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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. 2, pp. 177-195

Monitoring forest cover in riparian zones along the rivers of Mari El Republic using satellite data

L.V. Tarasova 1 , E.A. Kurbanov 1 , O.N. Vorobiev 1 , H. Bui 2 , S.A. Lezhnin 1 , D.M. Dergunov 1 
1 Volga State University of Technology, Yoshkar-Ola, Russia
2 Vietnam National University of Forestry, Hanoi, Vietnam
Accepted: 13.03.2024
DOI: 10.21046/2070-7401-2024-21-2-177-195
Landsat satellite data is widely used for monitoring forest cover. The use of the Google Earth Engine (GEE) cloud platform allows for the analysis of these data using various methods. The aim of this study is to investigate the dynamics and disturbance of forest cover in riparian zones from 1984 to 2022 based on Landsat time-series imagery. The study focuses on forested areas located within a 200-meter buffer zone along the 23 largest rivers in the Republic of Mari El. The classification of Landsat time-series data into four land cover classes using the Random Forest (RF) algorithm in GEE was carried out. At the first stage, 85 Landsat time-series images were classified, and the structure and dynamics of the classes were analyzed, resulting in a map of land cover changes. At the second stage, the LandTrendr algorithm was applied to detect areas of disturbance in the forest cover over the study period. The analysis of Landsat data revealed that overall, from 1984 to 2022, there has been an increase in forest cover area due to the replacement of non-forest areas and coniferous forests with deciduous forests. The greatest changes in disturbance indices were observed during the periods of 1985–1992 and 2010–2011, which can be attributed to the consequences of flooding in riparian forests and major wildfires in 2010. The application of cloud technologies and methodological approaches for change detection allows for the assessment of dynamics and disturbance based on multi-temporal satellite imagery at a regional scale.
Keywords: riparian forests, remote sensing, Google Earth Engine, Landsat, Random Forest, LandTrendr
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