DETERMINATION OF LANDSCAPE DIVERSITY OF AGROLANDS BASED ON REMOTE SENSING DATA

Abstract

The article analyzes scientific and methodological approaches to determining and assessing the landscape diversity of agricultural landscapes based on materials from remote sensing of the Earth from space. The theoretical basis of the landscapemetric approach is considered - from classical concepts of landscape ecology to modern geoinformation technologies. Five basic categories of the spatial structure of agricultural landscapes are analyzed: size, shape, integrity, fragmentation and diversity of fragments. A system of ten key landscape metrics for monitoring agrolandscapes of the Forest-Steppe of Ukraine is substantiated, which demonstrate a statistically significant correlation with the level of plowing, the share of natural lands and the density of forest belts. A five-level classification of the state of agrolandscapes according to landscape diversity indices is proposed and threshold values for each class are established. An improved method for automated decoding of land cover classes by synthesizing Landsat-8 optical images and Sentinel-1 radar data is described, which provides a spatial resolution of 8.25 m when using free data. The practical application of landscape metrics for analyzing land use dynamics, modeling scenarios for optimizing agrolandscapes, and assessing the effectiveness of soil protection contour and reclamation measures is shown. It has been established that remote sensing of the Earth appears as a key tool for the rapid detection of changes that cannot be tracked by traditional field survey methods due to limited access to territories. The results of the study confirmed the high efficiency of using Sentinel-2 satellite data to detect changes in the structure of agricultural landscapes. Multispectral indices, in particular NDVI (Normalized Difference Index vegetation), allow us to trace the dynamics of vegetation cover, the level of land degradation and signs of erosion processes. The integration of these data into the geoinformation environment makes it possible to create spatial models that reflect both the current state of territories and the trends in the transformation of agricultural landscapes over time.