Human activities significantly alter the geomorphology, ecology, and climatology of the Earth at local, regional, and global scales. Nowhere is this more apparent than in cities, which can be thought of as human-dominated ecosystems. Cities alter surface and subsurface hydrologic and biogeochemical processes by replacing pervious with impervious materials; change local to regional climate by altering surface energy balances and releasing various pollutants into the atmosphere; and influence biodiversity by fragmenting or destroying habitats.

The expansion of cities due to population growth and migration from rural to urban areas increasingly exposes humans to a variety of obvious (volcanoes, earthquakes, hurricanes) and more subtle (fugitive dust, subsidence, slope failures) geohazards. Use of remotely sensed data is frequently the only cost-effective and timely means to characterize and assess ecological, geological, and climatic changes resulting from urban expansion or redevelopment. The ASTER sensor provides high to moderately high resolution data in three wavelength regions useful for investigation of a wide range of urban processes.

Urban Land Cover and Spatial Structure, Phoenix AZ

The high spatial resolution of ASTER in the visible to near infrared bands (15 m/pixel) allows for detailed land cover classification of urban and peri-urban regions. Land cover classification of urban regions is useful for a range of applications including urban growth change detection, hydrology studies, and as input into climate models. Figure 1 is a land cover classification of the eastern portion of the Phoenix, AZ metropolitan region, and illustrates the spatial and class detail extractable from ASTER data.

Land cover data can be further analysed using landscape metrics to obtain quantitative measures of urban spatial structure useful for ecological, climatic, and demographic studies. There are a large number of metrics available for urban analysis; some examples include class area, mean patch size (a measure of the clump size of similarly classified pixels), edge density (a measure of patch or class neighborhood shape complexity), and interspersion/juxtaposition index (how dispersed or clumped together a class is on the landscape). Figure 2 depicts the interspersion/juxtaposition index (IJI) for the Built land cover classes in the Phoenix urban core area, and illustrates the relatively high degree of mixing with other land cover classes in the region.

Measurement of Surficial Biogeophysical Variables

The broad wavelength coverage of ASTER allows for measurement of important biogeophysical variables in urban/peri-urban regions such as vegetation density. Using simple vegetation indices, high spatial resolution urban vegetation maps can be made rapidly. This information is immediately useful to ecologists and city planners for assessment of urban park extent and health. Vegetion density is also important for modeling of urban climate, hydrology, and water use. Figure 3 is a color ramped Normalized Difference Vegetation Index (NDVI) of downtown London; regions with an NDVI value approaching 1 have high density of actively photosynthesizing vegetation, and regions with values approaching -1 have little to no vegetation. The ASTER visible-near infrared (vnir) data used to calculate the NDVI is included for comparison.

Collection of multispectral thermal infrared data is a particular strength of ASTER. Nighttime data acquisitions over urban regions can be used to create maps of urban/peri-urban surface temperature that are invaluable for assessment of urban heat islands. The distribution of built materials throughout the urban landscape are of obvious importance in constructing thermal budgets, but consideration of the potential contributions of surrounding natural materials to the regional thermal budget is also important. For example, the Phoenix, AZ metropolitan region is bounded by mountain ranges with little vegetation cover; these ranges act as large thermal emitters during the night and have surface temperatures equivalent to urban core asphalt and concrete (fig. 4). This surface temperature information is valuable for investigation of urban climatic patterns and initialization of climate models.