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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, 2023, Vol. 20, No. 6, pp. 51-66

Prospects for the use of pseudo-color image processing in analysis of long-term time series of satellite data in the task of assessing vegetation cover state

A.G. Terekhov 1 , G.N. Sagatdinova 1 , R.I. Mukhamediev 1 , I.Yu. Savin 2, 3 , E.N. Amirgaliyev 1 , S.B. Sairov 4 
1 Institute of Information and Computational Technologies, Almaty, Kazakhstan
2 V.V. Dokuchaev Soil Science Institute, Moscow, Russia
3 Institute of Environmental Engineering of RUDN University, Moscow, Russia
4 RSE Kazhydromet, Almaty, Kazakhstan
Accepted: 27.09.2023
DOI: 10.21046/2070-7401-2023-20-6-51-66
Satellite images and the NDVI (Normalized Difference Vegetation Index) are often used to monitor the state of vegetation cover and a significant amount of information has now been accumulated. When processing long-term time series of satellite data, mathematical difficulties arise, for example, in data clustering. The solution to the problem can be a parameterization of the satellite monitoring information using characteristic moments. For each pixel position, hundreds of NDVI values from satellite data can be reduced to several characteristic functional parameters, in particular to the extreme NDVI and the average long-term NDVI value, as well as other summing characteristics. This opens the way for the construction of pseudo-color images and subsequent clustering by any standard algorithm. This research examined Southern Kazakhstan, with a total area of more than 700 thousand km2. Using Google Earth Engine, time series of NDVI from Sentinel 2 scenes (resolution 10 m) for the period April – October 2018–2022 (about 160 covers) served as the basis for describing the state of vegetation cover. An additional parameter was the long-term maximum of the VSSI (Vegetation Soil Salinity Index). The RGB channels of the pseudo-color image were based on Sentinel 2 monitoring data from April – October 2017–2022 and included: Red — a long-term maximum of VSSI; Green — a long-term maximum of NDVI; Blue — a long-term average of NDVI. The resulting pseudo-color image displayed in detail the state of vegetation, with a clear separation of agricultural vegetation from natural, with a ranking of irrigated arable land according to the features of growth and development of agricultural crops. This information can serve as a basis for segmentation of preprocessed satellite data for analyzing the vegetation state in the South Kazakhstan in various applied tasks. As an example, using unsupervised ISODATA classification, salinity of irrigated arable land of Kyzylkum Rural District of Zhetysai District of Turkestan Oblast was estimated. The results demonstrated the prospects of such an analysis method and clarified the known results obtained earlier using MODIS satellite data.
Keywords: remote sensing, Sentinel 2, long-term time series of satellite data, state of vegetation cover, soil salinity, pseudo-color image
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References:

  1. Abayev N. N., Sagatdinova G. N., Maglinets Yu. A. et al., Satellite monitoring of winter irrigation activity in South Kazakhstan: A case study of Golognaya Step irrigated region, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2023, Vol. 20, No. 3, pp. 152–163 (in Russian), DOI: 10.21046/2070-7401-2023-20-3-152-163.
  2. Terekhov A. G., Abayev N. N., Vitkovskaya I. S. et al., Links between the vegetation state over Tien-Shan mountains and North Atlantic Oscillation indices of the upcoming season, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 2, pp. 275–281 (in Russian), DOI: 10.21046/2070-7401-2020-17-2-275-281.
  3. Terekhov A. G., Sagatdinova G. N., Murzabaev B. A., Regional-scale assessment of multi-year soil salinity using MODIS in the Syr Darya River valley, Kazakhstan, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 2, pp. 169–179 (in Russian), DOI: 10.21046/2070-7401-2022-19-2-169-179.
  4. Kharazmi R., Panidi E. A., Karkon Varnosfaderani M., Assessment of dry land ecosystem dynamics based on time series of satellite images, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2016, Vol. 13, No. 5, pp. 214–223 (in Russian), DOI: 10.21046/2070-7401-2016-13-5-214-223.
  5. Shinkarenko S. S., Bartalev S. A., NDVI seasonal dynamics of the North Caspian pasture landscapes according to MODIS data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 4, pp. 179–194 (in Russian), DOI: 10.21046/2070-7401-2020-17-4-179-194.
