Black Marble Nighttime Light Data for Disaster Damage Assessment

Author:

Zhang Danrong1ORCID,Huang Huili1ORCID,Roy Nimisha2ORCID,Roozbahani M. Mahdi2ORCID,Frost J. David3ORCID

Affiliation:

1. School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA

2. School of Computing Instruction, Georgia Institute of Technology, Atlanta, GA 30332, USA

3. School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA

Abstract

This research explores the utilization of the Black Marble nighttime light (NTL) product to detect and assess damage caused by hurricanes, tornadoes, and earthquakes. The study first examines average regional NTL trends before and after each disaster, demonstrating that NTL patterns for hurricanes closely align with the features of a resilience curve, unlike those for earthquakes and tornadoes. The relative NTL change ratio is computed using monthly and daily NTL data, effectively reducing variance due to daily fluctuations. Results indicate the robustness of the NTL change ratio in detecting hurricane damage, whereas its performance in earthquake and tornado assessment was inconsistent and inadequate. Furthermore, NTL demonstrates a high performance in identifying hurricane damage in well-lit areas and the potential to detect damage along tornado paths. However, a low correlation between the NTL change ratio and the degree of damage highlights the method’s limitation in quantifying damage. Overall, the study offers a promising, prompt approach for detecting damaged/undamaged areas, with specific relevance to hurricane reconnaissance, and points to avenues for further refinement and investigation.

Funder

US National Science Foundation

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Reference47 articles.

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3. (2022, December 01). Nighttime Lights: Backgrounder on VIIRS Day/Night Band and Its Application, Available online: https://www.earthdata.nasa.gov/learn/backgrounders/nighttime-lights.

4. Zhao, X., Yu, B., Liu, Y., Yao, S., Lian, T., Chen, L., Yang, C., Chen, Z., and Wu, J. (2018). NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies. Remote Sens., 10.

5. Detecting Disasters and Disaster Recovery in Southeast Asia: Findings from Space;Feeny;Nat. Hazards Rev.,2022

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