Urbanization and Land Cover Change in
Dakota County, Minnesota
FR 3262 Remote Sensing
Section 001/002
Kylee Berger and Julia Vang
Introduction
Land cover has changed immensely over time due to urbanization in Minnesota. As a
society, we do not always take into consideration the adverse effects of land cover changes we
make to accommodate the population growth. In Minnesota, from 1984 to 2005, the
population has grown drastically from 4,157,708 to 5,205,091, according to Leip and MnGeo.
This is a growth of about 1 million people within 21 years causing urbanization and more
natural lands to become developed. Also, cities are continuously growing as an effect of people
migrating to the cities. Another major threat to vegetation and/or natural area is the amount
of areas in Minnesota that has turned into agricultural land since 1984.
Therefore, to address the growing threat to vegetation such as forests and grassland
caused by rapid and widespread land-cover change in Minnesota, we monitored Dakota County,
Minnesota from 1984 to 2005 to see the quantity of these changes and where they are located.
This paper will analyze the use of remote sensing using Landsat Thematic Mapper (TM) data to
examine the images of Dakota County and to look at the effects of urbanization and agriculture
on vegetation.
Methodology
All data acquired for this project was from the University of Minnesota Twin Cities
Remote Sensing class Moodle site. We obtained two Landsat TM data images of the Twins
Cities area, one from year 1984 and the other from year 2005. In order to see a significant
amount of change in Dakota County we choose those years, which is 21 years. Other data
obtained was the Minnesota counties shape-file from the MN Data Deli website site,
deli.dnr.state.mn.us. The Minnesota counties shape-file is needed to clip out our area of
interest.
In order to work on our project, we used ERDAS Imagine 2011 and Arc-GIS programs.
ERDAS Imagine was used to change the UTM to Zone 15 N for both Landsat images, clip out the
Landsat images for Dakota County, run supervised classifications, run change detection, and run
accuracy assessment for our project. Arc-GIS was used to clip Dakota County from the
Minnesota counties shape-file and for our change detection map. We also used the Dakota
County GIS website for the classification and accuracy assessment part of the project to ensure
an area was correctly classified, if we were unsure of what class an area was.
After acquiring all our materials, we began using Arc-GIS to clip out Dakota County from
the Minnesota counties shape-file. Then, exported the Dakota County shape-file and loaded it
in ERDAS imagine as a vector layer. We then used this vector layer to clip both 1984 and 2005
Landsat images of the Twin Cities to obtain an image of Dakota County for faster processing.
Before we clipped out Dakota County from the Landsat images, we changed the UTM to Zone
15 N from the Landsat images to match with the vector layer UTM zone.
Once the Landsat images were all clipped to view only Dakota County, we began the
classification process on ERDAS imagine. We chose to use supervised classification to help
ensure we get the classes we are looking for and have a better control over class results. New
AOI layers for each image were created and 20 training sites/AOI were made for each class to
ensure we had various pixel values. Classes we sought to identify include urban, agriculture,
forest, herbaceous, wetland, and water. The training sites were then added and named into
the signature editor. After all training sites were found for each class, we merged the
signatures where we know they represent the same class to form more well-defined classes
within the signature editor. Again using the signature editor, we performed the supervised
classification after completing the merge. The supervised classification performed will then
assign a class to each pixel using the AOI identified. We then changed the colors to represent
each land cover: cyan for urban, sienna for agriculture, dark green for forest, light green for
herbaceous, pink for wetlands, and blue for water.
After completing the supervised classification for each image, we performed change
detection. We used the Matrix Union function to perform the change detection. This resulted
in one change image of our 1984 and 2005 classified image. We then uploaded the change
detection image and the 2005 classified image into Arc-GIS to perform a transparency. The
transparency function overlays the change detection photo onto the 2005 classified image to
see the changes that occurred over the 21 years time frame.
Lastly, we performed an accuracy assessment in ERDAS imagine. We were unable to
find aerial photos due to a lack of resources and time constraints therefore we used the
unclassified Dakota County Landsat data images for both the 1984 and 2005 to perform an
accuracy assessment on our classified images. In ERDAS imagine, under the Supervised tab, we
used the Accuracy Assessment function to get an Accuracy Assessment dialog box. In the dialog
box, we performed stratified random points with a search count of 1064, number of point of 70,
and minimum points of 10. After the search for stratified random points were found, we went
through all 70 points and filled the reference column in the accuracy assessment dialog box
without looking at the class column. We also used the Dakota County GIS website
(http://gisx.co.dakota.mn.us/website/dakotanetgis/default.htm?LayerIds=127,345&LayersVisib
le=1,1&ActiveLayerId=127) to aid us in identifying the class for the reference data if we were
unsure of what the class was in the unclassified image. Then, after we found all the reference
data, we produced an accuracy assessment report to see how accurate our classified images
were.
Results
Over the past 21 years there has been urban development in Dakota County, Minnesota.
Not only just by urbanization of roads and buildings, but also with the development of more
agricultural lands to support the growing population in the area. We found that 48,525 acres of
forest and herbaceous lands were developed into agriculture. Another 29,025 acres of both
forest and herbaceous lands are now urban lands. So nearly 77,550 acres of the forest and
grasses have been transformed. The loss of vegetation is depleting at a rapid rate to support
the growing economy and needs to be addressed. As far as water and wetlands go in
comparison with urban and agriculture, we found there was some change, but not as drastic.
