Whereas monthly and annual nighttime light (NTL) composite datasets are being increasingly used to estimate socioeconomic status, use of the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) daily data has been limited for detecting and assessing the impact of short-term disastrous events. This study explores the application of daily NPP-VIIRS DNB data in assessing the impact of three types of natural disasters: earthquakes, floods, and storms. Daily DNB images one month prior to and 10 days after a disastrous event were collected and a Percent of Normal Light (PNL) image was produced as the ratio of the mean DNB radiance of the pre- and post-disaster images. Areas with a PNL value lower than one were considered as being affected by the event. The results were compared with the damaged proxy map and the flood proxy map generated using synthetic aperture radar data as well as the reported power outage rates. Our analyses show that overall NPP-VIIRS DNB daily data are useful for detecting damages and power outages caused by earthquake, storm, and flood events. Cloud coverage was identified as a major limitation in using the DNB daily data; rescue activities, traffic, and socioeconomic status of the areas also affect the use of DNB daily data in assessing the impact of natural disasters. Our findings offer new insight into the use of the daily DNB data and provide a practical guide for researchers and practitioners who may consider using such data in different situations or regions.

The nighttime light (NTL) remote-sensing data have been widely applied in several applications for analyzing the urbanization process. The relationship between NTL intensity and human activity becomes a solid foundation for the applications using NTL data. However, there is no research, so far, revealing how the human activity seasonality could impact the seasonal change of NTL intensity. In this paper, a comparative analysis, box plot, and random forest algorithm were applied to NTL remote-sensing data and points of interest (POIs) data within Shanghai, China. The results show that in spring and autumn, the NTL is much brighter than that in summer and winter, especially within high human activity density area. The NTL intensity can be partly (approximately 40%) explained as the joint effects of the five POI categories. By analyzing the contributions of each POI category to NTL intensity, we found that the National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) could be used to dig more information about gross domestic product (GDP) and traffic-based applications with consideration of NTL seasonality.

Urban development status is closely related to the urban economy, environment, ecology, and health. Spatial and socioeconomic processes are the two key aspects of urban development, so the absence of any of them will affect the assessment of urban development status. In this study, using both spatial and socioeconomic information from land cover data and nighttime light data, respectively, we proposed an exponential model, Spatial–Socioeconomic Urban Development Curve (SSUDC), to provide a quantitative expression of the relationship between the two key processes of urban development and analyze urban development status. The SSUDC was calculated from the artificial surface ratio at 1% intervals obtained from Globeland30 land cover data and the corresponding average NPP-VIIRS nighttime light radiance data, using a nonlinear least-squares method. We generated SSUDCs for 330 prefecture-level cities in Mainland China, 208 of which had coefficients of determination (R²) greater than 0.6. Taking Ordos and Guiyang as two typical examples, we analyzed the importance and advantages of SSUDC. The coefficients α and β of the exponential SSUDC were shown to indicate the base intensity socioeconomic activity and the concentration of socioeconomic activities, respectively, and can be used to reveal the urban socioeconomic development status and functional type of cities. At the internal urban level, the residuals of SSUDC can imply the demand for urban physical or economic construction in different areas of the city, and even the urban growth type, together with the distribution of the artificial surface ratio. In summary, the proposed SSUDC provides a simple way to combine the spatial and socioeconomic processes of urban development, which is beneficial to the analysis of urban development at different scales and a rewarding tool for urban planning.

As spatial and socioeconomic processes are the two key aspects of urban development, revealing the relationship between these two key aspects is critical. Previous studies attempted to explain their correlation at the city or region level using built-up area metrics and nighttime light (NTL) data. However, more comprehensive studies on urban interior spatial characteristics and their relationship to NTL intensity are lacking in a three-dimension space. Using Luojia 1-01 nighttime light data, LiDAR digital surface model data, and other auxiliary data, this study applies an extreme gradient boosting regression model and Sharpley Additive exPlanations method to model and interpret the relationship between two-dimensional (2-D)/3-D landscape patterns and NTL intensity. Two study areas were selected to investigate the landscape–NTL relationship at the parcel and subdistrict levels. The major findings of this study include the following: 1) 2-D and 3-D urban landscape patterns have a close relationship with NTL intensity at the parcel and subdistrict scales; 2) the combinational metric of 2-D and 3-D landscape patterns has a stronger relationship with NTL intensity than either the 2-D or 3-D landscape metrics alone; 3) the correlations between most landscape metrics and NTL intensity are not simply positive or negative but change as metrics grow; and 4) the urban socioeconomic level is not only related to a single landscape metric sometimes but tends to the result of metrics interaction. These findings may help urban planners and government officials make more reasonable urban landscape planning policies under the goal of sustainable development.

An urban spatial cluster (USC) describes one or more geographic agglomerations and the linkages among cities. USCs are conventionally delineated based on predefined administrative boundaries of cities, without considering the dynamic and evolving nature of the spatial extent of USCs. This study uses Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light (NTL) satellite images to quantitatively detect and characterize the evolution of USCs. We propose a dynamic minimum spanning tree (DMST) and a subgraph partitioning method to identify the evolving USCs over time, which considers both the spatial proximity of urban built-up areas and their affiliations with USCs at the previous snapshot. China is selected as a case study for its rapid urbanization process and the cluster-based economic development strategy. Four DMSTs are generated for China using the urban built-up areas extracted from DMSP/OLS NTL satellite images collected in 2000, 2004, 2008, and 2012. Each DMST is partitioned into various subtrees and the urban built-up areas connected by the same subtree are identified as a potential USC. By inspecting the evolution of USCs over time, three different types of USCs are obtained, including newly emerging, single-core, and multicore clusters. Using the rank-size distribution, we find that large-sized USCs have greater development than medium- and small-sized USCs. A clear directionality and heterogeneity are observed in the expansions of the ten largest USCs. Our study provides further insight for the understanding of urban system and its spatial structures, and assists policymakers in their planning practices at national and regional scales.