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Rapid Remote Sensing Assessment of Impacts from Hurricane Maria on Forests of Puerto Rico
Published: March 01, 2018
Posted: June 26, 2018

Scientists can now provide the forest disturbance map and mortality estimation in a short period after the hurricanes.

The Science
Hurricane Maria made landfall as a strong Category 4 storm in southeast Puerto Rico on September 20, 2018. The powerful storm traversed the island in a northwesterly direction causing widespread destruction. Dramatic changes in forest structure across the entire island were evident from pre- and post-Maria composited Landsat 8 images. A non-photosynthetic vegetation (ΔNPV) map for only the forested pixels illustrated significant spatial variability in disturbance, with emergent patterns associated with factors such as slope, aspect, and elevation. An initial order-of-magnitude impact estimate based on remote sensing and previous field work indicated that Hurricane Maria may have caused mortality and severe damage to 23 to 31 million trees. Additional field work and image analyses are required to further detail the impact of Hurricane Maria to Puerto Rico forests.

The Impact
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.

Summary
Cyclonic storms represent a dominant natural disturbance in temperate and tropical forests in coastal regions of North and Central America. More recently, satellite remote sensing approaches have enabled the spatially explicit mapping of disturbance impacts on forested ecosystems, providing additional insights into the factors of storms. The team generated calibrated and corrected Landsat 8 image composites for the entire island using Google Earth Engine for a comparable pre-Maria and post-Maria time period that accounted for phenology. They carried out spectral mixture analysis (SMA) using image-derived endmembers on both composites to calculate the change in the 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. They produced a ΔNPV map for only the forested pixels illustrated significant spatial variability in disturbance, with emergent patterns associated with factors such as slope, aspect, and elevation. They also conducted hurricane simulations using the Weather Research and Forecasting (WRF) regional climate model to estimate wind speeds associated with forest disturbance.

Contacts
BER Program Managers
Daniel Stover
Terrestrial Ecosystem Science, SC-23.1
daniel.stover@science.doe.gov, 301-903-0289

Dorothy Koch
SC-23.1
Dorothy.koch@science.doe.gov (301-903-0105)

Principal Investigator
Jeffrey Chambers
Lawrence Berkeley National Laboratory
jchambers@lbl.gov, 510-495-2932

Lead Author Contact
Yanlei Feng
University of California, Berkeley
Lawrence Berkeley National Laboratory
Berkeley, CA 94720
yanleifeng@lbl.gov

Funding
This research was supported by the Office of Biological and Environmental Research, within the U.S. Department of Energy (DOE) Office of Science, under Contract No. DE-AC02-05CH11231, as part of the Next-Generation Ecosystem Experiments (NGEE)–Tropics project and the Regional and Global Climate Modeling Program. Resources were used from the National Energy Research Scientific Computing Center (NERSC), also supported by the DOE Office of Science under Contract No. DE-AC02-05CH11231.

Publications
Feng, Y., R.I. Negron-Juarez, C.M. Patricola, W.D. Collins, M. Uriarte, J.S. Hall, N. Clinton, and J.Q. Chambers. “Rapid remote sensing assessment of impacts from Hurricane Maria on forests of Puerto Rico.” PeerJ Preprints 6, e26597v1 (2018). [DOI:10.7287/peerj.preprints.26597v1]

Related Links

Topic Areas:

  • Research Area: Earth and Environmental Systems Modeling
  • Research Area: Next-Generation Ecosystem Experiments (NGEE)

Division: SC-33.1 Earth and Environmental Sciences Division, BER

 

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