ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 2, pp. 131-142

Remote control of early stages of different types of stress in various plants

O.V. Grigorieva 1 , V.N. Gruzdev 1, 2 , I.V. Drozdova 3 , B.V. Shilin 1, 2 
1 A.F. Mozhaisky Military Space Academy, Saint Petersburg, Russia
2 Saint Petersburg Scientific Research Center for Ecological Safety RAS, Saint Petersburg, Russia
3 Komarov Botanical Institute RAS, Saint Petersburg, Russia
Accepted: 15.12.2020
DOI: 10.21046/2070-7401-2021-18-2-131-142
Plants under a short- or long-term influence of adverse factors (stressors) quickly, from several to tens of hours, develop spectral anomalies in the near infrared range of 750–1000 nm, long before any visible morphological changes. The results of measurements of the anomalies in spectral reflectance coefficient (SRC) caused by such stressors as heavy metals, ionizing radiation, oil products, and mechanical damage are presented. The objects of research are beans, maple, oak, etc. It was mainly recorded, that due to the inhibitory effect of most stressors, anomalies were negative (SRC lowered), although in some cases a positive anomaly was observed in the form of an inversion of brightness contrasts. The spectral anomaly magnitude and its duration let us make a confident conclusion that we can detect and map areas of plants affected by stress with the help of aerospace video spectral systems of high spatial and spectral resolution.
Keywords: plant stress, spectral characteristics, video spectrometers, spectroradiometers
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References:

  1. Alekseev A. A., Shilin B. V., Shilin I. B., Operational experience of the imaging spectrometer using in fieldwork, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9, No. 4, pp. 89–94 (in Russian).
  2. Bakina L. G., Gruzdev V. N., Drozdova I. V., Shilin B. V., Remote Detection of Human Induced Stress on Vegetation in The Early Stages of Exposure to Heavy Metals, Regional’naya ekologiya, 2016, No. 1(43), pp. 81–89 (in Russian).
  3. Grigoryeva O. V., Sahidov A. G., Panin A. V., The Indicator of Soil Condition in Areas of Oil and Oil Products Circulation Obtained by Non-Contact Means of Monitoring, Ekologiya i promyshlennost’ Rossii, 2010, No. 10, pp. 50–53 (in Russian).
  4. Grigorieva O. V., Drozdova I. V., Shilin B. V., Experimental substantiation of the capabilities of videospectral remote indication of short-term vegetation stress, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 7, pp. 78–88 (in Russian), DOI: 10.21046/2070-7401-2018-15-7-78-88.
  5. Gruzdev V. N., Drozdova I. V., Kuznetsov A. Yu., Shilin B. V., Solving the Problems of Environmental Safety by Videospectral Method, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 1, pp. 8–17 (in Russian), DOI: 10.21046/2070-7401-2018-15-1-9-17.
  6. Kravtsov S. L., Golubtsov D. V., Lisova E. N., Optimal Spectral Channels of Aerospace Equipment for Vegetation Remote Sensing Monitoring (Foreign Publications Review by the Problem), Issledovaniya Zemli iz kosmosa, 2013, No. 1, pp. 79–91 (in Russian), DOI: 10.7868/S020596141301003X.
  7. Kronberg P., Fernerkundung der Erde. Grundlagen und Methoden des Remote Sensing in der Geologie, Stuttgart: Enke, 1985, 394 s.
  8. Shilin B. V., Gornyi V. I., Verem’ev V. I., History of Remote Sensing Methods Application During Elimination of Chernobyl Nuclear Power Plant Accident, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9, No. 2, pp. 27–36 (in Russian).
  9. Ayala-Silva T., Beyl C. A., Changes in spectral reflectance of wheat leaves in response to specific macronutrient deficiency, Advances in Space Research, 2005, Vol. 35, pp. 305–317, DOI: 10.1016/j.asr.2004.09.008.
  10. Bandaru V., Daughtry C. S., Codling E. E., Hansen D. J., White-Hansen S., Green C. E., Evaluating Leaf and Canopy Reflectance of Stressed Rice Plants to Monitor Arsenic Contamination, Intern. J. Environmental Research and Public Health, 2016, Vol. 13(6), Art. No. 606, 16 p., available at: https://doi.org/10.3390/ijerph13060606.
  11. Bowker D. E., Davis R. E., Myrick D. L., Stacy K., Jones W. T., Spectral Reflectances of Natural Targets for Use in Remote Sensing Studies, NASA Reference Publication, 1139, 1985, 188 p.
  12. Domínguez-Beisiegel M., Castañeda C., Mougenot B., Herrero J., Analysis and Mapping of the Spectral Characteristics of Fractional Green Cover in Saline Wetlands (NE Spain) Using Field and Remote Sensing Data, Remote Sensing, 2016, Vol. 8(7), Art. No. 590, 19 p., available at: https://doi.org/10.3390/rs8070590.
  13. Jacquemoud S., Ustin S. L., Leaf optical properties: a state of the art, Proc. 8th Intern. Symp. Physical Measurements and Signatures in Remote Sensing, Aussois, France, 2001, pp. 223–232.
  14. Kruse F. A., Boardman J. W., Characterization and Mapping of Kimberlites and Related Diatremes Using Hyperspectral Remote Sensing, Proc. IEEE AeroSpace Conf., Big Sky, Montana, 2000, Vol. 3, pp. 299–304, DOI: 10.1109/AERO.2000.879859.
  15. Kuhn F., Horig B., Hydrocarbon Index — An algorithm for hyperspectral detection of hydrocarbons, Intern. J. Remote Sensing, 2004, Vol. 25(12), pp. 2467–2473, DOI: 10.1080/01431160310001642287.
  16. Schwartz G., Eshel G., Ben-Haim M., Ben-Dor E., Reflectance spectroscopy as a rapid tool for qualitative mapping and classification of hydrocarbons soil contamination, Tel Aviv, Israel, 2009, 7 p., available at: https://pdfs.semanticscholar.org/fe4b/e6cb350831bf8067d8e6cb8519ca4781cb70.pdf.
  17. Van der Meer F. D., Imaging spectrometry: Basic Principles and Prospective Applications, Kluwer Academic Publishers, 2006, 403 p.
  18. Wahabzada M., Mahlein A.-K., Bauckhage C., Steiner U., Oerke E.-C., Kersting K., Metro Maps of Plant Disease Dynamics — Automated Mining of Differences Using Hyperspectral Images, PLoS ONE, 2015, Vol. 10(1), Art. No. e0116902, 20 p., available at: https://doi.org/10.1371/journal.pone.0116902.
  19. Zhang C., Ren H., Dai X., Qin Q., Li J., Zhang T., Sun Y., Spectral characteristics of copper-stressed vegetation leaves and further understanding of the copper stress vegetation index, Intern. J. Remote Sensing, 2019, Vol. 40(12), pp. 4473–4488, DOI: 10.1080/01431161.2018.1563842.