Spectral characteristics of some agricultural crops in different phenological phases of vegetation
https://doi.org/10.36305/2019-3-152-56-70
Abstract
For deciphering crops from satellite images at different time periods, it is necessary to have information about the spectral reflectivity of plants during their passage through the phenological phases of vegetation. An attempt was made to evaluate the spectral reflectivity of the main fruit crops and grapes in different phenological phases of the growing season using Sentinel-2 satellite images and the ENVI software package. Field research methods, plots were selected on which peach, grapes, cherries, apple trees, plums, and apricots grow are used. It was established that planting crops was carried out by mixing cultivars in order to reduce the risk of additional costs as a result of possible adverse natural processes and phenomena. For each section, the maximum, minimum, and average values of the spectral brightness coefficient were obtained and analyzed within 13 bands of Sentinel-2 satellite images. Space images were selected for 04/07/2019, 04/27/2019 and 05/12/2019, as the most suitable for the periods of the beginning of flowering (04/07/2019), the end of flowering (04/27/2019) and the beginning of fruit ripening (12/05/2019), with minimal cloud overlap values. To eliminate the external influence of the soil within each pixel of the image, the linear spectral separation module of the ENVI software package was used, a reference soil fragment was selected and its spectral characteristics were obtained, which made it possible to depict graphs of the spectral curves of the crops under study within each section. It was not possible to obtain a distinction of the spectral brightness coefficient for all sections, which is associated with the presence of additional external elements.
About the Authors
V. A. Tabunschik
ФГБУН ФИЦ «Институт биологии южных морей имени А.О. Ковалевского РАН»
Russian Federation
Russian Federation
Т. M. Chekmareva
ФГБУН ФИЦ «Институт биологии южных морей имени А.О. Ковалевского РАН»
Russian Federation
Russian Federation
R. V. Gorbunov
ФГБУН ФИЦ «Институт биологии южных морей имени А.О. Ковалевского РАН»
Russian Federation
Russian Federation
References
1. Bondur V.G. Modern approaches to processing large flows of hyperspectral and multispectral aerospace information. Issledovaniye Zemli iz kosmosa. 2014. 1: 4-17.
2. Borzov S.M., Potaturkin O.I. Detection of selective felling based on remote sensing data of high spatial resolution. Issledovaniye Zemli iz kosmosa. 2014. 4: 87
3. Huseynov G.A., Smokty O.I. Information properties of spectral brightness coefficients and vegetation indices for calibration of aerospace images // Trudy SPIIRAN. 2005. 2(2): 360-367
4. Zhirin V.M., Eidlina S.P., Knyazeva S. V. The experience of forestry analysis of the consequences of fires from space images // Sovremennyye problem distantsionnogo zondirovaniya Zemli iz kosmosa. 2013. 10 (3): 243-259.
5. The study of reflectivity. Moscow: State scientific and technical mining geological oil Publishing House, 1934. 84 p.
6. Kozoderov V.V., Kondranin T.V., Kazantsev О. Yu., Bobylev VI., Scherbakov M.V., Borzyak V.V., Dmitriev E.V., Egorov V.D., Kamentsev V.P., BelyakovA.Yu., Loginov S.B. Processing and interpretation of hyperspectral aerospace measurements for remote diagnostics of natural and technogenic objects. Issledovaniye Zemli iz kosmosa. 2009. 2: 36-54
7. Kozoderov V.V., Kondranin T.V., Kosolapov V.S., Golovko V.A., Dmitriev E.V. Recovering the volume of phytomass and other parameters of the state of the land cover based on the processing of multispectral satellite images. Issledovaniye Zemli iz kosmosa. 2007. 1: 57-65
8. Kolesnikova O.N., Cherepanov A.S. Possibilities ENVI for processing multispectral and hyperspectral data// Geomatika. 2009. 3: 24-27.
