Next Article in Journal
Estimation of River Discharge Solely from Remote-Sensing Derived Data: An Initial Study Over the Yangtze River
Previous Article in Journal
An Evaluation of MODIS-Retrieved Aerosol Optical Depth over AERONET Sites in Alaska
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2018, 10(9), 1386; https://doi.org/10.3390/rs10091386

Hurricane Maria in the U.S. Caribbean: Disturbance Forces, Variation of Effects, and Implications for Future Storms

USDA Forest Service International Institute of Tropical Forestry, Río Piedras, PR 00926, USA
*
Author to whom correspondence should be addressed.
Received: 11 July 2018 / Revised: 30 August 2018 / Accepted: 30 August 2018 / Published: 31 August 2018
(This article belongs to the Section Forest Remote Sensing)
Full-Text   |   PDF [13454 KB, uploaded 6 September 2018]   |  

Abstract

The impact of Hurricane Maria on the U.S. Caribbean was used to study the causes of remotely-sensed spatial variation in the effects of (1) vegetation index loss and (2) landslide occurrence. The vegetation index is a measure of canopy ‘greenness’, a combination of leaf chlorophyll, leaf area, canopy cover and structure. A generalized linear model was made for each kind of effect, using idealized maps of the hurricane forces, along with three landscape characteristics that were significantly associated. In each model, one of these characteristics was forest fragmentation, and another was a measure of disturbance-propensity. For the greenness loss model, the hurricane force was wind, the disturbance-propensity measure was initial greenness, and the third landscape characteristic was fraction forest cover. For the landslide occurrence model, the hurricane force was rain, the disturbance-propensity measure was amount of land slope, and the third landscape characteristic was soil clay content. The model of greenness loss had a pseudo R2 of 0.73 and showed the U.S. Caribbean lost 31% of its initial greenness from the hurricane, with 51% lost from the initial in the Luquillo Experimental Forest (LEF) from Hurricane Maria along with Hurricane Irma. More greenness disturbance was seen in areas with less wind sheltering, higher elevation and topographic sides. The model of landslide occurrence had a pseudo R2 of 0.53 and showed the U.S. Caribbean had 34% of its area and 52% of the LEF area with a landslide density of at least one in 1 km2 from Hurricane Maria. Four experiments with parameters from previous storms of wind speed, storm duration, rainfall, and forest structure over the same storm path and topographic landscape were run as examples of possible future scenarios. While intensity of the storm makes by far the largest scenario difference, forest fragmentation makes a sizable difference especially in vulnerable areas of high clay content or high wind susceptibility. This study showed the utility of simple hurricane force calculations connected with landscape characteristics and remote-sensing data to determine forest susceptibility to hurricane effects. View Full-Text
Keywords: Hurricane Maria; generalized linear model; remote sensing; forest fragmentation; U.S. Caribbean; Luquillo Experimental Forest Hurricane Maria; generalized linear model; remote sensing; forest fragmentation; U.S. Caribbean; Luquillo Experimental Forest
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Van Beusekom, A.E.; Álvarez-Berríos, N.L.; Gould, W.A.; Quiñones, M.; González, G. Hurricane Maria in the U.S. Caribbean: Disturbance Forces, Variation of Effects, and Implications for Future Storms. Remote Sens. 2018, 10, 1386.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top