The Impact of Hurricane Maria on the Vegetation of Dominica and Puerto Rico Using Multispectral Remote Sensing

As the worst natural disaster on record in Dominica and Puerto Rico, Hurricane Maria in September 2017 had a large impact on the vegetation of these islands. In this paper, multitemporal Landsat 8 OLI and Sentinel-2 data are used to investigate vegetation damage on Dominica and Puerto Rico by Hurricane Maria, and related influencing factors are analyzed. Moreover, the changes in the normalized difference vegetation index (NDVI) in the year 2017 are compared to reference years (2015 and 2016). The results show that (1) there is a sudden drop in NDVI values after Hurricane Maria’s landfall (decreased about 0.2) which returns to near normal vegetation after 1.5 months; (2) different land cover types have different sensitivities to Hurricane Maria, whereby forest is the most sensitive type, then followed by wetland, built-up, and natural grassland; and (3) for Puerto Rico, the vegetation damage is highly correlated with distance from the storm center and elevation. For Dominica, where the whole island is within Hurricane Maria’s radius of maximum wind, the vegetation damage has no obvious relationship to elevation or distance. The study provides insight into the sensitivity and recovery of vegetation after a major land-falling hurricane, and may lead to improved vegetation protection strategies.

[1]  Phillip B. Schafer,et al.  Orographic Precipitation in the Tropics: Experiments in Dominica , 2008 .

[2]  Mei Yu,et al.  Continued Reforestation and Urban Expansion in the New Century of a Tropical Island in the Caribbean , 2017, Remote. Sens..

[3]  Lawrence S. Bernstein,et al.  A new method for atmospheric correction and aerosol optical property retrieval for VIS-SWIR multi- and hyperspectral imaging sensors: QUAC (QUick atmospheric correction) , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[4]  Ainong Li,et al.  An Improved Physics-Based Model for Topographic Correction of Landsat TM Images , 2015, Remote. Sens..

[5]  Abduwasit Ghulam,et al.  Monitoring Tropical Forest Degradation in Betampona Nature Reserve, Madagascar Using Multisource Remote Sensing Data Fusion , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[6]  V. Klemas.  The Role of Remote Sensing in Predicting and Determining Coastal Storm Impacts , 2009 .

[7]  Yaw A. Twumasi,et al.  Hurricane Georges and vegetation change in Puerto Rico using AVHRR satellite data , 2004 .

[8]  Alison D. Nugent,et al.  The Dynamics of Ascent-Forced Orographic Convection in the Tropics: Results from Dominica* , 2013 .

[9]  Grant W. Staben,et al.  Estimates of tree canopy loss as a result of Cyclone Monica, in the Magela Creek catchment northern Australia , 2008 .

[10]  P. Teillet.  Effects of spectral, spatial, and radiometric characteristics on remote sensing vegetation indices of forested regions , 1997 .

[11]  Zhe Zhu,et al.  Object-based cloud and cloud shadow detection in Landsat imagery , 2012 .

[12]  Pedro Cabral,et al.  Cyclone Sidr Impacts on the Sundarbans Floristic Diversity , 2013 .

[13]  Eileen H. Helmer,et al.  The Forest Types and Ages Cleared for Land Development in Puerto Rico , 2007 .

[14]  Adam W. Murrah,et al.  The Impact of Hurricane Katrina on the Coastal Vegetation of the Weeks Bay Reserve, Alabama from NDVI Data , 2009 .

[15]  Mirco Boschetti,et al.  A multitemporal analysis of tsunami impact on coastal vegetation using remote sensing: a case study on Koh Phra Thong Island, Thailand , 2012, Natural Hazards.

[16]  Yanlei Feng,et al.  Rapid remote sensing assessment of impacts from Hurricane Maria on forests of Puerto Rico , 2018 .

[17]  Laura Schneider,et al.  Hurricane disturbance mapping using MODIS EVI data in the southeastern Yucatán, Mexico , 2011 .

[18]  Trude Storelvmo,et al.  Orographic Precipitation in the Tropics: The Dominica Experiment , 2010 .

[19]  T. L. Toan,et al.  Mapping of flood dynamics and spatial distribution of vegetation in the Amazon floodplain using multitemporal SAR data , 2007 .

[20]  Ying Wang,et al.  Remote-sensing assessment of forest damage by Typhoon Saomai and its related factors at landscape scale , 2013 .

[21]  Chris Roelfsema,et al.  Assessing tropical cyclone impacts using object-based moderate spatial resolution image analysis: a case study in Bangladesh , 2016 .

[22]  Yang Liu,et al.  Evaluating the vegetation destruction and recovery of Wenchuan earthquake using MODIS data , 2010 .

[23]  Siamak Khorram,et al.  Correspondence analysis for detecting land cover change , 2006 .

[24]  Elijah W. Ramsey,et al.  AVHRR imagery used to identify hurricane damage in a forested wetland of Louisiana , 1997 .

[25]  Eileen H. Helmer,et al.  Mapping the Forest Type and Land Cover of Puerto Rico, a Component of the Caribbean Biodiversity Hotspot , 2002 .

[26]  K. Walsh,et al.  A Technique to Determine the Radius of Maximum Wind of a Tropical Cyclone , 2008 .

[27]  C. Woodcock,et al.  Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images , 2015 .

[28]  Phillip Schafer,et al.  Orographic Enhancement of Precipitation inside Hurricane Dean , 2009 .

[29]  Keinosuke Gotoh,et al.  Evaluating salinity damage to crops through satellite data analysis: application to typhoon affected areas of southern Japan , 2014, Natural Hazards.

[30]  Calvin Blackwell,et al.  Power Law or Lognormal? Distribution of Normalized Hurricane Damages in the United States, 1900–2005 , 2015 .

[31]  Bin Xie,et al.  Study on Typhoon Center Monitoring Based on HY-2 and FY-2 Data , 2017, IEEE Geoscience and Remote Sensing Letters.

[32]  Julien Brun,et al.  Vegetation activity monitoring as an indicator of eco-hydrological impacts of extreme events in the southeastern USA , 2013 .

[33]  Daniel M. Mitchell,et al.  Hurricanes Harvey, Irma and Maria: how natural were these ‘natural disasters’? , 2017 .

[34]  Alison D. Nugent,et al.  Factors Leading to Extreme Precipitation on Dominica from Tropical Storm Erika (2015) , 2017 .

[35]  A. Skidmore,et al.  Interannual variability of NDVI and species richness in Kenya , 2002 .

[36]  George C. Hurtt,et al.  Multi-scale sensitivity of Landsat and MODIS to forest disturbance associated with tropical cyclones , 2014 .

[37]  M. Horner,et al.  The effects of transportation network failure on people’s accessibility to hurricane disaster relief goods: a modeling approach and application to a Florida case study , 2011 .

[38]  Tangao Hu,et al.  A Comparison of HY-2 and QuikSCAT Vector Wind Products for Tropical Cyclone Track and Intensity Development Monitoring , 2014, IEEE Geoscience and Remote Sensing Letters.

[39]  Michael E. Hodgson,et al.  Forest impact estimated with NOAA AVHRR and Landsat TM data related to an empirical hurricane wind-field distribution , 2001 .

[40]  Yuhong He,et al.  An object-based approach to delineate wetlands across landscapes of varied disturbance with high spatial resolution satellite imagery ☆ , 2015 .