Vol. 2, 2017



Kalinka Velichkova, Dora Krezhova

Pages: 276-282

DOI: 10.21175/RadProc.2017.56

Estimation and monitoring of plant health and influence of the environment are important components of the climate change researches. Recent hyperspectral remote sensing technologies, based on measurements of leaf reflectance in the visible and near infrared spectral ranges, allow detecting subtle absorption features in foliar spectra and to study correlations of these features linked to plant biophysical variables. The purpose of this study is to explore the sensitivity of several narrowband vegetation indices (VIs) when used to estimate the effect of a biotic stress (viral infection) on the biophysical parameters and physiological state of young potato plants. Two groups of plants were investigated – healthy and infected with Potato Virus Y (PVY). Hyperspectral reflectance data were collected by means of a portable fiber-optics spectrometer in the spectral range 400-1100 nm with a spectral resolution of 1.5 nm. The VIs – Normalized Difference VI (NDVI), modified Normalized Difference VI (mNDVI), Simple Ratio (SR); Chlorophyll Absorption Ratio Index (CARI), Modified CARI (MCARI), Chlorophyll Indices (ChIgreen and ChIred edge); Pigment index (PI), Disease index (fD) and Photochemical Reflectance Index (PRI) were evaluated for their potential to detect changes in physiological state and biochemical content of the infected plants. Three VIs appeared to be most sensitive – CARI, ChIgreen, and MCARI, the latter one has shown about two time better sensitivity than the others.
  1. A. F. H. Goetz, B. C. Gao, C. Wessman, “Vegetation biochemistry: what can imaging spectrometry tell us about canopies?” in vol. 3 Proc. of the 6th Australasian Remote Sensing Conference, Wellington, New Zealand, 1992, pp. 150-160.
  2. L. Chaerle, D. van der Straeten, “Seeing is believing: Imaging techniques to monitor plant health,” Biophys. Biochim. Acta – Gene Struct. Expr., vol. 1519, no. 3, pp. 153-166, Jun. 2001.
    DOI: 10.1016/S0167-4781(01)00238-X
  3. C. V. M. Barton, “Advances in remote sensing of plant stress,” Plant Soil, vol. 354, no. 1-2. pp. 41 – 44, May 2012.
    DOI: 10.1007/s11104-011-1051-0
  4. C. Panigada et al., “Chlorophyll concentration mapping with MIVIS data to assess crown discoloration in the Ticino Park oak forest,” Int. J. Remote Sens., vol. 31, no. 12, pp. 3307-3332, Jul. 2010.
    DOI: 10.1080/01431160903193497
  5. F. Fava et al., “Identification of hyperspectral vegetation indices for Mediterranean pasture characterization,” Int. J. Applied Earth Observ. and Geoinform., vol. 11, no. 4, pp. 233 – 243, Aug. 2009.
    DOI: 10.1016/j.jag.2009.02.003
  6. R. Colombo et al., “Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modelling,” Remote Sens. Environ., vol. 112, no. 4, pp. 1820-1834, Apr. 2008.
    DOI: 10.1016/j.rse.2007.09.005
  7. R. Colombo et al., “Retrieval of leaf area index in different vegetation types using high resolution satellite data”, Remote Sens. Environ., vol. 86, no. 1, pp. 120-131, Jun. 2003.
    DOI: 10.1016/S0034-4257(03)00094-4
  8. J. G. P. W. Clevers, L. Kooistra, “Using Hyperspectral Remote Sensing Data for Retrieving Canopy Chlorophyll and Nitrogen Content,” IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, vol. 5, no. 2, pp. 574-583, Apr. 2012.
    DOI: 10.1109/JSTARS.2011.2176468
  9. M. Meroni, R. Colombo and C. Panigada, “Inversion of a radiative transfer model with hyperspectral observations for LAI mapping in poplar plantations,” Remote Sens. Environ., vol. 92, no. 2, pp. 195-206, Aug. 2004.
    DOI: 10.1016/j.rse.2004.06.005
  10. F. C. Monteiro et al., “Assessing biophysical variable parameters of bean crop with hyperspectral measurements Priscylla,” Sci. Agric., vol. 69, no. 2, pp. 87-94, Mar-Apr. 2012.
    DOI: 10.1590/S0103-90162012000200001
  11. P. J. Zarco-Tejada et al., “A PRI-based water stress index combining structural and chlorophyll effects: Assessment using diurnal narrow-band airborne imagery and the CWSI thermal index,” Remote Sens. Environ., vol. 138, pp. 38-50, Nov. 2013.
    DOI: 10.1016/j.rse.2013.07.024
  12. W. Verhoef, “Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model,” Remote Sens. Environ., vol. 16, no. 2, pp. 125-141, Oct. 1984.
    DOI: 10.1016/0034-4257(84)90057-9
  13. A. J. Berjón et al., “Retrieval of biophysical vegetation parameters using simultaneous inversion of high resolution remote sensing imagery constrained by a vegetation index,” Precision Agric., vol. 14, no. 5, pp. 541-557, Oct. 2013.
    DOI: 10.1007/s11119-013-9315-8
  14. C. S. T. Daughtry et al., “Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance,” Remote Sens. Environ., vol. 74, no. 2, pp. 229-239, Nov. 2000.
    DOI: 10.1016/S0034-4257(00)00113-9
  15. N. Petrov. V. Lyubenova, “Variability in P1 gene region of Potato virus Y isolates and its effect on potato crops,” in Proc. Conf. The Man and the Universe, Smolyan, Bulgaria, 2011, pp. 671 – 677.
    Retrieved from: https://www.researchgate.net/publication/260244852_VARIABILITY_IN_P1_GENE_REGION_OF_POTATO_

