Abstract
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.
Highlights
Population is one of the most explicit indicators for characterizing human activities, and population data are extensively used in the fields of ecological and environmental modeling, urban planning, public health assessment, and resource development [1,2,3,4]
Both vegetation temperature light population index (VTLPI) and human settlement index (HSI) use vegetation index (VI) as an essential variable for population estimation, in which the premise is that vegetation coverage and normalized difference vegetation index (NDVI) values are much smaller in towns and villages, but the NDVI values in human settlements in arid regions such as deserts and oasis are even larger than the bare lands
This study presented a robust mapping approach for population distribution based on multi-sensor remote sensing data
Summary
Population is one of the most explicit indicators for characterizing human activities, and population data are extensively used in the fields of ecological and environmental modeling, urban planning, public health assessment, and resource development [1,2,3,4]. Accurate and detailed information of the spatial patterns of population density is an important part of disaster risk management [1,5], and spatial superposition analysis of the strength of natural hazard factors such as seismic intensity and the elements at risk such as population exposure is a key step in risk assessment of various natural disasters [5]. It is necessary for emergency rescue and disaster relief to access quality data of population exposure in disaster situations. Due to the change of unit boundaries among different census years, the temporal inconsistency problem may exist, which impedes demographic temporal comparison and analysis [12,13,14]
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