Remote sensing-based land surface change identification and prediction in the Aral Sea bed, Central Asia
The human-induced desiccation of the Aral Sea has generated large amounts of salt dust and has been posing a great threat to local ecological environment and human health. Monitoring its land cover changes is essential to obtaining information about the desertification process and dynamics of potential salt/sand dust source. To this end, long-term Landsat imagery was applied for the land use/cover change analysis based on support vector machine approach. The land cover distribution of the study area for 1977, 1987, 1996, 2006 and 2015 was mapped. In addition, the Markov–cellular automata integrated approach was used to predict the land cover change in 2015 and project changes in 2025 by extrapolating current trends. The classification results revealed that water surface of the Aral Sea shrunk by more than 66%, leading to the dramatic expanding of the salt soil and bare area. Change detection analysis indicated a serious land degradation trend as well as a major land cover evolution mode in the Aral Kum that could predict shifts in dust composition. The Markov–cellular automata technique was successful in predicting land cover distribution in 2015, and the projected land cover for 2025 revealed more desertification of the landscape with potential expansion in the salt soils and bare area. It is worth noting that the vegetation cover of the region represented an obvious increase in recent years that may be a good signal of ecological recovery.
KeywordsRemote sensing Aral Sea Markov–cellular automata Land use and land cover Support vector machine
The authors thank the USGS for providing the Landsat data. And special thanks go to the editor’s and three reviewers’ precious comments and suggestions for the manuscript. This research was conducted under the support of the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDA2006030102) and the National Natural Science Foundation of China (Grant Nos. U1603242, 41471098, 41601440).
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