Mapping the annual dynamics of land cover in Beijing from 2001 to 2020 using Landsat dense time series stack

Compared to change detection using two-dates satellite images, monitoring the changes at high temporal frequencies using dense observations can provide more comprehensive understanding of the land cover dynamics for a given place. Beijing, the capital of China, has undergone fast urban growth in the past decades. The existing studies on Landsat-derived land cover dynamics in Beijing mainly focus on 5- or 10-year intervals, or annual mapping of single land cover type; however, the dynamics of all-types land cover in Beijing at one-year scale were rarely investigated. To fill this research gap, we presented a time-series land-cover mapping approach by combining the Continuous Change Detection and Classification (CCDC) algorithm with Markov random field (MRF) model to explore the annual dynamics of land cover in Beijing from 2001 to 2020 using Landsat time series. First, the annual land cover maps for Beijing were generated using CCDC algorithm. Then, the MRF model was applied to annual land cover maps to alleviate the salt and pepper noise arising from the classification of CCDC at the pixel level. Results showed that CCDC-MRF proposed in this study could produce temporally and spatially consistent results which have higher annual average overall accuracy (81.93%) than the results derived from CCDC (79.18%). In addition, the accuracy of annual land cover changes for CCDC-MRF was 92.50% in spatial domain and 80.49% in temporal domain, which were higher than the results for CCDC with 89.36% in spatial domain and 78.38% in temporal domain. The major land cover change in Beijing over the last two decades was characterized by urban expansion with the replacement of cultivated land, leading to 13.53% of cultivated land being replaced by artificial impervious surface, mainly occurring between the fifth and sixth ring roads. The method proposed in this study could generate accurate land cover maps at high temporal frequencies and the findings of this research could provide a better understanding for sustainable urban development and management.