Search academic texts

Information × Registration Number 0126U000259, R & D request Title Remote aerospace methods for monitoring natural and natural-anthropogenic ecosystems under conditions of uncertainty and incomplete data (code: III-2-2026) Head Mykhailo O. Popov, Доктор технічних наук Registration Date 15-01-2026 Organization State Institution "Scientific Center for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine" popup.description1 The purpose of the work is to increase the effectiveness of remote methods for solving problems of monitoring natural and natural-anthropogenic ecosystems in conditions of uncertainty and incomplete data. This goal is planned to be achieved by improving known and developing new methods and techniques for processing, analyzing and interpreting materials of aerial and space surveys obtained using sensors of various physical nature, as well as taking into account additional data and information from other available information sources. In the course of the research, a number of new remote methods for solving problems of monitoring natural and natural-anthropogenic ecosystems in conditions of uncertainty and incomplete data will be developed. These are methods for assessing long-term and short-term environmental changes based on heterogeneous geospatial data; a method for assessing the state of natural and natural-anthropogenic ecosystems based on the theory of evidence; methods for determining various soil quality indicators; a method for assessing the state of soils using the soil “health” index; a biodiversity monitoring methodology based on the use of heterogeneous geospatial data. Conceptual models of a geographic information system for processing and analyzing data from remote monitoring of natural and anthropogenic ecosystems will also be proposed; a set of factors affecting soil quality will be substantiated; the influence of climate and relief on the properties of productivity zones of plant communities will be studied using machine learning methods. For most of the methods proposed in the research, appropriate (demonstration) software will be developed, which will allow assessing their effectiveness through testing. popup.nrat_date 2026-01-15 Close