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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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