The accurate and efficient extraction of urban areas is of great significance for better understanding of urban sprawl, built environment, economic activities, and population distribution. Night-Time Light (NTL) data have been widely used to extract urban areas. However, most of the existing NTL indexes are incapable of identifying non-luminous built-up areas. The high-resolution NTL imagery derived from the Luojia 1-01 satellite, with low saturation and the blooming effect, can be used to map urban areas at a finer scale. A new urban spectral index, named the Modified Normalized Urban Areas Composite Index (MNUACI), improved upon the existing Normalized Urban Areas Composite Index (NUACI), was proposed in this study, which integrated the Human Settlement Index (HSI) generated from Luojia 1-01 NTL data, the Normalized Difference Vegetation Index (NDVI) from Landsat 8 imagery, and the Modified Normalized Difference Water Index (MNDWI). Our results indicated that MNUACI improved the spatial variability and differentiation of urban components by eliminating the NTL blooming effect and increasing the variation of the nighttime luminosity. Compared to urban area classification from Landsat 8 data, the MNUACI yielded better accuracy than NTL, NUACI, HSI, and the EVI-Adjusted NTL Index (EANTLI) alone. Furthermore, the quadratic polynomial regression analysis showed the model based on MNUACI had the best R² and Root-Mean Square Error (RMSE) compared with NTL, NUACI, HSI, and EANTLI in terms of estimation of impervious surface area. It is concluded that MNUACI could improve the identification of urban areas and non-luminous built-up areas with better accuracy.

The COVID-19 pandemic has presented unprecedented disruptions to human society worldwide since late 2019, and lockdown policies in response to the pandemic have directly and drastically decreased human socioeconomic activities. To quantify and assess the extent of the pandemic’s impact on the economy of Hebei Province, China, nighttime light (NTL) data, vegetation information, and provincial quarterly gross domestic product (GDP) data were jointly utilized to estimate the quarterly GDP for prefecture-level cities and county-level cities. Next, an autoregressive integrated moving average model (ARIMA) model was applied to predict the quarterly GDP for 2020 and 2021. Finally, economic recovery intensity (ERI) was used to assess the extent of economic recovery in Hebei Province during the pandemic. The results show that, at the provincial level, the economy of Hebei Province had not yet recovered; at the prefectural and county levels, three prefectures and forty counties were still struggling to restore their economies by the end of 2021, even though these economies, as a whole, were gradually recovering. In addition, the number of new infected cases correlated positively with the urban NTL during the pandemic period, but not during the post-pandemic period. The study results are informative for local government’s strategies and policies for allocating financial resources for urban economic recovery in the short- and long-term.

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.

Although urban expansion statistics have been widely carried out, large-scale and rapid monitoring is still worth doing in order to improve the efficiency of statistics, as well as make up for the omissions and deficiencies of construction expansion statistics with multi-year intervals. This paper presents a study of urban expansion in the Beijing–Tianjin–Hebei plain based on ALOS Global Digital Surface Model “ALOS World 3D-30 m” (AW3D30 DSM), Shuttle Radar Topography Mission (SRTM) DSM, and Landsat 7 ETM+ images. Through the evaluation of errors and the elimination of non-building changes, a relatively objective result is derived. The neighborhood block statistics of the construction height expansion reveal that from 2000 to 2009, the largest centralized construction expansion mainly occurred between the Second Ring Road and the Fifth Ring Road of Beijing, followed by Yizhuang, Shunyi, Tianjin Central City, and Langfang. Zonal statistics also show a significant imbalance in the expansion of construction in the counties of the Beijing–Tianjin–Hebei plain. For example, Chaoyang, Dongcheng, Xicheng, Xuanwu, Chongwen, Nankai, Heping, and Hexi have a larger construction expansion; however, other counties present a relatively slow rate of building expansion. Furthermore, the correlation coefficient between the statistical average building height expansion per unit area (ABHE, by our method) and the actual average completed building floor space per unit area (ACBFS) derived from the Beijing Statistical Yearbook (BSY) is 0.9436, which proves that this method is feasible. With the continuous improvement of DSM data quality in the future, the method proposed in this paper can provide rapid and large-scale statistics to study more urban construction expansion in the world.

1. Since the emergence of the coronavirus disease 2019 (COVID-19) in December 2019, the Chinese government has taken decisive action by implementing rigorous measures, including lockdowns, quaranti...