Rakhmatullina I. R., Rakhmatullin Z. Z., Odintsov G. E., Nekhoroshikh V. P., Budanova A. V. Monitoring of Burned Areas Based on Sentinel-2 Temporal Composites in the Google Earth Engine Environment under Conditions of High Cloudiness
1 Bashkir State Agrarian University
50 let Oktyabrya str., 34, Ufa, Republic of Bashkortostan, 450001 Russian Federation
2 Autonomous Non-Profit Organization Institute of Rational Nature Management
8 Marta str., 17, Ufa, Republic of Bashkortostan, 450001 Russian Federation
3 Limited Liability Company «Institute of Project Forest Research»
Dagestanskaya str., 35, Ufa, Republic of Bashkortostan, 450050 Russian Federation
E-mail: rahmat_irina@mail.ru, zagir1983@mail.ru, odintsov94@inbox.ru, sai.ved@yandex.ru,
Abstract
UDC 528.85:630*43
How to cite: Rakhmatullina I. R.1, Rakhmatullin Z. Z.1, Odintsov G. E.1, 2, Nekhoroshikh V. P.3, Budanova A. V.1 Monitoring of burned areas based on Sentinel-2 temporal composites in the Google Earth Engine environment under conditions of high cloudiness // Sibirskij Lesnoj Zurnal (Sib. J. For. Sci.). 2026. N. 4. P. … (in Russian with English abstract and references).
DOI: 10.15372/SJFS20260405
EDN: …
© Rakhmatullina I. R., Rakhmatullin Z. Z., Odintsov G. E., Nekhoroshikh V. P., Budanova A. V., 2026
Forest fires cause significant damage to ecosystems, making rapid and accurate assessment of their consequences a critically important task. The aim of this study was to test an automated methodology for mapping pyrogenic damage using the Google Earth Engine (GEE) cloud platform and time series of Sentinel-2 L2A satellite images. The study was conducted in the Beloretsky District of the Republic of Bashkortostan, an area affected by a large forest fire in July 2023. A methodological feature was the use of median composites for pre‑fire and post‑fire periods, along with cloud and shadow masking, which helped overcome the problem of persistent cloud cover characteristic of the mountain‑forest area on the western macroslope of the Southern Urals. A script was implemented in the GEE environment for data filtering, cloud and shadow masking, calculation of spectral indices (NBR, NDVI) and their difference values (dNBR, dNDVI). For validation and interpretation of the results, VIIRS/FIRMS thermal hotspot data, open data from the Federal State Information System of the Forestry Complex (FSIS FC), and the Information System for Remote Monitoring (ISDM) of the Federal Forestry Agency (Rosleskhoz) were used. The methodology not only allowed burn scars to be delineated and classified by severity with high accuracy (deviation from official data ranged from 0.2 % to 28.2 %), but also to identify areas of anthropogenic disturbance (clear‑cuts) that were erroneously interpreted as burns by the dNBR index. It was shown that the dNBR index has greater contrast and specificity to pyrogenic damage compared to dNDVI. Limitations of the method related to signal mixing during a long composite period are discussed, and prospects for long‑term monitoring of vegetation recovery are outlined.
Article
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