Using Landsat ETM + Imagery to Measure Population Density in Indianapolis, Indiana, USA

Abstract

Remote sensing techniques have been previously used in urban analysis, settlement detection, and population estimation. This research explores the potentials of integra- tion of Landsat ETMdata with census data for estimation of population density in City of Indianapolis, Indiana. Spectral signatures, principal components, vegetation indices, fraction images, textures, and temperature were used as predictive indicators. Correlation analysis was used to explore the relationships between remote sensing variables and population, and stepwise regression analysis was then used to develop models for estimating population quantities. Two sampling schemes (non-stratified versus stratified sampling) were compared. It was found that the integration of textures, temperatures, and spectral responses substantially improved the accuracy of estima- tion. Stratification of the population into three categories of low-, medium-, and high-densities and development of different models for individual population density category provided better estimation results than a non-stratified scheme. The total population for City of Indianapolis was estimated to be 832,792 in 2000 yielding an accuracy of 96.8 percent.