Date of Graduation

12-2018

Document Type

Thesis

Degree Name

Master of Science in Geography (MS)

Degree Level

Graduate

Department

Geosciences

Advisor/Mentor

Jason A. Tullis

Committee Member

Fred Limp Jr.

Second Committee Member

John V. Brahana

Keywords

NDVI, Nutrients, Phenology

Abstract

Over 25 percent of the world’s population either lives on or obtains water from karst aquifers. The complex interactions between subsurface karst geologic features, the constant motion of the plant life cycle, and significant water resource demand all suggest the need to better define those interactions. The relationship of historical land surface phenology and water quality in karst topography were investigated in the Headwaters Illinois River watershed in Northwest Arkansas (NWA). This area represents high vulnerability to surface water and groundwater contamination, with both natural and anthropogenic processes such as over application of broil litter for enhanced cattle browse, affecting groundwater quality. Land surface phenology patterns influenced by these processes were identified using Landsat satellite imagery and object-based image analysis (OBIA). A normalized difference vegetation index (NDVI) time series was produced using Google Earth Engine for all passes over the study area that meet atmospheric and data quality criteria over two decades from 1999 to 2018. Analysis of NDVI and ancillary data over time allowed insight into vegetation health norms, deviation from those norms, and human impact upon regional vegetation. OBIA techniques were used to segment vegetation index time series pixels into polygons based on adjacency and similarity. Resulting polygons were categorized using an unsupervised clustering approach, and were labeled based on visual and expert interpretation of the study area. The relation of the image analysis results to groundwater quality was determined using data organized by hydrologic catchments within the study area. Comparison of the decadal water quality data and NDVI image analysis resulted in meaningful temporal patterns within the datasets but showed a near 0 slope for NDVI and water quality metrics. Future LSP studies should consider areas with greater spatial and temporal availability of water quality metrics and variable surface/groundwater interactions.

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