Identifying vegetation indices as the rational spectral indicators of vegetation state under conditions of laboratory experiment

  • Vadim Lyalko Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine, Oles Honchar st, 55-B, 01054, Kyiv, Ukraine https://orcid.org/0000-0002-7552-5915
  • Galyna Zholobak Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine, Oles Honchar st, 55-B, 01054, Kyiv, Ukraine https://orcid.org/0000-0003-4053-6101
  • Stanislav Dugin Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine, Oles Honchar st, 55-B, 01054, Kyiv, Ukraine https://orcid.org/0000-0002-2960-3783
  • Oksana Sybirtseva Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine, Oles Honchar st, 55-B, 01054, Kyiv, Ukraine https://orcid.org/0000-0003-1181-0474
  • Yelizaveta Dorofey Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine, Oles Honchar st, 55-B, 01054, Kyiv, Ukraine https://orcid.org/0000-0002-4348-0628
Keywords: vegetation indices, durum spring wheat, spectrometric and gasometric measurements, satellite data, carbon cycle, climatic effects, sustainable development

Abstract

For the vegetation classes, which cover the ground surface with a rather small area and studied by means of the ground-based remote sensors it’s necessary to select the suitable spectral indices, which cloud responded in a fast and effective way to dynamic environmental conditions induced by the different stress factors. The vegetation indices (VIs) can be such indicators calculated by the mathematical operations using reflectances in the different spectral ranges measured by the field remote sensors, i.e. spectroradiometers. Application of VIs allows identifying these changes of vegetation state, which aren’t visible at the visual observation. In order to select these VIs we have conducted the laboratory experiment with the cultivation of durum spring wheat variety “Diana” at the different higher seeding rates to provide by this way, first, the fast 100% plant cover, and, second, establish the stressed conditions for the plants. During this experiment the gasometric and spectrometric observation of the constantly growing phytomass carried out, when the measurements of intensity of СО2 absorption and release by the plants in process of photosynthesis/respiration and spectrometric ones have been performed practically simultaneously that allowed further calculation of VIs.
Three vegetation indices such as MTCI, Clrededge and Clgreen were identified as the most sensitive to the changes of vegetation state and, thus, they can serve as the proper spectral indicators of vegetation condition, which are extremely necessary to develop the technique of estimating the variables of carbon cycle in the different ecosystems using satellite data and field measurements, improve of assessment for the climatic effects at the regional and local levels and estimating the IAEG-SDGs indicators of sustainable development.

References

Bardysh B., Burshtynska Kh. (2014). Application of the vegetation indices for identification of the objects of the earth’s surface. Modern achievements in geodesic science and production. Iss. II (28), p. 82-88 (in Ukrainian).

Dugin S., Sybirtseva O., Golubov S., Dorofey Ye. (2019) Verification of multispectral data processing for the Sentinel-2A bands, field ASD FieldSpec®3FR and UAV with the DJI STS-VIS. Ukrajinsjkyj zhurnal dystancijnogho zonduvannja Zemli. 21, p. 29-39. DOI: https://doi.org/10.36023/ujrs.2019.21.147 (in Ukrainian).

Zholobak G., Sybirtseva O., Vakolyuk M., Zakharchuk Yu. (2017) Remote monitoring of the state of winter wheat during the spring-summer vegetation of 2016 year, by using vegetation indices of Sentinel-2A satellite (case study of forest steppe area of Ukraine). Ukrajinsjkyj zhurnal dystancijnogho zonduvannja Zemli. 15, p. 23-30. URL: http://ujrs.org.ua/ujrs/issue/viewIssue/15/pdf_19 (in Ukrainian).

Zholobak G., Dugin S., Sybirtseva O., Kazantsev N., Romanchuk I. (2020) Determination of nitrogen and chlorophyll content in two varieties of winter wheat plants means of ground and airborne spectrometry. Ukrajinsjkyj zhurnal dystancijnogho zonduvannja Zemli. 26, p. 4-13 DOI: https://doi.org/10.36023/ujrs.2020.26.178 (in Ukrainian).

