Abstract
In this study, the spatial and temporal patterns of the land cover were monitored within the Qazaly irrigation zone located in the deltaic zone of the Syrdarya river in the surroundings of the former Aral Sea. A 16-day MODIS (Moderate Resolution Imaging Spectroradiometer) Aqua NDVI (Normalized Difference Vegetation Index) data product with a spatial resolution of 250 meters was used for this purpose, covering the period between 2003 and 2018. Field survey results obtained in 2018 were used to build a sample dataset. The random forests supervised classification machine learning algorithm was used to map land cover, which produced good results with an overall accuracy of about 0.8. Statistics on land cover change were calculated and analyzed. The correctness of obtained classes was checked with Landsat 8 (OLI, The Operational Land Imager) images. Detailed land cover maps, including rice cropland, were derived. During the observation period, the rice croplands increased, while the generally irrigated area decreased.
Highlights
Vegetation is an important component of the environment, indicating environmental impacts
As far as the Syrdarya river is the main water transporting body in the Qazaly irrigation zone, all cropping agricultural plots across its canals were recognized by visual classification and the total area was found—14,730 square kilometers
Shrubland/grassland mosaics were found to be the dominant type of land cover, which is about half of the study area, followed by Dense herbaceous, while the least amount of area was occupied by the rice croplands
Summary
Vegetation is an important component of the environment, indicating environmental impacts. There are plenty of methods to collect information about vegetation, among which the remote-sensing-based method is appealing, mainly because it lets researchers accurately recognize large areas of vegetation cover in a short time period, decreasing the time and work spent for its analysis. The vegetation cover absorbs a specific wavelength of the light coming from the sun to produce chlorophyll and use it during photosynthesis, while for example snow does not absorb it and reflects it back [1]. This phenomenon helps researchers to distinguish cover types using different techniques. NDVI is the ratio of the difference between near-infrared (NIR) and red (R) surface reflectance bands to their sum, according to the Equation (1): NDVI
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