The catastrophic twin earthquakes that struck southern Turkey and northern Syria on 6 February 2023 caused massive casualties and extensive damage to infrastructure, with Hatay Province of Turkey bearing the brunt of the impact. To swiftly and thoroughly assess the damage caused by the earthquakes and the subsequent reconstruction efforts, this study initially investigated the application of light change ratios between the pre-earthquake monthly nighttime lights (NTLs) and the post-earthquake daily NTL data to identify earthquake damage in Hatay Province. Next, the monthly NTL data were employed to calculate the time series average lighting index (ALI). Subsequently, random noise and seasonal fluctuation were eliminated through data smoothing and seasonal decomposition techniques. Pre- and post-earthquake regression models were then utilised to establish a comprehensive framework for assessing economic recovery following the earthquake. The findings indicated that (1) the seismic damage identification method based on NTL data achieved an overall accuracy exceeding 71.55% in detecting building damage after a disaster. This method provided a swift and effective solution for rapidly assessing disaster-related destruction. (2) The reduced NTLs exhibited a strong correlation with the area of severely and moderately damaged buildings while showing a weaker correlation with areas of slightly damaged buildings. (3) The developed pre- and post-earthquake regression models demonstrated a high degree of fit, making them valuable tools for assessing regional economic recovery after the earthquake. At the county scale, such districts as Erzin and Kumlu exhibited promising signs of recovery, while such severely impacted areas as Antakya faced misconceptions of progress, primarily due to the brightening of NTLs caused by reconstruction efforts. Additionally, such districts as Dortyol and Samandag grappled with substantial short-term recovery challenges. Although the province experienced gradual economic recovery, achieving complete restoration has remained complex and time-intensive. The study offers valuable insights into earthquake damage assessment and economic recovery monitoring while serving as a scientific reference for disaster mitigation and post-disaster reconstruction efforts.

The COVID-19 pandemic, which spanned from 2020 to 2022, significantly influenced socio-economic activities in Shanghai, China. Analysing the recovery of the Shanghai economy from the effects of the pandemic is crucial for understanding broader trends in China's economic development. A novel light correction index was developed by integrating nighttime light (NTL) data, point of interest (POI), the enhanced vegetation index (EVI), and population data. This index was subsequently combined with provincial quarterly GDP data to estimate county-level GDP. Economic recovery frameworks were then established for all districts in Shanghai to comprehensively assess their performance in recovering from the pandemic. The findings indicate that the novel light index effectively quantifies the intensity of human socio-economic activities while mitigating the oversaturation and blooming effects associated with NTL data. The average coefficient of determination (R²) between the estimated county-level GDP and actual GDP is 0.801. Furthermore, the average R² for the developed economic recovery frameworks for the county-level districts before and after the pandemic is 0.948 and 0.748, respectively. These findings can serve as valuable indicators for assessing the economic recovery of Shanghai. It can be concluded that most districts in Shanghai demonstrated low economic resilience before and after the pandemic. Although the overall economy of Shanghai experienced growth, the growth rate was notably lower than the pre-pandemic level. As of the end of 2023, Shanghai's economy had not yet fully recovered to its pre-pandemic level.

Long-term monitoring of land cover dynamics is essential for evaluating conservation outcomes in World Heritage sites, yet remains challenging in conflict-affected regions with limited field observations. This study aims to reconstruct annual land cover dynamics and assess forest sustainability in the Okapi Wildlife Reserve (OWR), a World Heritage site in the Democratic Republic of Congo. Using Landsat dense time series observations (2000–2023), the Continuous Change Detection and Classification (CCDC) algorithm, landscape metrics, and the Sustainable Development Goal (SDG) 15.1.1 indicator, we developed an annual scale framework to quantify land cover changes and conservation status. The results showed that forest loss was the dominant land cover change process, with degradation accelerating after 2013. Forest disturbances were spatially heterogeneous, mainly concentrated in the boundary buffer zone and along National Road No. 4, while localized disturbances also occurred within the core protected area. Landscape fragmentation increased during the study period, and SDG 15.1.1 trends revealed differentiated conservation pressures among management zones. This study provides an annual-scale land cover history of the OWR and demonstrates the potential of Earth observation for monitoring endangered World Heritage sites.