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Monitoring and predicting land use/cover changes in the Aksu-Tarim River Basin, Xinjiang-China (1990–2030)

Environmental Monitoring and Assessment volume 191, Article number: 480 (2019) Cite this article

Abstract

Land use/cover (LCLU) is considered as one of the most serious environmental challenges that threatens developed and less developed countries. LCLU changes’ monitoring using the integration of remote sensing (RS) and geographical information systems (GIS) and their predicting using an artificial neural network (ANN) in the western part of the Tarim River Basin (Aksu), north-western Xinjiang-China, from 1990 to 2030 have been investigated first time through satellite imageries available. The imageries of 1990, 2000, 2005, 2010, and 2015 were downloaded from GLCF and USGS websites. After digital image processing, the object-oriented image classification approach was applied. The ANN method with MOLUSCE Plugin was used to simulate the LCLU changes in 2020, 2025, and 2030. GIS has also been used to calculate the distance from the road and water and etc. The simulation results of 2010 and 2015 were validated using classification data with Kappa coefficient. The results showed high accuracy of the classification and prediction as the validation of simulated 2010 and 2015 maps to the referenced maps have high accuracy of Kappa 84 and 88%, respectively. The results revealed that the land cover classes forest-, grass-, wet-, and barren land have been decreased from 50.01, 13.06, 8.24, and 1.06% in 1990 to 32.03, 3.06, 6.26, and 0.97% in 2015, respectively, while the land use classes, crop or farm land, and urban land have been increased almost double from 25.5 and 2.13% in 1990 to 53.71 and 3.86% from the total area in 2015, respectively. For the prediction, forest- and wetlands will loss more than half of their areas by 2030, the grass land will be cleared completely to be only 1.3% from the total study area, while the urban land will be increased to be 4.4% or the double of 1990. These results are attributed to population growth and expanding of agriculture land on the grass land, but the effect of climate was weak as the rainfall increased during the study period. Causes and effects of the LCLU changes were briefly discussed. The output of the study serves as useful tools for policy and decision makers combatting natural resources misused in arid lands.

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Notes

  1. Plugin provides a set of algorithms for land use change simulations such as ANN, LR, WoE, and MCE. There is also validation using kappa statistics.

  2. http://plugins.qgis.org/plugins/molusce

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Acknowledgements

This work was supported by National Key Research and Development Plan of China under Grant No. 2017YFB0504204 and CAS President’s International Fellowship Initiative (PIFI) for Visiting Fellows under Grant No. 2017VCA0012. We appreciate all of the anonymous editors and reviewers for providing significant comments that helped improve this paper.

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Authors and Affiliations

  1. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China

    Attia M. El-Tantawi, Anming Bao, Cun Chang & Ying Liu

  2. Key Laboratory of GIS & RS Application Xinjiang Uygur Autonomous Region, Urumqi, 830011, China

    Attia M. El-Tantawi, Anming Bao, Cun Chang & Ying Liu

  3. Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi, 830011, China

    Attia M. El-Tantawi, Anming Bao, Cun Chang & Ying Liu

  4. University of Chinese Academy of Sciences, Beijing, 100049, China

    Anming Bao, Cun Chang & Ying Liu

  5. Faculty of African Postgraduate Studies, Cairo University, Giza, 12613, Egypt

    Attia M. El-Tantawi

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  1. Attia M. El-Tantawi
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  2. Anming Bao
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Correspondence to Anming Bao.

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El-Tantawi, A.M., Bao, A., Chang, C. et al. Monitoring and predicting land use/cover changes in the Aksu-Tarim River Basin, Xinjiang-China (1990–2030). Environ Monit Assess 191, 480 (2019). https://doi.org/10.1007/s10661-019-7478-0

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  • DOI: https://doi.org/10.1007/s10661-019-7478-0

Keywords

  • Land use/cover change
  • Artificial neural network (ANN)
  • Reconnaissance Drought Index
  • Aksu
  • Tarim River Basin
  • Xinjiang