DOI QR코드

DOI QR Code

Evaluation of vegetation index accuracy based on drone optical sensor

드론 광학센서 기반의 식생지수 정확도 평가

  • Lee, Geun Sang (Dept. of Cadastre & Civil Engineering, Vision College of Jeonju) ;
  • Cho, Gi Sung (Dept. of Civil Engineering, Jeonbuk National University) ;
  • Hwang, Jee Wook (Dept. of Urban Engineering, Dept. of Urban Engineering, Jeonbuk National University) ;
  • Kim, Pyoung Kwon (Dept. of Civil Engineering, Jeonbuk National University)
  • Received : 2022.04.14
  • Accepted : 2022.04.26
  • Published : 2022.04.30

Abstract

Since vegetation provides humans with various ecological spaces and is also very important in terms of water resources and climatic environment, many vegetation monitoring studies using vegetation indexes based on near infrared sensors have been conducted. Therefore, if the near infrared sensor is not provided, the vegetation monitoring study has a practical problem. In this study, to improve this problem, the NDVI (Normalized Difference Vegetation Index) was used as a reference to evaluate the accuracy of the vegetation index based on the optical sensor. First, the Kappa coefficient was calculated by overlapping the vegetation survey point surveyed in the field with the NDVI. As a result, the vegetation area with a threshold value of 0.6 or higher, which has the highest Kappa coefficient of 0.930, was evaluated based on optical sensor based vegetation index accuracy. It could be selected as standard data. As a result of selecting NDVI as reference data and comparing with vegetation index based on optical sensor, the Kappa coefficients at the threshold values of 0.04, 0.08, and 0.30 or higher were the highest, 0.713, 0.713, and 0.828, respectively. In particular, in the case of the RGBVI (Red Green Red Vegetation Index), the Kappa coefficient was high at 0.828. Therefore, it was found that the vegetation monitoring study using the optical sensor is possible even in environments where the near infrared sensor is not available.

식생은 인간에게 다양한 생태공간을 제공하고 수자원 및 기후환경 측면에서도 매우 중요하기 때문에 근적외선 센서 기반의 식생지수를 활용한 식생 모니터링 연구가 많이 수행되어 왔다. 따라서 근적외선 센서를 구비하지 못할 경우 식생 모니터링 연구가 현실적으로 어려운 문제가 있었다. 본 연구에서는 이러한 문제를 개선하고자 NDVI 식생지수를 기준자료로 하여 광학센서 기반의 식생지수 정확도를 평가하였다. 먼저 현장에서 조사한 식생조사 지점과 NDVI 식생지수와의 중첩을 통해 Kappa 계수를 계산하였으며, 그 결과 Kappa 계수가 0.930으로 가장 높게 나타난 0.6 이상의 임계값을 갖는 식생영역을 광학센서 기반의 식생지수 정확도 평가의 기준자료로 선정할 수 있었다. NDVI 식생지수를 기준자료로 선정하여 광학센서 기반의 식생지수와 비교한 결과, 0.04, 0.08, 0.30 이상의 임계값 구간에서 Kappa 계수가 각각 0.713, 0.713, 0.828로 가장 높게 분석되었다. 특히 RGBVI 식생지수의 경우 Kappa 계수가 0.828로 높게 나타났으며, 따라서 근적외선 센서를 활용하지 못하는 환경에서도 광학센서를 활용한 식생 모니터링 연구가 가능함을 알 수 있었다.

Keywords

Acknowledgement

본 연구는 국토교통부 쇠퇴지역 재생역량 강화를 위한 기술개발사업의 연구비지원 (22TSRD-B151228-04)에 의해 수행되었습니다.

