Spatial improvement of human population distribution based on multi-sensor remote-sensing data: an input for exposure assessment

• Published in: International journal of remote sensing Vol. 34; no. 15; pp. 5569 - 5583
• Main Authors: Yang, Xuchao, Yue, Wenze, Gao, Dawei
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 2013
• Subjects: Animal, plant and microbial ecology; Applied geophysics; Assessments; Biological and medical sciences; Discrete element method; DMSP satellites; Earth sciences; Earth, ocean, space; Exact sciences and technology; exposure assessment; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Human; human population; human settlements; image analysis; Internal geophysics; Population density; Population distribution; Remote sensing; Risk analysis; Settlements; Teledetection and vegetation maps; vegetation; Zhejiang China; Southeast China; Far East; Asia; China; China, People's Rep., Zhejiang Prov; Availability; Human; risk assessment; Degree of exposure; Vegetation index; Estimation; digital elevation models; Population structure; remote sensing; exhibits; measurement sensor; Integrated management; System; Improvement; Meteorological satellite; Light; imagery; Defense mechanism; spatial resolution; programs; Pixel; Hazard; communities
• ISSN: 1366-5901; 0143-1161; 1366-5901
• DOI: 10.1080/01431161.2013.792970

A spatial mismatch of hazard data and exposure data (e.g. population) exists in risk analysis. This article provides an integrated approach for a rapid and accurate estimation of population distribution on a per-pixel basis, through the combined use of medium and coarse spatial resolution remote-sensing data, namely the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) night-time imagery, enhanced vegetation index (EVI), and digital elevation model (DEM) data. The DMSP/OLS night-time light data have been widely used for the estimation of population distribution because of their free availability, global coverage, and high temporal resolution. However, given its low-radiometric resolution as well as the overglow effects, population distribution cannot be estimated accurately. In the present study, the DMSP/OLS data were combined with EVI and DEM data to develop an elevation-adjusted human settlement index (EAHSI) image. The model for population density estimation, developed based on the significant linear correlation between population and EAHSI, was implemented in Zhejiang Province in southeast China, and a spatialized population density map was generated at a resolution of 250 m × 250 m. Compared with the results from raw human settlement index (59.69%) and single night-time lights (35.89%), the mean relative error of estimated population by EAHSI has been greatly reduced (17.74%), mainly due to the incorporation of elevation information. The accurate estimation of population density can be used as an input for exposure assessment in risk analysis on a regional scale and on a per-pixel basis.