Monitoring of agroecosystem desertification processes according to satellite data: experience and perspectives

Authors:
Open Access
Publisher website
Google Scholar
Abstract
Desertification is a major environmental threat that affects the lives of millions of people around the world. In order to effectively prevent this effect in agro-ecosystems at local and regional levels, it is necessary to have accurate information about its spatial distribution. Remote sensing plays a crucial role in this process due to its cost-effectiveness, ability to cover large areas, and use of a variety of sensors and data analysis methods. The combination of GIS, mathematical models and ground observations allows for more accurate data on land degradation. The purpose of this study is to summarize the experience of both Ukrainian and foreign researchers in the use of remote sensing for monitoring and assessment of desertification and land degradation processes at the global and national levels, as well as to identify areas for further improvement of remote sensing methods for integrated monitoring of desertification in agroecosystems and land degradation. This article reviews the experience of using satellite remote sensing data, mathematical models of desertification, and geographic information systems to identify areas prone to degradation in different parts of the world. Special attention is paid to the adaptation of these models to local conditions using additional parameters. The article also highlights the achievements of Ukrainian scientists in the field of satellite monitoring of agricultural landscapes, their classification and assessment based on remote sensing data. Prospects for further research are outlined. The use of an integrated approach combining geographic information systems, satellite data, analysis of environmental indicators, and mathematical models of desertification adapted to regional characteristics proves to be effective. The main directions remain the adaptation of models to regional conditions, the development of forecasting methods using remote sensing data, and the expansion of international data and experience exchange. It is important to continue to improve remote monitoring methods for the early detection of degradation processes and to strengthen research at the national level for the implementation of appropriate measures.

This publication has 20 references indexed in Scilit: