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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. 4, pp. 131-140

Variation in spectral indices in the context of natural and technogenic transformations of landscapes

N.D. Yakimov 1, 2 , E.I. Ponomarev 2, 3 , T.V. Ponomareva 2, 3 
1 Krasnoyarsk Science Center SB RAS, Krasnoyarsk, Russia
2 Siberian Federal University, Krasnoyarsk, Russia
3 Sukachev Institute of Forest SB RAS, Krasnoyarsk, Russia
Accepted: 26.06.2024
DOI: 10.21046/2070-7401-2024-21-4-131-140
We investigated the variability of spectral indices calculated from Landsat data for areas with signs of natural and technogenic transformations of vegetation and ground cover. An analysis of relative deviation of the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) indicators from the background values due to changes in the condition of the underlying surface covers was carried out. A classification of significant type-factors of transformation was produced for the territory of the Olimpiadinsky mining and processing plant (North Yenisei Region, Krasnoyarsk Krai), which is characterized by the signs of natural (wildfires) and man-made (mining) impacts for the period of 2000–2023. The degree of transformation of vegetation and ground cover under the influence of both natural and technogenic factors was described in terms of relative anomalies of the spectral indices ΔLST and ΔNDVI, calculated as the average for each cell of a regular network in the study area. A linear relationship was recorded between the anomalies of the ΔLST and ΔNDVI and the degree of natural/technogenic transformation of the vegetation and ground cover with a reliability of 0.31–0.81 for the thermal anomalies ΔLST and at the level of 0.28–0.85 for the vegetation anomalies ΔNDVI. It has been established that the degree of generalization of the initial data, depending on the size of the regular network cells used, does not affect critically the result of analyzing the state of the territory using the proposed method.
Keywords: forest ecosystems, predictive analyses, IPCC, RCP, MODIS, NDVI, LST, Pr
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