Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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An Analysis of Spectral Pattern for Detecting Pine Wilt Disease Using Ground-Based Hyperspectral Camera

지상용 초분광 카메라를 이용한 소나무재선충병 감염목 분광 특성 분석

  • 이정빈 (국립산림과학원 기후변화연구센터) ;
  • 김은숙 (국립산림과학원 기후변화연구센터) ;
  • 이승호 (산림교육원 재해방지교육과)
  • Received : 2014.09.05
  • Accepted : 2014.10.22
  • Published : 2014.10.31
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Abstract

In this paper spectral characteristics and spectral patterns of pine wilt disease at different development stage were analyzed in Geoje-do where the disease has already spread. Ground-based hyperspectral imaging containing hundreds of wavelength band is feasible with continuous screening and monitoring of disease symptoms during pathogenesis. The research is based on an hyperspectral imaging of trees from infection phase to witherer phase using a ground based hyperspectral camera within the area of pine wilt disease outbreaks in Geojedo for the analysis of pine wilt disease. Hyperspectral imaging through hundreds of wavelength band is feasible with a ground based hyperspectral camera. In this research, we carried out wavelength band change analysis on trees from infection phase to witherer phase using ground based hyperspectral camera and comparative analysis with major vegetation indices such as Normalized Difference Vegetation Index (NDVI), Red Edge Normalized Difference Vegetation Index (reNDVI), Photochemical Reflectance Index (PRI) and Anthocyanin Reflectance Index 2 (ARI2). As a result, NDVI and reNDVI were analyzed to be effective for infection tree detection. The 688 nm section, in which withered trees and healthy trees reflected the most distinctions, was applied to reNDVI to judge the applicability of the section. According to the analysis result, the vegetation index applied including 688 nm showed the biggest change range by infection progress.

본 연구에서는 소나무재선충병이 확산되어 있는 거제도를 대상으로 소나무재선충병 감염목 특성분석을 위하여 지상용 초분광 카메라를 활용하여 2012년과 2013년에 걸쳐 대상 임목을 촬영하였다. 영상 촬영은 소나무재선충병이 확산되는 시기인 6~9월 기간에 개체목 단위와 임분 단위로 구분하여, 개체목은 인위적으로 소나무재선충병을 주입한 공시목을 대상으로 실시하고, 임분은 소나무재선충병이 자연적으로 발생한 임분을 대상으로 실시하였다. 수백개의 파장대역 정보를 담고 있는 지상용 초분광 영상을 이용하여 소나무재선충병 감염단계에서부터 고사단계에 이르기까지 파장대역 변화와 특성분석을 진행하였다. 그 결과, 전체 파장대역 중 적색영역(550~700 nm)의 변화가 두드러지게 나타났으며 특히, 688 nm 전후의 파장대역에서 고사목과 정상목간의 가장 많은 변화폭이 관측되었다. 향후 초분광 항공사진을 활용한 소나무재선충병 감염목 탐지 활용가능성 판단을 위하여 개체목 단위 촬영영상보다 대면적의 임분단위 촬영영상을 활용한 분석이 진행되었다. 가장 큰 변화를 나타낸 688 nm 구간의 식생지수 활용을 위하여 Normalized Difference Vegetation Index(NDVI), Red Edge Normalized Difference Vegetation Index(reNDVI), Photochemical Reflectance Index(PRI), Anthocyanin Reflectance Index 2(ARI2) 식생지수에 대한 비교 분석을 실시하였다. 감염목 탐지에 효율성이 높다고 판단되는 지수는 NDVI와 reNDVI으로 나타났으며 688 nm를 NDVI와 reNDVI식 적색영역에 적용한 결과 688 nm를 포함하여 적용한 지수값에서 감염진행에 따른 가장 큰 변화폭을 나타내어 감염목 탐지에 가장 효율적인 것으로 판단되었다.

