Urbanization and Land Cover Change inDakota County, Minnesota

FR 3262 Remote SensingSection 001/002

Kylee Berger and Julia Vang

IntroductionLand 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.

MethodologyAll 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&LayersVisible=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.

ResultsOver 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 1984URBANFORESTAGRICULTUREHERBACOUSWETLANDWATER

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

URBANAGRICULTUREHERBACOUS/FORESTWATER/WETLAND

Table 1: Percent Change to UrbanChange Area, Acres

1984-2005 Percent (%) AcresForest 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 Reference Classified Number Producers Users Name Totals Totals Correct Accuracy 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 2005Class Reference Classified Number Producers Users

Name Totals Totals Correct Accuracy Accuracy Urban 10 12 8 80.00% 66.67%

Agriculture 20 14 11 55.00% 78.57% Herbaceous 9 13 5 55.56% 38.46%

Wetland 5 10 4 80.00% 40.00% Water 10 10 10 100.00% 100.00%

Forest 16 11 11 68.75% 100.00%Totals 70 70 49

Conclusion/DiscussionIn 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&LayersVisible=1,1&ActiveLayerId=127 BIBLIOGRAPHY 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.usMnGEO. 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