Land-cover change analysis in 50 global cities by using a combination of Landsat data and analysis of grid cells

Global urban expansion has created incentives to convert green spaces to urban/built-up area. Therefore, understanding the distribution and dynamics of the land-cover changes in cities is essential for better understanding of the cities' fundamental characteristics and processes, and of the impact of changing land-cover on potential carbon storage. We present a grid square approach using multi-temporal Landsat data from around 1985-2010 to monitor the spatio-temporal land-cover dynamics of 50 global cities. The maximum-likelihood classification method is applied to Landsat data to define the cities' urbanized areas at different points in time. Subsequently, 1 km² grid squares with unique cell IDs are designed to link among land-cover maps for spatio-temporal land-cover change analysis. Then, we calculate land-cover category proportions for each map in 1 km² grid cells. Statistical comparison of the land-cover changes in grid square cells shows that urban area expansion in 50 global cities was strongly negatively correlated with forest, cropland and grassland changes. The generated land-cover proportions in 1 km² grid cells and the spatial relationships between the changes of land-cover classes are critical for understanding past patterns and the consequences of urban development so as to inform future urban planning, risk management and conservation strategies.