9. Kondratiev K.Ya., Fechenko P.P. Spectral reflectance and vegetation recognition. Leningrad: Gidrometeoizdat, 1982. 216 p.
10. Krinov E.L. Spectral reflectance of natural formations. Moscow: Publishing USSR Academy of Sciences, 1947. 272 p.
11. Rachkulik V.N., Sitnikova M.V. Reflective properties and condition of vegetat/cw. Leningrad: Gidrometeoizdat, 1981. 267 p.
12. Chapursky L.I. Reflective properties of natural objects in the range of 400-2500 nm. Part 1. Leningrad: Ministry of Defense of the USSR, 1986. 160 p.
13. Schulz G.E. General phenology. Leningrad: Nauka, 1981.188 p.
14. Adamowski K. Spectral density of a river flow time series // Journal of Hydrology. 1971. Vol. 14(1). pp. 43-52. doi: 10.1016/0022-1694(71)90091-6
15. Ashish D., McClendon R.W., Hoogenboom G. Land use classification of multispectral aerial images using artificial neural networks // International Journal of Remote Sensing. 2009. Vol. 30(8). pp. 1989-2004. doi: 10.1080/01431160802549187
16. Asner G.P., Heidebrecht K.B. Spectral unmixing of vegetation, soil and dry carbon cover in arid regions: Comparing multispectral and hyperspectral observations // International Journal of Remote Sensing. 2002. Vol. 23(19). pp. 3939-3958. doi:10.1080/01431160110115960
17. Call K.A., Hardy J. T., Wallin D. O. Coral reef habitat discrimination using multivariate spectral analysis and satellite remote sensing // International Journal of Remote Sensing. 2003. Vol. 24(13). pp. 2627-2639. doi: 10.1080/0143116031000066990
18. Hirano A., Madden M., Welch R. Hyperspectral image data for mapping wetland vegetation/ / Wetlands. 2003. Vol. 23 (2). pp. 436-448. doi: 10.1672/18-20
19. Jacquemoud S., Baret E , Hanocq J.F. Modeling spectral and bidirectional soil reflectance/ / Remote Sensing of Environment. 1992. Vol. 41 (2-3). pp. 123-132.
20. Karami M., Rangzan K., Saberi A. Using GIS servers and interactive maps in spectral data sharing and administration: Case study of Ahvaz Spectral Geodatabase Platform (ASGP) // Computers & Geosciences. 2013. Vol. 60. pp. 23-33. doi:10.1016/j.cageo.2013.06.007
21. Landsberg H.E., Kaylor R.E. Spectral analysis of long meteorological series // Journal of Interdisciplinary Cycle Research. 1976. vol. 7. no. 3. pp. 237-243.
22. Lu D., Li G., Valladares G.S., Batistella M. Mapping soil erosion risk in Rondonia, Brazilian Amazonia: using RUSLE, remote sensing and GIS // Land Degradation & Development. 2004. Vol. 15(5). pp. 499-512. doi:10.1002/ldr.634
23. Luck-Vogel M., Mbolambi C., Rautenbach K., Adams J., van Niekerk L. Vegetation mapping in the St Lucia estuary using very high-resolution multispectral imagery and LiDAR // South African Journal of Botany. 2006. Vol. 107. pp. 188-199. doi:10.1016/j.sajb.2016.04.010
24. Lunetta RS., Ediriwickrema J., Hames J., Johnson D.M., Lyon J.G., Me Kerrow A., Pilant A. A Quantitative Assessment of a Combined Spectral and GIS Rule-Based LandCover Classification in the Neuse River Basin of North Carolina // Photogrammetric Engineering & Remote Sensing. 2009. Vol. 69(3). pp. 299-310. doi:10.14358/pers.69.3.299
25. Mayaux P., Bartholome E., Fritz S., Belward, A. A new land-cover map of Africa for the year 2000 // Journal of Biogeography. 2004. Vol. 31(6). pp. 861-877. doi: 10.1111/j.13652699.2004.01073.x.