References

  1. Chae, H.S., Kim, G.E., Kim, S.J., Kim, Y.S., Lee, G.S., Cho, G.S., and Cho, M.H. (2002), Envionmental Remote Sensing, Sigma Press.
  2. Crist, E.P. (1985), A thematic mapper tasseled cap equivalent for reflectance factor data, Remote Sensing of Environment, Vol. 17, pp. 301-306. https://doi.org/10.1016/0034-4257(85)90102-6
  3. Deering, D.W., Rouse, J.W., Haans, R.H., and Schell, J.A. (1975), Measuring forage production of grazing units from Landsat MSS data, Proceedings, Tenth International Symposium on Remote Sensing of Environment, Vol. 2, pp. 1169-1178.
  4. Francisco, A.V., Fernando, C.R., Monica, P.S., and Francisco, O.R. (2015), Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop, Biosystems Engineering, Vol. 32, pp. 19-27.
  5. Francisco, J.M.C., Maria, D.N.G., Jose, E.M., Manuel, S., and Alfonso, G.F.P. (2014), Validation of measurements of land plot area using UAV imagery, International Journal of Applied Earth Observation and Geoinformation, Vol. 33, pp. 270-279. https://doi.org/10.1016/j.jag.2014.06.009
  6. Frohn, R.C. (1998), Remote Sensing for Landscape Ecology, Boca Raton, FL; Lewis Publishers.
  7. Gao, J. (2006), Canopy Chlorophyll Estimation with Hyperspectral Remote Sensing. Kansas State University, Manhattan, Kansas, US.
  8. Genevieve, R., Mochael, S., and Frederic, B. (1996), Optimization of Soil-Adjusted Vegetation Indices, Remote Sensing of Environment, Vol. 55, pp. 95-107. https://doi.org/10.1016/0034-4257(95)00186-7
  9. Gitelson, A.A., Kaufman, Y.J., Stark, R., and Rundquist, D. (2002), Novel algorithms for remote estimation of vegetation fraction, Remote Sensing Environment, Vol. 80, pp. 76-97. https://doi.org/10.1016/S0034-4257(01)00289-9
  10. Huete, A. (1988), A Soil-adjusted vegetation index(SAVI), Remote sensing of Environment, Vol. 25, pp. 295-309. https://doi.org/10.1016/0034-4257(88)90106-X
  11. Heute, A. and Justice, C. (1999), MODIS Vegetation Index(MOD 13) Algorithm Theoretical Basis Document, Greenbelt: NASA Goddard Space Flight Center.
  12. Heute, A.F. and Liu, H.Q. (1994), An error and sensitivity analysis of the atmosphere and soil correcting variants of the normalized difference vegetation index for the MODIS-EOS, IEEE Transaction on Geoscience and Remote Sensing, Vol. 32, No. 4, pp. 897-905. https://doi.org/10.1109/36.298018
  13. Hunt, J., Cavigelli, E.R., Daughtry M., McMurtrey, J.E., and Walthall, C.L. (2005), Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status, Precision Agricuture, Vol. 6, pp. 359-378. https://doi.org/10.1007/s11119-005-2324-5
  14. Jones, K.B., Ritters, K.H., Wickham, J.D., Tankersley, R.D., O'Neill, R.V., Chaloud, D.J., Smith, E.R., and Neale, A.C. (1998), An Ecological Assessment of the United States, EPA.
  15. Jose, P.S.V. and Paulo, B. (2010), Post-fire vegetation regrowth detection in the Deiva Marina region using Landsat TM and ETM+ data, Ecological Modeling, Vol. 221, pp. 75-84. https://doi.org/10.1016/j.ecolmodel.2009.03.011
  16. Juan, I.C., Jose, F.O., David, H., and Miguel, A.M. (2013), Estimation of lear area index in onion using an unmanned aerial vehicle, Biosystems Engineering, Vol. II5, pp. 31-42.
  17. Juliane, B., Kang, Y., Helge, A., Andreas, B., Simon, B., Broscheit, J., Martin, L., and Gnyp, G.B. (2015), Combinating UAV-based plant height from crop surface models, visible and near infrared vegetation indices for biomass monitoring in barley, International Journal of Applied Earth Observation and Geoinformation, Vol. 39, pp. 79-87. https://doi.org/10.1016/j.jag.2015.02.012
  18. Kim, Y.S., Park, N.W., Hong, S.Y., Lee, K.D., and Yoo, H.Y. (2014), Early Production of Large-area Crop Classification Map using Time-series Vegetation Index and Past Crop Cultivation Patterns, Korean Journal of Remote Sensing, Vol. 30, No. 4, pp. 493-503. https://doi.org/10.7780/KJRS.2014.30.4.7
  19. Kumar, L., Schmidt, K., Dury, S., and Skidmore, A. (2001), Imaging spectrometry and vegetation science, Kluwer Academic Publishers, Dordrecht, Netherlands, pp. 111-155.
  20. Landis, J.R. and Koch, G.G. (1977), The measurement of observer and agreement for categorical data, Biometrics, Vol. 33, No. 1, pp. 159-174. https://doi.org/10.2307/2529310
