Research of oil polluted soil and vegetation cover grown in laboratory by hyperspectral remote sensing method using the ASD FieldSpec 3FR Spectroradiometer

  • Galina Zholobak Scientific Centre for Aerospace Research of the Earth Institute of Geological Science National Academy of Sciences of Ukraine, Kyiv, Ukraine
  • Stanislav Dugin Scientific Centre for Aerospace Research of the Earth Institute of Geological Science National Academy of Sciences of Ukraine, Kyiv, Ukraine
  • Oksana Sybirtseva Scientific Centre for Aerospace Research of the Earth Institute of Geological Science National Academy of Sciences of Ukraine, Kyiv, Ukraine
  • Yelizaveta Dorofey Scientific Centre for Aerospace Research of the Earth Institute of Geological Science National Academy of Sciences of Ukraine, Kyiv, Ukraine
Keywords: vegetation indices, oil pollution, spring crops

Abstract

The development of oil extraction and refining industry causes the environment pollution primarily the aquatic and terrestrial ecosystems. The vegetation and soils as the components of terrestrial ecosystems expose to oil pollution especially. The research of oil-polluted soil and vegetation cover grown in laboratory by hyperspectral remote sensing method using the ASD FieldSpec® 3FR spectroradiometer is performed. The vegetation cover of spring cereals (wheat, barley, and corn) is formed by the growing in the containers with soil. The hyperspectral vegetation indices together with the reflectance in the red edge of spectrum (ТСІ, GrNDVI and REP) are the higher values for corn cover in comparison with the different spring small grain cereals. The additional dose of oil applied in the polluted soils from the Staryi Sambir oil deposit induces the reduction in the values of above-mentioned vegetation indices for the every spring crop of interest. The research of oil-polluted soil and vegetation cover grown in laboratory by hyperspectral remote sensing method using the ASD FieldSpec 3FR spectroradiometer is performed. The vegetation cover of spring cereals (wheat, barley, and corn) is formed by the growing in the containers with the different soils. The hyperspectral vegetation indices together with the reflectance in the red edge of spectrum (ТСІ, GrNDVI and REP) are the higher values for corn cover in comparison with the different spring small grain cereals. The additional dose of oil applied in the polluted soils from the Staryi Sambir oil deposit induces the reduction in the values of above-mentioned vegetation indices for the every spring crop of interest. Accumulated GrNDVI values for the corn and TCI for the corn and spring barley are similar to the laboratory chlorophyll content in these crops (according to the ratio of cultivation substrates). The further remote study of oil polluted soils and their influence on vegetation demands to improve the spectral measurements using satellite image data.

References

GOST 17.1.4.01-80 (1983). General requirements for methods for the determination of petroleum products in natural and waste waters. Moscow. (in Russian). https://internet-law.ru/gosts/gost/23834/

Jura, N., Tsvilinuk, O., Terek, O. (2006) Reactions of sedge rough to oil pollution. Visn. Ljviv. Uni-tu. Ser. biol., no. 42, pp. 142–146. (in Ukrainian).

Jura, N. M. (2011). Possibilities of using plant test systems for biomonitoring of oil-contaminated soils. Biologhichni Studiji (Studia Biologica), vol. 5, no. 3, pp. 183–196. (in Ukrainian).

Jura, N., Podan, I. (2017). Environmental Impacts of Long-Term Oil Production on the Starosambirskoye Field of Lviv Oblast. Visn. Ljviv. Uni-tu. Ser. biol., vol. 76, pp. 120–127. (in Ukrainian).

Lyalko, V. I., Shportiuk, Z. M., Sibirtseva, O. M., Dugin, S. S. (2015). Hyperspectral indices for differentiation of oil-saturated soils according to the data of remote spectrometry. Geol. zhurn., no. 4 (353), pp. 105–112. (in Ukrainian). http://geojournal.igs-nas.org.ua/article/view/138759

Nazarov, A.V. (2007). Influence of oil pollution of soil on plants. Vest. Perm. un-ta. Biol., no. 5 (10), pp. 134–141. (in Russian).

Petrosyan, A. G., Dyatlov, S. E., Tarasenko, A. O., Dyatlova, O. S. (2002). Biotesting as a method for express evaluation of soil toxicity. Visnyk Odesjkogho nacionaljnogho universytetu, vol. 7, no. 1, pp. 139–145. (in Ukrainian). http://liber.onu.edu.ua/pdf/vest_biol_1_02.pdf

Terek, O. I. (2007) Plant growth: teach. manual. Lviv: View. Center of LNU Ivan Franko, 247 p. (in Ukrainian).

Shanda, V. I., Shanda, L. V. (2009). Soil as an environment for plant interactions. Gruntoznavstvo, vol. 10 (1–2), pp. 14–22. http://www.ussj.cv.ua/2009_t10_1-2/Shanda.pdf

Allen, C. S., Krekeler, M. P. S. Reflectance spectra of crude oils and refined petroleum products on a variety of common substrates. Proc. SPIE 7687, Active and Passive Signatures, 76870L, (4 May 2010). https://doi.org/10.1117/12.852200

Andreoli, G., Bulgarelli, B., Hosgood, B., Tarchi, D. (2007) Hyperspectral Analysis of Oil and Oil-Impacted Soils for Remote Sensing Purposes, ISSN 1018-5593, EU Commission Directorate-General JRC Institute for the Protection and Security of the Citizen, Italy . https://www.ugpti.org/smartse/research/citations/downloads/Andreoli-HSI_for_Oil_and_Spills-2007.pdf

