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opencc-by-4.0Dec 2008View details →

Fig. 2 in Body size differentiation in selected carabid species inhabiting Puszcza Piska forest stands disturbed by the hurricane

Fig. 2. The differences between the mean body length and the median value of body length for Carabus arcensis individuals inhabiting post-hurricane (Pisz) and control (Maskulińskie) stands in years 2003-2007.

opencc-by-4.0Dec 2008View details →

Fig. 1 in Body size differentiation in selected carabid species inhabiting Puszcza Piska forest stands disturbed by the hurricane

Fig. 1. The mean body length for Carabus arcensis individuals inhabiting post-hurricane (Pisz) and control (Maskulińskie) stands in years 2003-2007.

Fig. 7 in Ground beetle assemblages (Coleoptera, Carabidae) in the third year of regeneration after a hurricane in the Puszcza Piska pine forests

Fig. 7. CCA ordination of carabid beetle assemblages inhabiting stands affected by the hurricane (P) and control stands (M) in age classes I-V. Abbreviations of the environmental variables: N – nitrogen content of soil, C – carbon content of soil, C.N – C/N ratio of soil, Dyf – soil CO 2 efflux rate. (For full names of species, see Table 1).

Fig. 4 in Ground beetle assemblages (Coleoptera, Carabidae) in the third year of regeneration after a hurricane in the Puszcza Piska pine forests

Fig. 4. Proportion of hygrophilous species individuals in carabid beetle assemblages inhabiting stands affected by the hurricane (P) and control stands (M) in age classes I-V.

Fig. 3 in Ground beetle assemblages (Coleoptera, Carabidae) in the third year of regeneration after a hurricane in the Puszcza Piska pine forests

Fig. 3. Proportion of forest species individuals in carabid beetle assemblages inhabiting stands affected by the hurricane (P) and control stands (M) in age classes I-V.

Fig. 5 in Ground beetle assemblages (Coleoptera, Carabidae) in the third year of regeneration after a hurricane in the Puszcza Piska pine forests

Fig. 5. Proportion of xerophilous species individuals in carabid beetle assemblages inhabiting stands affected by the hurricane (P) and control stands (M) in age classes I-V.

Fig. 1 in Ground beetle assemblages (Coleoptera, Carabidae) in the third year of regeneration after a hurricane in the Puszcza Piska pine forests

Fig. 1. Cluster analysis for the carabid beetle assemblages inhabiting stands affected by the hurricane (P) and control stands (M) in age classes I-V. Ward˙s method, Euclidean distance.

Fig. 6 in Ground beetle assemblages (Coleoptera, Carabidae) in the third year of regeneration after a hurricane in the Puszcza Piska pine forests

Fig. 6. The SPC/MIB (SCP/SBO) model of carabid beetle assemblages inhabiting stands affected by the hurricane (P) and control stands (M) in age classes I-V.

opengpl-3.0-or-laterJul 2024View details →

Data and Analyses supporting "Human-caused ocean warming has intensified recent hurricanes"

Notebooks and data from "Human-caused ocean warming has intensified recent hurricanes", in press at Environmetal Research: Climate. A pre-print of the paper can be found at <a href="https://essopenarchive.org/users/667788/articles/1232538-human-caused-ocean-warming-has-intensified-recent-hurricanes">https://essopenarchive.org/users/667788/articles/1232538-human-caused-ocean-warming-has-intensified-recent-hurricanes</a> Included are study analysis notebooks, figure plotting notebooks supportiung the paper publication, and data file outputs forming the computation foundation of paper results. Please email <a href="mailto:dgilford@climatecentral.org" target="_blank" rel="noopener">dgilford@climatecentral.org</a> with any questions or feedback.   Funding for this work was provided by the Bezos Earth Fund, The Schmidt Family Foundation, and the CO2 Foundation.

opencc-by-4.0Jul 2021View details →

Hurricane Michael - SAMURAI analysis files created from NOAA P-3 tail Doppler radar data

