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Spatially Quantifying Forest Damage from a Category 5 Hurricane

Citation

St. Peter, Joseph; Anderson, Chad; Drake, Jason; Medley, Paul (2019), Spatially Quantifying Forest Damage from a Category 5 Hurricane, Dryad, Dataset, https://doi.org/10.5061/dryad.905qfttgc

Abstract

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.

Methods

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.

N

RMSE

R2

Adjusted R2

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

Usage Notes

For convenience and informational purposes only.

Funding

Gulf Coast Ecosystem Restoration Council, Award: 17-IA-11083150- 001