A new map of global urban extent from MODIS satellite data

By Annemarie Schneider, Mark A. Friedl, David T. Potere. 2009

822 citations recorded by the source corpus.

Although only a small percentage of global land cover, urban areas significantly alter climate, biogeochemistry, and hydrology at local, regional, and global scales. To understand the impact of urban areas on these processes, high quality, regularly updated information on the urban environment—including maps that monitor location and extent—is essential. Here we present results from efforts to map the global distribution of urban land use at 500 m spatial resolution using remotely sensed data from the Moderate Resolution Imaging Spectroradiometer (MODIS). Our approach uses a supervised decision tree classification algorithm that we process using region-specific parameters. An accuracy assessment based on sites from a stratified random sample of 140 cities shows that the new map has an overall accuracy of 93% (k = 0.65) at the pixel level and a high level of agreement at the city scale (R2 = 0.90). Our results (available at http://sage.wisc.edu/urbanenvironment.html) also reveal that the land footprint of cities occupies less than 0.5% of the Earth’s total land area.

Subfields: Global and Planetary Change, Media Technology

View Paper: https://www.semanticscholar.org/paper/1cf4417915ad5edf507f7b43335a2edbb534a980

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Citations in the corpus

Assessing vulnerabilities and limits in the transition to renewable energies: Land requirements under 100% solar energy scenarios (2017, methodology): “Further, the method applied does not escape the fact that there is a large uncertainty in the estimation of urban/built-up areas, with differences of an order of magnitude (Schneider et al., 2009).”
The long-term effect of increasing the albedo of urban areas (2012, methodology): “Importantly, the estimated global urban areas in GRUMP are more than five times larger than in the MODIS data (Schneider et al 2009); this represents an important area of uncertainty pertinent to our estimate of global CO2 offset.”
Changes in observed climate extremes in global urban areas (2015, methodology): “Our selection of urban areas based on population was further verified with urban extent derived using the MODIS data (Schneider et al 2009).”
Estimation of Gridded Population and GDP Scenarios with Spatially Explicit Statistical Downscaling (2016, background): “Variables Description Unit Source Year City pop City population 67,934 cities GRUMP 1 1990, 1995, 2000 Urban area Urban area [km2] 0.5-degree grids Schneider et al., 2009 2 2001–2002Agri area Agricultural area [km2] Road dens Total l ngth [km] ofprincipal roads Natural Earth 3 2012 Airport dist…”
Climate–vegetation control on the diurnal and seasonal variations of surface urban heat islands in China (2016, background): “Following the convention (Schneider et al 2009), areas dominated by high-density built-up land (>50”
Change Detection from Temporal Sequences of Class Labels: Application to Land Cover Change Mapping (2013, background): “Empirical evaluation on real and synthetic data suggests that the proposed approach is effective in capturing the characteristics of land cover classification and change processes, and produces significantly improved classification and change detection products.”
Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach (2012, background): “Moreover, there is a growing body of work looking at urbanization and its effects from a regional to global perspective (Mills, 2007; Pataki et al., 2006) which requires medium to coarse resolution large-area maps of urban extent and urban change (Schneider et al., 2009, 2010).”
Urban agriculture: a global analysis of the space constraint to meet urban vegetable demand (2014, methodology): “We used the global urban dataset derived from the 500 m MODerate resolution Imaging Spectroradiometer (MODIS) circa year 2001 (Schneider et al 2009, 2010).”