Post-disaster recovery assessment and driving factors of the 2013 Lushan Earthquake affected area based on multi-temporal nighttime light data

Although severe earthquakes continue to challenge the resilience of local communities, fine-scale knowledge of post-earthquake recovery remains scarce. Nighttime light data facilitate the continuous monitoring and assessment of post-earthquake impacts and recovery through multi-temporal scales, thereby providing crucial scientific support for disaster management and the formulation of mitigation strategies. In this study, we applied spatiotemporal analysis to evaluate the post-disaster recovery process in the 2013 Lushan Earthquake affected area. We further explored the driving factors of the nighttime light changes after the earthquake and examined the natural and social characteristics underlying the post-disaster recovery process. The relationship between nighttime-light changes and the post-disaster recovery process varied across different temporal scales. Interday nighttime lighting provided a near-real-time reflection of earthquake disaster impacts. Intermonthly changes primarily reflected mid-term recovery progress, revealing two distinct recovery patterns: urban and rural. Interannual changes provided a macro-level overview of recovery across disaster zones, identifying three distinct recovery patterns: non-hit townships, hit townships, and main urban areas. The trend in light intensity over disaster-affected areas showed significant consistency with changes in the regional gross domestic product (GDP). Based on Pearson's correlation analysis, the correlation coefficient between the two at the county level reached 0.857, indicating that nighttime light data can accurately reflect the spatial dynamics of economic activity. Natural factors including elevation and social factors, such as the distribution of construction land, had a significant impact on light variations. Furthermore, we established a multi-timescale post-earthquake recovery analysis framework, which clarifies the correspondence between night-time lights and recovery processes across different scales. It also provides a quantitative representation of socioeconomic recovery and distinguishes the roles of key influencing factors. This study provides methodological foundations and empirical references for precise post-disaster assessments and optimized recovery strategies.