Predicting infrastructure damage and economic impact of sinkholes requires high accuracy mapping distribution and development. The study mapped sinkholes and sinkhole hotpsots in Johnson City, TN using LiDAR-derived Digital Elevation Model (DEM) and a database of known sinkholes which were matched to LiDAR-derived depressions. For all matched depressions (n = 404), three metrics were calculated: circularity index, ratio of length to width of the Minimum Bounding Rectangle (MBR) and percent coverage of the MBR by the depression, and 3,634 new sinkholes were identified. Newly developed hotspots were identified in north Johnson City and other areas in the south near the Johnson City Medical Center. The methodology developed can be applied to identify hotspots in other small metropolitan cities and the hotspot map produced can be employed in hazard mitigation planning, resource allocation, and made available publicly to property owners and insurance companies.
| Identifer | oai:union.ndltd.org:ETSU/oai:dc.etsu.edu:etd-4964 |
| Date | 01 December 2018 |
| Creators | Fasesin, Kingsley |
| Publisher | Digital Commons @ East Tennessee State University |
| Source Sets | East Tennessee State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
| Rights | Copyright by the authors. |
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