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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, 2021, Vol. 18, No. 4, pp. 115-127

Method for estimating vegetation cover transformations in the Syrian Mediterranean region based on remotely sensed satellite data using heuristic rules

A. Khatib 1 , V.A. Malinnikov 1 
1 Moscow State University of Geodesy and Cartography, Moscow, Russia
Accepted: 22.06.2021
DOI: 10.21046/2070-7401-2021-18-4-115-127
The post-classification method is one of the most widely used methods for assessing vegetation cover transformations using remotely sensed satellite data. However, the use of the method has several disadvantages. First, the errors of independent vegetation cover classification based on satellite data at different times can accumulate, which leads, in turn, to a decrease in the accuracy of assessing vegetation cover transformations. Second, when evaluating the reliability of the map of vegetation cover transformations, the size of the error matrix grows as the second power of the error matrix for one date. In this case, it is difficult to create a control sample and assess the accuracy of the obtained results since some transformations do not occur or occur rarely. This work presents and tests an approach for comparing the automated satellite image classification at different times and filtering possible errors using heuristic rules based on a priori knowledge of the probable and unlikely transformations of land cover types in the Syrian Mediterranean region for 2010–2018. To create a control sample and assess the reliability of the map of vegetation cover transformation all changes on the map were reduced to generalized thematic classes of interest. The overall accuracy of the map of vegetation cover transformation was 92 %. The producer’s accuracy of thematic classes on the map varied in the range of 87–100 %, and the user’s accuracy — 73–99 %, which indicates a high reliability of the proposed approach. In addition, the proposed method gives information on the degree of uncertainty in vegetation cover transformation estimates.
Keywords: remote sensing, Landsat, vegetation cover transformations, post-classification method, heuristic rules, confidence intervals, automated interpretation, supervised classification, random forest, textural features, digital elevation model
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References:

  1. Bartalev S. A., Loupian E. A., R&D on methods for satellite monitoring of vegetation by the Russian Academy of Sciences’ Space Research Institute, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2013, Vol. 10, No. 1, pp. 197–214 (in Russian).
  2. Bartalev S. A., Egorov V. A., Zharko V. O., Loupian E. A., Plotnikov D. E., Khvostikov S. A., Current state and development prospects of satellite mapping methods of Russia’s vegetation cover, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2015, Vol. 12, No. 5, pp. 203–221 (in Russian).
  3. Bartalev S. A., Egorov V. A., Zharko V. O., Loupian E. A., Plotnikov D. E., Khvostikov S. A., Shabanov N. V., Sputnikovoe kartografirovanie rastitel’nogo pokrova Rossii (Land cover mapping over Russia using Earth observation data.), Moscow: IKI RAN, 2016, 208 p. (in Russian).
  4. Bartalev S. A., Elkina E. S., Loupian E. A., Plotnikov D. E., Tolpin V. A., Remote sensing of 2017 winter crops in the Russian Federation, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, Vol. 14, No. 4, pp. 275–280 (in Russian), DOI: 10.21046/2070-7401-2017-14-4-275-280.
  5. Denisov P. V., Sereda I. I., Troshko K. A., Loupian E. A., Plotnikov D. E., Tolpin V. A., Development of winter and spring crops in European Russia in the season 2019–2020, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 4, pp. 306–311 (in Russian), DOI: 10.21046/2070-7401-2020-17-4-306-311.
  6. Zharko V. O., Bartalev S. A., Egorov V. A., Investigation of forest growing stock volume estimation possibilities over Russian Primorsky Krai region using Proba-V satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 1, pp. 157–168 (in Russian), DOI: 10.21046/2070-7401-2018-15-1-157-168.
  7. Loupian E. A., Denisov P. V., Sereda I. I., Troshko K. A., Plotnikov D. E., Tolpin V. A., Analysis of winter crops development in the southern regions of Russia in spring 2020 based on remote monitoring, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 2, pp. 285–291 (in Russian), DOI: 10.21046/2070-7401-2020-17-2-285-291.
