Climate change is greatly affecting glaciers in Alaska. Since the Earth’s temperature is increasing, glaciers have been thinning, receding, and completely melting away. This is causing the sea level to rise. The impacts of climate change are going to keep getting worse. We are all affected by this no matter where we live in the world.
The area of this study is in Southcentral Alaska. It is roughly 60 miles south of Alaska’s largest city, Anchorage, and about 30 miles northeast of Whittier. This area is frequently visited by tourists and cruise ships. A massive slippage here could be detrimental not only to the tourism industry but also for the locals.
The purpose of my research is to show the extent of glaciers slipping because this problem has a domino effect that will hurt us and other living things. It can create a huge tsunami, extinct wildlife, and ruin the coral reefs. The list can go on and on and it will not get better unless we do something soon.
Figure 1: Study Area Source: USGS.gov
Anchorage could be the scene of a catastrophic tsunami. According to ECO Watch, an environmental news source for a healthier planet and life said a letter was signed by a few scientists that warns people about the possibilities of a tsunami happening within the next year and highly likely within 20 years (Davidson 2020).
The most clearly defined evidence that global warming is present is the disappearance of mountain glaciers around the world. Scientists will use the distance in thickness of snow from the previous years’ measurements to determine whether or not the glacier has grown or shrunk. (Lindsey 2020).
I first used Google Maps to determine the region I wanted to use for my research. Once I found that, I used the USGS’s Earth Explorer to download two remote sensing data—one from August 4th, 2015, and the other from April 27th, 2020. Both images were from Landsat 8 OLI/TIRS C1 level-1. I stacked only bands 2 through 7 of the individual TIF images to create the new multi-layer image that I used to run the supervised classification. I gave both images the same 6 classes: water, bare soil, partial melting/ thinner ice, snow-ice, compacted snow, and snow/ice/debris mixture. To make sure you clearly understand each class, I provide the operation definition for each (except for water and bare soil since those are commonly known):
Partial melting/ thinner ice: piece of the glacier that is melting or receding, thus creating a thin ice sheet over the pre-existing area.
Snow-ice: a combination of lighter snow over a sheet of ice.
Compacted snow: heavy and dense snow. (not “fresh” snowfall)
Snow/ice/debris mixture: the materials left behind from glaciers. These can be rocks, sand, or clay. There could also be some mild vegetation in the mixture. All of this is partial covered by a layer or snow or ice depending on the time of the year and weather.
I followed Jingxiao Zhang et al mythology. In their research, they explored the efficiency of machine-learning technique by doing a classification analysis on glaciers.
After comparing the two classified Landsat images, I found that most of the snow-ice areas in the 2015 image turned into partial melting/ thinner ice areas in the 2020 image. The reason for this is global warming. When the earth gets warmer, the glaciers melt faster. The melted water flows into nearby water sources which contributes to the accelerating sea level. I validated my classification results by using the collected testing samples. I discovered there was significantly more snow, ice, and debris mixture in the 2020 data. The mixture is created from the materials left behind by glaciers which means the compacted snow and snow-ice are melting. There was also an increase of water which creates a big problem in many coastal regions. Besides the obvious flooding problem, it could interfere with farming, costal plant life, and wildlife. Below are the images of the classification (Figure 2 and Figure 3).
If you look at the table below (Figure 4), you can see the percentages of each class for the 2015 and 2020 data. I created the summary report table by listing the classification results. From there, I checked the option to show the class distribution for my selected area. I found this feature in MultiSpec to be extremely useful given the text output was large. I copied the results into Excel so I could make a clustered column chart (Figure 5).
Area (Hectares, 2015)
Area (Hectares, 2020)
Partial Melting/ Thinner Ice
Snow, Ice, and Debris Mixture
Figure 4 - Summary Report Table
Water has increased by 3.7% between 2015 and 2020. This is due to the glaciers melting which moves mass from the land to the sea. We can tell that the glaciers are disappearing because there is an increase in bare soil, thinner ice, and the debris mixture. In addition to this there is a decrease in compacted snow which is found in the northeast region of the data set.
Figure 5 - Summary Report Graph
The accuracy of my data is 85.2%. I think some things were misclassified because it can be difficult to distinguish the melting from debris-free and debris-covered glaciers. If a glacier has debris on it, this could contribute to accelerated melting rates. In addition, seasonal snow can make it more difficult to distinguish the snow-ice because they have a similar spectral curve.
If we use these machine-learning technologies as stated in the Zhang et al study, we could not only better prepare for a tsunami, but we could also use these tsunami signals to understand the relationship between glaciers and oceans—the key factor in predicting the future of glaciers.
Want to learn more about climate research and glaciers? Click here: https://www.usgs.gov/programs/climate-research-and-development-program/science/glaciers-and-climate-project
Davidson, J. (2020, May 15). Alaska Faces Major Tsunami Threat Scientists Warn. Retrieved June 16, 2022, from https://www.ecowatch.com/alaska-tsunami-threat-2646006013.html?rebelltitem=1
Lindsey, R. (2020, February 14). Climate Change: Glacier Mass Balance: NOAA Climate.gov. Retrieved June 16, 2022, from https://www.climate.gov/news-features/understanding-climate/climate-change-glacier-mass-balance
Zhang, J. et al (2019, February 22). Glacier Facies Mapping Using a Machine-Learning Algorithm: The Parlung Zangbo Basin Case Study. Retrieved June 16, 2022, from https://www.mdpi.com/2072-4292/11/4/452/htm#