Authors

Class Years:

Alyssa J. Kaewwilai '20, Gettysburg College

Charlie E. Reisman '21, Gettysburg College

Document Type

Student Research Paper

Date of Creation

Fall 2019

Department 1

Environmental Studies

Department 2

Anthropology

Abstract

Remote sensing mechanisms through the use of technology like the Landsat 5-7 Land Manager satellites are commonly used in conjunction with multispectral methods such as unsupervised classification to record and analyze changes in snow and ice over time in areas such as the Greenland Ice Sheet (GrIS). Unsupervised classification is a method of identifying, grouping, and labeling features in an image according to their spectral values and is therefore a good method of classifying snow and ice in areas such as Greenland. The goal of unsupervised classifications is to assign pixels into potentially meaningful subsurface classes based on similarities of geophysical responses, in order to create a final product that displays an accurate class map of the land cover of the area (Kvamme, 2019). Within the southwestern region of Greenland is the town Maniitsoq along the (GrIS) that is surrounded by natural canal-like channels and located in the Qeqqata municipality. The southwestern Maniitsoq Greenland territory lies within the northernmost area south of the Arctic Circle and contains big, long, and deep glaciers. Temperatures in southern Greenland do not exceed 20 °C within the summer months of June, July, or August. Greenland is warmest and driest on land closest to the ice sheet during these summer months (Topas Travel, 2019).

The location of our study area, the southwestern region of Greenland near Maniitsoq, was captured with Landsat imagery. The ice land cover was classified into three different sectional groups: changed snow/ice, unchanged snow/ice, and other. The research question of this project is as follows: how have the Greenland Ice Sheet glaciers along the eastern coast and inland Greenland changed from June through August of 1994-2004 and 2009-2019?

Comments

Written for ES 363: Remote Sensing.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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