Southeastern United States frequently experience tornadoes, necessitating rapid response and recovery efforts by state and federal agencies. Accurate information about the extent and severity of tornado-induced damage, especially debris volume and locations, is crucial for these efforts. This study, therefore, focuses on post-tornado debris assessment in Leon County, Florida, which was hit by two EF-2 and an EF-1 tornadoes in May 2024. Using satellite imagery from the Planetscope satellite and Geographic Information Systems (GIS), a macro-level evaluation of tornado debris impact was conducted, particularly on roadways and impacted communities. The proposed approach includes an evaluation of the overall post-tornado debris impact across the entire county and its population, and a detailed analysis of debris impact on roadways and its effect on accessibility. Spectral indices from satellite images, specifically the Normalized Difference Vegetation Index (NDVI), were utilized to derive assessment parameters. By comparing NDVI values from pre- and post-tornado images, we analyzed changes in vegetation and debris accumulation along roadway segments leading to possible roadway closures. This integrated method provides critical insights for enhancing disaster response and recovery operations in tornado-prone regions. Findings indicate that high volumes of vegetative debris were present in the south-central parts of the county, which is occupied by the highest population of county residents. The roadway segments in this region also recorded highest debris volumes, which is a critical information for agencies that need to know highly impacted locations. Comparing the results to ground truth damage data, the accuracy recorded was 74%.
more »
« less
Assessing Tornado Impacts in the State of Kentucky with a Focus on Demographics and Roadways Using a GIS-Based Approach
Although the literature provides valuable insight into tornado vulnerability and resilience, there are still research gaps in assessing tornadoes’ impact on communities and transportation infrastructure, especially in the wake of the rapidly changing frequency and strength of tornadoes due to climate change. In this study, we first investigated the relationship between tornado exposure and demographic-, socioeconomic-, and transportation-related factors in our study area, the state of Kentucky. Tornado exposures for each U.S. census block group (CBG) were calculated by utilizing spatial analysis methods such as kernel density estimation and zonal statistics. Tornadoes between 1950 and 2022 were utilized to calculate tornado density values as a surrogate variable for tornado exposure. Since tornado density varies over space, a multiscale geographically weighted regression model was employed to consider spatial heterogeneity over the study region rather than using global regression such as ordinary least squares (OLS). The findings indicated that tornado density varied over the study area. The southwest portion of Kentucky and Jefferson County, which has low residential density, showed high levels of tornado exposure. In addition, relationships between the selected factors and tornado exposure also changed over space. For example, transportation costs as a percentage of income for the regional typical household was found to be strongly associated with tornado exposure in southwest Kentucky, whereas areas close to Jefferson County indicated an opposite association. The second part of this study involves the quantification of the tornado impact on roadways by using two different methods, and results were mapped. Although in both methods the same regions were found to be impacted, the second method highlighted the central CBGs rather than the peripheries. Information gathered by such an investigation can assist authorities in identifying vulnerable regions from both transportation network and community perspectives. From tornado debris handling to community preparedness, this type of work has the potential to inform sustainability-focused plans and policies in the state of Kentucky.
