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A volumetric approach to population estimation using lidar remote sensing / Zhong Lu in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 11 (November 2011)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 77 n° 11 (November 2011) . - pp 1145 - 1156 Titre : A volumetric approach to population estimation using lidar remote sensing Type de document : Article/Communication Auteurs : Zhong Lu, Auteur ; J. Im, Auteur ; L. Quackenbush, Auteur Année de publication : 2011 Article en page(s) : pp 1145 - 1156 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Denver [Termes IGN] détection du bâti [Termes IGN] données lidar [Termes IGN] estimation statistique [Termes IGN] habitat urbain [Termes IGN] ilot [Termes IGN] lasergrammétrie [Termes IGN] population urbaine [Termes IGN] recensement démographique [Termes IGN] régression [Termes IGN] volume (grandeur) [Termes IGN] zone urbaine Résumé : (Auteur) 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 lidar-derived 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 over-whelmingly 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. Numéro de notice : A2011-448 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.77.11.1145 En ligne : https://doi.org/10.14358/PERS.77.11.1145 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31226 [article]