Application of remote sensing method for determination of arable land degradation

Abstract

The article studies an issue of application of remote sensing technology for determination of arable land degradation. Under the modern land use conditions, the processes of soil layer destruction, particularly on arable land, have been recognized as negative large-scale development factor. It is caused byimpact of excessive anthropogenic processes on agriculture land. The authors analyse determination of arable land degradation, using data of transformations of the vegetation cover, referring to the space multispectral satellite images Landsat. The process of investigationrequires anti-erosion organization of the territory. While processing multispectral satellite images, the principal task is to reproduce the information, which provides the best depiction of the processes of arable land degradation. Transformation of vegetation cover and dynamics of the processes of soil erosion have been chosen as indicators, characterizing arable land degradation. To measure these indicators, it is necessary to use multispectral satellite images of the medium spatial resolution along with supplementary geospatial data -digital model of topography, soil map and climatic characteristics of the studied territory. The authors of the article have performed estimation of the erosion processes by determination of the Normalized Difference Vegetation Index (NDVI). According to the results of the conducted research, the authors have developed thelocal level thematicland degradation map on the territory of village Velyka Ozymyna in Lviv region.This map demonstrates that in the period 2000-2019 18 % of the studied territory was subjected to degradation processes. Considering the discovered changes in land use, the authorsperformed modelling of the expected land degradation level on the studied territory by 2050.According to the data of modern remote sensing, it is possible to make forecast concerning the progress of soil destruction and arable land degradation in the future. It will secure efficient decision-making on optimization of agricultural land use and land protective activities.