Award Date

5-1-2019

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Geoscience

First Committee Member

Steven Rowland

Second Committee Member

Lynn Fenstermaker

Third Committee Member

Gabriel Judkins

Fourth Committee Member

Rebecca Martin

Number of Pages

99

Abstract

This thesis is a combination of two separate but related projects. The first project is a Potential Fossil Yield Classification (PYFC) survey. The PFYC is a Bureau of Land Management funded survey designed to synthesize paleontologic information into a geographic information system (GIS) as a distributable geodatabase. The database is designed to represent surficial geologic deposits contained in a polygon shapefile. Throughout the State of Nevada each polygon represents a mapped geologic unit at a scale of at least 1:250 k. Each mapped geologic unit is then assigned a “potential fossil yield classification”, a numerical ranking value of 1-5 based on the known fossils within a geologic unit. Fossil type and abundance are considered in the assignment of a PFYC value, 1 being the lowest, and 5 being the highest.

The second project consists of a multi-temporal land-use/land-cover change detection analysis designed to measure effects of rapid urbanization within a geologic unit identified to have the highest fossil potential based on the results of the PFYC survey. The Las Vegas Formation (LVfm) is a Pleistocene groundwater discharge deposit that has been shown to contain significant vertebrate fossils, thus being assigned a PFYC value of 5. The proximity of the LVfm to the densely populated city of Las Vegas provides a unique opportunity quantify effects of urbanization to lands rich with fossil resources. This project is designed to utilize remotely sensed imagery and aerial light detection and ranging (LiDAR) point clouds to accurately quantify urbanization effects

Keywords

Change-detectioin; GIS; Las Vegas; Remote-sensing; Urbanization

Disciplines

Environmental Sciences | Geographic Information Sciences | Remote Sensing

Language

English


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