Disaster-Caused Power Outage Detection at Night Using VIIRS DNB Images

Abstract

Rapid disaster assessment is critical for public security and rescue. As a secondary disaster of large-scale meteorological disasters, power outages cause severe outcomes and thus need to be monitored efficiently and without being costly. Power outage detection from space-borne remote sensing imagery offers a broader coverage and is more temporally sensitive than ground-based surveys are. However, it is challenging to determine the affected area accurately and quantitatively evaluate its severity. Therefore, a new method is proposed to solve the above problems by building a power outage detection model (PODM) and drawing a power outage spatial distribution map (POSDM). This paper takes the winter storm Uri, of 2021, as the meteorological disaster background and Harris County, Texas, which was seriously affected, as the research object. The proposed method utilises the cloud-free VIIRS DNB nadir and close nadir images (<60 degrees) collected during the 3 months before and 15 days after Uri. The core idea beneath the proposed method is to compare the radiance difference in the affected area before and after the disaster, and a large difference in radiance indicates the happening of power outages. The raw radiance of night light measurement is first corrected to remove lunar and atmospheric effects to improve accuracy. Then, the maximum and minimum pixels in the target area of the image are considered outliers and iteratively eliminated until the standard deviation change before and after elimination is less than 1% to finalize the outlier removals. The case study results in Harris show that the PODM detects 28% of outages (including traffic area) compared to 17% of outages (living area only) reported by ground truth data, indicating general agreement with the proposed method.

Assessment of Wildfire Susceptibility and Wildfire Threats to Ecological Environment and Urban Development Based on GIS and Multi-Source Data: A Case Study of Guilin, China

“…The use of nighttime remote sensing data has been widely applied to assess urban development levels, estimate population and GPD, among other fields. A higher night-time light DN (NTLDN) indicates a higher level of urban development in the area [58]. The acquisition and processing of nighttime light data was performed on the GEE platform by using the "NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG" function to select the "avg_rad" band to obtain the 2022 NTLDN with a resolution of 463.83 m. Despite the product being subjected to stray light correction, there still exists noise interference from unstable light sources and stray light [59].…”

A Cost-Effective Earthquake Disaster Assessment Model for Power Systems Based on Nighttime Light Information

“…Under this circumstance, as a secondary disaster of large-scale disasters, power damages cause severe outcomes and thus need to be monitored efficiently and without being costly [14]. Moreover, the correlation between city electricity power system and NTL has been verified in many studies [15][16][17] and used in disaster research [4,14,18].…”

“…Under this circumstance, as a secondary disaster of large-scale disasters, power damages cause severe outcomes and thus need to be monitored efficiently and without being costly [14]. Moreover, the correlation between city electricity power system and NTL has been verified in many studies [15][16][17] and used in disaster research [4,14,18]. For instance, NTL data have been used to model the earthquake effects on the local economies [19,20], access economic loss and recovery after natural disaster [21], detect real-time flood [22] and identify fires and power outages [23].…”