69(1):1-14. https://doi.org/10.1016/j.jaridenv.2006.08.016
  24. Lee, K.D., S.I. Na, S.C. Baek, K.D. Park, J.S. Choi, S.J. Kim, H.J. Kim, H.S. Yun, and S.Y. Hong, 2015, Estimating the amount of nitrogen in hairy vetch on paddy fields using unmaned aerial vehicle imagery, Korean Journal of Soil Science and Fertilizer 48(5):384-390 (in Korean with English abstract). https://doi.org/10.7745/KJSSF.2015.48.5.384
  25. Lee, K.D., Y.E. Lee, C.W. Park, S.Y. Hong and S.I. Na. 2016. Study on Reflectance and NDVI of Aerial Images using a Fixed-Wing UAV"Ebee". Korean Journal of Soil Science and Fertilizer 49(6):731-742 (in Korean with English abstract). https://doi.org/10.7745/KJSSF.2016.49.6.731
  26. Lee, G.S., Y.W. Choi, K. Jung and G.S. Cho. 2015. Analysis of the Spatial Information Accuracy According to Photographing Direction of Fixed Wing UAV. Journal of the Korean Cadastre Information Associstion 17(3):141-149
  27. Lee, G.S., Y.W. Choi, S.B. Lee and S.G. Kim. 2016. Estimation of reservoir area and capacity curve equation using UAV photogrammetry. Journal of the Korean Society for Geo-spatial Information Science 24(3):93-101 https://doi.org/10.7319/kogsis.2016.24.3.093
  28. Lee, J.W., G.A. Park, H.K. Joh, K.H. Lee, S.I. Na, J.H. Park, and S.J. Kim. 2011. Analysis of relationship between vegetation indices and crop yield using KOMPSAT (KoreaMulti-Purpose SATellite)-2 imagery and field investigation data. Journal of The Korean Society of Agricultural Engineers 53(3):75-82 https://doi.org/10.5389/KSAE.2011.53.3.075
  29. Morel, A. and L. Prieur. 1977. Analysis of variation in ocean. Limnology and Oceanography 22:709-722. https://doi.org/10.4319/lo.1977.22.4.0709
  30. Na, S.I., S.Y. Hong, C.W. Park, K.D. Kim, K.D. Lee. 2016. Estimation of highland Kimchi cabbage growth using UAV NDVI and agro-meteorological factors. Korean Journal of Soil Science and Fertilizer 49(5):420-428 (in Korean with English abstract). https://doi.org/10.7745/KJSSF.2016.49.5.420
  31. Na, S.I., S.Y. Hong, Y.H. Kim, K.D. Lee, and S.Y. Jang. 2013. Prediction of rice yield in Korea using paddy rice NPP index: Application of MODIS data and CASA model. Korean Journal of Remote Sensing 29(5):461-476 https://doi.org/10.7780/kjrs.2013.29.5.2
  32. Na, S.I., C.W. Park, Y.K. Cheong, C.S. Kang, I.B. Choi and K.D. Lee. 2016. Selection of Optimal Vegetation Indices for Estimation of Barley & Wheat Growth based on Remote Sensing: An Application of Unmanned Aerial Vehicle and Field Investigation Data. Korean Journal of Remote Sensing 32(5):483-497 https://doi.org/10.7780/kjrs.2016.32.5.7
  33. Nam W.H., T. Tadesse, B.D. Wardlow, M.W. Jang and S.Y. Hong. 2015. Satellite-based hybrid drought assessment using vegetation drought response index in South Korea (VegDRISKorea). Journal of the Korean Society of Agricultural Engineers 57(4):1-9 https://doi.org/10.5389/KSAE.2015.57.4.001
  34. Pajares, G. 2015. Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVs). Photogrammetric Engineering & Remote Sensing 81(4):281-329. https://doi.org/10.14358/PERS.81.4.281
  35. Park, J.K. and J.H. Park. 2015. Crops classification using imagery of unmanned aerial vehicle(UAV). Journal of the Korean Society of Agricultural Engineers 57(6):91-97 https://doi.org/10.5389/KSAE.2015.57.6.091
  36. Pearson, R.L. and L.D. Miller. 1972. Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie. Proceeding of the Eighth International Symposium on Remote Sensing of Environment, II, Michigan, Ann Arbor, pp. 1357-1381.
  37. Rock, B.N., J.E. Vogelmann, D.L. Williams, A.F. Vogehnann and T. Hoshizaki. 1986. Remote detection of forest damage. Bioscience 36:439-445. https://doi.org/10.2307/1310339
  38. Rouse, J,W., R.H. Haas, J.A. Schell and D.W. Deering. 1974. Monitoring Vegetation Systems in the Great Plains with ERTS. Proceedings, Third Earth Resources Technology Satellite-1 Symposium, Volume 1: Technical Presentations, section A. pp.309-317.
  39. Shin, Y.H., J.H. Park and M.S. Park. 2003. Spectral reflectance characteristics and vegetation indices of field crops. KCID journal 10(2):43-54
  40. Su, T.C. and H.T. Chou. 2015. Application of multispectral sensors carried on unmanned aerial vehicle(UAV) to trophic state mapping of small reservoirs: A case study of Tain-Pu reservoir in Kinmen, Taiwan. Remote Sensing 7(8):10078-10097. https://doi.org/10.3390/rs70810078
  41. Toevs, G.R., J.W. Karl, J.J. Taylor, G.S. Spurrier, M. Karl, M.R. Bobo and J.E. Herrick. 2011. Consistent indicators and methods and a scalable sample design to meet assessment, inventory, and monitoring information needs across scales. Rangelands 33:14-20.
  42. Tomas, J.R. and H.W. Gausman. 1977. Leat reflectance vs. leaf chlorophyll and carotenoid concentrations for eight crops. Agronomy Journal 69:799-802. https://doi.org/10.2134/agronj1977.00021962006900050017x
  43. Thompson, L.J., Y. Shi, R.B. Ferguson. 2017. Getting Started with Drones in Agriculture. NebGuide, Nebraska Extension Publications. 12pp.
  44. Watanabea, Y. and Y. Kawaharab. 2016. UAV photogrammetry for monitoring changes in river topography and vegetation. Procedia Engineering 154:317-325. https://doi.org/10.1016/j.proeng.2016.07.482
  45. Zaman, B., A. Jensen, S.R. Clemens and M. McKee. 2014. Retrieval of spectral reflectance of high resolution multispectral imagery acquired with an autonomous unmanned aerial vehicle. Photogrammetric Engineering & Remote Sensing 80(12):1139-1150. https://doi.org/10.14358/PERS.80.12.1139