Authors: Noah Christensen, Matt Olson
Mentors: Matt Olson
Insitution: Utah Valley University
Geologic maps serve as a valuable tool with diverse applications, one of which is resource exploration. Understanding available resources in America is beneficial for enhancing energy security, environmental sustainability, and economic growth. As political discourse rises, it is crucial to address national security concerns and head towards short- and long-term goals of energy independence. Traditional geologic mapping is characterized by time-intensive and high-cost fieldwork, but through multispectral and hyperspectral remotely sensed imagery it is possible to instantly map extensive areas using unique absorption patterns of minerals in the reflected spectral signature of the electromagnetic spectrum. This study explores an intercomparison of mineral mapping using Hyperion (EO-1), ASTER, and AVIRIS imagery in the Marysvale Volcanic Field, Utah. The satellites and high-altitude aircraft were chosen due to their varying number of spectral channels, spectral ranges, and spatial resolutions, allowing for a cross-analysis of their strengths, weaknesses, and overall capabilities. While previous studies have utilized AVIRIS and ASTER imagery within the Marysvale Volcanic Field, no prior research has explored a comparison of mineralogical maps using these sensors at a specific location. All analysis will be conducted through open-source applications to promote accessibility in future research and reproducibility of image generation through the sharing of R code. This research will enhance our comprehension of the necessary spectral and spatial resolutions for generating accurate mineral identification. Published geologic maps and in-situ field samples were used to validate the generated maps. We expect to find one of two outcomes: a strong contrast in classified mineralogy depending on the image source used, pointing towards a need for higher spatial and spectral resolutions to achieve accurate mapping, or minimal variation in classification, indicative of an unnecessary number of spectral bands. Developing accurate and accessible mineral mapping tools may be the next step in strengthening our knowledge of resource availability, without the need for rigorous traditional mapping methods.