Lykhochvor V., Petrychenko V. (2006). Horticulture. Modern intensive technologies for the cultivation of the main field crops. Lviv: NVF ”Ukrainski teknolohii”. – 730 p. (in Ukrainian).

Kravchenko V.S. (2016) Optimization for the technology elements of spring wheat cultivation in the south of the Right-Bank Forest Steppe Ukraine. Thesis for a Candidate Degree in Agricultural Science, Uman, - 188 ps. (in Ukrainian).

Barnes, E.M., Clarke, T.R., Richards, S.E., Colaizzi, P.D., Haberland, J., Kostrzewski, M., Waller, P., Choi, C., Riley, E., Thompson, T., Lascano, R.J., Li, H., Moran, M.S. (2000). Coincident detection of crop water stress, nitrogen status and canopy density using ground-based multispectral data. 5th International Conference on Precision Agriculture, Bloomington, 16-19 July 2000, 1-15. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.463.8007&rep=rep1&type=pdf

Dash J., Curran P.J. (2004) The MERIS terrestrial chlorophyll index. Int. Journal of Remote Sensing. 2004. 25. P. 5403-5413

Dotzler S., Hill J., Buddenbaum H., Stoffe J. The Potential of EnMAP and Sentinel2 Data for Detecting Drought Stress Phenomena in Deciduous Forest Communities. Remote Sens.2015. Vol. 7. P. 14227–14258. doi:10.3390/rs71014227.

Du S., Du S. Land cover classification using remote sensing images and LiDAR data // IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 2479-2482, doi: 10.1109/IGARSS.2019.8899840

Frampton, W. J., Dash, J., Watmough, G., Milton, E. J. (2013).Evaluating the capabilities of Sentinel2 for quantitative estimation of biophysical variables in vegetation. ISPRS J. Photogram. Remote Sens. 82, 83–92.

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. – 1996. – V. 58, № 3. – P. 289-298.

Gitelson, A. A., Keydan, G. P., Merzlyak, M. N. (2006)Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophysical Research.– 2006.–Letters 33, L11402.

Gitelson, A., Merzlyak, M.N. (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.

Huete A. R. (1988) A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment. – 1988.– V.25, N 3.– P.295-309.

Jiang, Z., Huete, A.R., Didan, K., Miura, T.(2008) Development of a two band enhanced vegetation index without a blue band. Remote Sens. Environ.2008. V. 112. P. 3833–3845

Lang, W.; Chen, X.; Liang, L.; Ren, S.; Qian, S. Geographic and Climatic Attributions of Autumn Land Surface Phenology Spatial Patterns in the Temperate Deciduous Broadleaf Forest of China. Remote Sens. 2019, 11, 1546. https://doi.org/10.3390/rs11131546

Merzlyak, M.N., Gitelson, A.A., Chivkunova, O.B., Rakitin, V.Y. (1999) Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening, Physiol. Plant. 106 (1) : 135-141 http://dx.doi.org/10.1034/j.1399-3054.1999.106119.x

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. – 1973. – 93 p.

Zarco-Tejada P. J., Miller J. R., Noland T. L., Mohammed G. H., Sampson P. H. Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data // IEEE Transactions on Geoscience and Remote Sensing, 2001. Vol. 39, No. 7. P. 1491−1507.

Zhang X., Long T., He G., Guo Y., Yin R., Zhang Zh., Xiao H., Li M., Cheng B. Rapid generation of global forest cover map using Landsat based on the forest ecological zones // J. of Applied Remote Sensing, 14(2), 022211 (2020). https://doi.org/10.1117/1.JRS.14.022211

Zhu L., Suomalainen J., Liu J., Hyyppä J., Kaartinen H., Haggren H. A Review: Remote Sensing Sensors // Multi-purposeful Application of Geospatial Data : IntechOpen, 2018.– DOI: 10.5772/intechopen.71049

Section
Earth observation data applications: Challenges and tasks