Remote sensing and statistical analysis of the effects of hurricane María on the forests of Puerto Rico

Feng, Yanlei; Negrón-Juárez, Robinson I.; Chambers, Jeffrey Q.

doi:10.1016/j.rse.2020.111940

Remote sensing and statistical analysis of the effects of hurricane María on the forests of Puerto Rico

Journal Article · Wed Jun 17 00:00:00 EDT 2020 · Remote Sensing of Environment

DOI:https://doi.org/10.1016/j.rse.2020.111940· OSTI ID:1782192

^[1]; Negrón-Juárez, Robinson I. ^[2]; Chambers, Jeffrey Q. ^[3]

Univ. of California, Berkeley, CA (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Widely recognized as one of the worst natural disaster in Puerto Rico's history, hurricane María made landfall on September 20, 2017 in southeast Puerto Rico as a high-end category 4 hurricane on the Saffir-Simpson scale causing widespread destruction, fatalities and forest disturbance. This study focused on hurricane María's effect on Puerto Rico's forests as well as the effect of landform and forest characteristics on observed disturbance patterns. In this work, we used Google Earth Engine (GEE) to assess the severity of forest disturbance using a disturbance metric based on Landsat 8 satellite data composites with pre and post-hurricane María. Forest structure, tree phenology characteristics, and landforms were obtained from satellite data products, including digital elevation model and global forest canopy height. Our analyses showed that forest structure, and characteristics such as forest age and forest type affected patterns of forest disturbance. Among forest types, highest disturbance values were found in sierra palm, transitional, and tall cloud forests; seasonal evergreen forests with coconut palm; and mangrove forests. For landforms, greatest disturbance metrics was found at high elevations, steeper slopes, and windward surfaces. As expected, high levels of disturbance were also found close to the hurricane track, with disturbance less severe as hurricane María moved inland. Results demonstrated that forest and landform characteristics accounted for 34% of the variation in spatial forest spectral disturbance patterns. This study demonstrated an informative regional approach, combining remote sensing with statistical analyses to investigate factors that result in variability in hurricane effects on forest ecosystems.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1782192

Alternate ID(s):: OSTI ID: 1763801

Journal Information:: Remote Sensing of Environment, Journal Name: Remote Sensing of Environment Vol. 247; ISSN 0034-4257

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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