Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2026, V. 23, No. 4, pp. 272-281
Detection of fire-hazardous areas in forest logging zones based on Landsat and Sentinel satellite data
V.A. Khamedov 1 , E.I. Baranova 1 1 Siberian State University of Geosystems and Technologies, Novosibirsk, Russia
Accepted: 26.06.2026
DOI: 10.21046/2070-7401-2026-23-4-272-281
The paper investigates the potential for preventive detection of fire-hazardous logging sites heavily cluttered with logging residues (slash) utilizing medium spatial resolution satellite imagery. The proposed method is based on surface albedo differences in the 0.845–0.885 and 2.10–2.30 μm wavelength ranges during the active snowmelt period. During this time, areas covered with logging residues heat up faster than the surrounding landscape due to differences in heat capacity. Landsat and Sentinel satellite data, processed using the VEGA-Science platform, served as the primary data source. The methodology was validated across three study areas located in Vologda and Irkutsk regions, as well as Krasnoyarsk Krai, over the 2019–2024 period. A total of 160 new clear-cut areas encompassing 5,043 hectares were successfully interpreted. It was established that the temperature contrast between debris-covered and cleared areas can reach several degrees Celsius. This allows for the detection of potentially fire-hazardous sites smaller than 1 hectare 2 to 4 weeks prior to the official onset of the fire season. Mapping confirmed the presence of debris-laden logging sites in Vologda and Irkutsk regions, whereas no such areas were recorded in Krasnoyarsk Krai, indirectly suggesting stricter compliance with slash disposal regulations in the latter. The results demonstrate a high effectiveness of this approach for timely updating natural fire hazard classification maps, planning field inspection routes, and enhancement of monitoring regarding compliance with forest fire safety regulations.
Keywords: logging residues, fire hazard period, satellite images, Landsat, Sentinel, VEGA-Science
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