    Retrieved on: Jan. 20, 2017
  16. D. Noordam, Identification of plant viruses: methods and experiments, 1st ed., Wageningen, The Netherlands: Centre for Agricultural Publishing and Documentation, 1973.
  17. M. A. Aqeel Ashraf, M. J. Maach, I. Yusoff, “Introduction to Remote Sensing of Biomass,” in Biomas and Remote Sensing of Biomass, I. Atazadeh, Ed., Rijeka, Croatia: InTech, 2011, ch. 8, sec. 1.5, p. 135.
    Retrieved from: https://cdn.intechopen.com/pdfs-wm/19222.pdf
    Retrieved on: Jan. 20, 2017
  18. G. R. Mahajan et al., “Using hyperspectral remote sensing techniques to monitor nitrogen, phosphorus, sulphur and potassium in wheat (Triticum aestivum L.),” Precision Agric., vol. 15, no. 5, pp. 499-522, Oct. 2014.
    DOI: 10.1007/s11119-014-9348-7
  19. D. Krezhova, A. Stoev and S. Maneva, “Detection of biotic stress caused by apple stem grooving virus in apple trees using hyperspectral reflectance analysis,” Compt. rend. Acad. Bulg. Sci., vol. 68, no. 2, pp. 175-182, Jun. 2015.
    Retrieved from: https://www.researchgate.net/publication/278406271_Detection_of_biotic_stress_caused

