1. Quantifying Urbanization with Landsat Imagery in Rochester, Minnesota
Patrick Landisch & Stephanie Zahler
May 3rd, 2013
FR 3262

2. Rochester, Minnesota Third largest city in Minnesota Population:
*U.S. Decennial Census

3. Original Landsat 5 Imagery
May, 1985
April, 2001

5. Image Preparatory Process
Nad 83 UTM Zone 15N
Determine area of interest in Arc GIS
Minnesota Data Deli PLS Townships
Export shapefile of area of interest
Clip image to area of interest
4 townships containing Rochester in Olmsted County
Range 13W, Township 107N
Range 13W, Township 106N
Range 14W, Township 107N
Range 14W, Township 106N

6. Clipped Imagery
May, 1985
April, 2001

7. Classification Supervised classification Classified:
10-20 training sites for each class
Classified:
Urban Areas
Agriculture
Forest Land
Water
Bare Soil

8. Classified Images
May, 1985
April, 2001

9. Change Detection Thematic Change Image Difference
Uses the classified images
Detects change within five “Zones”
Zones determined by classified image
20% threshold on “highlight change” image
Pixel by pixel method

10. Thematic Change Detection Image

11. Image Difference: “Highlighted Change”

12. Accuracy Assessment
Used NAIP imagery for the entire state of Minnesota
2006 land cover data as reference for 2001 imagery
Clipped to our area of interest

13. Accuracy Assessment
71.43%

14. Results Percent Change to Urban Area 1985-2001 Percent Change Acres
Percent Change
Acres
Bare Agriculture to Urban
0.1367
Water to Urban
0.1998
37.42
Green Vegetation to Urban
0.1085
566.1
Forest to Urban
0.1513
4829.1
Urban to Urban (Acres in 1985)
15503
Total
0.5963

15. Discussion
2001 as an endpoint for our study, but may be used as a starting point for future studies
Add another photo or two to monitor increment change between ’85 and ‘01
Rough study of 15 years of urban growth
It would be interesting to start this study during the 30’s or 40’s
Added two more photos from the fall
Improve classification process

16. Limitations/Challenges
First time using ERDAS to do data analysis
Did not find images with no cloud cover
10% proved to be acceptable
No reference map used for 1985 classification
Provided more confidence in our results
Could not find images from the same month
Interpreting the data with confidence

17. Conclusion Learning experience with ERDAS Imagine
Urban expansion is important to monitor
Rapid
Useful in city planning and allocating resources
Historical information regarding previous land use
Provide a helpful resource for anyone attempting to do a similar project

18. References http://deli.dnr.state.mn.us/
FR 3262 Lab Lesson 6- Image Operations and Clip
FR 3262 Lab Lesson 11- Supervised Classification
FR 3262 Lab Lesson 12- Change Detection
FR 3262 Lab Lesson 11a- Accuracy Assessment