This paper reviews how remotely sensed data have been used to understand the impact of urbanization on global environmental change. We describe how these studies can support the policy and science communities’ increasing need for detailed and up-to-date information on the multiple dimensions of cities, including their social, biological, physical, and infrastructural characteristics. Because the interactions between urban and surrounding areas are complex, a synoptic and spatial view offered from remote sensing is integral to measuring, modeling, and understanding these relationships. Here we focus on three themes in urban remote sensing science: mapping, indices, and modeling. For mapping we describe the data sources, methods, and limitations of mapping urban boundaries, land use and land cover, population, temperature, and air quality. Second, we described how spectral information is manipulated to create comparative biophysical, social, and spatial indices of the urban environment. Finally, we focus how the mapped information and indices are used as inputs or parameters in models that measure changes in climate, hydrology, land use, and economics. Full article

The world is rapidly urbanizing, but there is no single urbanization process. Rather, urban areas in different regions of the world are undergoing myriad types of transformation processes. The purpose of this paper is to examine how well data from DMSP/OLS nighttime lights (NTL) can identify different types of urbanization processes. Although data from DMSP/OLS NTL are increasingly used for the study of urban areas, to date there is no systematic assessment of how well these data identify different types of urban change. Here, we randomly select 240 sample locations distributed across all world regions to generate urbanization typologies with the DMSP/OLS NTL data and use Google Earth imagery to assess the validity of the NTL results. Our results indicate that where urbanization occurred, NTL have a high accuracy (93%) of characterizing these changes. There is also a relatively high error of commission (42%), where NTL identified urban change when no change occurred. This leads to an overestimation of urbanization by NTL. Our analysis shows that time series NTL data more accurately identifies urbanization in developed countries, but is less accurate in developing countries, suggesting the need to exert caution when using or interpreting NTL in developing countries. Full article