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

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

References

  • Adam, A. H. M., Elhag, A. M. H., & Salih, A. M. (2013). Accuracy assessment of land use & land cover classification (LU/LC), case study of Shomadi area, Renk County, Upper Nile State, South Sudan. International Journal of Scientific and Research Publications, 3, 1–6.

    Google Scholar 

  • Amut, A., Gong, L., Yuan, Z., Crovello, T., & Gao, Z. (2006). Estimation of the ecological degeneration from changes in land use and land covers in the upper reaches of the Tarim River. In Proceedings of SPIE.

    Google Scholar 

  • Appiah, M. K., Feike, T., Wiredu, A. N., & Mamitimin, Y. (2014). Cotton production, land use change and resource competition in the Aksu-Tarim River Basin, Xinjiang, China. Quarterly Journal of International Agriculture, 53(3), 243-261.

  • Bao, A., Huang, Y., Ma, Y., Guo, H., & Wang, Y. (2017). Assessing the effect of EWDP on vegetation restoration by remote sensing in the lower reaches of Tarim River. Ecological Indicators, 74, 261–275.

    Article  Google Scholar 

  • Brown, D. G., Walker, R., Manson, S., & Seto, K. (2012). Modeling land use and land cover change. In G. Gutman et al. (Eds.), Land change science. Remote sensing and digital image processing (Vol. 6). Dordrecht: Springer.

    Google Scholar 

  • Chen, X., Yan, J., Chen, Z., Luo, G., Song, Q., & Xu, W. (2009). A spatial geostatistical analysis of impact of land use development on groundwater resources in the Sangong Oasis Region using remote sensing imagery and data. Journal of Arid Land, 1(1), 1–8.

    Google Scholar 

  • Dewidar, K. H. M. (2004). Detection of land use/land cover changes for the northern part of the Nile delta (Burullus region), Egypt. International Journal of Remote Sensing, 25(20), 4079–4089.

    Article  Google Scholar 

  • Esmail, A. M., Masria, A., & Negm, A. (2016). Monitoring land use/land cover changes around Damietta Promontory, Egypt, using RS/GIS. 12th International Conference on Hydro-informatics, HIC 2016.Procedia Engineering, 154, 936-942.

    Article  Google Scholar 

  • Gismondi, M. (2013). MOLUSCE—an open source land use change analyst, http://2013.foss4g.org/conf/programme/presentations/107. Accessed 19 Sep 2013

  • Halmy, M. W. A., Gessler, P. E., Hicke, J. A., & Salem, B. B. (2015). Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Applied Geography, 63, 101-112.

    Article  Google Scholar 

  • Harris, I., Jones, P. D., Osborn, T. J., & Lister, D. H. (2014). Updated high-resolution grids of monthly climatic observations—the CRU TS3.10. Dataset. International Journal of Climatology, 34, 623–642. https://doi.org/10.1002/joc.3711.

    Article  Google Scholar 

  • Hartmann, H., Snow, J. A., Stein, S., Buda, S., Zhai, J., Jiang, T., Krysanova, V., & Kundzewicz, Z. W. (2016). Predictors of precipitation for improved water resources management in the Tarim River basin: creating a seasonal forecast model. Journal of Arid Environments, 125, 31-42.

    Article  Google Scholar 

  • Hossen, H., & Negm, A. (2016). Change detection in the water bodies of Burullus Lake, Northern Nile Delta, Egypt, using RS/GIS, 12th International Conference on Hydro informatics, HIC 2016. Procedia Engineering, 154, 936–942.

    Article  Google Scholar 

  • Jogun, T. (2016). The simulation model of land cover change in the Požega-Slavonia County. Diploma thesis, Faculty of Science, Department of Geography. http://digre.pmf.unizg.hr/4908/,. Accessed 27 June 2017

  • Kaufmann, R. K., & Seto, K. C. (2001). Changes detection, accuracy, and bias in a sequential analysis of Landsat imagery in the Pearl River delta, China: econometric techniques. Agriculture Ecosystems & Environment, 85(1), 95–105.

    Article  Google Scholar 

  • Keilholz, P., Disse, M., & Halik, U. (2015). Effects of land use and climate change on groundwater and ecosystems at the middle reaches of the Tarim River using the MIKE SHE integrated hydrological model. Water, 7(6), 3040–3056.

    CAS  Article  Google Scholar 

  • Lambin, E. F., Geist, H. J., & Lepers, E. (2003). Dynamics of land-use and land-cover change in tropical regions. Annual Review of Environment and Resources, 28, 205–241.

    Article  Google Scholar 

  • Li, Z., Chen, Y., Wang, Y., & Li, W. (2016). Drought promoted the disappearance of civilizations along the ancient Silk Road. Environmental Earth Sciences, 75(14), 1116.

    Article  Google Scholar 

  • Liu, Y. B., & Chen, Y. (2006). Impact of population growth and land-use change on water resources and ecosystems of the arid Tarim River Basin in Western China. International Journal of Sustainable Development and World Ecology, 13(4), 295–305.

    Article  Google Scholar 

  • Narayan, K., & Khanindra, P. (2015). Remote Sensing and GIS Based Land use/Land cover Change Detection Mapping in Saranda Forest, Jharkhand, India. International Research Journal of Earth Sciences, 3(10), 1–6.

