Expansion of Major Urban Areas in the US Great Plains from 2000 to 2009 Using Satellite Scatterometer Data

A consistent dataset delineating and characterizing changes in urban environments will be valuable for socioeconomic and environmental research and for sustainable urban development. Remotely sensed data have been long used to map urban extent and infrastructure at various spatial and spectral resolutions. Although many datasets and approaches have been tried, there is not yet a universal way to map urban extents across the world. Here we combined a microwave scatterometer (QuikSCAT) dataset at ~1 km posting with percent impervious surface area (%ISA) data from the National Land Cover Dataset (NLCD) that was generated from Landsat data, and ambient population data from the LandScan product to characterize and quantify growth in nine major urban areas in the US Great Plains from 2000 to 2009. Nonparametric Mann-Kendall trend tests on backscatter time series from urban areas show significant expanding trends in eight of nine urban areas with p-values ranging 0.032 to 0.001. The sole exception is Houston, which has a substantial non-urban backscatter at the northeastern edge of the urban core. Strong power law scaling relationships between ambient population and either urban area or backscatter power (r2 of 0.96 in either model) with sub-linear exponents (β of 0.911 and 0.866, respectively) indicate urban areas become more compact with more vertical built-up structure than lateral expansion to accommodate the increased population. Increases in backscatter and %ISA datasets between 2001 and 2006 show agreement in both magnitude and direction for all urban areas except Minneapolis-St. Paul (MSP), likely due to the presence of many lakes and ponds throughout the MSP metropolitan area. We conclude discussing complexities in the backscatter data caused by large metal structures and rainfall

Urban environments, Beijing case study

Various remote sensing methods and demographic datasets are used in the Beijing case study to illustrate their capability to observed physical and demographic characteristics of the urban environment. NL data serve well to identify the outer limit of not only large urban areas but also small settlements. For each large urban contour limit from NL, DSM scatterometer data can detect urban extent and typology. Within each urban type classified by DSM data, Landsat spectral signatures can provide highresolution details of the urban land cover. It is found that DSM s0 has the highest correlation with ambient population of Beijing. To monitor urban change, data can be partitioned into different timescales. The combination of multiple remote sensing methods together with demographic measures is necessary to effectively observe urban environments, rather than each dataset standing alone - both by adding shape and contour to urban population estimates as well as to describe patterns of association between population models and those detecting the rapidly changing built environment. Although Beijing may have local characteristics in detail, it shares many issues of a megacity common to other megacities across the world, where methods and results in this Beijing study can be applicable.</p