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DEVELOPMENT OF INFORMATION APPLICATION FOR DECISION SUPPORTING DURING ECOLOGICAL RESTORATION

S.P. Mesyats

Abstract

The progress (evolution) of a civilization based on natural resources development has resulted in permanent natural environment destruction. By the beginning of the third millennium it has become evident that exploitation of the planet's mineral resources, being the basis for the existence and progress of civilization, is the cause of the global environmental crisis. The Mining Institute has substantiated a methodology of restoration of natural ecosystems (ecological restoration), which consists in using an idea of their adaptive system organization. A restoration technology for the disturbed lands of mining landscapes has been developed. The restoration is implemented by creation of biologically active environment, which can be attributed to nature-friendly technologies, i.e. technologies, created by a type of natural processes. An information resource has been developed in order to provide information support in restoring the disturbed lands of mining landscapes, in accordance with the principle of self-organization of natural ecosystems. The resource includes databases on disturbed lands state and factors that limit their self-restoration, the resource potential for self-restoration of disturbed lands, methods for creating biologically active environment and monitoring restored lands. Impact zones of mining enterprises, as a rule, are zones of the crisis environmental state. The multifactor impact of mining enterprises on the natural environment determines the relevance of searching approaches to an integral assessment of their impact with a view to making verified decisions on the ecological restoration. To assess the state of natural ecosystems, it is optimal to use remote sensing data on the Earth's surface because of the global nature of the man-made impact. An algorithm has been developed for estimating the state of the vegetation cover of mining industrial impact zones by satellite observations. The algorithm is based on selection of monitoring testing sites, calculation of the maximum value of the vegetation index and calendar days of its observation over a long period. A back-analysis has been made of the dynamics of the vegetation cover state of the mining industrial impact zones based on the long-term satellite observations for the Murmansk region. The data received have been integrated into the information system to optimize the algorithm for automated selection of ways to restore disturbed lands of mining landscapes in order to support decision making during ecological restoration.

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DOI resolver: doi.org/10.5593/sgem2018/1.3/s03.038

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