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An Analysis of Spectral Characteristic Information on the Water Level Changes and Bed Materials

수위변화에 따른 하상재료의 분광특성정보 분석

  • Kang, Joongu (Department of Land, Water and Environment Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Lee, Changhun (Nature and Technology Inc.) ;
  • Kim, Jihyun (Department of Land, Water and Environment Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Ko, Dongwoo (Nature and Technology Inc.) ;
  • Kim, Jongtae (Nature and Technology Inc.)
  • 강준구 (한국건설기술연구원 국토보전연구본부) ;
  • 이창훈 (주식회사 자연과기술) ;
  • 김지현 (한국건설기술연구원 국토보전연구본부) ;
  • 고동우 (주식회사 자연과기술) ;
  • 김종태 (주식회사 자연과기술 연구소)
  • Received : 2019.11.14
  • Accepted : 2019.11.26
  • Published : 2019.12.31

Abstract

The purpose of this study is to analyze the reflectance of bed materials according to changes in the water level using a drone-based hyperspectral sensor. For this purpose, we took hyperspectral images of bed materials such as soil, gravel, cobble, reed, and vegetation to compare and analyze the spectral data of each material. To adjust the water level, we constructed an experimental channel to control the discharge and installed the bed materials within the channel. In this study, we configured 3 cases according to the water level (0.0 m, 0.3 m, 0.6 m). After the imaging process, we used the mean value of 10 points for each bed material as analytical data. According to the analysis, each material showed a similar reflectance by wavelength and the intrinsic reflectance characteristics of each material were shown in the visible and near-infrared region. Also, the deeper the water level, the lower the peak reflectance in the visible and near-infrared region, and the rate of decrease differed depending on the bed material. We expect the intrinsic properties of these bed materials to be used as basic research data to evaluate river environments in the future.

본 연구는 드론 기반의 초분광 센서를 활용하여 수위변화에 대한 하상재료의 분광정보 차이를 분석하는 것이 목적이다. 하상재료는 흙, 자갈, 호박돌, 갈대, 식생을 대상으로 하였으며 5개 하상재료에 대한 초분광 영상촬영을 실시하고 각 재료의 분광정보를 비교 분석하였다. 수위 변화를 위해 유량조절이 가능한 실험수로를 제작하고 수로 내 하상재료를 설치하였다. 수위 조건은 0.0 m, 0.3 m, 0.6 m이며 수위에 따라 CASE를 구분하였다. 영상촬영 후 각 하상재료별 10개 포인트를 평균한 값을 분석 자료로 사용하였다. 분석 결과 흙, 자갈, 호박돌, 갈대의 파장별 반사율은 비슷한 유형을 보이지만 각 재료별 가시광선과 근적외선 영역에서는 분광정보의 고유특징이 나타났다. 또한 수위가 깊어질수록 가시광선과 근적외선 영역에서 반사율은 감소하고 있으며 감소 비율은 하상재료에 따라 차이가 발생하였다. 하상재료에 대한 고유정보는 향후 하천환경평가를 위한 기초연구 자료로 활용될 것으로 기대된다.

