Critical Disaster Indicators (CDIs): Deriving the Duration, Damage Degree, and Recovery Level from Nighttime Light Image Time Series

Abstract

Deriving timely natural disaster information is critical in emergency risk management and disaster recovery efforts. Due to the limitation of data availability, such information is difficult to obtain in a timely manner. In this research, VIIRS nighttime light (NTL) image time series from January 2014 to July 2019 were employed to reflect key changes between pre- and post-disasters. The Automated Valley Detection (AVD) model was proposed and applied to derive critical disaster indicators in the 2017 Hurricane Maria event in Puerto Rico. Critical disaster indicators include outage duration, damage degree, and recovery level. Two major findings can be concluded. First, the AVD model is a robust and useful approach to detecting sudden changes in NTL in terms of their location and duration at the census tract level. Second, the AVD-estimated disaster metrics are able to capture disaster information successfully and match with two types of reference data. These findings will be valuable for emergency planning and the energy industry to monitor and restore power outages in future natural disasters.

Keywords

• VIIRS Day/Night Band (DNB)
• nighttime remote sensing
• power outage detection
• electricity recovery
• natural hazards