Xi Li

The armed conflict in Sudan, spanning from April 2023 to the present, has escalated into a severe humanitarian disaster. Night-time light data can serve as a valuable proxy for monitoring the humanitarian conditions in conflict-affected area where ground-based data collection is severely constrained. This study utilized the Black Marble monthly night-time light product (VNP46A3) to analyze the spatiotemporal dynamics of night-time light in Sudan during the conflict period, with particular focus on major conflict hotspots such as Khartoum State and the Darfur region. Additionally, we examined refugee movements from Sudan to Chad by analyzing night-time light changes along the Sudan-Chad border. Furthermore, we employed a two-way fixed effects (TWFE) model to quantify the relationship between night time light change (NLC), conflict frequency, and internally displaced persons (IDPs). The results indicate that night-time light in Sudan exhibited a sharp and persistent decline following the onset of the conflict, with nearly half of the states experiencing a light reduction exceeding 50%. Along the Sudan Chad border, night-time light decreased on the Sudanese side but increased on the Chadian side, suggesting an influx of Sudanese refugees into Chad. The TWFE model demonstrated that NLC has a negative correlation with both conflict frequency and the net outflow of IDPs. These findings can provide the international community with a deeper understanding of the evolving humanitarian situation in Sudan, and highlight the value of night-time light remote sensing in effectively capturing conflict induced socioeconomic disruption and population displacement.

Urban expansion is a key driver of land-use change and environmental pressure in rapidly urbanizing regions. Existing assessments of urban expansion often rely on predefined indicator systems and fixed weighting schemes, which limits their adaptability to evolving research priorities and regional contexts. This study develops an adaptive framework for urban expansion assessment by integrating a transformer-based language model with multi-source spatial data. A BERT-based semantic extraction process is used to identify relevant indicators and derive their relative weights from the scientific literature, enabling the construction of a literature-driven Urban Expansion Index (UEI). The framework is applied to the Central Plains Mega-city Region (CPMR), China, to examine spatial patterns and temporal dynamics of urban expansion between 2010 and 2020. Results show that UEI is primarily driven by land-use expansion indicators, while socioeconomic, infrastructure, and environmental indicators jointly reflect the multidimensional nature of expansion processes. Spatial patterns reveal a persistent concentration of high expansion intensity in core cities, alongside heterogeneous environmental responses and gradual outward growth. Changes in UEI display weaker spatial coherence than static levels, indicating differentiated local expansion dynamics. Local spatial autocorrelation analysis further identifies shifting clusters of urban expansion intensity, suggesting a reorganization of expansion centers within the agglomeration over time. By linking transformer-based indicator extraction with spatial analysis, this study advances urban expansion assessment beyond outcome-oriented mapping toward a more adaptive and knowledge-informed approach. The proposed framework is transferable to other mega-city regions and provides a useful tool for supporting territorial spatial planning and sustainable urban development.

The City Emission Function (CEF) characterizing the angular distribution patterns of urban nighttime light emissions, plays a decisive role in modeling the night sky radiant intensity. However, seldom studies have improved the CEF incorporating the vegetation phenological characteristics. The multi-angle, high-frequency nighttime light (NTL) observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) provide vital data support for estimating CEF coefficients. In this study, we developed a satellite-derived NTL model, which is a function of viewing zenith angle (VZA) and Normalized Difference Vegetation Index (NDVI). Based on this model, we performed angular normalization on Black Marble time series (2016–2018) for 11 deciduous vegetation-dominated study areas across North America. The proposed approach yields two main outcomes: (1) high-frequency pixel-level CEF coefficients, and (2) angular-normalized NTL time series, which were evaluated against near-nadir monthly composite products using two metrics, the coefficient of determination (R²) and the root mean square error (RMSE). The results demonstrated that (1) the magnitude of the normalized CEF tends to decrease with increasing VZA and increase with rising NDVI, (2) angular normalization reduced the impact of VZA on NTL temporal dynamics, and (3) the addition of vegetation characteristics improved the accuracy of angular normalization algorithms, yielding an average increase in R² of 0.028 and a decrease in RMSE of 0.166 nW/sr. This study provides an effective approach to estimating urban emitted lights using nighttime light remote sensing data, and is expected to provide insights into urban environmental monitoring and sustainable lighting management.

