Abstract
This study provides an assessment of the process of sand landslide degradation of the Abay Region of the Republic of Kazakhstan and space materials (RS-Remote Sensing, GIS-Geographic Information Systems and machine learning data of the soilgrid platform, QGIS Trends). Using the technology of the Earth module and field research, the amount of landslide sand g/kg at a depth of 5 cm was determined and the SDG land degradation target, the land productivity indicator (productivity), the indicator of the impact assessment of the change in the type of surface cover (land cover), the indicator of the change in the carbon stock of soil organic matter (carbon velocity) were calculated in the machine learning module, which uses integrated research to monitor, map and quantify the threat of land degradation. The study was analyzed and obtained between 2015 and 2022 by interpreting data from machine learning images with visual, interactive interpretation. The data of dynamic reverse changes from Sandy and worn areas, in these two stages, were obtained by overlapping the database. The results of the study showed that in the proportion of years obtained, 3009.29 km2 of newly degraded land was formed, and most of the newly degraded land is the area where sand landslides occur, as is known from the quantitative data of the study, it was found that the share of sand landslide degradation of the surveyed 272 410.4 km2 area within 7 years is 4.56 % of the total area. By analyzing the results of the study, some constructive measures were proposed aimed at ecological protection of forest clusters in the North-West of Abay Region and planting new forest seedlings.