more »
« less
- Award ID(s):
- 1940319
- PAR ID:
- 10494403
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Sustainability
- Volume:
- 16
- Issue:
- 3
- ISSN:
- 2071-1050
- Page Range / eLocation ID:
- 1180
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Liu, Weifeng (Ed.)Each year, numerous tornadoes occur in forested regions of the United States. Due to the substantial number of fallen trees and accessibility issues, many of these tornadoes remain poorly documented and evaluated. The process of documenting tree damage to assess tornado intensity is known as the treefall method, an established and reliable technique for estimating near-surface wind speed. Consequently, the demand for documenting fallen trees has increased in recent years. However, the treefall method proves to be extremely expensive and time-consuming, requiring a laborious assessment of each treefall instance. This research proposes a novel approach to evaluating treefall in large, forested regions using deep learning-based automated detection and advanced image processing techniques. The developed treefall method relies on high-resolution aerial imagery from a damaged forest and involves three main steps: (1) instance segmentation detection, (2) estimating tree taper and predicting fallen tree directions, and (3) obtaining subsampled treefall vector results indicating the predominant flow direction in geospatial coordinates. To demonstrate the method’s effectiveness, the algorithm was applied to a tornado track rated EF-4, which occurred on 10 December 2021, cutting through the Land Between the Lakes National Recreation Area in Kentucky. Upon observation of the predicted results, the model is demonstrated to accurately predict the predominant treefall angles. This deep-learning-based treefall algorithm has the potential to speed up data processing and facilitate the application of treefall methods in tornado evaluation.more » « less
-
Guided by influential theories of disaster research and gerontology, this study examines health resilience associated with tornadoes, particularly focusing on how individuals’ tornado-associated stress, financial losses, and family members’ well-being affected posttraumatic distress (PTD), posttraumatic growth (PTG), and self-reported changes in health among adults. To reach this goal, this study collected data from residents affected by two violent tornadoes in 2013, with the assistance of a professional survey lab which implemented a random-digit-dialling telephone survey. The working sample included 517 respondents with oversampled older adults. Multinomial logistic regression, Poisson regression, and Ordinary Least Square Regression were conducted separately for younger and older adults. The results indicated a significant effect of stress levels on PTG among older adults only. Nonetheless, the differences in effect sizes between the two groups were not significant. Meanwhile, respondents’ financial losses and their family members’ declined health were significant predictors of improved health among older adults. Similarly, family members’ declined mental health was a significant predictor of PTD among older adults, but not younger adults. Compared to young adults, older adults were more vulnerable to their family members’ declined mental health, but also more resilient to stressful situations, financial losses, and family members’ declined physical health. Lastly, although risk and resilience factors could be constructed with the same set of items, they function differently among different groups of people. Hence, more studies on heterogeneity are needed to further refine resilience frameworks.more » « less
-
Guided by influential theories of disaster research and gerontology, this study examines health resilience associated with tornadoes, particularly focusing on how individuals' tornado-associated stress, financial losses, and family members' well-being affected posttraumatic distress (PTD), posttraumatic growth (PTG), and self-reported changes in health among adults. To reach this goal, this study collected data from residents affected by two violent tornadoes in 2013, with the assistance of a professional survey lab which implemented a random-digit-dialling telephone survey. The working sample included 517 respondents with oversampled older adults. Multinomial logistic regression, Poisson regression, and Ordinary Least Square Regression were conducted separately for younger and older adults. The results indicated a significant effect of stress levels on PTG among older adults only. Nonetheless, the differences in effect sizes between the two groups were not significant. Meanwhile, respondents' financial losses and their family members' declined health were significant predictors of improved health among older adults. Similarly, family members' declined mental health was a significant predictor of PTD among older adults, but not younger adults. Compared to young adults, older adults were more vulnerable to their family members' declined mental health, but also more resilient to stressful situations, financial losses, and family members' declined physical health. Lastly, although risk and resilience factors could be constructed with the same set of items, they function differently among different groups of people. Hence, more studies on heterogeneity are needed to further refine resilience frameworks.more » « less
-
Recent research suggests that surface elevation variability may influence tornado activity, though separating this effect from reporting biases is difficult to do in observations. Here we employ Bayes’s law to calculate the empirical joint dependence of tornado probability on population density and elevation roughness in the vicinity of Arkansas for the period 1955–2015. This approach is based purely on data, exploits elevation and population information explicitly in the vicinity of each tornado, and enables an explicit test of the dependence of results on elevation roughness length scale. A simple log-link linear regression fit to this empirical distribution yields an 11% decrease in tornado probability per 10-m increase in elevation roughness at fixed population density for large elevation roughness length scales (15–20 km). This effect increases by at least a factor of 2 moving toward smaller length scales down to 1 km. The elevation effect exhibits no time trend, while the population bias effect decreases systematically in time, consistent with the improvement of reporting practices. Results are robust across time periods and the exclusion of EF1 tornadoes and are consistent with recent county-level and gridded analyses. This work highlights the need for a deeper physical understanding of how elevation heterogeneity affects tornadogenesis and also provides the foundation for a general Bayesian tornado probability model that integrates both meteorological and nonmeteorological parameters.more » « less
An official website of the United States government

