Aaron Gaither & Darin Erickson
Project Report
FNRM 3262
12/03/14
Jakobshavn Isbrae Glacial Retreat
Project 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
Reference Classified Number
Class Name
Totals
Totals
Correct
Snow Cover
0
0
0
Glacier
20
20
20
Non Vegetated
2
2
2
Water
6
6
6
Vegetation
13
13
13
Fjord Ice
8
8
8
Cloud Cover
1
1
1
Totals
50
50
50
Overall Classification Accuracy = 100%
Producers
Users
Accuracy Accuracy
----100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
Fig. 3
1990 Image
Reference Classified
Class Name
Totals
Totals
Glacier
16
18
Water
8
8
Fjord
7
5
Bare Soil
3
4
Vegetation
10
9
Snow Cover
6
6
Overall Classification Accuracy = 94%
Number
Correct
16
8
5
3
9
6
Producers
Accuracy
100%
100%
71%
100%
90%
100%
Users
Accuracy
89%
100%
100%
75%
100%
100%
Fig. 4
2001 Image
Reference Classified
Class Name
Totals
Totals
Water
13
9
Vegetation
10
12
Fjord
7
10
Non
Vegetation
1
2
Glacier
19
17
Snow
0
0
Totals
50
50
Overall Classification Accuracy = 80%
Fig. 5
Number
Correct
9
10
5
1
15
0
40
Producers Users
Accuracy Accuracy
69%
100%
100%
83%
71%
50%
100%
79%
---
50%
88%
---
2009 Image
Reference Classified Number
Class Name
Totals
Totals
Correct
Scan Line
0
0
0
Water
8
5
5
Non
Vegetation
1
3
1
Vegetative
13
14
12
Fjord
5
11
3
Glacier
23
17
15
Totals
50
50
36
Overall Classification Accuracy = 72%
Producers Users
Accuracy Accuracy
----63%
100%
100%
92%
60%
65%
33%
86%
27%
88%
Fig. 6
2014 Image
Reference Classified Number
Class Name
Totals
Totals
Correct
Water
10
9
9
Vegetation
12
12
11
Non Vegetation
1
2
1
Fjord
7
10
7
Glacier
20
17
17
Totals
50
50
45
Overall Classification Accuracy = 90%
Producers Users
Accuracy Accuracy
90%
100%
92%
92%
100%
50%
100%
70%
85%
100%
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.
Northern Outlet
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
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
Retreat Distance
Change in Area
(km)
(km2)
Year
1985 -1990
1990 -2001
2001- 2009
2009-2014
-1.15
2.57
12.52
2.27
-7.88
22.98
144.35
31.6
North Outlet
Retreat Distance
Change in Area
(km)
(km2)
-0.745
0.957
2.05
0.453
-1.64
2.11
6.19
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.
Year since 1985
5
16
24
29
Main Glacier
Retreat
Distance since
1985 (km)
-1.15
1.42
13.94
16.20
North Outlet
Area lost
since 1985
(km2)
-7.88
15.1
159.45
191.05
Total
Distance since
1985 (km )
-0.75
0.20
2.25
2.70
Area since
1985 (km2)
-1.64
0.47
6.66
8.95
Fig. 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.
Main Glacial Retreat Distance
2009-2014
Years
2001- 2009
1990 -2001
1985 -1990
-2.00
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
Distance (km)
Fig. 11
North Outlet Glacial Retreat Distance
Years
2009-2014
2001- 2009
1990 -2001
1985 -1990
-1
-0.5
0
0.5
1
1.5
2
2.5
Distance (km)
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.
Jakobshavn Yearly and Monthly Average Temperature
Degrees Celsius
15
10
5
0
-5
-10
1990
1995
2000
2005
2010
2015
Year
Average annualTemp. C deg.
Average Monthly Temp. July
Linear (Average annualTemp. C deg.)
Linear (Average Monthly Temp. July)
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:
Average Annual Temp. C
Deg.
Year
Average Monthly
Temp. July C. Deg.
Year
1992
-7.1
1992
6.4
1993
-6.9
1993
8.3
1994
-5.9
1994
7.5
1995
-4.9
1995
9.1
1996
-3.9
1996
6.8
1997
-3.7
1997
5.9
1998
-2.9
1998
8.9
1999
-4.7
1999
9.1
2000
-2.9
2000
8
2001
-3.2
2001
9
2002
-3.8
2002
7
2003
-2.7
2003
8.5
2004
-6.5
2004
4.3
2005
-4.2
2005
6.7
2006
-3.4
2006
13.4
2007
-2.7
2007
9.6
2008
-3.7
2008
9.3
2009
-3.2
2009
9
2010
-0.1
2010
8.7
2011
-4
2011
10.2
2012
-3
2012
9.3
2013
-3.2
2013
7.5