Spatial refinement of census population distribution using remotely sensed estimates of impervious surfaces in Haiti

Abstract

Previous research on the relationship between impervious surfaces and population has focused on limited areas. This paper considers the relationship for an entire country. Multiple spatial resolution optical imagery was integrated with census data to refine the spatial distribution of population in Haiti. A classification and regression tree (CART) methodology was used to create a percent impervious-area layer based on ten high-resolution training chips and a Landsat Enhanced Thematic Mapper (ETM+) mosaic. This estimate then became an input for a dasymetric mapping approach, where population was distributed proportionately to impervious areas with input from ancillary data sources. Haiti's section boundaries (third administrative level) provided the mapping base because a strong relationship between imperviousness and population was observed at this administrative level. The accuracy of the technique was tested for a set of 110 municipalities in Haiti, making use of recent census data. The potential usefulness of imagery-based population estimates for areas where census data do not exist was also tested by dividing Haiti into northern and southern portions and using one to predict the other.