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With the continuous development of satellite techniques, it is now possible to acquire a regular series of images concerning a givengeographical zone with both high accuracy and low cost. Research on how best to effectively process huge volumes of observationaldata obtained on different dates for a specific geographical zone, and to exploit the valuable information regarding land covercontained in these images has received increasing interest from the remote sensing community. In contrast to traditional land coverchange measures using pair-wise comparisons that emphasize the compositional or configurational changes between dates, thisresearch focuses on the analysis of the temporal sequence of land cover dynamics, which refers to the succession of land cover typesfor a given area over more than two observational periods. Using a time series of classified Landsat images, ranging from 2006 to2011, a sequential pattern mining method was extended to this spatiotemporal context to extract sets of connected pixels sharingsimilar temporal evolutions. The resultant sequential patterns could be selected (or not) based on the range of support values. Theseselected patterns were used to explore the spatial compositions and temporal evolutions of land cover change within the study region.Experimental results showed that continuous patterns that represent consistent land cover over time appeared as quite homogeneouszones, which agreed with our domain knowledge. Discontinuous patterns that represent land cover change trajectories weredominated by the transition from vegetation to bare land, especially during 2009–2010. This approach quantified land cover changesin terms of the percentage area affected and mapped the spatial distribution of these changes. Sequential pattern mining has beenused for string mining or itemset mining in transactions analysis. The expected novel significance of this study is the generalizationof the application of the sequential pattern mining method for capturing the spatial variability of landscape patterns, and theirtrajectories of change, to reveal information regarding process regularities with satellite imagery

Spatiotemporal Mining of Time-Series Remote Sensing Images Based on Sequential Pattern Mining