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Im, Jungho
Intelligent Remote sensing and geospatial Information Science (IRIS) Lab
Research Interests
  • Remote sensing, Geospatial modeling, Disaster monitoring and management, Climate change

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A Volumetric Approach to Population Estimation Using Lidar Remote Sensing

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Title
A Volumetric Approach to Population Estimation Using Lidar Remote Sensing
Author
Lu, ZhenyuIm, JunghoQuackenbush, Lindi
Keywords
Accurate estimation;  Area-based;  Building detection;  Calibration site;  Denver , Colorado;  Housing characteristic;  LIDAR data;  LIDAR remote sensing;  Multiple linear regressions;  Regression model;  Regression tree models;  Regression trees;  Residential building;  Simple linear regression;  Single variable;  Study areas;  Volumetric approach
Issue Date
2011-11
Publisher
AMER SOC PHOTOGRAMMETRY
Citation
PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, v.77, no.11, pp.1145 - 1156
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.
URI
https://scholarworks.unist.ac.kr/handle/201301/8326
ISSN
0099-1112
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