  8. Plotnikov D. E., Bartalev S. A., Loupian E. A., Tolpin V. A., Accuracy assessment for winter crops mapping in spring-summer growing season with MODIS data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, Vol. 14, No. 4, pp. 132–145 (in Russian), DOI: 10.21046/2070-7401-2017-14-4-132-145.
  9. Sereda I. I., Denisov P. V., Troshko K. A., Loupian E. A., Plotnikov D. E., Tolpin V. A., The unique situation of winter crops development observed from remote sensing data in the European territory of Russia in October 2020, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 5, pp. 304–310 (in Russian), DOI: 10.21046/2070-7401-2020-17-5-304-310.
  10. Terekhin E. A., Estimation of forest disturbance in the forest-steppe zone at the beginning of the XXI century using satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 2, pp. 134–146 (in Russian), DOI: 10.21046/2070-7401-2020-17-2-134-146.
  11. Khatib A., Malinnikov V. A., Automated classification of the vegetation cover of Mediterranean landscape using spectral-textural and topographic features of high spatial resolution satellite imagery, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 2, pp. 51–63 (in Russian), DOI: 10.21046/2070-7401-2021-18-2-51-63.
  12. Congalton R., Green K., Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Second Edition, Congalton R. G., Green K. F. L. (eds.), Boca Raton, Florida: CRC Press, 2009, 183 p.
  13. Banskota A., Kayastha N., Falkowski M., Wulder M., Froese R., White J., Forest Monitoring Using Landsat Time Series Data: A Review, Canadian J. Remote Sensing, 2014, Vol. 40, No. 5, pp. 362–384, DOI: 10.1080/07038992.2014.987376.
  14. Bartalev S., Plotnikov D., Loupian E., Mapping of arable land in Russia using multi-year time series of MODIS data and the LAGMA classification technique, Remote Sensing Letters, 2016, Vol. 7, No. 3, pp. 269–278, DOI: 10.1080/2150704X.2015.1130874.
  15. Coppin P., Jonckheere I., Nackaerts K., Muys B., Lambin E., Digital change detection methods in ecosystem monitoring: a review, Intern. J. Remote Sensing, 2004, Vol. 25, No. 9, pp. 1565–1596, DOI: 10.1080/0143116031000101675.
  16. Hansen M., Potapov P., Moore R., Hancher M., Turubanova S., Tyukavina A., Thau D., Stehman S., Goetz S., Loveland T., Kommareddy A., Egorov A., Chini L., Justice C., Townshend J., High-Resolution Global Maps of 21st-Century Forest Cover Change, Science, 2013, Vol. 342, No. 6160, pp. 850–853, DOI: 10.1126/science.1244693.
  17. Hanzeyu X., Shuhua Q., Peng G., Chong L., Junbang W., Long-term monitoring of citrus orchard dynamics using time-series Landsat data: a case study in southern China, Intern. J. Remote Sensing, 2018, Vol. 39, No. 22, pp. 8271–8292, DOI: 10.1080/01431161.2018.1483088.
  18. Henits L., Jürgens C., Mucsi L., Seasonal multitemporal land-cover classification and change detection analysis of Bochum, Germany, using multitemporal Landsat TM data, Intern. J. Remote Sensing, 2016, Vol. 37, No. 15, pp. 3439–3454, DOI: 10.1080/01431161.2015.1125558.
  19. Hermosilla T., Wulder M., White J., Coops N., Hobart G., Disturbance-Informed Annual Land Cover Classification Maps of Canada’s Forested Ecosystems for a 29-Year Landsat Time Series, Canadian J. Remote Sensing, 2018, Vol. 44, No. 1, pp. 67–87. DOI: 10.1080/07038992.2018.1437719.
  20. Hussain M., Chen D., Cheng A., Wei H., Stanley D., Change detection from remotely sensed images: From pixel-based to object-based approaches, ISPRS J. Photogrammetry and Remote Sensing, 2013, Vol. 80, pp. 91–106, DOI: 10.1016/j.isprsjprs.2013.03.006.
