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

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 30years. 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.