Small Area Population Estimation with High-Resolution Remote Sensing and Lidar

Timely small-area population estimates are critical for both public and private sector decision-making. State and local governments must allocate resources, and private businesses need to identify and delineate customer profiles, market areas and site locations. Currently, such population data is only available for one date per decade through the national census. This study focuses on developing methods for intercensal small-area population estimation from integrative use of airborne light detection and ranging (lidar) and high spatial resolution aerial photographs. Particularly, it addresses the following question:What level of information extracted from lidar and high-spatial resolution imagery can be effectively infused in deriving small-area population estimation? This question was addressed through a comparative study of seven linear models parameterized in terms of building count, building area and/or building volume, at two different land use levels: single-family dwelling, multifamily dwelling and other types, versus residential and other types. Results showed that while building volume is more relevant to population counts at census block level; it also represents the most challenging parameter to measure by automated analyses of lidar and high resolution remote sensing imagery. Because of that, a simple model that primarily utilizes residential building counts resulted in more reliable population estimation.