Aaron Gaither & Darin Erickson

Project Report

FNRM 3262

12/03/14

Jakobshavn Isbrae Glacial RetreatProject Final Report

Project Description and Objective:

We want to be able to develop time lapse imagery showing the transformation of the

Jakobshavn Isbrae glacier in Greenland.  The importance of this data can be used globally and

locally.  It can show how global warming is altering glaciers.  This issue will also have an impact

on global hydrology. As glaciers around the world are retreating they continuously release their

stored water, this in turn will have an impact on sea levels. Due to the glacial retreat, fresh water

loss may become an important issue in the near future to the local environment. We will analyze

5 different images to determine how far the glacier has retreated in the last 30 years. If data is

available, we will try to compare average local temperature to glacial retreat.

The area of interest for our project is located on the west shore of Greenland. Our study

area encompasses the entire Jakobshavn Isbrae glacier which covers roughly 3,819 km2 and

extends westward to the Arctic Ocean. Our data collection of glacial retreat covers roughly 2,050

km2 within the study area (Alley). However, our data suggests that this area is dropping each

year.

Materials, Tools and Concepts:

We used 5 different Landsat images for our data collection ranging from 1985 – 2014.

We used images from USGS Earth Explorer. All the images were collected from path 9 – row 11

with a Lat. (69.6) and Lon._ (- 49.4) with cloud cover less than 20 %. The table below describes

the data correlated with our images including: Image ID, Landsat #, Image acquisition date and #

of bands.

We also used Google Earth to initially identify the extent of the glacier and identify the

coordinates we should use for the Landsat images.

Image ID Landsat # Image Acquisition # of Bands

LT50090111985184KIS00 Landsat 4-5 TM July 3rd, 1985 7

LT50090111990166KIS00 Landsat 4-5 TM June 15th, 1990 7

LE70090112001188EDC00 Landsat 7 SLC-on July 7th, 2001 9

LE70090112009210EDC00 Landsat 7 SLC – off July 29th, 2009 9

LC80090112014184LGN00 Landsat 8 OLI July 3rd, 2014 12

Fig. 1

Procedures & Pre-processing

We found 5 Landsat images with less than 20% cloud cover that accurately portrayed our

area of interest, over a 30 year time frame. We had difficulty finding images from Landsat 7 that

truly displayed our area of interest. Coincidently, our 2009 image contains scan lines, but does

not pose any problems with classification. All of the images are from the month of July except

for year 1990 which is from June. In order to analyze our images, we needed to retrieve them

from USGS Earth Explorer. For each image, we selected “download option” and chose level 1

product which provided use with data from each spectral band as well as other meta-data.

After downloading each image, we needed to extract the data from each zip file in order

to work in Erdas Imagine. We assigned each image to a specific folder corresponding with the

date of acquisition. Once in Erdas, we opened an empty 2D viewer. We then selected “Raster”,

“Spectral” and “Layer Stack” in that order. We then selected each individual Tiff file to be

stacked in ascending order. After completing these steps for each of the five images, we were

able to clearly view our area of interest. We set the spectral bands at 4 (red), 3 (green), 2 (blue)

for all images up to 2009. For our Landsat 8 image from 2014, we set the spectral bands at 5

(red), 4 (green), 3 (blue). With all of our images successfully extracted and stacked, we were

then able to create subset the images.

To create a subset image we used the “help” tool to identify the “subset” function. After

identifying this function, we chose to do a four corner subset image. The coordinates for each

image are as follows:

ULX 486690.98

ULY 7699142.5

LRX 581497.47

LRY 7658999.21

URX 581497.47

URY 7699142.5

LLX 486690.98

LLY 7658999.21

Fig. 2

Classification and Analysis

After completion of our pre-processing, it was time to classify the images. Initially we

used an unsupervised classification with 7 classes, 50 maximum iterations and .98 convergence

threshold to identify the different cover types. We chose to identify: Snow Cover, Open Water,

Cloud Cover, Fjord Ice, Glacier, Vegetative Cover and Non-Vegetative Cover. The unsupervised

classification was unsuccessful with all images to correctly identify the cover types. We then

decided to run a supervised classification with maximum likelihood as our parametric rule. We

created 10 polygons within each cover type, all within our area of interest. We then generated

classified images for each Landsat image. This approach worked moderately well except for a

few minor errors.

