Rapid remote sensing assessment of impacts from Hurricane Maria on forests of Puerto Rico

Department of Geography, University of California, Berkeley, Berkeley, CA, United States
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
Department of Ecology, Evolution & Environmental Biology, Columbia University, New York, NY, United States
Google, Mountain View, CA, United States
Subject Areas
Biodiversity, Ecology, Ecosystem Science, Climate Change Biology, Environmental Impacts
carbon, biomass, disturbance, tree mortality, extreme events, climate change, tropical cyclone, remote sensing, tropical forest
© 2018 Feng et al.
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Feng Y, Negron-Juarez RI, Patricola CM, Collins WD, Uriarte M, Hall JS, Clinton N, Chambers JQ. 2018. Rapid remote sensing assessment of impacts from Hurricane Maria on forests of Puerto Rico. PeerJ Preprints 6:e26597v1


Hurricane Maria made landfall as a strong Category 4 storm in southeast Puerto Rico on September 20th, 2018. The powerful storm traversed the island in a northwesterly direction causing widespread destruction. This study focused on a rapid assessment of Hurricane Maria’s impact to Puerto Rico’s forests. Calibrated and corrected Landsat 8 image composites for the entire island were generated using Google Earth Engine for a comparable pre-Maria and post-Maria time period that accounted for phenology. Spectral mixture analysis (SMA) using image-derived endmembers was carried out on both composites to calculate the change in the non-photosynthetic vegetation (ΔNPV) spectral response, a metric that quantifies the increased fraction of exposed wood and surface litter associated with tree mortality and crown damage from the storm. Hurricane simulations were also conducted using the Weather Research and Forecasting (WRF) regional climate model to estimate wind speeds associated with forest disturbance. Dramatic changes in forest structure across the entire island were evident from pre- and post-Maria composited Landsat 8 images. A ΔNPV map for only the forested pixels illustrated significant spatial variability in disturbance, with patterns that associated with factors such as slope, aspect and elevation. An initial order-of-magnitude impact estimate based on previous work indicated that Hurricane Maria may have caused mortality and severe damage to 23-31 million trees. Additional field work and image analyses are required to further detail the impact of Hurricane Maria to Puerto Rico forests.

Author Comment

The analyses and results from this work represent a rapid response capability following natural disasters impacting forested ecosystems. Datasets are publicly available, and a set of user interface tools is being developed for a variety of stakeholder end uses. These tools and resources will be further developed, and provided on the NGEE-Tropics website (https://ngee-tropics.lbl.gov/).