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Shining a Light on Hurricane Damage Estimation via Nighttime Light Data: Pre-processing Matters

Nancy Thomas, Saba Rahimi, Annita Vapsi, Cathy Ansell, Elizabeth Christie, Daniel Borrajo, Tucker Balch, Manuela Veloso

Abstract

Amidst escalating climate change, hurricanes are inflicting severe socioeconomic impacts, marked by heightened economic losses and increased displacement. Previous research utilized nighttime light data to predict the impact of hurricanes on economic losses. However, prior work did not provide a thorough analysis of the impact of combining different techniques for pre-processing nighttime light (NTL) data. Addressing this gap, our research explores a variety of NTL pre-processing techniques, including value thresholding, built masking, and quality filtering and imputation, applied to two distinct datasets, VSC-NTL and VNP46A2, at the zip code level. Experiments evaluate the correlation of the denoised NTL data with economic damages of Category 4-5 hurricanes in Florida. They reveal that the quality masking and imputation technique applied to VNP46A2 show a substantial correlation with economic damage data.

Shining a Light on Hurricane Damage Estimation via Nighttime Light Data: Pre-processing Matters

Abstract

Amidst escalating climate change, hurricanes are inflicting severe socioeconomic impacts, marked by heightened economic losses and increased displacement. Previous research utilized nighttime light data to predict the impact of hurricanes on economic losses. However, prior work did not provide a thorough analysis of the impact of combining different techniques for pre-processing nighttime light (NTL) data. Addressing this gap, our research explores a variety of NTL pre-processing techniques, including value thresholding, built masking, and quality filtering and imputation, applied to two distinct datasets, VSC-NTL and VNP46A2, at the zip code level. Experiments evaluate the correlation of the denoised NTL data with economic damages of Category 4-5 hurricanes in Florida. They reveal that the quality masking and imputation technique applied to VNP46A2 show a substantial correlation with economic damage data.

Paper Structure

This paper contains 16 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Data
  3. Hurricane Damage Data
  4. Nighttime Lights
  5. VSC-NTL
  6. VNP46A2
  7. Dynamic World
  8. Methods
  9. Data Extraction
  10. Data pre-processing
  11. Value Thresholding
  12. Built Masking
  13. Quality Filtering and Imputation
  14. Results
  15. Discussion
  16. ...and 1 more sections

Figures (2)

  • Figure 1: The DNB Radiance variations for two datasets in a Florida Area: Pre-, During, and Post-Hurricane Michael.
  • Figure 2: Monthly percentage change in average NTL from October 2017 to 2019 for Hurricane Michael's high/low impacted zip codes. The event date is shown with a vertical line.