QUANTIFICATION OF PERMAFROST THAW DEPTH AND SNOW DEPTH IN INTERIOR ALASKA AT MULTIPLE SCALES USING FIELD, AIRBORNE, AND SPACEBORNE DATA

• Other Authors: Brodylo, David (author), Zhang, Caiyun (Thesis advisor), Florida Atlantic University (Degree grantor), Department of Geosciences, Charles E. Schmidt College of Science
• Format: Text
• Language: English
• Published: Florida Atlantic University
• Subjects: Permafrost > Alaska; Remote sensing; Machine learning; Arctic; permafrost; Alaska
• Online Access: http://purl.flvc.org/fau/fd/FA00014229; https://fau.digital.flvc.org/islandora/object/fau%3A98136/datastream/TN/view/QUANTIFICATION%20OF%20PERMAFROST%20THAW%20DEPTH%20AND%20SNOW%20DEPTH%20IN%20INTERIOR%20ALASKA%20AT%20MULTIPLE%20SCALES%20USING%20FIELD,%20AIRBORNE,%20AND%20SPACEBORNE%20DATA.jpg

Much of Interior Alaska contains permafrost, which is a permanently frozen layer found within or at the surface of the Earth. Historically, this permafrost has experienced relative stability, with limited thaw during warmer summer months and fire events. However, largely due to the impact of a warming climate, among other factors, permafrost that would typically experience limited thawing during the summer season has recently been thawing at an unprecedented rate. Trapped by this layer of permafrost is a large quantity of carbon (C), which could be released into the atmosphere as greenhouse gases such as carbon dioxide (CO2) and methane (CH4). Due to the remoteness of the Arctic, there is a lack of yearly recorded permafrost thaw depth and snow depth values across much of the region. As such, the focus of this research was to establish a framework to identify how permafrost thaw depth and snow depth can be predicted across both a 1 km2 local scale and a 100 km2 regional scale in Interior Alaska by a combination of 1 m2 field data, airborne and spaceborne remote sensing products, and object-based machine learning techniques from 2014 – 2022. Machine learning techniques Random Forest, Support Vector Machine, k-Nearest Neighbor, Multiple Linear Regression, and Ensemble Analysis were applied to predict the permafrost thaw depth and snow depth. Results indicated that this methodology was able to successfully upscale both the 1 m2 field permafrost thaw depth and snow depth data to a 1 km2 local scale before successfully further upscaling the estimated results to a 100 km2 regional scale, while also linking the estimated values with ecotypes. The best results were produced by Ensemble Analysis, which tended to have the highest Pearson’s Correlation Coefficient, alongside the lowest Mean Absolute Error and Root Mean Square Error. Both Random Forest and k-Nearest Neighbor also provided encouraging results. The presence or absence of a thick canopy cover was strongly connected with thaw depth and snow depth estimates. ...