Remote sensing for natural disaster recovery: Lessons learned from Hurricanes Irma and Maria in Puerto Rico

Abstract

In September 2017, Hurricanes Irma and Maria devastated the Commonwealth of Puerto Rico. Following these hurricanes, comprehensive and timely data collection was, and continues to be, required to assess both the severity of damage across Puerto Rico and to inform recovery and mitigation strategies. In this manuscript, we present how remote sensing data was incorporated into this assessment and planning process, focusing on the applications for Puerto Rico’s natural resources in the months following the hurricanes. We first describe how different types of satellite and airborne remote sensing data, along with existing and newly developed data processing methodologies, were applied to the damage assessment and recovery planning process for three natural resource applications: terrestrial forests, landslides, and coastal systems. We show that while remote sensing data offered a critical first assessment of the damage caused by the hurricanes, it was not always easily integrated into the recovery planning process and the variable timelines required by decisionmakers. Remote sensing data remains a powerful, if sometimes underutilized, tool in immediate and long-term disaster recovery efforts, and we conclude by suggesting future areas for improvement to facilitate the integration into natural disaster planning, assessment, and response.