  21. Lee, G.S. and Lee, J.J. (2016), Test of Fault Detection to Solar-Light Module Using UAV Based Thermal Infrared Camera, Journal of the Korean Association of Geographic Information Studies, Vol. 19, No. 4, pp. 106-117. https://doi.org/10.11108/KAGIS.2016.19.4.106
  22. Lee, G.S. and Choi, Y.W. (2019), Analysis of Cropland Spectral Properties and Vegetation Index Using UAV, Journal of the Korean Association of Geographic Information Studies, Vol. 22, No. 4, pp. 86-101. https://doi.org/10.11108/KAGIS.2019.22.4.086
  23. Lee, K.D., Lee, Y.E., Park, C.W., Hong, S.Y., and Na, S.I. (2016), Study on Reflectance and NDVI of Aerial Images using a Fixed-Wing UAV eBee, Korean Journal of Soil Science and Fertilizer, Vol. 49, No. 6, pp. 731-742. https://doi.org/10.7745/KJSSF.2016.49.6.731
  24. Lorenzo, C., Paolo, G., Jacopo, P., and Davide, R.A. (2015), Vineyard detection from unmanned aerial systems images, Computers and Electronics in Agriculture, Vol. 114, pp. 78-87. https://doi.org/10.1016/j.compag.2015.03.011
  25. Mireia, G., Marta, C., David, M., Payam, D., Antonia, V., Antoni, P., and Mark, J.N. (2016), Normalized difference vegetation index (NDVI) as a marker of surrounding greenness in epidemiological studies: The case of Barcelona city, Urban Forestry & Urban Greening, Vol. 19, pp. 88-94. https://doi.org/10.1016/j.ufug.2016.07.001
  26. Motohka, T., Nasahara, K.N., Oguma, H., and Tsuchida, S. (2010), Applicability of green-red vegetation index for remote sensing of vegetation phenology, Remote Sensing, Vol. 2, pp. 2369-2387. https://doi.org/10.3390/rs2102369
  27. Na, S.I., Park, C.W., Cheong, Y.K., Kang, C.S., Choi, I.B., and Lee, K.D. (2016), Selection of Optimal Vegetation Indices for Estimation of Barley & Wheat Growth based on Remote Sensing, Korean Journal of Remote Sensing, Vol. 32, No. 5, pp. 483-497. https://doi.org/10.7780/KJRS.2016.32.5.7
  28. Qi, J., Cabot, F., Moran, M.S., and Dedieu, G. (1995), Biophysical Parameters Estimations Using Multidirectional Spectral Measurements, Remote Sensing of Environment, Vol. 54, pp. 71-83. https://doi.org/10.1016/0034-4257(95)00102-7
  29. Ramsey, R.D., Falconer, A., and Jensen, J.R. (1995), The relationship between NOAA-AHRR NDVI and Ecoregions in Utah, Remote Sensing of Environment, Vol. 53, pp. 188-198. https://doi.org/10.1016/0034-4257(95)00019-W
  30. Ranjay, S., Liping, D., Eugene, G.Y., Lingjun, K., Yuan-zheng, S., and Yu-qi, B. (2017), Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer, Journal of Integrative Agriculture, Vol. 16, pp. 398-407. https://doi.org/10.1016/s2095-3119(16)61502-2
  31. Rouse, J.W., Haans, R.H., Schell, J.A., and Deering, D.W. (1974), Monitoring Vegetation Systems in the Great Plains with ERTS, Proceedings, Third Earth Resources Technology Satellite-1 Symposium, NASA SP-351, pp. 301-317.
  32. Running, S.W., Justice, C.O., Solomonson, V., Hall, D., Barker, J., Kaufmann, Y.J., Strahler, A.H., Huete, A.R., Muller, J.P., Vanderbilt, V., Wan, Z.M., Teillet, P., and Carneggie, D. (1994), Terrestrial Remote Sensing Science and Algorithms Planned for EOS/MODIS, International Journal of Remote Sensing, Vol. 15, No. 17, pp. 3587-3620. https://doi.org/10.1080/01431169408954346
  33. Torres-Sanchez, J., Lopez-Granados, F., and Pena, J.M. (2015), An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops, Computers and Electronics in Agriculture, Vol. 114, pp. 43-52. https://doi.org/10.1016/j.compag.2015.03.019
  34. Tucker, C.J. (1979), Red and photographic infrared linear combinations for monitoring vegetation, Remote Sensing Environment, Vol. 8, pp. 127-150. https://doi.org/10.1016/0034-4257(79)90013-0
  35. Xingwang, F. and Yuanbo, L. (2016), A global study of NDVI difference among moderate-resolution satellite sensors, Journal of Photogrammetry and Remote Sensing, Vol. 121, pp. 177-191. https://doi.org/10.1016/j.isprsjprs.2016.09.008
  36. Zhang, F., Zhang, L.W., Shi, J.J., and Huang, J.F. (2014), Soil Moisture Monitoring Based on Land Surface Temperature Vegetation Index Space derived from MODIS Data, PEDOSPHERE, Vol. 24, No. 4, pp. 450-460. https://doi.org/10.1016/s1002-0160(14)60031-x