Disaster damage assessments are a critical component to response and recovery operations. In recent years, the field of remote sensing has seen innovations in automated damage assessments and UAS collection capabilities. However, little work has been done to explore the intersection of automated methods and UAS photogrammetry to detect tornado damage. UAS imagery, combined with Structure from Motion (SfM) output, can directly be used to train models to detect tornado damage. In this research, we develop a CNN that can classify tornado damage in forests using SfM-derived orthophotos and digital surface models. The findings indicate that a CNN approach provides a higher accuracy than random forest classification, and that DSM-based derivatives add predictive value over the use of the orthophoto mosaic alone. This method has the potential to fill a gap in tornado damage assessment, as tornadoes that occur in wooded areas are typically difficult to survey on the ground and in the field; an improved record of tornado damage in these areas will improve our understanding of tornado climatology. / Master of Science / Disaster damage assessments are a critical component to response and recovery operations. In recent years, the field of remote sensing has seen innovations in automated damage assessments and Unmanned Aerial System (UAS) collection capabilities. However, little work has been done to explore the intersection of automated methods and UAS imagery to detect tornado damage. UAS imagery, combined with 3D models, can directly be used to train machine learning models to automatically detect tornado damage. In this research, we develop a machine learning model that can classify tornado damage in forests using UAS imagery and 3D derivatives. The findings indicate that the machine learning model approach provides a higher accuracy than traditional techniques. In addition, the 3D derivatives add value over the use of only the UAS imagery. This method has the potential to fill a gap in tornado damage assessment, as tornadoes that occur in wooded areas are typically difficult to survey on the ground and in the field; an improved record of tornado damage in these areas will improve our understanding of tornado climatology.

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Strong tornadoes have impacted the central Appalachian Mountains multiple times in recent years. The topography of this region leads to unique spatial patterns of tornado damage as the tornado vortices pass over ridges in forested areas, and this damage can be detected with vegetation indices derived from remotely sensed imagery. The objectives of this study were to 1) Classify forest damage from the April 19, 2019 EF-3 tornado in Franklin County, VA using remotely-sensed images, 2) Quantify the spatial patterns of forest damage intensity across the path using derived vegetation indices and terrain variables (primarily slope, aspect, elevation, and exposure), and 3) Use regression models to determine if relationships exist among terrain variables along the and forest damage patterns. I generated EVI and NDII vegetation indices from Sentinel-2 imagery and compared the derived damage to the underlying terrain variables. Results revealed that the two vegetation indices were effective for classifying tornado damage, and discrete damage classes aligned well with NWS EF-scale tornado intensity estimations. ANOVA testing suggested that EF-3 equivalent damage was more likely to occur on downslope topography, leeward of the tornado's direction of travel. OLS and geographically weighted regression (GWR) modeling performed poorly, suggesting that an alternative method may be more suitable for modeling, the scale of assessment was inadequate, or that important predictor variables were not captured. Overall, the intensity of the tornado was clearly modified by terrain interactions, and the remote sensing methodology used was effective for reliably identifying and rating damage in forested areas. / Master of Science / Strong tornadoes have impacted the central Appalachian Mountains multiple times in recent years. The topography of this region leads to unique spatial patterns of tornado damage as the tornado vortices pass over ridges in forested areas, and this damage can be detected with vegetation indices derived from remotely sensed imagery. The objectives of this study were to 1) Classify forest damage from the April 19, 2019 EF-3 tornado in Franklin County, VA using remotely-sensed images, 2) Quantify the spatial patterns of forest damage intensity across the path using derived vegetation indices and terrain variables (primarily slope, aspect, elevation, and exposure), and 3) Use regression models to determine if relationships exist among terrain variables along the and forest damage patterns. I generated EVI and NDII vegetation indices from Sentinel-2 imagery and compared the derived damage to the underlying terrain variables. Results revealed that the two vegetation indices were effective for classifying tornado damage, and discrete damage classes aligned well with NWS EF-scale tornado intensity estimations. ANOVA testing suggested that EF-3 equivalent damage was more likely to occur on downslope topography, leeward of the tornado's direction of travel. OLS and geographically weighted regression (GWR) modeling performed poorly, suggesting that an alternative method may be more suitable for modeling, the scale of assessment was inadequate, or that important predictor variables were not captured. Overall, the intensity of the tornado was clearly modified by terrain interactions, and the remote sensing methodology used was effective for reliably identifying and rating damage in forested areas.

Thesis (M.S.)--University of Tennessee, Knoxville, 2009. / Title from title page screen (viewed on Oct. 23, 2009). Thesis advisor: Wayne Clatterbuck. Vita. Includes bibliographical references.

The primary purpose of this research program is to confirm the spontaneous planning behavior in post-disaster operations while at the same time contribute to the development of the concept in a tornado type disaster. An additional goal also includes examining how the process takes place in resolving unanticipated problems as a disaster unfolds. This study uses qualitative methodology which is case study to probe the concept of spontaneous planning behavior to solve unexpected challenges as a disaster develops. Specifically, semi-structured, open-ended questions were utilized to collect data from stakeholders in eleven functional organizations in three impacted cities during the North Texas April 3, 2012, tornadoes. Findings indicate that debris removal and ensuring public safety, search and rescue, securing damaged neighborhoods, activation of emergency operations centers, damage assessment, restoration of communication system, public relations and media, and volunteer and donation management activities appear to have benefited from spontaneous planning behavior. Further findings suggest that the driving forces behind the phenomenon were gathering valuable new information, learning opportunity within the disaster, relative freedom and significant high degree of discretion, response was innovative with flexibility, and solutions waiting for problems features proposed in the integrated decision-making model (IDMM). However, it was uncovered that interview respondents’ answers tend to indicate that mixed organizational structures helped in problem resolutions rather than just flat organizational structure as some decision making literature may suggest. Analysis of this decision-making model expanded the understanding of how spontaneous planning behavior took place in resolving unforeseen problems in post-disaster operations. This research project confirmed the concept of spontaneous planning in the North Texas tornadoes as well as suggesting how it occurred. The research program validates spontaneous planning behavior in tornadoes; advances and develops the concept of spontaneous planning; increases understanding, description, and management of post-disaster operations; improves emergency management operations; promotes spontaneous planning as a key principle among responders and others involved in emergency management; and proposes IDMM as a useful model that explains decision-making behavior during a disaster.