A Volumetric Approach to Population Estimation Using Lidar Remote Sensing

Abstract

This research investigated the applicability of lidar data for estimating population at the census block level using a volumetric approach. The study area, near the urban downtown area of Denver, Colorado, was selected since it includes dense distribution of different types of residential buildings. A modified morphological building detection algorithm was proposed to extract buildings from the lidarderived surfaces. The extraction results showed that the modified morphological building detection algorithm can effectively recover building pixels occluded by nearby trees. The extracted buildings were further refined to residential buildings using parcel data. Two approaches (i.e., area- and volume-based) to population estimation were investigated at the census block level. Four regression models (i.e., simple linear regression, multiple linear regression, regression tree using one variable, and regression tree using multiple variables) were used to identify the relationship between census population and the area or volume information of the residential buildings. The volume-based models overwhelmingly outperformed the area-based models in the study area, and the models using multiple variables yielded more accurate estimation than the single variable models. The volume-based regression tree model using multiple variables yielded the most accurate estimations: R2=0.89, RMSE=21 people, and RRMSE=26.8 percent in the calibration site; and R2=0.80, RMSE=27 people, and RRMSE=30.1 percent in the validation site. As the results show, the volumetric approach using lidar remote sensing is effective for population estimation in regions with heterogeneous housing characteristics.