Characterization of Surface Karst Using LiDAR and Field Traverses, Fort Hood Military Installation, Coryell County, Texas
type: CreativeWork
description: The Fort Hood Military Installation is a karst landscape that has been significantly altered for training exercises that include heavy vehicle maneuvers and simulated combat. Traditional karst surveys are often time-consuming and require extensive field analyses to adequately characterize large areas. Bias is given to areas that are most easily accessible and false negatives are common. Previous studies conducted in the eastern and western portion of the base have understated the abundance and spatial distribution of karst, particularly in the western portion. This study used field traverses and 0.5-meter Light Detection and Ranging (LiDAR) data to characterize surface karst depressions, create a set of new and refined filters and buffering mechanisms to remove non-karst depressions, and determine the accuracy of the model. LiDAR data was used to create a digital elevation model (DEM), which was used to extract areas with localized depressions at a sub-meter scale. In order to isolate features that were formed through karst processes, data were processed through a series of filters with parameters based on features found during traverse surveys. Field verifications to assess the accuracy of the LiDAR conducted with previous filters and buffering mechanisms had an overall accuracy of 77.3%, indicating this model overestimated the number of features in the study area. To assess the accuracy of the new filters and buffering parameters, field verified features from a random point survey and a remote verification survey of features within each of the filters was conducted. The overall accuracy was 84.1%, indicating that the new filters and buffering parameters improved depression characterization and the ability to determine those features that were influenced by natural and anthropogenic processes.
abstract: The Fort Hood Military Installation is a karst landscape that has been significantly altered for training exercises that include heavy vehicle maneuvers and simulated combat. Traditional karst surveys are often time-consuming and require extensive field analyses to adequately characterize large areas. Bias is given to areas that are most easily accessible and false negatives are common. Previous studies conducted in the eastern and western portion of the base have understated the abundance and spatial distribution of karst, particularly in the western portion. This study used field traverses and 0.5-meter Light Detection and Ranging (LiDAR) data to characterize surface karst depressions, create a set of new and refined filters and buffering mechanisms to remove non-karst depressions, and determine the accuracy of the model. LiDAR data was used to create a digital elevation model (DEM), which was used to extract areas with localized depressions at a sub-meter scale. In order to isolate features that were formed through karst processes, data were processed through a series of filters with parameters based on features found during traverse surveys. Field verifications to assess the accuracy of the LiDAR conducted with previous filters and buffering mechanisms had an overall accuracy of 77.3%, indicating this model overestimated the number of features in the study area. To assess the accuracy of the new filters and buffering parameters, field verified features from a random point survey and a remote verification survey of features within each of the filters was conducted. The overall accuracy was 84.1%, indicating that the new filters and buffering parameters improved depression characterization and the ability to determine those features that were influenced by natural and anthropogenic processes.
author: Dailey, Heather Jaclyn
keywords: Surface Karst, Geographic Information Systems, Light Detection and Ranging, Sinkhole, Sinkholes, Earth Sciences, Geology, Geomorphology, Physical Sciences and Mathematics, Speleology
date published: 2020-05-01T07:00:00Z
date modified: 2026-07-24T04:30:30Z
provider: Stephen F. Austin State University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Stephen F. Austin State University