Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Quantitative Analysis of Thermophysical Properties of Lava Flows on Earth and Mars

Simurda, Christine (2019) Quantitative Analysis of Thermophysical Properties of Lava Flows on Earth and Mars. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

[img]
Preview
PDF
Download (12MB) | Preview

Abstract

Multi-instrument approaches, at different spatial and spectral resolutions, are used to investigate the thermophysical properties of lava flows at the subpixel scale. Development of remote sensing aerial and terrain technology has provided higher spatial resolution data that can improve the derivation of surface properties from satellite datasets. TIR data have applications to interpret surface properties of planetary bodies, but are limited by the lower spatial resolution. This research utilizes multi-instrument approaches to improve the understanding of the subpixel surface properties derived from TIR data, specifically to quantify the presence of shadowing, mixed pixels, and complex surfaces with horizontal mixing and vertical layering. Visible data, with higher spatial resolutions, are used to interpret the surface topography and/or structures and TIR data, with lower spatial resolutions, are used to understand thermal properties to derive particle size and composition. Two study areas were the focus of this research: a terrestrial analog at the North Coulee, part of the Mono-Inyo Crater System, and the Daedalia Planum flow field on Mars. At the North Coulee, studies assessed the effect of shadows on ATI and aimed to better understanding the relationship between mixed pixels (with subpixel particle and block sizes variability) and ATI. The locations of shadows were identified using a DEM and a correction applied based on the areal percentage of a pixel in shadow. Analysis of the relationship between mixed pixels and ATI demonstrates that the current assumption of uniform material at the pixel scale will cause incorrect derivation of moderate and coarse materials at higher ATI values. The studies on Daedalia Planum, Mars, aim to determine the cause of the thermophysical variation between lava flows and define the areal percentage of dust, sand, and lava outcrops on the flow surfaces. Through this quantitative analysis, the variability was determined to be caused by different vertical layering and horizontal mixing of these components and that some flows have up to 40% identifiable lava outcrops with a dust layer of 0.2 mm. These techniques demonstrate the application of multi-instrument approaches to investigate complex surfaces with mixtures and layering below the spatial resolution of current TIR instruments.


Share

Citation/Export:
Social Networking:
Share |

Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Simurda, ChristineSimurda_C@pitt.educms2560000000157996396
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairRamsey, Michaelmramsey@pitt.edu
Committee MemberCrown, Davidcrown@psi.edu
Committee MemberHarbert, Williamharbert@pitt.edu
Committee MemberShelef, Eitanshelef@pitt.edu
Committee MemberStewart, Brainbstewart@pitt.edu
Date: 25 September 2019
Date Type: Publication
Defense Date: 1 March 2019
Approval Date: 25 September 2019
Submission Date: 29 July 2019
Access Restriction: 2 year -- Restrict access to University of Pittsburgh for a period of 2 years.
Number of Pages: 216
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Geology and Planetary Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Apparent Thermal Inertia, Horizontal Mixing and Vertical Layering of Materials, Multi-instrument Approach, Subpixel Properties, Thermal Inertia, Thermophysical Surface Properties
Date Deposited: 25 Sep 2019 16:23
Last Modified: 25 Sep 2021 05:15
URI: http://d-scholarship.pitt.edu/id/eprint/37030

Metrics

Monthly Views for the past 3 years

Plum Analytics


Actions (login required)

View Item View Item