  6. Shinkarenko S. S., Bartalev S. A., Long-term arid pasture landscapes NDVI dynamics in European Russia and adjacent territories, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 6, pp. 108–123 (in Russian), DOI: 10.21046/2070-7401-2022-19-6-108-123.
  7. Abbas A. W., Minallh N., Ahmad N., Abid S. A. R., Khan M. A. A., K-Means and ISODATA clustering algorithms for landcover classification using remote sensing, Sindh University Research J. — SURJ (Science Series), 2016, Vol. 48, No. 2, pp. 315–318.
  8. Asfaw E., Suryabhagavan K. V., Argaw M., Soil salinity modeling and mapping using remote sensing and GIS: The case of Wonji sugar cane irrigation farm, Ethiopia, J. Saudi Society of Agricultural Sciences, 2018, Vol. 17, Issue 3, pp. 250–258, DOI: 10.1016/j.jssas.2016.05.003.
  9. Chelali M., Kurtz C., Puissant A., Vincent N., Deep-STaR: Classification of image time series based on spatio-temporal representations, Computer Vision and Image Understanding, 2021, Pt. 1, Vol. 208–209, Article 103221, DOI: 10.1016/j.cviu.2021.103221.
  10. Colomines L., Kurtz C., Puissant A., Vincent N., Dealing with Incomplete Land-Cover Database Annotations Applied to Satellite Image Time Series Semantic Segmentation, In: Pattern Recognition and Artificial Intelligence, ICPRAI 2022, Lecture Notes in Computer Science, El Yacoubi M., Granger E., Yuen P. C., Pal U., Vincent N. (eds.), Cham: Springer, 2022, Vol. 13363, pp. 211–222, DOI: 10.1007/978-3-031-09037-0_18.
  11. Dehni A., Lounis M., Remote sensing techniques for salt affected soil mapping: application to the Oran region of Algeria, Procedia Engineering, 2012, Vol. 33, pp. 188–198, DOI: 10.1016/j.proeng.2012.01.1193.
  12. Dhodhi M. K., Saghri J. A., Ahmad I., Ul-Mustafa R., D-ISODATA: A Distributed Algorithm for Unsupervised Classification of Remotely Sensed Data on Network of Workstations, J. Parallel and Distributed Computing, 1999, Vol. 59, Issue 2, pp. 280–301, DOI: 10.1006/jpdc.1999.1573.
  13. Doi R., Improved discrimination among similar agriculturalplots using red-and-green-based pseudo-colour imaging, Intern. Agrophysics, 2016, Vol. 30, No. 2, pp. 151–163, DOI: 10.1515/intag-2015-0086.
  14. Eamus D., Huete A., Yu Q., Vegetation dynamics, Cambridge University Press, 2016, 513 p.
  15. Forzieri G., Castelli F., Vivoni E. R., Vegetation Dynamics within the North American Monsoon Region, J. Climate, 2011, Vol. 24, pp. 1763–1783, DOI: 10.1175/2010JCLI3847.1.
  16. Gite K. R., Gupta P., GAN-FuzzyNN: Optimization Based Generative Adversarial Network and Fuzzy Neural Network Classification for Change Detection in Satellite Images, Sensing and Imaging, 2023, Vol. 24, Article 1, DOI: 10.1007/s11220-022-00404-3.
  17. Guarnieri A., Vettore A., Automated techniques for satellite image segmentation, Intern. Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 2002, Vol. 34, No. 4, pp. 406–410.
  18. Gunst R. F., Webster J. T., Regression analysis and problems of multicollinearity, Communications in Statistics-Theory and Methods, 1975, Vol. 4, No. 3, pp. 277–292, DOI: 10.1080/0361092708827246.
  19. Hatfield J. L., Prueger J. H., Sauer T. J. et al., Applications of vegetative indices from remote sensing to agriculture: Past and future, Inventions, 2019, Vol. 4, No. 4, Article 71, DOI: 10.3390/inventions4040071.
  20. Huang S., Tang L, Hupy J. P. et al., A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing, J. Forestry Research, 2021, Vol. 32, pp. 1–6, DOI: 10.1007/s11676-020-01155-1.
  21. Interdonato R., Ienco D., Gaetano R., Ose K., DuPLO: a DUal view Point deep Learning architecture for time series classification, ISPRS J. Photogrammetry and Remote Sensing, 2019, Vol. 149, pp. 91–104, DOI: 10.1016/j.isprsjprs.2019.01.011.