The 1985 image contained cloud cover on the South East portion of the image. However,

this does not affect our goal of analyzing glacial retreat. The 1990 classified image had difficulty

distinguishing between the glacial edge and snow cover of the northern inlet. We believe this is

due to the fact that it is the only image taken in June rather than July, thus comprising of more

snow cover. We will use the actual Landsat image to digitize a polyline to define the northern

glacial outlet. The last error we had with classification was scan lines in our 2009 image. This

was not too difficult to overcome. We assigned a pixel value to correspond to the scan lines. This

differentiated those pixels from already assigned classes. We were then able to accurately

identify the glacial edge and other cover types. While we were able to differentiate between

cover types, our accuracy assessment was negatively affected by this error due to a histogram

value of 0.

After classifying the images we needed to run an accuracy assessment of each image. To

do this we selected Raster > Supervised > Accuracy Assessment. After assigning the appropriate

images to the assessment, we were able to set our parameters. We used a search count of 1024,

50 points with a minimum of 10 points. We chose stratified random for our distribution

parameters. By choosing stratified random, we were able to have an equal distribution of points

across the image while still maintaining randomization within each classified pixel value. The

accuracy results are as follows:

1985 Image

Class NameReference Totals

Classified Totals

Number

Correct

Producers

Accuracy

Users Accurac

y Snow Cover 0 0 0 --- ---

Glacier 20 20 20 100% 100%Non Vegetated 2 2 2 100% 100%

Water 6 6 6 100% 100% Vegetation 13 13 13 100% 100% Fjord Ice 8 8 8 100% 100%

Cloud Cover 1 1 1 100% 100% Totals 50 50 50

Overall Classification Accuracy = 100%Fig. 3

1990 Image

Class NameReference Totals

Classified Totals

Number

CorrectProducers Accuracy

Users Accurac

y Glacier 16 18 16 100% 89% Water 8 8 8 100% 100% Fjord 7 5 5 71% 100%

Bare Soil 3 4 3 100% 75% Vegetation 10 9 9 90% 100% Snow Cover 6 6 6 100% 100%Overall Classification Accuracy = 94%

Fig. 4

2001 Image

Class NameReference Totals

Classified Totals

Number Correct

Producers Accuracy

Users Accuracy

Water 13 9 9 69% 100% Vegetation 10 12 10 100% 83% Fjord 7 10 5 71% 50% Non Vegetation 1 2 1 100% 50% Glacier 19 17 15 79% 88% Snow 0 0 0 --- --- Totals 50 50 40

Overall Classification Accuracy = 80%

Fig. 5

2009 Image

Class NameReference Totals

Classified Totals

Number Correct

Producers Accuracy

Users Accuracy

Scan Line 0 0 0 --- --- Water 8 5 5 63% 100% Non Vegetation 1 3 1 100% 33% Vegetative 13 14 12 92% 86% Fjord 5 11 3 60% 27% Glacier 23 17 15 65% 88% Totals 50 50 36Overall Classification Accuracy = 72%

Fig. 6

2014 Image

Class NameReference Totals

Classified Totals

Number Correct

Producers Accuracy

Users Accuracy

Water 10 9 9 90% 100% Vegetation 12 12 11 92% 92% Non Vegetation 1 2 1 100% 50% Fjord 7 10 7 100% 70% Glacier 20 17 17 85% 100% Totals 50 50 45Overall Classification Accuracy = 90%

Fig. 7

We used ArcGIS to calculate the distance and area of total retreat of both the main glacier

and northern outlet glacier. We created polyline shape-files for each Landsat image to identify

glacial boundaries. To do this, we selected Catalog > Folder Connections > E: Drive >

Respective Folder. Within that folder we created a new polyline shape-file. After all polylines

were created, we were able to calculate the total distance of retreat and total change in area. We

retrieved these values by using the measure tool in ArcGIS, this allowed us to calculate area and

average distance. We computed values of change relative to 1985 and change relative to the

previous study year.

