The Changes in Nighttime Lights Caused by the Turkey–Syria Earthquake Using NOAA-20 VIIRS Day/Night Band Data

Abstract

The Turkey–Syria earthquake on 6 February 2023 resulted in losses such as casualties, road damage, and building collapses. We mapped and quantified the areas impacted by the earthquake at different distances and directions using NOAA-20 VIIRS nighttime light (NTL) data. We then explored the relationship between the average changes in the NTL intensity, population density, and building density using the bivariate local indicators of the spatial association (LISA) method. In Turkey, Hatay, Gaziantep, and Sanliurfa experienced the largest NTL losses. Ar Raqqah was the most affected city in Syria, with the highest NTL loss rate. A correlation analysis showed that the number of injured populations in the provinces in Turkey and the number of pixels with a decreased NTL intensity exhibited a linear correlation, with an R-squared value of 0.7395. Based on the changing value of the NTL, the areas with large NTL losses were located 50 km from the earthquake epicentre in the east-by-south and north-by-west directions and 130 km from the earthquake epicentre in the southwest direction. The large NTL increase areas were distributed 130 km from the earthquake epicentre in the north-by-west and north-by-east directions and 180 km from the earthquake epicentre in the northeast direction, indicating a high resilience and effective earthquake rescue. The areas with large NTL losses had large populations and building densities, particularly in the areas approximately 130 km from the earthquake epicentre in the south-by-west direction and within 40 km of the earthquake epicentre in the north-by-west direction, which can be seen from the low–high (L-H) pattern of the LISA results. Our findings provide insights for evaluating natural disasters and can help decision makers to plan post-disaster reconstruction and determine risk levels on a national or regional scale.

Spatiotemporal Evolution and Correlation Analysis of Carbon Emissions in the Nine Provinces along the Yellow River since the 21st Century Using Nighttime Light Data

“…Unlike daytime remote sensing, nighttime light remote sensing can reveal information that is not visible during the day [17,18]. Since most of the stable light at night comes from artificial sources in urban areas, remote sensing images of nighttime lights can more intuitively reflect differences in human activity at night [19][20][21][22]. Nighttime light data has become a new monitoring method with the advantages of large coverage, fast timeliness, and convenient access, making it suitable for multi-scale and long-term research on urban issues [23].…”