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8 results for “hurricane damage”
HURRECON Model for Estimating Hurricane Wind Speed, Direction and Damage
HURRECON is a simple meteorological model that estimates hurricane surface wind speed and direction based on the track, size, and intensity of a hurricane and the surface type (land or water). The model also estimates Fujita-scale wind damage as a function of peak 1/4 mile wind speed and wind gust factor. Estimates can be generated for a single site or a rectangular region. The model is based on published empirical studies of many hurricanes. HURRECON can be used to study the impacts of individual hurricanes or to reconstruct the hurricane disturbance regime for a particular region. For more information on the most recent version of the model please see the published paper (Boose, E. R., K. E. Chamberlin and D. R. Foster. 2001. Landscape and regional impacts of hurricanes in New England. Ecological Monographs 71: 27-48). Additional information is contained in the documentation that accompanies the program. For an updated version of the HURRECON model in R and Python, please see HF446.
Forest Damage Patterns at Harvard Forest in the 1938 Hurricane
This study examined landscape-level patterns of forest damage at the Harvard Forest caused by the 1938 New England Hurricane. For details on methods and results, please see the published paper (Foster, D. R. and E. R. Boose. 1992. Patterns of forest damage resulting from catastrophic wind in central New England, USA. Journal of Ecology 80: 79-98). The Abstract from the paper is reproduced below. "1. The effect of catastrophic winds on a forested landscape in central Massachusetts was examined to investigate the factors controlling the geographic pattern of damage. The study area, Tom Swamp Tract, Harvard Forest, comprises a valley and adjoining hillslopes supporting second growth hardwood and confer stands. Much of the study used records and maps that were analyzed cartographically with a geographic information system (GIS). "2. Areally, forest damage was distributed fairly evenly among different damage classes ranging from no damage to more than 75% of stems broken or uprooted. However, there was a negative exponential size distribution of contiguous areas of the same damage intensity, with a preponderance less than 2 ha; these areas ranged from less than 0.04 ha to more than 35 ha; hurricane damage exhibited a continuum ranging from minor damage of individual trees to extensive blow-down of broad areas of forest. "3. The spatial pattern of wind damage was controlled by vegetation height and composition and by site exposure, which is predominantly determined by slope orientation and angle. Approximately 3% of the stands in the study site occupied protected sites, 31% intermediate sites, and 66% exposed sites. "4. Forest type susceptibility followed the ranking (from highest to lowest): Pinus strobes, conifer plantations, Pinus strobus-hardwood = Tsuga canadensis-hardwood-Pinus strobes, hardwood-Pinus strobes, hardwood. Damage increased with increasing site exposure to wind and increased approximately linearly with stand height. "5. An empirical GIS model of landsca
HURRECON Model for Estimating Hurricane Wind Speed, Direction, and Damage (R and Python)
The HURRECON model estimates wind speed, wind direction, enhanced Fujita scale wind damage, and duration of EF0 to EF5 winds as a function of hurricane location and maximum sustained wind speed. Results may be generated for a single site or an entire region. Hurricane track and intensity data may be imported directly from the US National Hurricane Center's HURDAT2 database. HURRECON is available in R and Python. The R version is available on CRAN as HurreconR. The model is an updated version of the original HURRECON model written in Borland Pascal for use with Idrisi (see HF025). New features include support for: (1) estimating wind damage on the enhanced Fujita scale, (2) importing hurricane track and intensity data directly from HURDAT2, (3) creating a land-water file with user-selected geographic coordinates and spatial resolution, and (4) creating plots of site and regional results. The model equations for estimating wind speed and direction, including parameter values for inflow angle, friction factor, and wind gust factor (over land and water), are unchanged from the original HURRECON model. For more details and sample datasets, see the project website on GitHub (https://github.com/hurrecon-model).