Keywords

References

  1. Asner, G.P., Bustamante, M.M. and Townsend, A.R. 2003. Scale dependence of biophysical structure in deforested areas bordering the Tapaos National Forest, Central Amazon. Remote Sensing of Environment 87: 507-520. https://doi.org/10.1016/j.rse.2003.03.001
  2. Cho, H.G. and Lee, K.S. 2014. Comparison between Hyperspectral and Multispectral Images for the Classification of Coniferous Species. Korean Journal of Remote Sensing 30: 25-36. (in Korean) https://doi.org/10.7780/kjrs.2014.30.1.3
  3. Gao, B.C., Montes. M.J. and Davis, C.O. 2004. Refinement of wavelength calibrations of hyperspectral imaging data using a spectrum matching technique, Remote Sensing of Environment 90: 424-433. https://doi.org/10.1016/j.rse.2003.09.002
  4. Goetz, A.F.H. 2009. Three decades of hyperspectral remote sensing of the Earth: A personal view. Remote Sensing of Environment 113: S5-S16. https://doi.org/10.1016/j.rse.2007.12.014
  5. Han, D.Y., Cho, Y.W., Kim, Y.I. and Lee, Y.W. 2003. Feature selection for image clasiification of hyperion data. Korean Journal of Remote Sensing 19: 171-179. (in Korean)
  6. Herold, M., Roberts, D.A., Gardner, M.E. and Dennison, P.E. 2004. Spectrometry for urban area remote sensing Development and analysis of a spectral library from 350 to 2400 nm. Remote Sensing of Environment 91: 304-319. https://doi.org/10.1016/j.rse.2004.02.013
  7. Jang, A.J., Kim, Y.I., Choi, S.K., Han, D.Y., Choi, J.W., Kim, Y.M., Han, Y.K., Park, H.L., Wang, P. and Lim, H.C. 2013. Construction and data analysis of test-bed by hyperspectral airborne remote sensing. Korean Journal of Remote Sensing 29: 161-172. (in Korean) https://doi.org/10.7780/kjrs.2013.29.2.1
  8. Kang, J.G., Lee, C.H., Yeo, H.K. and Kim, J.T. 2019. Correlation analysis on the water depth and peak data value of hyperspectral imagery. Ecology and Resilient Infrastructure 6: 171-177. (in Korean) https://doi.org/10.17820/eri.2019.6.3.171
  9. Kim, Y.J., Han, H.J. and Kang, J.G. 2018. The study on spatial classification of riverine environment using UAV hyperspectral image. Journal of the Korea Academia-Industrial cooperation Society 19: 633-639. (in Korean)
  10. Kokaly, R.F., Asner, G.P., Ollinger, S.V., Martin, M.E. and Wessman, C.A. 2009. Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies. Remote Sensing of Environment 113: S78-S91. https://doi.org/10.1016/j.rse.2008.10.018
  11. Koponen, S., Pulliainen, J. Kallio, K. and Hallikainen. M. 2002. Lake water quality classification with airborne hyperspectral spectrometer and simulated MERIS data. Remote Sensing of Environment 79: 51-59. https://doi.org/10.1016/S0034-4257(01)00238-3
  12. Lee, J.D., Bang, K.J. and Kim, H.H. 2018. Land cover classification of coastal area by SAM from airborne hyperspectral images. Journal of the Korean Association of Geographic Information Studies 21: 35-45. (in Korean)
  13. Long, Y., Rivard, B., Rogge, D. and Tian, M. 2019. Hyperspectral band selection using the N-dimensional Spectral Solid Angle method for the improved discrimination of spectrally similar targets. International Journal of Applied Earth Observation and Geoinformation 79: 35-47. https://doi.org/10.1016/j.jag.2019.03.002
  14. Myers, T.L., Johnson, T.J., Gallagher, N.B., Bernacki, B.E., Beiswenger, T.N., Szecsody, J.E., Tonkyn, R.G., Bradley, A.M., Su, Y.F. and Danby, T.O. 2019. Hyperspectral imaging of minerals in the longwave infrared: The use of laboratory directional-hemispherical reference measurements for field exploration data. Journal of Applied Remote Sensing 13: 034527
  15. Park, Y.J., Jang, H.J., Kim, Y.S., Baik, K.H. and Lee, H.S. 2014. A Research on the Applicability of Water Quality Analysis using the Hyperspectral Sensor. Journal of the Korean Society for Environmental Analysis 17: 113-125. (in Korean)
  16. Pignatti, S., Cavalli, R.M., Cuomo, V., Fusilli, L., Pascucci, S. and Poscolieri, M. 2009. Evaluating Hyperion capability for land cover mapping in a fragmented ecosystem: Pollino National Park, Italy. Remote Sensing of Environment 113: 622-634. https://doi.org/10.1016/j.rse.2008.11.006
  17. Smith, M., Ollinger, S.V., Martin, M.E., Aber, J.D., Hallett, R.A. and Goodale, G.L. 2002. Direct estimation of aboveground forest prodectivity through hyperspectral remote sensing of canopy nitrogen. Ecological Applications 12: 1286-1302. https://doi.org/10.1890/1051-0761(2002)012[1286:DEOAFP]2.0.CO;2
  18. Song, K., Li, L., Tedesco, L.P., Li, S., Clercin, N.A., Hall, B.E., Li, Z. and Shi, K. 2012. Hyperspectral determination of eutrophication for a water supply source via genetic algorithm-partial least squares (GA-PLS) modeling. Science of the Total Environment 426: 220-232. https://doi.org/10.1016/j.scitotenv.2012.03.058
  19. Stratoulias, D., Balzter, H., Zlinszky, A. and Toth, V.R. 2015. Assessment of ecophysiology of lake shore reed vegetation based on chlorophyll fluorescence, field spectroscopy and hyperspectral airborne imagery. Remote Sensing of Environment 157: 72-84. https://doi.org/10.1016/j.rse.2014.05.021
  20. Yu, Y.H., Kim, Y.S. and Lee, S.G. 2008. A study on estimation of water depth using hyperspectral satellite imagery. Koera Aerospace Research Institute 7: 216-222. (in Korean)