Date of Award

2022

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Geography, Geology, and Anthropology

Abstract

This thesis describes the development and testing of an experimental method for monitoring the condition of archaeological features through computer-assisted 3D change detection. This method utilizes structure-from-motion (SfM) photogrammetry and the Model-to-Model Cloud Comparison (M3C2) algorithm to document and compare features in three dimensions. In collaboration with the Daniel Boone National Forest (DBNF) of eastern Kentucky, USA, this method was tested using a sample of nine ancient rock carvings, or petroglyphs, in hopes of identifying and tracking multiscale change. SfM photogrammetry is the process of creating 3D representations using 2D images, and the M3C2 algorithm measures the precise distance between co-registered 3D point clouds. Based on previous scholarship and case studies, M3C2 analysis is capable of identifying and tracking subtle changes along the surface of petroglyph rock carvings. These changes were expected to come in the form of both large-scale (meter) and small-scale (millimeter) changes associated with environmental weathering and tourist activity. This analysis was specifically designed to determine the effective temporal and spatial scale for a method using these techniques under in-field conditions. Each site was recorded using SfM photogrammetry at three epochs over the course of a year (May 2021 - May 2022) to create time-series datasets. Each epoch was then compared using the M3C2 algorithm generating millimeter accurate distance measurements. Overall, while the method provided reliable sub-centimeter change detection results, as of May 2022, little to no surface change has been detected across all nine petroglyph features being monitored.

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