NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies

Abstract

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.

Using Nighttime Light Data and POI Big Data to Detect the Urban Centers of Hangzhou

“…Sensors on a satellite can capture the brightness of cities, farms, industrial areas, fishing vessel lights, forest fires, and other human activity areas at night and form a nighttime light image [6,7]. Nighttime light data can make up for the deficiency of statistical data for urban research in some respects, and can be applied to studies related to human activities due to the strong correlation between human activities and the lightmaps of population, GDP, or power consumption [8].At present, nighttime light data are widely used in research on urban expansion [9], urban morphology and structure [10,11], estimation of socioeconomic status [12][13][14][15], fisheries [16,17], and energy [18,19]. It has been found that population distributions and light intensities have significant correlations [20].…”

“…At present, nighttime light data are widely used in research on urban expansion [9], urban morphology and structure [10,11], estimation of socioeconomic status [12][13][14][15], fisheries [16,17], and energy [18,19]. It has been found that population distributions and light intensities have significant correlations [20].…”

Preliminary Results from a Wildfire Detection System Using Deep Learning on Remote Camera Images

“…Lower altitude (500-2000 km) orbiting satellites have finer resolution that makes it easier to detect fires in their early phases, but they take several hours to days to return to the same view. For example, VIIRS has a 12 h revisit time, together the two MODIS satellites cover the Earth four times per day, and Landsat-8 has a 16 day revisit time [16][17][18]. It is rare, but has happened, that one of these satellites provides the first notification of a fire because the fire would have to start just as the satellites happens to be passing overhead.…”

Rapid Assessment of a Typhoon Disaster Based on NPP-VIIRS DNB Daily Data: The Case of an Urban Agglomeration along Western Taiwan Straits, China

“…Wang et al [38] used the National Aeronautics and Space Administration (NASA) Black Marble nighttime data (a type of high-quality NTL data from NPP-VIIRS DNB) and PNL to assess disaster-related power outages. Zhao et al [39] evaluated the power recovery after a hurricane disaster based on NPP-VIIRS images. In the Hurricane Hudhud incident, NPP-VIIRS image-based estimations of the proportion of users without power were consistent with the reported power-outage situation, showing that the NPP-VIIRS DNB daily data were effective for detecting damage and power outage caused by the storm.…”

“…In the Hurricane Hudhud incident, NPP-VIIRS image-based estimations of the proportion of users without power were consistent with the reported power-outage situation, showing that the NPP-VIIRS DNB daily data were effective for detecting damage and power outage caused by the storm. In summary, the most commonly used methods for detecting changes in NTL remote-sensing images include pixel-based image comparisons before and after events [33,35,37,39] and region-based time-series analyses [32,38].…”