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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, 2024, Vol. 21, No. 4, pp. 199-208

Detection of citrus crop plantations in Syria using Landsat-8 OLI satellite data

S. Nasser 1 , I.Yu. Savin 2 
1 RUDN University, Moscow, Russia
2 V.V. Dokuchaev Soil Science Institute, Moscow, Russia
Accepted: 05.08.2024
DOI: 10.21046/2070-7401-2024-21-4-199-208
Citrus cultivation is of great economic and social importance for the Mediterranean countries. Citrus production in the region varies considerably from year to year due to meteorological conditions and the dynamics of the plantation area. This predetermines high importance of monitoring of their condition, which is currently not carried out at all or is carried out on the basis of a statistical survey method with a large error. The aim of the research was to analyse the possibilities of estimating citrus plantation areas of a test plot in Syria based on normalized difference vegetation index (NDVI) time series calculated from Landsat-8 OLI satellite data. Detection of citrus plantations was carried out using the NDVI curve similarity analysis method. Data from field surveys conducted in 2016 were used to assess the detection accuracy. It was found that the averaged NDVI curves of citrus plantations are reliably separated from those of other tree crops. The predominant species of citrus crops (orange, lemon, clementine) were also detected quite reliably. Separation of plantations within species by age and cultivar was not reliable. The proposed approach showed high potential for organizing a satellite-based monitoring system for citrus plantation acreage in Syria.
Keywords: NDVI, Landsat-8, satellite monitoring of vegetation, citrus, Syria, ILWIS
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References:

  1. Ameisen E., Building machine learning powered applications: Going from idea to product, O’Reilly Media, Inc., 2020, 238 p.
  2. Kotovich N. V., Algorithms for clustering of symbol’s images, Trudy ISA RAN, 2008, Vol. 38, pp. 241–251 (in Russian).
  3. Lupyan E. A., Bartalev S. A., Savin I. Yu., Satellite monitoring technologies in agriculture of Russia, Aehrokosmicheskii kur’er, 2009, No. 6, pp. 47–49 (in Russian).
  4. Rybalko E. A., Baranova N. V., Loupian E. A. et al., Management of ground data and remote observations data processing aimed at vineyards remote monitoring, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2016, Vol. 13, No. 1, pp. 79–92 (in Russian), DOI: 10.21046/2070-7401-2016-13-1-79-92.
  5. Tolpin V. A., Rybalko E. A., Baranova N. V. et al., Building a repository of satellite and ground data for the development of viticulture remote monitoring methods in the Republic of Crimea, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, Vol. 14, No. 1, pp. 101–110 (in Russian), DOI: 10.21046/2070-7401-2017-14-1-101-110.
  6. Ali A., Imran M., Remotely sensed real-time quantification of biophysical and biochemical traits of Citrus (Citrus sinensis L.) fruit orchards: A review, Scientia Horticulturae, 2021, Vol. 282, Article 110024, DOI: 10.1016/j.scienta.2021.110024.
  7. Almohamed S., Darwish C., Review of the Syrian agriculture and future prospects for reconstruction, Jordan J. Agricultural Sciences, 2021, Vol. 15, No. 2, pp. 35–49, DOI: 10.35516/jjas.v15i2.44.
  8. Atzberger C., Zeug G., Defourny P. et al., Monitoring of forests through remote sensing: final report, Publications Office of the European Union, 2020, 149 p., DOI: 10.2779/175242.
  9. Huang C., Zhang C., He Y. et al., Land cover mapping in cloud-prone tropical areas using Sentinel 2 data: Integrating spectral features with NDVI temporal dynamics, Remote Sensing, 2020, Vol. 12, Article 1163, DOI: 10.3390/rs12071163.
  10. Galvañ A., Boughalleb-M’Hamdi N., Benfradj N. et al., Climate suitability of the Mediterranean Basin for citrus black spot disease (Phyllosticta citricarpa) based on a generic infection model, Scientific Reports, 2022, Vol. 12, Article 19876, DOI: 10.1038/s41598-022-22775-z.
  11. Moussaid A., Fkihi S., Zennayi Y., Citrus orchards monitoring based on remote sensing and artificial intelligence techniques: A review of the literature, Proc. 2 nd Intern. Conf. Advanced Technologies for Humanity (ICATH), 2021 pp. 172–178, DOI: 10.5220/0010432001720178.
  12. Prudnikova E., Savin I., Vindeker G. et al., Influence of soil background on spectral reflectance of winter wheat crop canopy, Remote Sensing, 2019, Vol. 11, No. 16, Article 1932, DOI: 10.3390/rs11161932.
  13. Rehman S. U., Abbasi K., Qayyum A. et al., Comparative analysis of citrus fruits for nutraceutical properties, Food Science and Technology, 2020, Vol. 40, Suppl. 1, pp. 153–157, DOI: 10.1590/fst.07519.
  14. Ronchetti G., Manfron G., Weissteiner C. J. et al., Remote sensing crop group-specific indicators to support regional yield forecasting in Europe, Computers and Electronics in Agriculture, 2023, Vol. 205, Article 107633, DOI: 10.1016/j.compag.2023.107633.
  15. Rouse J. W., Haas R. H., Scheel J. A., Deering D. W., Monitoring vegetation systems in the great plains with ERTS, Proc. 3 rd Earth Resource Technology Satellite (ERTS) Symp., 1974, Vol. 1, pp. 48–62.
  16. Salwa A., Cheikh D., Review of the syrian agriculture and future prospects for reconstruction, Jordan J. Agricultural Sciences, 2021, Vol. 15, No. 2, pp. 35–49, DOI: 10.35516/jjas.v15i2.44.
  17. Santoro F., Tarantino E., Figorito B. et al., A tree counting algorithm for precision agriculture tasks, Intern. J. Digital Earth, 2013, Vol. 6, No. 1, pp. 94–102, DOI: 10.1080/17538947.2011.642902.
  18. Savin I., Klyukina A., Dragavtseva I., About possibilities of apple trees flowering date detection based on MODIS data, Proc. 20 th Intern. Multidisciplinary Scientific GeoConf. (SGEM), 2020, Vol. 20, Article 2.2, pp. 157–164, DOI: 10.5593/sgem2020/2.2/s10.019.
  19. Usha K., Singh B., Potential applications of remote sensing in horticulture — A review, Scientia Horticultu­rae, 2013, Vol. 153, pp. 71–83, https://doi.org/10.1016/j.scienta.2013.01.008.
  20. Ward J. H., Hierarchical grouping to optimize an objective function, J. American Statistical Association, 1963, Vol. 58, No. 301, pp. 236–244, DOI: 10.1080/01621459.1963.10500845.