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arXiv · 1807.05854

Assessment of electrical and infrastructure recovery in Puerto Rico following hurricane Maria using a multisource time series of satellite imagery

Jacob Shermeyer
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Abstract

Puerto Rico suffered severe damage from the category 5 hurricane (Maria) in September 2017. Total monetary damages are estimated to be ~92 billion USD, the third most costly tropical cyclone in US history. The response to this damage has been tempered and slow moving, with recent estimates placing 45% of the population without power three months after the storm. Consequently, we developed a unique data-fusion mapping approach called the Urban Development Index (UDI) and new open source tool, Comet Time Series (CometTS), to analyze the recovery of electricity and infrastructure in Puerto Rico. Our approach incorporates a combination of time series visualizations and change detection mapping to create depictions of power or infrastructure loss. It also provides a unique independent assessment of areas that are still struggling to recover. For this workflow, our time series approach combines nighttime imagery from the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP VIIRS), multispectral imagery from two Landsat satellites, US Census data, and crowd-sourced building footprint labels. Based upon our approach we can identify and evaluate: 1) the recovery of electrical power compared to pre-storm levels, 2) the location of potentially damaged infrastructure that has yet to recover from the storm, and 3) the number of persons without power over time. As of May 31, 2018, declined levels of observed brightness across the island indicate that 13.9% +/- ~5.6% of persons still lack power and/or that 13.2% +/- ~5.3% of infrastructure has been lost. In comparison, the Puerto Rico Electric Power Authority states that less than 1% of their customers still are without power.

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Jacob Shermeyer. 2018-07-16. Assessment of electrical and infrastructure recovery in Puerto Rico following hurricane Maria using a multisource time series of satellite imagery. https://doi.org/10.1117/12.2325585

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