Keywords

References

  1. Gamon, J.A., J. Penuelas, and C.B. Field, 1992. A Narrow-Waveband Spectral Index That Tracks Diurnal Changes in Photosynthetic Efficiency, Remote Sensing of Environment, 41: 35-44. https://doi.org/10.1016/0034-4257(92)90059-S
  2. Gitelson, A.A. and M.N. Merzlyak, 1994. Spectral Reflectance Changes Associated with Autumn Senescence of Aesculus Hippocastanum L. and Acer Platanoides L. Leaves. Spectral Features and Relation to Chlorophyll Estimation. Journal of Plant Physiology, 143: 286-292. https://doi.org/10.1016/S0176-1617(11)81633-0
  3. Gitelson, A.A., Y. Zur, O.B. Chivkunova, and M.N. Merzlyak, 2002. Assessing Carotenoid Content in Plant Leaves with Reflectance Spectroscopy. Photochemistry and Photobiology, 75: 272-281. https://doi.org/10.1562/0031-8655(2002)075<0272:ACCIPL>2.0.CO;2
  4. Kim, J.B., D.Y. Kim, and N.C. Park, 2010. Development of an aerial precision forecasting techniques for the pine wilt disease damaged area based on GIS and GPS, Journal of Korean Association of Geographic Information Studies, 13(1): 28-34. https://doi.org/10.11108/kagis.2010.13.1.028
  5. Kim, J.B., M.H. Jo, I.H. Kim, and Y.K. Kim, 2003. A study on the extraction of damaged area by pine wood nematode using high resolution IKONOS satellite images and GPS, Journal of Korean Forest Society, 92(4): 362-366.
  6. Kim, S.R., W.K. Lee, K. Nam, Y. Song, H. Yu, M.I. Kim, J.Y. Lee, and S.H. Lee, 2013. Investigation into reflectance characteristics of trees infected by pine wilt disease, Journal of Korean Forest Society, 102(4): 499-505. https://doi.org/10.14578/jkfs.2013.102.4.499
  7. Kim, Y., D.M. Glenn, J. Park, H.K. Ngugi, and B.L. Lehman, 2011. Hyperspectral image analysis for water stress detection of apple trees, Computers and Electronics in Agriculture, 77: 155-160. https://doi.org/10.1016/j.compag.2011.04.008
  8. Kim, Y.S., S.E. Jung, W.K. Lee, J.B. Kim, and T.H. Kwon, 2008. Analyzing vegetation index change of damaged trees by pine wilt disease using portable near infrared camera, Journal of Korean Forest Society, 97(6): 561-564.
  9. Korea Forest Service, 2014. 2014 Plan for forecasting and control of forest insect pests and disease, Korea Forest Service, Daejeon, Korea.
  10. Lee, S.H., H.K. Cho, and W.K. Lee, 2007. Detection of the pine trees damaged by pine wilt disease using high resolution satellite and airborne optical imagery. Korean Journal of Remote Sensing, 23(5): 409-420 https://doi.org/10.7780/kjrs.2007.23.5.409
  11. Penuelas, J., I. Filella, and L. Sweeano, 1996. Cell wall elastivity and water index (R970 nm/R900 nm) in wheat under different nitrogen availabilities, International Journal of Remote Sensing, 17: 373-382. https://doi.org/10.1080/01431169608949012
  12. Penuelas, J., I. Filella, C. Biel, L. Sweeano, and R. Save, 1993. The reflectance at the 950-970 nm region as an indicator of plant water status, International Journal of Remote Sensing, 14: 1887-1905. https://doi.org/10.1080/01431169308954010
  13. Pu, R., L. Foschi, and P. Gong, 2004. Spectral feature analysis for assessment of water status and health level of coast live oak (Quercus agrifolia) leaves, International Journal of Remote Sensing, 25(20): 4267-4286. https://doi.org/10.1080/01431160410001705114
  14. Pu, R., S. Ge, N.M. Kelly, and P. Gong, 2003. Spectral absorption features as indicators of water status in Quercus agrifolia leaves, International Journal of Remote Sensing, 24(9): 1799-1810. https://doi.org/10.1080/01431160210155965
  15. Rouse, J.W., R.H. Haas, J.A. Schell, and D.W. Deering, 1974. Monitoring Vegetation Systems in the Great Plains with ERTS. 3rd Earth Resource Technology Sstellite(ERTS) Symposium, vol 1: pp. 48-62.
  16. Suarez, L., P.J. Zarco-Tejada, G. Sepulcre-Canto, O. Perez-Priego, J.R. Miller, J.C. Jimenez-Munoz, and J. Sobrino, 2008. Assessing canopy PRI for water stress detection with diurnal airborne imagery, Remote Sensing of Environment, 112: 560-575. https://doi.org/10.1016/j.rse.2007.05.009
  17. Wu, C., Z. Niu, Q. Tang, and W. Huang, 2008. Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation, Agricultural and Forest Meteorology, 148: 1230-1241. https://doi.org/10.1016/j.agrformet.2008.03.005
  18. Zarco-Tejada, P.J., V. Gonzalez-Dugo, and J.A.J. Berni, 2012. Fluorescence, temperature and narrowband indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera, Remote Sensing of Environment, 117: 322-337. https://doi.org/10.1016/j.rse.2011.10.007

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