Discipline: Mathematics and Statistics
Subcategory: Geosciences and Earth Sciences
Session: 4
Room: Senate
Edmund Robbins - Florida Institute of Technology
Co-Author(s): Jonathan Webb,University of Idaho, Moscow, Idaho; Thu Thu Hlaing, Ithaca College, Ithaca, New York
Glaciers are important indictors of climate change, as changes in physical features such as their area give measurable evidence of fluctuating temperature, precipitation, and other climate factors. The importance of mountain glaciers beyond the physical processes related to climate change are significant. On a social scale this significance cannot be underscored enough, given that millions of people around the world depend on the glacial runoff for water access for various activities. A clear understanding of the factors related to glacier recession is critical to understand potential impacts from climate change. The remote nature of mountain glaciers renders direct measurement impractical on anything other than a local scale. This project employs multispectral data collected in the form of satellite imagery, taken by Landsat at regular intervals in conjunction with climate factor measurements taken from a network of national and international sensors in time series format. The goal of this project is to quantify changes in the terminal point and area of Franz Josef and Gorner glaciers in response to climate factors identified during the modeling process. Multiple Linear Regression and Generalized Additive Models (GAM) are employed to identify significant climate factors that can explain and predict variations in the terminal point and area of the glaciers. The independent climate variables included Local Max Temp, Local Min Temp, Global Average Temp, Precipitation and Atmospheric Carbon Dioxide. The Dependent variables were Terminal point recession distance and glacier area. For each glacier 4 models were constructed, two multiple regression models and two GAMs. Starting with the full model and using the metrics of multiple R^2, AIC, and BIC. The best combination of climate factors was selected for each glacier. The GAMs developed in the modelling process identify the global CO2 level, temperature (local and global), and precipitation as significant factors in explaining the variability found in both the terminal point locations and area for both glaciers. These finding are in line with what is currently known about the physics of glacier motion. For the regression models it became clear from a QQ plot and analysis of the residuals that these models were not sufficiently flexible to capture the nonlinearity present in the physical processes being modeled. Additional modifications and extensions of the current process focus towards the future development of a human off the loop method for terminal point detection and glacier area quantification. In order to achieve this, additional work must be done to improve the image segmentation which still requires manual adjustment by modeler. This greatly slows if not prevents a global scale use of the current method. In addition, the application of Deep Learning models for image segmentation and object identification.
Funder Acknowledgement(s): Funding was provided by an NSF REU Grant to Dr. Nezamoddin N. Kachouie, Florida Institute of Technology
Faculty Advisor: Dr. Nezamoddin N. Kachouie, nezamoddin@fit.edu
Role: This project was one of the projects in the REU Summer 2021 Program of Statistical Modeling with Applications to Geoscience (SMAG ) at Florida Institute of Technology under direct supervision of Dr. Nezamoddin N. Kachouie and Dr. Lazarus. As a graduate student participant, I was involved in all aspects of this project working with a team of two undergrad students.