期刊
REMOTE SENSING
卷 13, 期 6, 页码 -出版社
MDPI
DOI: 10.3390/rs13061171
关键词
Saudi Arabia; Riyadh; population; nighttime; DMSP-OLS; NTL; land cover; use
类别
资金
- King Abdulaziz City for Science and Technology (KACST)
- Royal Commission for Riyadh City (RCRC)
- General Authority for Statistics (GASTAT)
This paper introduces a novel index called VBANTLI, which integrates DMSP-OLS data with vegetation and bare land areas to improve population mapping accuracy. Experimental results show that VBANTLI successfully reduces overglow and saturation effects, outperforming traditional indices.
Knowledge of the spatial pattern of the population is important. Census population data provide insufficient spatial information because they are released only for large geographic areas. Nighttime light (NTL) data have been utilized widely as an effective proxy for population mapping. However, the well-reported challenges of pixel overglow and saturation influence the applicability of the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) for accurate population mapping. This paper integrates three remotely sensed information sources, DMSP-OLS, vegetation, and bare land areas, to develop a novel index called the Vegetation-Bare Adjusted NTL Index (VBANTLI) to overcome the uncertainties in the DMSP-OLS data. The VBANTLI was applied to Riyadh province to downscale governorate-level census population for 2004 and 2010 to a gridded surface of 1 km resolution. The experimental results confirmed that the VBANTLI significantly reduced the overglow and saturation effects compared to widely applied indices such as the Human Settlement Index (HSI), Vegetation Adjusted Normalized Urban Index (VANUI), and radiance-calibrated NTL (RCNTL). The correlation coefficient between the census population and the RCNTL (R = 0.99) and VBANTLI (R = 0.98) was larger than for the HSI (R = 0.14) and VANUI (R = 0.81) products. In addition, Model 5 (VBANTLI) was the most accurate model with R-2 and mean relative error (MRE) values of 0.95% and 37%, respectively.
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