The Central Sahel region is beset by ongoing armed conflicts and confronts a critical humanitarian crisis. Nighttime light data can serve as a valuable, objective source for monitoring conflict crises. In this study, we quantified total nighttime light (TNL) and the change rate (CR) of TNL in the Central Sahel region and analyzed the correlation between TNL and the number of conflicts using a fixed-effects regression model. For large conflict events, such as the Malian civil war and the siege in Burkina Faso, we conducted more detailed local analyses using nighttime light data to assess the impact of the conflict. The results indicate that the number of conflicts in the region has been on the rise since 2012, and there is a significant negative correlation between TNL and the number of conflicts, with an R2 of 0.515. The Malian civil war in 2012 led to an 80% decline in TNL in Kidal and Timbuktu, accompanied by a massive refugee outflow to refugee camps in neighboring countries, where TNL gradually increased. In Burkina Faso, TNL in reported besieged cities has significantly decreased since 2019. Additionally, we identified four unreported suspected besieged cities: Namssiguia, Boala, Bourzanga, and Tougouri. It is anticipated that the findings of this study will enhance international community's understanding of the situation in the Central Sahel region and enable more effective assistance.

A comprehensive understanding of fine particulate matter (PM2.5) distribution is vital for addressing health concerns related to deteriorating air quality. While remotely sensed nighttime light observations have proven effective in monitoring PM _2.5 concentrations, their coarse resolution limits their ability to capture the fine-scale spatial variations within urban environments. To address this limitation, an improved spatial random forest (ISSRF) model was employed to estimate PM _2.5 concentrations using SDGSAT-1 Glimmer nighttime light data. The resultant PM _2.5 concentration maps, with a resolution of 300 meters, were generated for the winter of 2021 (R ² = 0.81) and cover four urban agglomerations (UAs) in China. Two population-weighted indicators were utilized to assess the nighttime population exposure to PM _2.5 . The findings suggest that population exposure to PM _2.5 is highest in the Beijing-Tianjin-Hebei UA (66.84 μg/m ³ ), followed by Chengdu-Chongqing (62.66 μg/m ³ ), Yangtze River Delta (52.04 μg/m ³ ), and Guangdong-Hong Kong-Macao Greater Bay Area (33.74 μg/m ³ ). Notably, the Chengdu-Chongqing UA exhibits the highest levels of exposure among children (≤ 5 years) and the elderly (≥ 65 years). These findings provide valuable insights for policymakers to prioritize pollution control strategies and measures.

The timely and accurate monitoring of high-seas fisheries is essential for effective management. However, efforts to monitor industry fishing vessels in the central-eastern North Pacific have been hampered by frequent cloud cover and solar illumination interference. In this study, enhanced fishing extraction algorithms based on computer vision were developed and tested. The results showed that YOLO-based computer vision models effectively detected dense small fishing targets, with original YOLOv8 achieving a precision (P) of 89% and a recall (R) of 79%, while refined versions improved these metrics to 93% and 99%, respectively. Compared with traditional threshold methods, the YOLO-based enhanced models showed significantly higher accuracy. While the threshold method could identify similar trend changes, it lacked precision in detecting individual targets, especially in blurry scenarios. Using our trained computer vision model, we established a dataset of dynamic changes in fishing vessels over the past decade. This research provides an accurate and reproducible process for precise monitoring of lit fisheries in the North Pacific, leveraging the operational and near-real-time capabilities of Google Earth Engine and computer vision. The approach can also be applied to dynamic monitoring of industrial lit fishing vessels in other regions.