This repository contains analyses of Hurricane Michael created from quality controlled data from the NOAA P3 tail Doppler radar. The TDR data can be found in its raw format at the following link under the folders 20181008H1, 20181009H1, 20181009H2, and 20181010H1: <a href="http://seb.noaa.gov/pub/acdata/2018/RADAR/">https://seb.noaa.gov/pub/acdata/2018/RADAR/</a> The analyses are the topic of the manuscript 'Vertical Vortex Development in Hurricane Michael (2018) during Rapid Intensification' which is in review as of dataset publication. Please cite the manuscript when using this data as it contains methodological information on how the analyses were created. More information about the center fix times each analysis file is related to are available in the manuscript. Code to run the SAMURAI analysis tool which created these files, information on how SAMURAI works, and directions can be found at the following 2 links: <a href="http://github.com/mmbell/samurai">https://github.com/mmbell/samurai</a> <a href="http://wiki.lrose.net/index.php/SAMURAI">http://wiki.lrose.net/index.php/SAMURAI</a>

opencc-zeroNov 2022View details →

Demographic study of a tropical epiphytic orchid with stochastic simulations of hurricanes, herbivory, episodic recruitment, and logging

In a time of global change, having an understanding of the nature of biotic and abiotic factors that drive a species' range may be the sharpest tool in the arsenal of conservation and management of threatened species. However, such information is lacking for most tropical and epiphytic species due to the complexity of life history, the roles of stochastic events, and the diversity of habitat across the span of a distribution. In this study, we conducted repeated censuses across the core and peripheral range of Trichocentrum undulatum, a threatened orchid that is found throughout the island of Cuba (species core range) and southern Florida (the northern peripheral range). We used demographic matrix modeling as well as stochastic simulations to investigate the impacts of herbivory, hurricanes, and logging (in Cuba) on projected population growth rates (𝜆 and 𝜆s) among sites.

opencc-by-4.0Mar 2023View details →

Hurricane Disturbance Vegetation Anomaly (HDVA) [Data set for Turner et al.]

The Hurricane Disturbance Vegetation Anomaly (HDVA) is a rapid assessment approach to understand the severity of ecological damage from a high intensity storm event on an otherwise healthy, mature mangrove forest. Data archived here focuses on Cuba, where Hurricane Irma (Category 5) hit the northern coast in September 2017 and caused wide-spread damage to mangroves and coastal forests. Local scientists were not able to assess the full extent or severity of damages through field work due to limited infrastructure and resources, so they turned to remote sensing analysis. We developed a multitemporal, multiresolution approach to assess the damage 1) extent and 2) relative severity using changes from the typical green-leaf phenology represented by the Enhanced Vegetation Index with MODIS and Sentinel-2 data. All data was processed in Google Earth Engine API to access and utilize large amounts of historical data, as well as compare sensors’ spatial resolution impacts on results. This data set includes the HDVA products and categorization of data by quartile of damage (catastrophic, severe, moderate, mild, no loss).

opencc-zeroJun 2023View details →

Data for: Saturation of ocean surface wave slopes observed during hurricanes

<div>Observational wave data and modeled wind data to accompany the article "Saturation of ocean surface wave slopes observed during hurricanes," which is currently in revision for publication in Geophysical Research Letters (a preprint is attached as a supplement). The observations include targeted aerial deployments into Hurricane Ian (2022) and opportunistic measurements from the free-drifting Sofar Ocean Spotter global network in Hurricane Fiona (2022). Surface wind speeds are modeled using the U.S. Naval Research Laboratory's Coupled Ocean-Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC). The datasets are table-like and include the following: 1) hourly records of surface wave statistics in the form of scalar energy spectra, directional moments, derived products (including mean square slope), and modeled wind speeds; 2) data to reproduce the binned mean square slopes and model wind speeds presented in the article; 3) data to reproduce the mean energy density versus wind speed plot; and 4) data to reproduce the mean energy density versus wave age plot.</div>

opencc-by-4.0Dec 2019View details →

Spatially Quantifying Forest Damage from Hurricane Michael using Sentinel-2 Imagery

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 on 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 revisited, 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 principal 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, revisited 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 principal components from a principal 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. <table> <tbody> <tr> <td> N </td> <td> RMSE </td> <td> R2 </td> <td> Adjusted R2 </td> </tr> <tr> <td> 231 </td> <td> 2.811 </td> <td> 0.51 </td> <td> 0.50 </td> </tr> </tbody> </table> Road, water and urban areas were masked out of this raster dataset using a 1 m landcover product created by the author and located here - <a href="https://doi.org/10.2737/RDS-2017-0014">https://doi.org/10.2737/RDS-2017-0014</a>

publicJun 2023View details →

Data for: Saturation of ocean surface wave slopes observed during hurricanes

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publicNov 2022View details →

Demographic study of a tropical epiphytic orchid with stochastic simulations of hurricanes, herbivory, episodic recruitment, and logging

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