Analysis of the spectral properties of wheat growth in different vegetation periods
Keywords:spectral brightness coefficient, multispectral images, Landsat-8, atmospheric correction, wavelength, range, cadastral number
The article presents a technique for studying space images based on the analysis of the spectral brightness coefficient (SBC) of space images of the earth's surface.
Recognition of plant species, soils, and territories using satellite images is an applied task that allows to implement many processes in agriculture and automate the activities of farmers and large farms. The main tool for analyzing satellite imagery data is the clustering of data that uniquely identifies the desired objects and changes associated with various reasons.
Based on the data obtained in the course of experiments on obtaining numerical SBC values, the patterns of behavior of the processes of reflection of vegetation, factors that impede the normal growth of plants, and the proposed clustering of the spectral ranges of wave propagation, which can be used to determine the type of objects under consideration, are revealed. Recognition of these causes through the analysis of SBC satellite images will create an information system for monitoring the state of plants and events to eliminate negative causes. SBC data is divided into non-overlapping ranges, i.e. they form clusters reflecting the normal development of plant species and deviations associated with negative causes. If there are deviations, then there is an algorithm that determines the cause of the deviation and proposes an action plan to eliminate the defect.
It should be noted that the distribution of the brightness spectra depends on the climatic and geographical conditions of the plant species and is unique for each region. This study refers to the Akmola region, where grain crops are grown
Fisenko, E. V. (2019). Analysis of the results of using the technique of multimedia processing of spectral images of the underlying surface using complex remote sensing data. Geodesy and Aerophotosurveying, 63 (3), 324–332. doi: https://doi.org/10.30533/0536-101x-2019-63-3-324-332
Bajsholanov, S. S., Polevoj, A. N. (2016). Ocenka vlagoobespechennosti vegetacionnogo perioda v severnoj zernoseyushchej territorii Kazahstana. Fizicheskaya geografiya i geomorfologiya, 3 (83), 95–102.
Botvich, I. Yu., Volkova, A. I., Kononova, N. A., Ivanova, Yu. D., Shevyrnogov, A. P. (2017). Spectrometry of herbaceous vegetation of the krasnoyarsky krai and Republic of Khakassia: the method of measurement, storage and processing of data. Reshetnevskie chteniya, 398–400. Available at: https://cyberleninka.ru/article/n/spektrometrirovanie-travyanistoy-rastitelnosti-krasnoyarskogo-kraya-i-respubliki-hakasiya-metodika-izmereniy-hranenie-i-obrabotka
Danilov, R. Yu., Kremneva, O. Yu., Ismailov, V. Ya., Tretyakov, V. A., Rizvanov, A. A., V.V. Krivoshein, Pachkin, A. A. (2020). General methods and results of ground hyperspectral studies of seasonal changes in the reflective properties of crops and certain types of weeds. Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli Iz Kosmosa, 17 (1), 113–127. doi: https://doi.org/10.21046/2070-7401-2020-17-1-113-127
Yerzhanova, A. Ye., Kerimkhulle, S. Ye., Makhanov, M., Abdikerimova, G. B., Beglerova, S. T., Taszhurekova, Zh. K. (2021). Atmospheric correction of Landsat-8 / OLI data using the FLAASH algorithm: obtaining information about agricultural crops. Journal of Theoretical and Applied Information Technology, 99 (13), 3110–3119. Available at: http://www.jatit.org/volumes/Vol99No13/3Vol99No13.pdf
Yerzhanova, A. Y. (2021). Spectral properties of plants by vegetation periods for analysis of satellite images. Vestnik KazNRTU, 143 (1), 226–232. doi: https://doi.org/10.51301/vest.su.2021.v143.i1.28
De Keukelaere, L., Sterckx, S., Adriaensen, S., Knaeps, E., Reusen, I., Giardino, C. et. al. (2018). Atmospheric correction of Landsat-8/OLI and Sentinel-2/MSI data using iCOR algorithm: validation for coastal and inland waters. European Journal of Remote Sensing, 51 (1), 525–542. doi: https://doi.org/10.1080/22797254.2018.1457937
Stycenko, E. A. (2018). Razrabotka metodiki avtomaticheskoj rasshifrovki rastitel'nogo pokrova s kompleksnym ispol'zovaniem mnogosezonnyh zonal'nyh kosmicheskih snimkov. Moscow, 213.
Kruse, F. A. (1988). Use of airborne imaging spectrometer data to map minerals associated with hydrothermally altered rocks in the northern grapevine mountains, Nevada, and California. Remote Sensing of Environment, 24 (1), 31–51. doi: https://doi.org/10.1016/0034-4257(88)90004-1
Kolesnikova, O., Cherepanov, A. (2009). Vozmozhnosti PK ENVI dlya obrabotki mul'tispektral'nyh i giperspektral'nyh dannyh. Geomatika, 3, 24–27. Available at: https://sovzond.ru/upload/iblock/65b/2009_03_004.pdf
EarthExplorer. Available at: https://earthexplorer.usgs.gov/
Andreev, G. A., Bazarskiy, O. V., Glauberman, A. S., Kolesnikov, A. I., Korzhik, Yu. V., Khlyavich, Ya. L. (1984). Analiz i sintez sluchaynykh prostranstvennykh tekstur. Zarubezhnaya radioelektronika, 2, 3–33.
Kharalik, R. M. (1979). Statisticheskiy i strukturnyy podkhody k opisaniyu tekstur. TIIER, 67 (5), 98–119.
Potapov, A. A. (2003). Novye informatsionnye tekhnologii na osnove veroyatnostnykh teksturnykh i fraktal'nykh priznakov v radiolokatsionnom obnaruzhenii malokontrastnykh tseley. Radiotekhnika i elektronika, 48 (9), 1101–1119.
Kolodnikova, N. V. (2004). Obzor teksturnykh priznakov dlya zadach raspoznavaniya obrazov. Doklady Tomskogo gosudarstvennogo universiteta sistem upravleniya i radioelektroniki, 113–124.
Sidorova, V. S. (2012). Hierarchical cluster algorithm for remote sensing data of earth. Pattern Recognition and Image Analysis, 22 (2), 373–379. doi: https://doi.org/10.1134/s1054661812020149
Irons, J. R., Dwyer, J. L., Barsi, J. A. (2012). The next Landsat satellite: The Landsat Data Continuity Mission. Remote Sensing of Environment, 122, 11–21. doi: https://doi.org/10.1016/j.rse.2011.08.026
How to Cite
Copyright (c) 2021 Akbota Yerzhanova, Akmaral Kassymova, Gulzira Abdikerimova, Manshuk Abdimomynova, Zhuldyz Tashenova, Elmira Nurlybaeva
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.