    Retrieved on: Jan. 20, 2017
  20. D. A. Fuentes et al., “Mapping Canadian boreal forest vegetation using pigment and water absorption features derived from the AVIRIS sensor,” J. Geophys. Res., vol. 106, no. D24, pp. 33565-33577, Dec. 2001.
    DOI: 10.1029/2001JD900110
  21. D. Krezhova et al., “Detection of environmental changes using hyperspectral remote sensing,” in Proc. of 9th Int. Physics Conf. of the Balkan Physical Union (BPU9), Istanbul, Turkey, 2015.
    DOI: 10.1063/1.4944275
  22. USB2000 Fiber Optic Spectrometer Installation and Operation Manual, Ocean Optics, Inc., Dunedin (FL), USA.
    Retrieved from: https://oceanoptics.com/wp-content/uploads/USB2000-Operating-Instructions.pdf
    Retrieved on: Jan. 20, 2017
  23. D. Krezhova et al., “Method for detecting stress induced changes in leaf spectral reflectance,” Compt. Rend. Acad. Bulg. Sci., vol. 58, no. 5, pp. 517-522, 2005.
  24. P. J. Zarco-Tejada et al., “Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops,” Remote Sens. Environ., vol. 90, no. 4, pp. 463-476, Apr. 2004.
    DOI: 10.1016/j.rse.2004.01.017
  25. G. A. Carter, “Ratios of leaf reflectances in narrow wavebands as indicators of plant stress,” Int. J. Remote Sens., vol. 15, no. 3, pp. 697-704, 1994.
    DOI: 10.1080/01431169408954109
  26. C. F. Jordan, “Derivation of leaf area index from quality of light on the forest floor,” Ecology, vol. 50, no. 4, pp. 663-666, Jul. 1969.
    DOI: 10.2307/1936256
  27. D. R. Tilley, M. Ahmed, J. Son and H. Badrinarayanan, “Hyperspectral reflectance of emergent macrophytes as an indicator of water column ammonia in an oligohaline, subtropical marsh,” Ecol. Eng., vol. 21, no. 2-3, pp. 153-163, Dec. 2003.
    DOI: 10.1016/j.ecoleng.2003.10.004
  28. C. J. Tucker, “Red and photographic infrared linear combinations for monitoring vegetation,” Remote Sens. Environ., vol. 8, no. 2, pp. 127-150, May 1979.
    DOI: 10.1016/0034-4257(79)90013-0
  29. J. W. Rouse et al., “Monitoring the vernal advancement of retrogradation of natural vegetation,” NASA GSFC, Greenbelt (MD), USA, Rep. 1-371, 1974.
    Retrieved from: https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19740022555.pdf
    Retrieved on: Jan. 21, 2017
  30. A. D. Roberts, L. K. Roth and L. R. Perroy, “Hyperspectral Vegetation Indices,” in Hyperspectral Remote Sensing of Vegetation, A. Thenkabail, P. S. Lyon, J. G. Huete Eds., Boca Raton (FL), USA: CRC Press, 2011, ch. 14, sec. 2, pp. 309-328.
    DOI: 10.1201/b11222-20
  31. C. Jurgens, “The modified normalized difference vegetation index (mNDVI) a new index to determine frost damages in agriculture based on Landsat TM data,” Int. J. Remote Sens., vol. 18, no. 17, pp. 3583-3594, 1997.
    DOI: 10.1080/014311697216810
  32. M. N. Merzlyak et al., “Non-destructive optical detection of leaf senescence and fruit ripening,” Physiol. Plant., vol. 106, no. 1, pp. 135-141, May 1999.
    DOI: 10.1034/j.1399-3054.1999.106119.x
  33. A. A. Gitelson, A. Viña, V. Ciganda, D. C. Rundquist, T. J. Arkebauer, “Remote estimation of canopy chlorophyll content in crops,” Geophys. Res. Lett., vol. 32, no. 8, Apr. 2005.
    DOI: 10.1029/2005GL022688
  34. J. A. Gamon, J. Peñuelas, C. B. Field, “A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency,” Remote Sens. Environ., vol. 41, no. 1, pp. 35-44, Jul. 1992.
    DOI: 10.1016/0034-4257(92)90059-S
  35. D. A. Sims, J. A. Gamon, “Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages,” Remote Sens. Environ., vol. 81, no. 2-3, pp. 337-354, Aug. 2002.
    DOI: 10.1016/S0034-4257(02)00010-X
  36. D. Haboudane et al., “Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture,” Remote Sens. Environ., vol. 81, no. 2-3, pp. 416–426, Aug. 2002.
    DOI: 10.1016/S0034-4257(02)00018-4
  37. M. S. Kim, C. S. T. Daughtry, E. W. Chappelle, J. E. McMurtrey, C. L. Walthall, “The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation (Apar),” in Proc. of the 6th Symposium on Physical Measurements and Signatures in Remote Sensing, Val D’Isere, 1994, pp. 299-306.
    Retrieved from: https://goobi.tib.eu/viewer/content/?action=pdf&metsFile=830289488.xml&targetFileName=THE_USE_OF

    Retrieved on: Jan. 21, 2017
  38. S. L. Ustin et al., “Remote sensing based assessment of biophysical indicators for land degradation and desertification,” in Recent advances in remote sensing and geo-information processing for land degradation assessment, vol. 8, A. Röder, J. Hill, Eds., Boca Raton (FL), USA: CRC Press, 2009, ch. 2, pp. 15–44.
    Retrieved from: https://books.google.ca/books?isbn=0203875443
    Retrieved on: Jan. 21, 2017
  39. D. Haboudane et al., “Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture,” Remote Sens. Environ., vol. 90, no. 3, pp. 337–352, Apr. 2004.
    DOI: 10.1016/j.rse.2003.12.013