Water is more difficult to convert, therefore less change. According to our change detection
3,568 acres of wetland and water have been transformed into agriculture and 2,983 acres to
urban. Not as much as the forest and herbaceous lands, but still a substantial amount. See
figures 1-3 and tables 1-2 for classified images and change detection image and data.
Figure 1: 1983 Classified image of Dakota County, Minnesota
Map Key of 1984
URBAN
FOREST
AGRICULTURE
HERBACOUS
WETLAND
WATER
Figure 2: 2005 Classified image of Dakota County, Minnesota
Map Key of 2005
URBAN
FOREST
AGRICULTURE
HERBACOUS
WETLAND
WATER
Figure 3: Change Detection of 1984-2005 of Dakota County, Minnesota
Map Key of Change Detection
URBAN
AGRICULTURE
HERBACOUS/FOREST
WATER/WETLAND
Table 1: Percent Change to Urban
Change Area, Acres
1984-2005
Percent (%)
Acres
Forest to Urban
12.4
2,560
Herbacous to Urban
26.4
26,465
Agriculture to Urban
10.4
15,487
Wetland to Urban
13.5
1,451
Water to Urban
18.5
1,532
Table 2: Percent Change to Agriculture
Change Area, Acres
1984-2005
Percent (%)
Acres
Forest to Agriculture
37.1
17,283
Herbaceous to Agriculture
31.2
31,242
Wetland to Agriculture
30.2
2,500
Water to Agriculture
9.9
1,068
Urban to Agriculture
20.5
12,488
For our accuracy assessment results, percentages presented for 1984 and 2005 were
low, 68.6% and 70%. The result of these low percentages may be due to high percentage cloud
cover, reference data error, and/or the use of the same data set. For the 1984 classified image,
there were 48 out of 70 stratified random points correct. With the 2005 classified image, it
resulted in 49 out of 70 points correct. The classes that caused the greatest amount of error
include herbaceous and wetland. Herbaceous and wetland were the hardest to identify in the
images given and were very similar to agriculture. See Tables for Accuracy Assessment tables
for 1984 and 2005.
Table 3: Accuracy Assessment of 1984
Class
Name
Reference
Totals
Classified
Totals
Number
Correct
Producers
Accuracy
Users
Accuracy
FOREST
12
11
9
75.00%
81.82%
URBAN
12
12
9
75.00%
75.00%
AGRICULTURE
16
14
9
56.25%
64.29%
HERBACOUS
14
13
10
71.43%
76.92%
WETLAND
6
10
3
50.00%
30.00%
WATER
10
10
8
80.00%
80.00%
Totals
70
70
48
Table 4: Accuracy Assessment of 2005
Class
Name
Urban
Agriculture
Herbaceous
Wetland
Water
Forest
Totals
Reference
Totals
10
20
9
5
10
16
70
Classified
Totals
12
14
13
10
10
11
70
Number
Correct
8
11
5
4
10
11
49
Producers
Accuracy
80.00%
55.00%
55.56%
80.00%
100.00%
68.75%
Users
Accuracy
66.67%
78.57%
38.46%
40.00%
100.00%
100.00%
Conclusion/Discussion
In conclusion, urbanization in Dakota County has depleted vegetation in Minnesota. Not
only have the development of urban created a depletion of vegetation in Minnesota, but
agriculture has also been a problem source. If we were to do this project for the whole
Minnesota, we can see that our prediction will hold true for all of Minnesota. Therefore, we
should learn to protect and preserve Minnesota vegetation if we want to have natural areas in
the future.
In this project, there were many implications. Parts of our results were skewed due to
cloud cover in our images. Therefore, for future studies, we suggest doing an atmospheric
correction on images with cloud cover or find data with low to 0% cloud cover. If atmospheric
correction were done in our project, our results would be more accurate. Even with the issue
of clouds we maintained a relatively decent accuracy, when we performed the accuracy
assessment on both images.
In order to get better results in mapping an area, a higher resolution than Landsat
would have had fewer errors in both image classification and accuracy assessment and having a
greater knowledge of the remote sensing programs will increase the accuracy of this project.
This project has been a challenge for the both of us being new to remote sensing and
trying to figure out ERDAS imagine, the data involved in remote sensing, and with minimal use
of Arc-GIS. Other challenges presented were finding aerial photos for our accuracy assessment
and manipulating the data images to show what we wanted to get across, such as a map
properly showing the changes. We also ran into complications of understanding our data fully.
When we got the values for the “from-to” results, interpretation was a challenge we faced.
Understanding what values meant. This was a learning curve for the both of us. Lastly, to
improve the project, we could have used better images and also downloaded more images
from varies seasons from the same year to increase the accuracy of our classified images.
References:
Campbell, James B., and Wynne, Randolph H. Introduction to Remote Sensing, Fifth Edition
(2011). The Guilford Press: New York.
Dakota County GIS website:
http://gisx.co.dakota.mn.us/website/dakotanetgis/default.htm?LayerIds=127,345&Laye
rsVisible=1,1&ActiveLayerId=127
Leip, David. 1984 Presidential General Election Results - Minnesota. 2012. 9 December
2012 <http://uselectionatlas.org/RESULTS/state.php?f=0&fips=27&year=1984>.
MN Data Deli: deli.dnr.state.mn.us
MnGEO. Minnesota Population. 9 December 2012
<http://www.lmic.state.mn.us/datanetweb/php/census2000/estimate/menu.php>.
University of Minnesota Remote Sensing Moodle Site:
https://moodle2.umn.edu/course/view.php?id=12495