ISSN 0137-0944
eISSN 2949-6144
En Ru
ISSN 0137-0944
eISSN 2949-6144
Forecasting the ecological state of lands in regions of the Russian Federation for sustainable development

Forecasting the ecological state of lands in regions of the Russian Federation for sustainable development

Abstract

An electronic raster map of the ecological state of lands of Russian regions was prepared for the reference period from 2001 to 2020 based on the characteristics of the primary photosynthesis products derived from Earth remote sensing materials from space in the form of indicators of «productivity», land ransformation and dynamics of organic matter content in the soil and their generalizing indicator of sustainable development goals — SDG 15.3.1. Land degradation indicator. Th e calculation shows that 2.2 million square kilometers, which constitutes 13% of the territory of Russian Federation, belong to the category of degraded by the end of 2020. 45% of the territory is occupied by land from the stable category, which has not changed over the past 19 years. 41% of the territory belongs to the lands that have improved during the reference period. A statistically reliable nonlinear regular relationship of the indicator of the ecological state of lands of Russian Federation was established in the form of an indicator of the share of non-degraded lands, considering the share of non-established lands with the amount of specific emissions of pollutants into the atmospheric air from stationary sources located on the territory of the subject. The threshold value of specifi c emissions from stationary sources at the level of the subject of Russian Federation was established, amounting to 1610 kg per sq. km. Th e probability of exceeding the threshold value of specifi c emissions is 37%. The risk of increasing land degradation with an increase in emissions is typical for 36% of territorial units of Russian Federation.
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Received: 12/01/2022

Accepted: 02/13/2023

Accepted date: 04/01/2023

Keywords: land degradation; mathematical modeling; analysis of Earth remote sensing data; vegetation index; risk assessment

DOI: 10.55959/MSU0137-0944-17-2023-78-2-63-74

Available in the on-line version with: 01.04.2023

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Issue 2, 2023