Detection and prediction of land use/land cover change using spatiotemporal data fusion and the Cellular Automata–Markov model

  • Yuting Lu
  • Penghai WuEmail author
  • Xiaoshuang Ma
  • Xinghua Li


The detection and prediction of land use/land cover (LULC) change is crucial for guiding land resource management, planning, and sustainable development. In the view of seasonal rhythm and phenological effect, detection and prediction would benefit greatly from LULC maps of the same seasons for different years. However, due to frequent cloudiness contamination, it is difficult to obtain same-season LULC maps when using existing remote sensing images. This study utilized the spatiotemporal data fusion (STF) method to obtain summer Landsat-scale images in Hefei over the past 30 years. The Cellular Automata–Markov model was applied to simulate and predict future LULC maps. The results demonstrate the following: (1) the STF method can generate the same inter-annual interval summer Landsat-scale data for analyzing LULC change; (2) the fused data can improve the LULC detection and prediction accuracy by shortening the inter-annual interval, and also obtain LULC prediction results for a specific year; (3) the areas of cultivated land, water, and vegetation decreased by 33.14%, 2.03%, and 16.36%, respectively, and the area of construction land increased by 200.46% from 1987 to 2032. The urban expansion rate will reach its peak until 2020, and then slow down. The findings provide valuable information for urban planners to achieve sustainable development goals.


Land use and land cover Spatiotemporal data fusion ESTARFM CA–Markov Prediction 



We thank the data providers of USGS and the International Scientific & Technical Data Mirror Site, Compute Network Information Center, Chinese Academy of Sciences. We thank Dr. Xiaolin Zhu (The Hong Kong Polytechnic University) for providing access to the ESTARFM IDL code.

Funding information

This work was supported in part by the Mining environmental restoration and wetland ecological security Collaborative Innovation Center, the National Natural Science Foundation of China under Grant 41501376.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Resources and Environmental EngineeringAnhui UniversityHefeiChina
  2. 2.Anhui Province Key Laboratory of Wetland Ecosystem Protection and RestorationAnhui UniversityHefeiChina
  3. 3.Institute of Physical Science and Information TechnologyAnhui UniversityHefeiChina
  4. 4.School of Remote Sensing and Information EngineeringWuhan UniversityWuhanChina

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