26. Molleri G. S. F , Kampel, M., de Moraes Novo E. M. L. Spectral classification of water masses under the influence of the Amazon River plume // Acta Oceanologica Sinica. 2010. Vol. 29(3). pp. 1-8. doi:10.1007/sl3131-010-0031-l
27. Parrish D.F., Derber J.C. The National Meteorological Center's spectral statisticalinterpolation analysis system // Monthly Weather Review. 1992. vol. 120. no. 8. pp. 17471763.
28. Rivero R. G., Grunwald S., BinfordM. W., Osborne, T. Z. Integrating spectral indices into prediction models of soil phosphorus in a subtropical wetland // Remote Sensing of Environment. 2009. Vol. 113(11). pp. 2389-2402. doi:10.1016/j.rse.2009.07.015
29. Roberts D.A., Ustin S.L., Ogunjemiyo S., Greenberg J., Dobrowski S.Z., Chen J., Hinckley T.M. Spectral and Structural Measures of Northwest Forest Vegetation at Leaf to Landscape Scales//Ecosystems. 2004. Vol. 7(5). doi:10.1007/sl0021-004-0144-5
30. Salih A.A. M., Ganawa E.-T, Elmahl A.A. Spectral mixture analysis (SMA) and change vector analysis (CVA) methods for monitoring and mapping land degradation/desertification in arid and semiarid areas (Sudan), using Landsat imagery // The Egyptian Journal of Remote Sensing and Space Science. 2017. Vol. 20, S21-S29. doi:10.1016/j.ejrs.2016.12.008
31. Sanyal J., Lu X.X. Remote sensing and GIS-based flood vulnerability assessment of human settlements: a case study of Gangetic West Bengal, India // Hydrological Processes. 2005. Vol. 19(18). pp. 3699-3716. doi:10.1002/hyp.5852
32. Small C. Estimation of urban vegetation abundance by spectral mixture analysis // International Journal of Remote Sensing. 2001. Vol. 22(7). pp. 1305-1334.
33. Smith M. J., Pain C.F. Applications of remote sensing in geomorphology // Progress in Physical Geography. 2009. Vol. 33(4). pp. 568-582. doi: 10.1177/0309133309346648
34. Tabunshchyk V.A., Petlukova E.A., Hytrin M.O. The use of Satellite Imagery Sentinel 2 for Analysis of Land Used in Agriculture (for Example Razdolnensky District of the Republic of Crimea) // Proceedings of the T.I. Vyazemsky Karadag scientific station Nature Reserve of the RAS. 2018. no 1. pp. 43-57.
35. Tansey K., Chambers I., Anstee A., Denniss A., Lamb A. Object-oriented classification of very high resolution airborne imagery for the extraction of hedgerows and field margin cover in agricultural areas // Applied Geography. 2009. Vol. 29(2). pp. 145-157. doi:10.1016/j.apgeog.2008.08.004
36. Yuan F. Land cover change and environmental impact analysis in the Greater Mankato area of Minnesota using remote sensing and GIS modelling // International Journal of Remote Sensing. 2008. Vol. 29(4). pp. 1169-1184. doi: 10.1080/01431160701294703
37. YuanM., Dickens-Micozzi M., MagsigM.A. Analysis of Tornado Damage Tracks from the 3 May Tornado Outbreak Using Multispectral Satellite Imagery // Weather and Forecasting. 2002. Vol. 17(3). pp. 382-398. doi :10.1175/15200434(2002)017<0382:aotdtf>2.0.co;2.
Review
For citations:
Tabunschik V.A., Chekmareva Т.M., Gorbunov R.V. Spectral characteristics of some agricultural crops in different phenological phases of vegetation. Plant Biology and Horticulture: theory, innovation. 2019;(152):56-70. (In Russ.) https://doi.org/10.36305/2019-3-152-56-70
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