Abstract
ABSTRACT The potentiality and specificity of using multitemporal stacks of thermal infrared (TIR) satellite scenes for modelling changes in tundra land cover and predicting geohazards in areas under development are studied using the example of two areas in northern West Siberia. The study aims at developing and testing a method for relative radiometric normalization of Land Remote-Sensing Satellite (Landsat) TIR images applied to estimate changes in land surface temperature (LST) in the tundra with regard to the high-latitude climate conditions. The suggested normalization method (RNUC) differs from other similar approaches in the use of unsupervised classification to determine pseudo-invariant features where master and slave images are spectrally identical. The error in LST contrasts is minimized iteratively by choosing the optimal number of land-cover classes and linear regression coefficients by cross-validation. The normalization procedure ensures error reduction by a factor of two to four, which is evidence of its high performance. The results of processing and analysis of 19 pairs of Landsat thermal images at the sites of the Marre-Sale and Urengoy oil and gas-condensate fields using the RNUC method showed that the relative error of LST contrast normalization could be in the range of approximately 3% to 10%. Based on the time series of normalized values of LST and the normalized difference vegetation index (NDVI) of sites with pyrogenic damage in the area of the Urengoy oil and gas-condensate field, it is shown that the process of stabilization of the tundra cover is characterized by a negative correlation of these parameters and lasts 25 to 28 years. A positive correlation between LST and NDVI characterizes a different type of landscape transformation; in particular, it may be an increase in the thickness of secondary vegetation cover or a change in the hydrological conditions.
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