Population Estimation Using Landsat Enhanced Thematic Mapper Imagery

An assessment of two groups of approaches for estimating urban population with remote-sensing information is presented in this article. These approaches, zonal and pixel-based models, are applied to Landsat Enhanced Thematic Mapper images of a portion of Columbus, Ohio , to generate population estimates. The zonal approach uses impervious surface fraction, spectral radiance, and land-use/land-cover classification to derive population estimates. The pixel-based approach uses impervious surface fraction and spectral radiance to estimate the population of residential areas. To assess robustness, these models were applied to Dayton, Ohio . A comparative study indicates that the models generated promising results in estimating regional population counts. However, zonal regression with spectral radiance produced large errors (76%) for census block groups, whereas other models gave significantly better estimation accuracy. Comparing the performance of the indicators, impervious surface fraction is competitive, and slightly but consistently better than land-use classification. In comparison with traditional zonal approaches, pixel-based models give somewhat better estimation accuracy.