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Lake Area Analysis Using Exponential Smoothing Model and Long Time-Series Landsat Images in Wuhan, China

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  • Gonghao Duan

    (Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China)

  • Ruiqing Niu

    (College of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China)

Abstract

The loss of lake area significantly influences the climate change in a region, and this loss represents a serious and unavoidable challenge to maintaining ecological sustainability under the circumstances of lakes that are being filled. Therefore, mapping and forecasting changes in the lake is critical for protecting the environment and mitigating ecological problems in the urban district. We created an accessible map displaying area changes for 82 lakes in the Wuhan city using remote sensing data in conjunction with visual interpretation by combining field data with Landsat 2/5/7/8 Thematic Mapper (TM) time-series images for the period 1987–2013. In addition, we applied a quadratic exponential smoothing model to forecast lake area changes in Wuhan city. The map provides, for the first time, estimates of lake development in Wuhan using data required for local-scale studies. The model predicted a lake area reduction of 18.494 km 2 in 2015. The average error reached 0.23 with a correlation coefficient of 0.98, indicating that the model is reliable. The paper provided a numerical analysis and forecasting method to provide a better understanding of lake area changes. The modeling and mapping results can help assess aquatic habitat suitability and property planning for Wuhan lakes.

Suggested Citation

  • Gonghao Duan & Ruiqing Niu, 2018. "Lake Area Analysis Using Exponential Smoothing Model and Long Time-Series Landsat Images in Wuhan, China," Sustainability, MDPI, vol. 10(1), pages 1-16, January.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:1:p:149-:d:126117
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    References listed on IDEAS

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    1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    2. Shuisheng Zhou & Jiangtao Cui & Feng Ye & Hongwei Liu & Qiang Zhu, 2013. "New smoothing SVM algorithm with tight error bound and efficient reduced techniques," Computational Optimization and Applications, Springer, vol. 56(3), pages 599-617, December.
    3. Corberán-Vallet, Ana & Bermúdez, José D. & Vercher, Enriqueta, 2011. "Forecasting correlated time series with exponential smoothing models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 252-265, April.
    4. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    5. Taylor, James W., 2003. "Exponential smoothing with a damped multiplicative trend," International Journal of Forecasting, Elsevier, vol. 19(4), pages 715-725.
    6. Dong, Zibo & Yang, Dazhi & Reindl, Thomas & Walsh, Wilfred M., 2013. "Short-term solar irradiance forecasting using exponential smoothing state space model," Energy, Elsevier, vol. 55(C), pages 1104-1113.
    7. F. Pozzi & T. Matteo & T. Aste, 2012. "Exponential smoothing weighted correlations," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 85(6), pages 1-21, June.
    8. E. Y. Pee & J. O. Royset, 2011. "On Solving Large-Scale Finite Minimax Problems Using Exponential Smoothing," Journal of Optimization Theory and Applications, Springer, vol. 148(2), pages 390-421, February.
    9. Corberán-Vallet, Ana & Bermúdez, José D. & Vercher, Enriqueta, 2011. "Forecasting correlated time series with exponential smoothing models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 252-265.
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    Cited by:

    1. Jingjing Yan & Wei Shi & Fei Li, 2018. "Evaluation and Countermeasures of the Implementation of the Lake Protection and Governance System in Wuhan City, Middle China," Sustainability, MDPI, vol. 10(10), pages 1-15, September.
    2. Ivanize Silva & Rafael Santos & António Lopes & Virgínia Araújo, 2018. "Morphological Indices as Urban Planning Tools in Northeastern Brazil," Sustainability, MDPI, vol. 10(12), pages 1-18, November.

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