Open AccessUsing remote sensing data in population density estimation
Author(s) -
E. G. Shvetsov,
Nadezhda M. Tchebakova,
E. I. Parfenova
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.25743/sdm.2021.34.56.066
Subject(s) - population , human settlement , population density , census , geography , spatial distribution , index (typography) , vegetation (pathology) , environmental science , remote sensing , geographic information system , statistics , physical geography , mathematics , computer science , demography , medicine , archaeology , pathology , sociology , world wide web
In recent decades, remote sensing methods have often been used to estimate population density, especially using data on nighttime illumination. Information about the spatial distribution of the population is important for understanding the dynamics of cities and analyzing various socio-economic, environmental and political factors. In this work, we have formed layers of the nighttime light index, surface temperature and vegetation index according to the SNPP/VIIRS satellite system for the territory of the central and southern regions of the Krasnoyarsk krai. Using these data, we have calculated VTLPI (vegetation temperature light population index) for the year 2013. The obtained values of the VTLPI calculated for a number of settlements of the Krasnoyarsk krai were compared with the results of the population census conducted in 2010. In total, we used census data for 40 settlements. Analysis of the data showed that the relationship between the value of the VTLPI index and the population density in the Krasnoyarsk krai can be adequately fitted (R 2 = 0.65) using a linear function. In this case, the value of the root-meansquare error was 345, and the relative error was 0.09. Using the obtained model equation and the spatial distribution of the VTLPI index using GIS tools, the distribution of the population over the study area was estimated with a spatial resolution of 500 meters. According to the obtained model and the VTLPI index, the average urban population density in the study area exceeded 500 people/km2 . Comparison of the obtained data on the total population in the study area showed that the estimate based on the VTLPI index is about 21% higher than the actual census data.