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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. 

Infrastructures such as roadways, power lines, and communications networks play a critical role in our society. However, they are also susceptible to failures, especially after natural events, easily affecting large geographical areas. Predicting where and when these failures will occur with high confidence is very difficult due to the stochastic nature of such events. Nevertheless, it is possible to know which areas are more vulnerable in advance and plan accordingly. This paper aims to use just remote sensing techniques based on satellite images to detect roadways vulnerabilities to hurricanes. The framework exhibits a modular architecture that enables detecting and mapping in 3D vegetation and detecting buildings. We propose a risk function based on the information retrieved from the satellite image which can be used to create a risk map of the area. The study area has been selected in Tallahassee, Florida where a high-resolution satellite image has been acquired in September 2018, before Hurricane Michael main hit. The findings of this work can help the management teams and city responders to identify the most vulnerable regions which are under the risk of disruption and to organize the resources prior to the event. The advantages of our approach are that the entire framework can be use as an end-to-end standalone solution for risk analysis at city level and can be easily expanded with other source of data. 

Transportation systems are vulnerable to hurricanes and yet their recovery plays a critical role in returning a community to its pre-hurricane state. Vegetative debris is among the most significant causes of disruptions on transportation infrastructure. Therefore, identifying the driving factors of hurricane-caused debris generation can help clear roadways faster and improve the recovery time of infrastructure systems. Previous studies on hurricane debris assessment are generally based on field data collection, which is expensive, time consuming, and dangerous. With the availability and convenience of remote sensing powered by the simple yet accurate estimations on the vigor of vegetation or density of manufactured features, spectral indices can change the way that emergency planners prepare for and perform vegetative debris removal operations. Thus, this study proposes a data fusion framework combining multispectral satellite imagery and various vector data to evaluate post-hurricane vegetative debris with an exploratory analysis in small geographical units. Actual debris removal data were obtained from the City of Tallahassee, Florida after Hurricane Michael (2018) and aggregated into U.S. Census Block Groups along with four groups of datasets representing vegetation, storm surge, land use, and socioeconomics. Findings suggest that vegetation and other land characteristics are more determinant factors on debris generation, and Modified Soil-Adjusted Vegetation Index (MSAVI2) outperforms other vegetation indices for hurricane debris assessment. The proposed framework can help better identify equipment stack locations and temporary debris collection centers while providing resilience enhancements with a focus on the transportation infrastructure. 

Hurricanes affect thousands of people annually, with devastating consequences such as loss of life, vegetation and infrastructure. Vegetation losses such as downed trees and infrastructure disruptions such as toppled power lines often lead to roadway closures. These disruptions can be life threatening for the victims. Emergency officials, therefore, have been trying to find ways to alleviate such problems by identifying those locations that pose high risk in the aftermath of hurricanes. This paper proposes an integrated methodology that utilizes both Google Earth Engine (GEE) and geographical information systems (GIS). First, GEE is used to access Sentinel-2 satellite images and calculate the Normalized Difference Vegetation Index (NDVI) to investigate the vegetation change as a result of Hurricane Michael in the City of Tallahassee. Second, through the use of ArcGIS, data on wind speed, debris, roadway density and demographics are incorporated into the methodology in addition to the NDVI indices to assess the overall impact of the hurricane. As a result, city-wide hurricane impact maps are created using weighted indices created based on all these data sets. Findings indicate that the northeast side of the city was the worst affected because of the hurricane. This is a region where more seniors live, and such disruptions can lead to dramatic consequences because of the fragility of these seniors. Officials can pinpoint the identified critical locations for future improvements such as roadway geometry modification and landscaping justification.