DOI: 10.5061/dryad.905qfttgc
title: Spatially Quantifying Forest Damage from a Category 5 Hurricane

description: Hurricane Michael made landfall on Mexico Beach, Florida panhandle as a
 Category 5 storm on October 10th, 2018. The storm had a large impact on
 the forests in the Florida panhandle and into Georgia. In this study we
 use Sentinel-2 imagery and 248 forest plots collected prior to landfall in
 2018 in the forests impacted by Hurricane Michael to build a general
 linear model of tree basal area across the landscape. The basal area model
 was constrained to areas where trees were present using a tree presence
 model as a hurdle.  We informed the model with post hurricane
 Sentinel-2 imagery and compared the pre and post hurricane basal area maps
 to assess the loss of basal area following the hurricane. The basal area
 model had an r-squared value of 0.508. Our results provide a detailed map
 showing the extent of basal area loss across the Florida panhandle at 10m
 spatial scale. Plots were revisited to ground truth the modelled results
 and showed that the model performed well at categorizing forest hurricane
 damage. This study demonstrates the use of remotely sensed imagery and
 in-situ forest measurements to rapidly quantify, using common forestry
 metrics, forest damage from large natural disturbances at spatial
 resolution useful to inform disaster response management decisions.
description: The Restore .csv file is data from forestry plots established by
 the Florida Natural Areas Inventory (FNAI) as a baseline for a Restore Act
 project focused in the Florida panhandle. A total of 248 plots were
 visited between December 2017 and March 2018. These temporary plots were
 navigated to using handheld GPS units and laid out in 36m squares
 containing four 9m diameter non overlapping subplots. Measurements of
 vegetative cover, tree species count, tree condition, and diameter at
 breast height were taken for all trees in the subplots. Post hurricane
 Michael 70 plots were revisisted, measurements at these plots were a
 subjective plot hurricane damage categorization, and count and diameter of
 downed or damaged trees as well as miscellaneous notes regarding site
 damage. The state parks .csv file is data from forestry
 plots established by the Florida Natural Areas Inventory (FNAI) at Florida
 State parks post hurricane Michael. These plots are 20 m circular radius
 that include subjective plot hurricane damage categorization, and count of
 downed or damaged trees, herbaceous cover, as well as miscellaneous notes
 regarding site damage.  Several fields were added to
 these plot .csv files post field visit as a variables extracted from a
 principle component analysis that used Sentinel-2 imagery to inform a
 remote sensing analysis of basal area. A file
 geodatabase is attached that contains the project area boundary,
 apalachicola national forest boundary, the three shapefiles of all restore
 plots, revisisted restore plots, and state parks plots. Raster outputs
 from our analysis are also available in this gdb, they contain metadata in
 their item descriptions. The general metadata for these rasters
 follows:  A general linear regression model was built
 to estimate tree basal area across the study area of 11 counties in the
 Florida Panhandle. Basal area (BA) was calculated from Restore field plots
 where trees were present located in and around the Apalachicola National
 Forest. Plot measurements include all trees within four non-overlapping 9m
 radius circular subplots within a 36m square plot. Tree diameter at breast
 height (DBH), species, count, and condition measurements were recorded.
 Measurements were summarized to the plot and DBH (square inches) was
 converted to basal area per acre (square feet per acre) using the formula
 0.005454 * DBH^2. Plot measurements of basal area per acre were related to
 the top ten principle components from a principle component analysis (PCA)
 at the spatial resolution of the Restore plots (40 m). The PCA used the
 normalized Sentinel-2 spectral values and two texture values from the 7
 mosaicked images from two time periods, the winter of 2017-2018 and the
 spring of 2018, before Hurricane Michael. A softmax neural network model
 was built from the PCA and Restore plot datasets to identify areas where
 trees were present. The basal area model was applied to the pre and post
 hurricane PCA imagery to create modelled surfaces of estimated Basal Area
 in pixels that where over 50% likely to contain trees according to the
 softmax neural network model. The basal are linear regression model
 results and predictors for the model are in the tables below.
 Table Linear
 Regression Model. Model fit and predictors for BAA.
 
 <b>N</b> 
 <b>RMSE</b> 
 <b>R2</b> 
 <b>Adjusted R2</b> 
 
 231 
 2.811 
 0.51 
 0.50 
 Road, water and urban areas were masked out of
 this raster dataset using a 2013 1 m landcover product created by the
 author and located here - https://doi.org/10.2737/RDS-2017-0014
description: For convenience
 and informational purposes only.