Monitoring of agricultural land in the Aral Sea region using remote sensing data of the Earth

Sabine Akzhigit, Marzhan Melsovna Toguzova, Almagul Abdygalieva, Zhanat Kanatuly, Dauren Kulenov

2024

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.35595/2414-9179-2024-2-30-171-180
Data confidence 70 medium
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Abstract

The article researches the use of satellite monitoring to analyze changes in vegetation and soil cover of agricultural land in the Aral Sea Region, located in Kyzylorda Region, on the border of Kazakhstan and Uzbekistan, using geographic information systems (GIS). These technologies are widely used to monitor natural disasters, agriculture, forest and water resources and analyze environmental pollution and predict its effects. The purpose of the study is to assess and analyze the impact of environmental degradation on agricultural land using Earth remote sensing data. The methodology includes analyzing time series of space images to calculate vegetation indices, which makes it possible to identify the dynamics of changes and assess the level of land degradation. Landsat-8 space images data for different time intervals from 2014 to 2023 were used. The object of the study was agricultural plots in Kazaly district, located on the territory of the Aral Sea. Analysis and assessment of changes in the study area were carried out using Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Salinity Index (NDSI) on Landsat satellite images. Data processing was performed in the EOSDA Land Viewer and QGIS software environment. As a result of satellite data processing, maps of the dynamics of agricultural crop development using NDVI, maps of water availability in the study area using NDWI, and maps assessing the degree of salinization of the soil cover were obtained. The obtained results allow us to understand more deeply the scale of the Aral Sea ecological problem and contribute to the development of actual effective strategies of adaptation and restoration of the affected agroecosystems.