Doctoral Dissertations

Date of Award

12-2016

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Major

Anthropology

Major Professor

Amy Z. Mundorff

Committee Members

Devin White, Dawnie Steadman, Budhendra Bhaduri

Abstract

This study is an analysis of terrestrial remote sensing data sets collected at the University of Tennessee’s Anthropology Research Facility (ARF). The objective is to characterize human burial signatures using spectroscopy and laser scanning technologies. The development of remote human burial detection methodologies depends on basic research to establish signatures that inform forensic investigations. This dissertation provides recommendations for future research on remote sensing of human burials, and for investigators who wish to apply these technologies to case work.

Data used in this study include terrestrial spectra, aerial hyperspectral imagery, satellite multispectral imagery, terrestrial light detection and ranging (LIDAR), and aerial LIDAR. In February 2013, ten individuals donated through the Forensic Anthropology Center body donation program were buried in three differently sized graves at the ARF. The graves contain one, three, and six bodies, respectively. An empty experimental control grave was also created. Terrestrial data collections were made from two-days pre-burial to 21-months post-burial. Aerial data were collected from 19 to 27-months post-burial. Satellite imagery was collected from six-months pre-burial to 23-months post-burial. Analytical emphasis is placed on the terrestrial data sets, which are of the highest spatial and spectral fidelity. Results of terrestrial data analysis reveal separable spectral and topographic signatures between the disturbed locations and surrounding undisturbed area. Aerial and satellite data were used to attempt validation of terrestrial data analysis findings, but findings were inconclusive.

This study demonstrates that live vegetation spectral samples can be correctly classified as disturbed or undisturbed groups at rates from 52.0 – 78.3% using statistically-based classification models. Additionally, this study documents localized elevation change at burial surfaces as a result of initial digging activity, subsequent soil settling and subsurface decomposition. The findings of this research are significant to both researchers and practitioners. It is the first study to compare live vegetation spectra associated with human burials and is the first to document burial elevation change using LIDAR. This work contributes to a collective understanding of human burial signatures that can be used together or with other geophysical methods to assist in locating unmarked human burials.

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