Date of Award

Spring 1-1-2012

Document Type


Degree Name

Master of Arts (MA)



First Advisor

Mark Serreze

Second Advisor

Noah Molotch

Third Advisor

David Gallaher


This thesis analyzes the long-term albedo variability of the Greenland Ice Sheet (GrIS) using 25 years of Advanced Very High Resolution Radiometer (AVHRR) Polar Pathfinder satellite data. Previous studies of the GrIS have generally limited their spatiotemporal extents to either coarse resolutions or relatively short time spans, or else concentrated on small, dynamic areas. The premise of this thesis is that these limitations may be obscuring larger patterns or changes in the GrIS albedo.

Analyzing massive remotely-sensed data sets presents formidable challenges. In response, and concurrent with the albedo analysis, this thesis demonstrates new techniques for managing and analyzing large satellite-derived data sets. Prototype database technology is introduced that enables scalable storage and rapid, random-access to data at high spatiotemporal resolutions. To answer the scientific questions posed, analysis tools are included with the database architecture and applied to the entire data set.

This thesis determines the spatial distributions of albedo means, medians and variances, as well as any long-term trends. The spatial and temporal relationships between synoptic weather forcings and albedo changes are also explored. Where possible, ground-truth data is supplied to support the satellite-based conclusions. In the course of assembling and analyzing the data, the current study reveals several types of previously undetected errors in the data set. This outcome validates the original premise of the thesis and demonstrates additional capabilities of the database and analysis system.

The final results show that the AVHRR/Polar Pathfinder data set used herein is of poor quality. However, the analysis methods, quality assurance, and data management techniques used in this thesis demonstrate that the technology now exists to manage massive remotely-sensed data sets within distributed databases. This capability may open new possibilities for rapid data analysis, and prove useful for managing and analyzing large data sets beyond the field of remote sensing.