Arellano, P., Tansey, K., Balzter, H. Boyd, D.S. (2015) Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images. Environmental Pollution. Vol. 205. pp. 225–239. https://doi.org/10.1016/j.envpol.2015.05.041

Cloutis, E. A. (1989) Spectral Reflectance Properties of Hydrocarbons: Remote-Sensing Implications. Science. Vol. 245. no 4914. pp. 165–168. https://doi.org/10.1126/science.245.4914.165

Dash, J., Curran, P. J. (2004) The MERIS terrestrial chlorophyll index. Int. Journal of Remote Sensing. Vol. 25. pp. 5403–5413. URL: http://www.informaworld.com/smpp/. https://doi.org/10.1080/0143116042000274015

Gitelson, A. A., Kaufman, Y. J., Merzlyak, M. N. (1996) Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment. Vol. 58, no. 3. pp. 289–298. https://doi.org/10.1016/s0034-4257(96)00072-7

Herrmann, I., Pimstein, A. A., Karnieli, Y., Cohen, V., Alchanatis, D., Bonfil, J. (2011) LAI assessment of wheat and potato crops by VENUS and Sentinel-2 bands. Remote Sensing of Environment. Vol.115. no 8. pp. 2141–2151. https://doi.org/10.1016/j.rse.2011.04.018

Horler, D. N. H., Dockray, M., Barber, J. (1983) The red edge of plant leaf reflectance. Int. Journal of Remote Sensing. Vol. 4. pp. 273–288. https://doi.org/10.1080/01431168308948546

Huete, A. R. (1988) A modified soil vegetation adjusted index (SAVI). Remote Sensing of Environment. Vol. 25. no. 1. pp. 295–309.

Jensen, J. R. (2007) Remote sensing of the environment: an Earth resource perspective: 2nd ed. Upper Saddle River, NJ: Prentice-Hall, Inc., 592 р. https://www.scirp.org/(S(i43dyn45teexjx455qlt3d2q))/reference/ReferencesPapers.aspx?ReferenceID=1509053

Kiang, N. Y., Siefert, J., Govindjee, Blankenship, R. E. (2007) Spectral signatures of photosynthesis. I. Review of Earth organisms. Astrobiology. Vol. 7. no. 1. pp. 222–251. https://doi.org/10.1089/ast.2006.0105

Kuhn, F., Oppermann, K., Horig, B. (2004) Hydrocarbon index – an algorithm for hyperspectral detection of hydrocarbons. Int. J. Remote Sensing. V. 25. no. 12. pp. 2467–2473. https://doi.org/10.1080/01431160310001642287

Li, L., Ustin, S. L., Lay, M. (2005) Application of AVIRIS data in detection of oil-induced vegetation stress and cover change at Jornada, New Mexico. Remote Sens. Environ. V 94. no. 1. pp. 1–16. https://doi.org/10.1016/j.rse.2004.08.010

Noomen, M. F., van der Meer, F. D., Skidmore, A. K. Hyperspectral remote sensing for detecting the effects of three hydrocarbon gases on maize reflectance. Global monitoring for sustainability and security: Proc. of the 31st Int. Symp. On Remote Sensing of Environment (Saint-Petersburg, 20–24 June 2005). Saint-Petersburg, 2005. pp. 4. https://www.researchgate.net/publication/228723419_Hyperspectral_remote_sensing_for_detecting_the_effects_of_three_hydrocarbon_gases_on_maize_reflectance

Prince, S. D. (1991) A model of regional primary production for use with coarse resolution satellite date. Int. J. Remote Sensing. Vol. 6. no. 7. pp. 1313–1330. https://doi.org/10.1080/01431169108929728

Rasmussen, M. S. (1996) Operational yield forecast using AVHRR NDVI data: reduction of environmental and inter annual variability. Int. J. Remote Sensing. Vol. 18. no. 5. pp. 1059–1077. https://doi.org/10.1080/014311697218575

Rouse, J. W., Jr., Haas, R. H., Schell, J. A., Deering, D. W. (1973) Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. Prog. Rep. RSC 1978-1, 93 p. https://ntrs.nasa.gov/search.jsp?R=19740022555

Short, N. M., Bolton, J. (2006) NASA Remote Sensing Tutorial, URL: http://rst.gsfc.nasa.gov/Sect5/Sect5 5.html.

Strong, C. J., Burnside, N. G., Llewellyn, D. (2017) The potential of small-Unmanned Aircraft Systems for the rapid detection of threatened unimproved grassland communities using an Enhanced Normalized Difference Vegetation Index. PLoS ONE. № 12 (10). pp. 1–16. https://doi.org/10.1371/journal.pone.0186193.

Tian, Q. Study on Oil-Gas Reservoir detecting methods using hyperspectral remote sensing // XXII ISPRS Congress, 25 August – 01 September 2012, Melbourne, Australia, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2012.Vol. XXXIX-B7. pp. 157–162. https://doi.org/10.5194/isprsarchives-xxxix-b7-157-2012

Ustin, S. L., Gitelson, A. A., Jacquemoud, S., Schaepman, M., Asner, G. P., Gamon, J lement 1 (Imaging Spectroscopy Special Issue). pp. S67–S77.

Section
Earth observation data applications: Challenges and tasks