    Google Scholar 

  • NOAA. (2017). What is the difference between land cover and land use, National Ocean Service, NOAA, U.S Department of Commerce, https://oceanservice.noaa.gov/facts/lclu.html. Accessed 25 June 2018.

  • Noha, S., & Hanan, F. (2012). Monitoring Burullus lake using remote sensing techniques, Sixteenth International Water Technology Conference. Istanbul, Turkey: IWTC 16 2012.

    Google Scholar 

  • Otukei, J. R., & Blaschke, T. (2010). Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 2010(12), S27–S31.

    Article  Google Scholar 

  • Pakhale, G. K., & Gupta, P. K. (2010). Comparison of Advanced Pixel Based (ANN and SVM) and Object-Oriented Classification Approaches Using Landsat-7 ETM+ Data. International Journal of Engineering & Technology, 2(4), 245-251.

  • Pittock, A. B. (1988). The greenhouse effect and future climatic change. In S. Gregory (Ed.), 1988: Recent climatic change (a regional approach) (pp. 306–315). London: Belhaven Press.

    Google Scholar 

  • Praveen, K. M., & Jayarama, R. S. R. (2013). Analysis of land use/land cover changes using remote sensing data and GIS at an urban area, Tirupati, India, Hindawi Publishing Corporation. The Scientific World Journal, 2013, Article ID 268623, 1–6. https://doi.org/10.1155/2013/268623.

    Article  Google Scholar 

  • Rahman, M. T. U., Tabassum, F., Rasheduzzaman, M., Saba, H., Sarkar, L., Ferdous, J., Uddin, S., & Islam, A. Z. M. Z. (2017). Temporal dynamics of land use/land cover change and its prediction using CA-ANN model for southwestern coastal Bangladesh. Environmental Monitoring and Assessment, 189(11), 565. https://doi.org/10.1007/s10661-017-6272-0.

    Article  Google Scholar 

  • Sala, O. E., Chapin, F. I. S., Armesto, J. J., Berlow, E. L., Bloomfield, J. B., Dirzo, R. H., et al. (2000). Global biodiversity scenarios for the year 2100. Science, 287(5459), 1770–1774.

    CAS  Article  Google Scholar 

  • Shah, R., Manekar V. L., Christian, R. A.,& Mistry, N. J. 2013. Estimation of Reconnaissance Drought Index (RDI) for Bhavnagar District, Gujarat, India. World Academy of Science, Engineering and Technology, International Journal of Environmental and Ecological Engineering, 7(7), 507-510.

  • Shaltout, K. H., & Al-Sodany, Y. M. (2008). Vegetation analysis of Burullus Wetland: a RAMSAR site in Egypt. Wetlands Ecology and Management, 16, 421–439.

    Article  Google Scholar 

  • Shi, Y., Wang, R., Fan, L., Li, J., & Yang, D. (2010). Analysis on land-use change and its demographic factors in the original-stream watershed of Tarim River based on GIS and statistic. Procedia Environmental Sciences, 2(6), 175–184.

    Article  Google Scholar 

  • Tsakiris, G., & Vangelis, H. (2005). Establishing a drought index incorporating evapotranspiration. European Water, 9(10), 3–11.

    Google Scholar 

  • World Meteorological Organization (WMO) and Global Water Partnership (GWP). (2016). Handbook of drought indicators and indices. In M. Svoboda & B. A. Fuchs (Eds.), Integrated Drought Management Programme (IDMP). Geneva: Integrated Drought Management Tools and Guidelines Series 2.

    Google Scholar 

  • Yang, H., Mu, S., & Li, J. (2014). Effects of ecological restoration projects on land use and land cover change and its influences on territorial npp in Xinjiang, China. Catena, 115(4), 85–95.

    Article  Google Scholar 

  • Yang, X., Chen, R., & Zheng, X. Q. (2016). Simulating land use change by integrating ANN-CA model and landscape pattern indices. Geomatics, 7(3), 1–15.

    CAS  Google Scholar 

  • Yang, P., Xia, J., Zhan, C., Mo, X., Chen, X., Hu, S., & Chen, J. (2018). Estimation of water consumption for ecosystems based on vegetation interfaces processes model: a case study of the Aksu river basin, Northwest China. Science of the Total Environment, 613-614, 186–195.

    CAS  Article  Google Scholar 

  • Zhao, R., Chen, Y., Zhou, H., Li, Y., Qian, Y., & Zhang, L. (2009). Assessment of wetland fragmentation in the Tarim River Basin, Western China. Environmental Geology, 57(2), 455–464.

    Article  Google Scholar 

  • Zhao, R., Chen, Y., Shi, P., Zhang, L., Pan, J., & Zhao, H. (2013). Land use and land cover change and driving mechanism in the arid inland river basin: a case study of Tarim River, Xinjiang, China. Environmental Earth Sciences, 68(2), 591–604.

    Article  Google Scholar 

  • Zhou, Q. (1998). Use of GIS technology for land resource inventories and modelling for sustainable regional development. AMBIO., 27(6), 444–450.

    Google Scholar 

Download references

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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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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Keywords

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