Canopy damage and recovery following Hurricane Maria using multitemporal lidar data, Mar-2017 - Mar-2020, Puerto Rico
The data archive is here: http://dx.doi.org/10.15486/ngt/1797399 please use this DOI when citing this dataset. Hurricane Maria (Category 4) snapped and uprooted canopy trees, removed large branches, and defoliated vegetation across Puerto Rico. The magnitude of forest damages and the rates and mechanisms of forest recovery following Maria provide important benchmarks for understanding the ecology of extreme events. We used airborne lidar data acquired before (2017) and after Maria (2018, 2020) to quantify landscape-scale changes in forest structure along a 439-ha elevational gradient (100 to 800 m) in the Luquillo Experimental Forest. Damages from Maria were widespread, with 73% of the study area losing ≥1 m in canopy height (mean = -7.1 m). Taller forests at lower elevations suffered more damage than shorter forests above 600 m. Yet only 13% of the study area had canopy heights ≤2 m in 2018, a typical threshold for forest gaps, highlighting the importance of damaged trees and advanced regeneration on post-storm forest structure. Heterogeneous patterns of regrowth and recruitment yielded shorter and more open forests by 2020. Nearly 45% of forests experienced initial height loss (<-1 m, 2017-2018) followed by rapid height gain (>1 m, 2018-2020), whereas 21.6% of forests with initial height losses showed little or no height gain, and 17.8% of forests exhibited no structural changes >|1| m in either period. Canopy layers <10 m accounted for most increases in canopy height and fractional cover between 2018-2020, with gains split evenly between height growth and lateral crown expansion by surviving individuals. These findings benchmark rates of gap formation, crown expansion, and canopy closure following hurricane damage. Included in the attached zip file are four TIF and four KML files. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National
Tree damage by Hurricane Hugo on the Luquillo Forest Dynamics Plot (LFDP), Puerto Rico
Hurricane Hugo struck the Caribbean national forest in September 1989. Files LFDP_HURRDAM.TXT and LFDP_HURRDAMa.TXT contain data on the damage to trees caused by the hurricane collected by Mr. R. DeLeon between August 1990 and September 1991. Mr. DeLeon walked throughout the plot to find stems >= 10 cm diameter that had apparently been damaged or killed by the hurricane in an effort to collect information before the damaged stems rotted. The information on these stems was later combined with the results of the first census to reconstruct the forest, as it would have appeared, at the time of Hurricane Hugo. This file contains the hurricane damage data collected for stems damaged by Hurricane Hugo combined with data for the stems recorded subsequently in the first complete LFDP census starting in 1990. Some stems that were measured in Census 1 survey 2 and survey 3 or Census 2 that were believed to have been missed in Census 1 survey 1, are also included (see census history above) and are assumed to have been undamaged by Hurricane Hugo. The structure of the data files is the same for both files LFDP_HURRDAM.TXT and LFDP_HURRDAMa.TXT but the diameter of the trees in LFDP_HURRDAMa.TXT have been calculated by extrapolating diameters backwards from subsequent measurements to the time of the Census 1 survey 1. Diameters in file LFDP_HURRDAMa.TXT can not be used for growth measurements. For our publications we treat files LFDP_HURRDAM.TXT and LFDP_HURRDAMa.TXT as one data set. The National Science Foundation requires that data from projects it funds are posted on the web two years after any data set has been organized and "cleaned". The data from each census of the LFDP will be updated at intervals as each survey of the LFDP shows errors in the previous data collection. After posting on the web, researchers who are not part of the project are then welcome to use the data. Given the enormous amount of time, effort and resources required to manage the LFDP, obtain these d
Spatially Quantifying Forest Damage from Hurricane Michael using Sentinel-2 Imagery
<p><strong>ABSTRACT:</strong></p> <p>Hurricane Michael made landfall on Mexico Beach, Florida panhandle as a Category 5 storm on October 10<sup>th</sup>, 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.</p> <p><strong>METHODS:</strong></p> <p>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.</p> <p>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. </p> <p>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.</p> <p>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: </p> <p>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.</p> <p>Table Linear Regression Model. Model fit and predictors for BAA.</p> <table> <tbody> <tr> <td> <p><strong>N</strong></p> </td> <td> <p><strong>RMSE</strong></p> </td> <td> <p><strong>R2</strong></p> </td> <td> <p><strong>Adjusted R2</strong></p> </td> </tr> <tr> <td> <p>231</p> </td> <td> <p>2.811</p> </td> <td> <p>0.51</p> </td> <td> <p>0.50</p> </td> </tr> </tbody> </table> <p>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></p>
Replication Dataset for Improving Normalized Hurricane Damages
<p>This dataset can be used to replicate the results in Martinez (2020) Improving Normalized Hurricane Damages. https://doi.org/10.1038/s41893-020-0550-5</p> <p>The dataset was constructed using data from Weinkle et al. Nature Sustainability https://doi.org/10.1038/s41893-018-0165-2</p> <p>Building cost information was obtained from Robert Shiller: http://www.econ.yale.edu/~shiller/data/Fig3-1.xls</p>
Spatially Quantifying Forest Damage from a Category 5 Hurricane
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