Current Results and Images:

Throughout our project we were able to generate many images and tables to help evaluate

the retreat of Jakobshavn Glacier. The image shown below is a 2014 Landsat image with each

glacial boundary from respective years. The two main study areas we focused on were the

Northern Outlet and the Main Glacier.

Fig. 8

The extent of glacial retreat from 1985 can be evaluated from this image. It is relatively

simple to notice a change in glacial boundary. From 1985 – 1990 there was an advance in glacial

Northern Outlet

Main Glacier

edge. In years following 1990, there was significant retreat of glacial boundaries within both

study areas. Below is a table that represents retreat distance and change in area relative to the

previous study year.

Main Glacier North Outlet

YearRetreat Distance (km)

Change in Area (km2)

Retreat Distance (km)

Change in Area (km2)

1985 -1990 -1.15 -7.88 -0.745 -1.641990 -2001 2.57 22.98 0.957 2.112001- 2009 12.52 144.35 2.05 6.192009-2014 2.27 31.6 0.453 2.29

Fig. 9

Each value represents change relative to the previous study year. Negative values

represent growth in glacier while positive values represent retreat. We noticed a significant

retreat distance and change in area in years 2001 – 2009. We calculated 75% of the total glacial

retreat occurred between the years of 2001 – 2009. Below is a table that represents retreat

distance and change in area from 1985.

Main Glacier North Outlet

Year since 1985

Retreat Distance since 1985 (km)

Area lost since 1985 (km2)

Distance since 1985 (km )

Area since 1985 (km2)

5 -1.15 -7.88 -0.75 -1.6416 1.42 15.1 0.20 0.4724 13.94 159.45 2.25 6.6629 16.20 191.05 2.70 8.95

TotalFig. 10

Each value represents change relative to 1985. Negative values represent growth in

glacier while positive values represent retreat. With this data we were able to calculate an

average retreat distance of .56 km/yr and a total retreat distance of 16.2 km. We also calculated

an area loss of 6.6 km2/yr and a total of 8.95 km2 since 1985. According to the Polar Research

Center of Ohio State University, “Jakobshavn glacier is one of the world’s fastest moving

glaciers reaching speeds up to 7 km/year” (Hong-Gyoo). Figures 11 and 12 help visualize the

change in retreat distance of the main glacier and northern outlet.

1985 -1990

1990 -2001

2001- 2009

2009-2014

-2.00 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00

Main Glacial Retreat Distance

Distance (km)

Year

s

Fig. 11

1985 -1990

1990 -2001

2001- 2009

2009-2014

-1 -0.5 0 0.5 1 1.5 2 2.5

North Outlet Glacial Retreat Distance

Distance (km)

Year

s

Fig. 12

We were able to retrieve data of average monthly and yearly temperature near

Jakobshavn glacier from tutiempo.net. We chose to collect data from the month of July to not

only represent our image retrieval date but also the months consistently above freezing. The

information collected is represented in Figure 13 below. We noticed a slight increase in average

yearly temperature from 1985 – 2013. While an increase of only 1-2 deg. Celsius may not appear

to be significant, it warrants a correlation between glacial retreat and rising temperature. We also

noticed a substantial increase in temperature of 9.1 deg. Celsius between the years 2003 – 2008.

This is also the time frame of the major glacial retreat.