  22. Li S., Xu L., Jing Y. et al., High-quality vegetation index product generation: A review of NDVI time series reconstruction techniques, Intern. J. Applied Earth Observation and Geoinformation, 2021, Vol. 105, Article 102640, DOI: 10.1016/j.jag.2021.102640.
  23. Lucchese L., Mitra S. K., Unsupervised segmentation of color images based on k-means clustering in the chromaticity plane, Proc. IEEE Workshop on Content-Based Access of Image and Video Libraries (CBAIVL’99), Fort Collins, CO, USA, 1999, pp. 74–78, DOI: 10.1109/IVL.1999.781127.
  24. Pelletier C., Webb G. I., Petitjean F., Temporal convolutional neural network for the classification of satellite image time series, Remote Sensing, 2019, Vol. 11, Issue 5, Article 523, DOI: 10.3390/rs11050523.
  25. Petitjean F., Kurtz C., Passat N., Gançarski P., Spatio-temporal reasoning for the classification of satellite image time series, Pattern Recognition Letters, 2012, Vol. 33, Issue 13, pp. 1805–1815, DOI: 10.1016/j.patrec.2012.06.009.
  26. Phiri D., Simwanda M., Salekin S. et al., Sentinel 2 data for land cover/use mapping: a review, Remote Sensing, 2020, Vol. 12, Issue 14, Article 2291, DOI: 10.3390/rs12142291.
  27. Roerink G. J., Menenti M., Soepboer W., Su Z., Assessment of climate impact on vegetation dynamics by using remote sensing, Physics and Chemistry of the Earth, Parts A/B/C, 2003, Vol. 28, Issues 1–3, pp. 103–109, DOI: 10.1016/S1474-7065(03)00011-1.
  28. Sami Kh., Kushal K., Fulton J. P. et al., Remote sensing in agriculture—accomplishments, limitations, and opportunities, Remote Sensing, 2020, Vol. 12, No. 22, Article 3783, DOI: 10.3390/rs12223783.
  29. Singh S., Pattern recognition of infrared images and pseudo-color image processing: Forest fire in Himalaya, Intern. J. Engineering Research and Applications (IJERA), 2018, Vol. 8, No. 6, pp. 25–28, DOI: 10.9790/9622-0806042528.
  30. Stoian A., Poulain V., Inglada J. et al., Land cover maps production with high resolution satellite image time series and convolutional neural networks: Adaptations and limits for operational systems, Remote Sensing, 2019, Vol. 11, Issue 17, Article 1986, DOI: 10.3390/rs11171986.
  31. Tucker C. J., Vanpraet C. L., Sharman M. J., Van Ittersum G., Satellite Remote Sensing of Total Herbaceous Biomass Production in the Senegalese Sahel, Remote Sensing of Environment, 1985, Vol. 17, pp. 233–249, DOI: 10.1016/0034-4257(85)90097-5.
  32. Woodward F. I., Lomas M. R., Vegetation dynamics-simulating responses to climatic change, Biological Reviews, 2004, Vol. 79, No. 3, pp. 643–670, DOI: 10.1017/S1464793103006419.
  33. Xi W., Du Sh., Wang Yi-Ch., Zhang X., A spatiotemporal cube model for analyzing satellite image time series: Application to land-cover mapping and change detection, Remote Sensing of Environment, 2019, Vol. 231, Article 111212, DOI: 10.1016/j.rse.2019.111212.
  34. Xue J., Su B., Significant remote sensing vegetation indices: A review of developments and applications, Sensors, 2017, Vol. 2017, Article 1353691, DOI: 10.1155/2017/1353691.
  35. Yan J., Zhang G., Ling H., Han F., Comparison of time-integrated NDVI and annual maximum NDVI for assessing grassland dynamics, Ecological Indicators, 2022, Vol. 136, Article 108611, DOI: 10.1016/j.ecolind.2022.108611.
  36. Yavariabdi A., Kusetogullari H., Change Detection in Multispectral Landsat Images Using Multiobjective Evolutionary Algorithm, IEEE Geoscience and Remote Sensing Letters, 2017, Vol. 14, No. 3, pp. 414–418, DOI: 10.1109/LGRS.2016.2645742.