文章基本信息

标题：Optimization of Modelling Population Density Estimation Based on Impervious Surfaces
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作者：Jinyu Zang ; Ting Zhang ; Longqian Chen 等
期刊名称：Land
印刷版ISSN：2073-445X
出版年度：2021
卷号：10
期号：8
页码：791
DOI：10.3390/land10080791
语种：English
出版社：MDPI, Open Access Journal
摘要：Population data are key indicators of policymaking, public health, and land use in urban and ecological systems; however, traditional censuses are time-consuming, expensive, and laborious. This study proposes a method of modelling population density estimations based on remote sensing data in Hefei. Four models with impervious surface (IS), night light (NTL), and point of interest (POI) data as independent variables are constructed at the township scale, and the optimal model was applied to pixels to obtain a finer population density distribution. The results show that: (1) impervious surface (IS) data can be effectively extracted by the linear spectral mixture analysis (LSMA) method; (2) there is a high potential of the multi-variable model to estimate the population density, with an adjusted Rsup2/sup of 0.832, and mean absolute error (MAE) of 0.420 from 10-fold cross validation recorded; (3) downscaling the predicted population density from the township scale to pixels using the multi-variable stepwise regression model achieves a more refined population density distribution. This study provides a promising method for the rapid and effective prediction of population data in interval years, and data support for urban planning and population management.