Dr. Lei Zou from the GEAR Lab recently published a collaborative article in the Journal of Remote Sensing to develop a novel method to derive critical disaster indicators (CDI) from time-series nighttime light remote sensing images for emergency risk management and disaster recovery efforts. The article is led by Ms. Weiying Lin from Department of Geography, Texas A&M University. The details of this publication can be found below:
Lin, Weiying, et al. "Critical Disaster Indicators (CDIs): Deriving the Duration, Damage Degree, and Recovery Level from Nighttime Light Image Time Series." Remote Sensing 15.23 (2023): 5471. https://doi.org/10.3390/rs15235471.
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.
Figure 2. The analytical framework in this study.
Figure 3. (A) The AVD-estimated tract-level degree of damage; (B) hurricane threat based on
tract-level averaged kernel density.
Congratulations! We look forward to hearing more achievements from GEAR Lab! Gig'em!