  21. Introductory Digital Image Processing: A Remote Sensing Perspective, Fourth Edition, Jensen J. U. (ed.), Utah: Pearson Education, 2015, 623 p.
  22. Lu D., Mausel P., Brondizio E., Moran E., Change detection techniques, Intern. J. Remote Sensing, 2004, Vol. 25, No. 12, pp. 2365–2401, DOI: 10.1080/0143116031000139863.
  23. Lu D., Li G., Moran E., Current situation and needs of change detection techniques, Intern. J. Image and Data Fusion, 2014, Vol. 5, No. 1, pp. 13–38, DOI: 10.1080/19479832.2013.868372.
  24. Mengna L., Congcong L., Hong J., Chengyuan F., Jun Y., Zhiliang Z., Lei S., Shirong L., Peng G., Tracking bamboo dynamics in Zhejiang, China, using time-series of Landsat data from 1990 to 2014, Intern. J. Remote Sensing, 2016, Vol. 37, No. 7, pp. 1714–1729, DOI: 10.1080/01431161.2016.1165885.
  25. Olofsson P., Foody G., Stehman S., Woodcock C., Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation, Remote Sensing of Environment, 2013, Vol. 129, pp. 122–131, DOI: 10.1016/j.rse.2012.10.031.
  26. Olofsson P., Foody G., Herold M., Stehman S., Woodcock C., Wulder M., Good practices for estimating area and assessing accuracy of land change, Remote Sensing of Environment, 2014, Vol. 148, pp. 42–57, DOI: 10.1016/j.rse.2014.02.015.
  27. Potapov P., Turubanova S., Tyukavina A., Krylov A., McCarty J., Radeloff V., Hansen M., Eastern Europe’s forest cover dynamics from 1985 to 2012 quantified from the full Landsat archive, Remote Sensing of Environment, 2015, Vol. 159, pp. 28–43, DOI: 10.1016/j.rse.2014.11.027.
  28. Salah H., Goldin S., Rezgui A., Nour-Ilislam B., Ait-Aoudia S., What is a remote sensing change detection technique? Towards a conceptual framework, Intern. J. Remote Sensing, 2019, Vol. 41, No. 5, pp. 1788–1812, DOI: 10.1080/01431161.2019.1674463.
  29. Singh A., Digital change detection techniques using remotely-sensed data, International J. Remote Sensing, 1989, Vol. 10, No. 6, pp. 989–1003, DOI: 10.1080/01431168908903939.
  30. Stehman S., Estimating area from an accuracy assessment error matrix, Remote Sensing of Environment, 2013, Vol. 132, pp. 202–211, DOI: 10.1016/j.rse.2013.01.016.
  31. Tsarouchi G., Mijic A., Moulds S., Buytaert W., Historical and future land-cover changes in the Upper Ganges basin of India, Intern. J. Remote Sensing, 2014, Vol. 35, No. 9, pp. 3150–3176, DOI: 10.1080/01431161.2014.903352.
  32. Waldner F., Fritz S., Gregorio A., Plotnikov D., Bartalev S., Kussul N., Gong P., Thenkabail P., Hazeu G., Klein I., Low F., Miettinen J., Dadhwal V., Lamarche C., Bontemps S., Defourny P., A Unified Cropland Layer at 250 m for Global Agriculture Monitoring, Data, 2016, Vol. 1, No. 1, Art. No. 3, 13 p., DOI: 10.3390/data1010003.
  33. Zhang L., Weng Q., Annual Dynamics of Impervious Surface in the Pearl River Delta, China, from 1988 to 2013, Using Time Series Landsat Imagery, ISPRS J. Photogrammetry and Remote Sensing, 2016, Vol. 113, pp. 86–96, DOI: 10.1016/j.isprsjprs.2016.01.003.
  34. Zhu Z., Change detection using Landsat time series: A review of frequencies, preprocessing, algorithms, and applications, J. Photogrammetry and Remote Sensing, 2017, Vol. 130, pp. 370–384, DOI: 10.1016/j.isprsjprs.2017.06.013.