1990 1995 2000 2005 2010 2015-10

-5

0

5

10

15

Jakobshavn Yearly and Monthly Average Temperature

Average annualTemp. C deg. Linear (Average annualTemp. C deg.)Average Monthly Temp. July Linear (Average Monthly Temp. July)

Year

Degr

ees C

elsiu

s

Fig. 13

Discussion:

The evaluation of glacial retreat of Jakobshavn Glacier has revealed many important

findings. However, there were several challenges to overcome in order to accurately assess

glacial retreat. Our first challenge was to find suitable Landsat images. This meant finding

downloadable images, scan-line free, low atmospheric interference and date acquisition. We

were able to find four images that clearly represented our area. Our fifth image of 2009

contained scan lines, but we were able to accurately identify the glacial edge and compute data.

As discussed earlier, we had difficulty classifying our images correctly due to snow cover

over vegetated and non-vegetated areas. We used the previous images to help determine the

correct classification. We conducted an accuracy assessment of all of the classified images. In

1985 we obtained 100% accuracy. We are skeptical of the results because it is highly unlikely to

achieve this level of accuracy. We may have simply had incredible luck with that particular

assessment.

We initially hypothesized that temperature will have a strong correlation with the retreat

of the glacier. We were surprised that the glacier advanced between the years of 1985 – 1990.

This is important to understand because there can be many influences on glacial retreat that we

have not fully identified. We also obtained our average temperatures of the local area rather than

the average temperature of the entire area of Greenland. This was important because the West

coast, Jakobshavn glacier, is consistently warmer than the rest of Greenland. While we fail to

reject our hypothesis, we will take into consideration that the increase in temperature was

minimal resulting in a weak correlation between temperature and glacial retreat.

While the data collected is sufficient to monitor glacial retreat, there can be extended

research on the area. This research would include data collection of surrounding Labrador Sea

temperature, change detection of vegetation and exposed land area, comparison to similar

glaciers and further understanding of historical measurements prior to 1985. With more research

and data collection of the area, we might be able to present a stronger correlation between

temperature and glacial retreat.

Sources:

Alley, Richard B., Peter U. Clark, Philippe Huybrechts, Ian Joughin. Ice Sheet and Sea Level Changes. < http://www.sciencemag.org/content/310/5747/456.full > 10/21/05. Web. 12/3/14

Hong-Gyoo, Sohn. Jakobshavn Glacier, West Greenland: 30 Years of Space Born Obstervation. < onlinelibrary.wiley.com/doi/10.1029/98GLO1973/pdf > 7/15/1998. Web. 12/3/14

Software used: Erdas Imagine and ArcMap10.2. 2014

Tu Tiempo: World Weather - Local Weather Forecast. Historical Weather: ILULISSAT.weather station 42210 “BGQQ” < http://www.tutiempo.net/en/Climate/ILULISSAT_JAKOBSHA/42210.htm > 2014. Web. 11/26/14

U.S. U.S. Geological Survey. Glovis. Path 9, Row 11: 07/03/1985, 06/15/1990, 07/07/2001, 07/29/2009, and 07/03/2014. < http://glovis.usgs.gov/ > 2014. Web.Oct/15-21 of 2014

Appendix:

YearAverage Annual Temp. C Deg. Year

Average Monthly Temp. July C. Deg.

1992 -7.1 1992 6.41993 -6.9 1993 8.31994 -5.9 1994 7.51995 -4.9 1995 9.11996 -3.9 1996 6.81997 -3.7 1997 5.91998 -2.9 1998 8.91999 -4.7 1999 9.12000 -2.9 2000 82001 -3.2 2001 92002 -3.8 2002 72003 -2.7 2003 8.52004 -6.5 2004 4.32005 -4.2 2005 6.72006 -3.4 2006 13.42007 -2.7 2007 9.62008 -3.7 2008 9.32009 -3.2 2009 92010 -0.1 2010 8.72011 -4 2011 10.22012 -3 2012 9.32013 -3.2 2013 7.5