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Open AccessArticle

Modeling Population Density Using a New Index Derived from Multi-Sensor Image Data

Institute of Remote Sensing and Geographic Information System, Peking University, 5 Summer Palace Road, Beijing 100871, China
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Remote Sens. 2019, 11(22), 2620; https://doi.org/10.3390/rs11222620
Received: 30 September 2019 / Revised: 6 November 2019 / Accepted: 7 November 2019 / Published: 8 November 2019
The detailed information about the spatial distribution of the population is crucial for analyzing economic growth, environmental change, and natural disaster damage. Using the nighttime light (NTL) imagery for population estimation has been a topic of interest in recent decades. However, the effectiveness of NTL data in population estimation has been impeded by some limitations such as the blooming effect and underestimation in rural regions. To overcome these limitations, we combine the NPP-VIIRS day/night band (DNB) data with normalized difference vegetation index (NDVI) and land surface temperature (LST) data derived from the moderate resolution imaging spectroradiometer (MODIS) onboard the Terra satellite, to create a new vegetation temperature light population index (VTLPI). A statistical model is developed to predict 250m grid-level population density based on the proposed VTLPI and the least square regression approach. After that, a case study is implemented using the data of Sichuan Province, China in 2015, and the results indicates that the VTLPI-estimated population density outperformed the results from other two methods based on nighttime light imagery or human settlement index, and the three publicized population products, LandScan, WorldPop, and GPW. When using the census data as reference, the mean relative error and median absolute relative error on a township level are 0.29 and 0.12, respectively, and the root-mean-square error is 212 persons/km2. The results show that our VTLPI-based model can achieve a better estimation of population density in rural areas and urban suburbs and characterize more spatial variations at 250m grid level both in both urban and rural areas. The resultant population density offers better population exposure data for assessing natural disaster risk and loss as well as other related applications. View Full-Text
Keywords: population density; spatial distribution; nighttime light imagery; multi-sensor; vegetation temperature light population index population density; spatial distribution; nighttime light imagery; multi-sensor; vegetation temperature light population index
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MDPI and ACS Style

Luo, P.; Zhang, X.; Cheng, J.; Sun, Q. Modeling Population Density Using a New Index Derived from Multi-Sensor Image Data. Remote Sens. 2019, 11, 2620.

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