The global trend of urbanization: spatiotemporal analysis of megacities using multi-temporal remote sensing, landscape metrics, and gradient analysis

Abstract

Today's mega cities could serve as good predictors of future urbanization processes in incipient mega cities. Measuring and analysing the past effects of urban growth in the largest category of urban agglomerations aims at understanding spatial dynamics. In this study we use remote sensing, landscape metrics and gradient analysis to measure, quantify, and analyze spatiotemporal effects of massive urbanization in 10 sample mega cities throughout the world. By using timeseries of Landsat data, we classify urban footprints since the 1970s. This lets us detect temporal and spatial urban patterns, sprawl and densification processes and various types of urban development. A multi-scale analysis starts at city level using landscape metrics to quantify spatial urban patterns. We relate the metrics, like e.g. landscape shape index, edge density or class area to each other in spider charts. Furthermore, we use gradient analysis to provide insight into spatial pattern development from the urban core to the periphery. The results paint a characteristic picture of spatiotemporal urbanization for the individual mega cites and enable comparison of all cities across the board. Spatial characteristics of urbanization dynamics allow indirectly conclusions on causes or future consequences.