Spatio-temporal cross-city comparison using multisensoral remote sensing for Mexican cities

Abstract

Our planet is more and more transforming into an urban world, in which the dynamics of urbanization have overcome the ability to govern cities. In a situation of uncoordinated urban growth, regional and urban planning lack technologies and methodologies to measure, monitor and analyze the spatio-temporal pattern of dynamic urban sprawl. This paper focuses on methods using remote sensing data to analyze, quantify and compare spatial urbanization processes. Urban sprawl is detected at the level of urban footprints using a postclassification change detection approach based on multi-sensoral Landsat and TerraSAR-X data. Spatio-temporal analysis combines absolute parameters (e.g. areal growth), location-based zonal statistics and gradient analysis (e. g. urban core versus the urban fringes) as well as spatial metrics (e.g. Largest Patch Index) to quantitatively characterise the spatial pattern of city developments. The study aims to detect spatial analogies as well as differences for the four largest Mexican urban agglomerations.