Characterizing urban sprawl using multi-stage remote sensing images and landscape metrics

This study intends to explore the spatial analytical methods to identify both general trends and more subtle patterns of urban land changes. Landsat imagery of metropolitan Kansas City, USA was used to generate time series of land cover data over the past three decades. Based on remotely sensed land cover data, landscape metrics were calculated. Both the remotely sensed data and landscape metrics were used to characterize long-term trends and patterns of urban sprawl. Land cover change analyses at the metropolitan, county, and city levels reveal that over the past three decades the significant increase of built-up land in the study area was mainly at the expense of non-forest vegetation cover. The spatial and temporal heterogeneity of the land cover changes allowed the identification of fast and slow sprawling areas. The landscape metrics were analyzed across jurisdictional levels to understand the effects of the built-up expansion on the forestland and non-forest vegetation cover. The results of the analysis suggest that at the metropolitan level both the areas of non-forest vegetation and the forestland became more fragmented due to development while large forest patches were less affected. Metrics statistics show that this landscape effect occurred moderately at the county level, while it could be only weakly identified at the city level, suggesting a scale effect that the landscape response of urbanization can be better revealed within larger spatial units (e.g., a metropolitan area or a county as compared to a city). The interpretation of the built-up patch density metrics helped identify different stages of urbanization in two major urban sprawl directions of the metropolitan area. Land consumption indices (LCI) were devised to relate the remotely sensed built-up growth to changes in housing and commercial constructions as major driving factors, providing an effective measure to compare and characterize urban sprawl across jurisdictional boundaries and time periods.