ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 4, pp. 149-165

Ecological assessment of lands contaminated by a complex of heavy metals in the vicinity of Norilsk for the period from 2004 to 2019 based on NDVI MODIS materials from the Vega-Science server

M.V. Evdokimova 1 , G.P. Glazunov 1 , A.S. Yakovlev 1 , I.O. Plekhanova 1 , R.A. Aimaletdinov 1 , M.V. Shestakova 1 
1 Lomonosov Moscow State University, Moscow, Russia
Accepted: 24.07.2021
DOI: 10.21046/2070-7401-2021-18-4-149-165
The article analyzes the regularities of spatial and temporal (interannual and intra-seasonal) variability of the vegetation index NDVI in the vicinity of the source of heavy metals entering the environment in connection with their concentrations in soils. The aim of the study was to provide a scientific justification for the possibility of an objective quantitative assessment of the ecological state of lands contaminated with the complex of heavy metals based on remote sensing data based on a theoretical model of the response of living beings in the form of NDVI to the effects of stressors in the form of the complex of heavy metals with its preliminary parameterization based on subsatellite measurements. It is established that the intra-seasonal dynamics of NDVI and the response of the ecosystem in the form of NDVI to the content of heavy metals in soils in the vicinity of Norilsk, which are affected by metallurgical production, are satisfactorily described by the theoretical model. It is possible to generalize the results of satellite (NDVI) and subsatellite (the concentration of heavy metals in the soil) observations to obtain an assessment of the ecological state of the land for the purposes of justifying measures to restore their quality and developing regional standards for soil quality.
Keywords: ecological state, bioindication, mathematical modeling, analysis of Earth remote sensing data, vegetation index, risk assessment, natural background
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