By:Nick Severson Brian Trick Land Cover Change of Twin Cities Metro and Scott County ______________________________FR Fall 2013

Project Goals Identify land cover change over 20 year period for 5 main classes using Landsat 5 imagery in false IR, bands (4, 3, 2). 1)Water, 2)Agriculture, 3)Natural Vegetation, 4)Residential, 5)Urban / Impervious Calculate change from Agriculture and Vegetation to Urban and Residential classes. Assess classification accuracy against a higher resolution image. (time dependent) Use qualitative assessment (visual comparison against professional urban growth change map). Use census information to reinforce growth info for AOI.

7-County Metro, MN Landsat tile doesn’t cover entire Metro area. AOI clipped to retain valid data. How to validate 7-County growth against reference without a complete metro mosaic of both tiles? Open to suggestions?

Supervised Classification Attempt Roads easily identifiable in rural areas. Loss of definition when closer to urban areas. Makes classification problematic. Planned for another attempt after unsupervised first cut.

Supervised Classification Attempt Above: Stillwater Area Left: Shakopee Area Road networks visible, but development becomes problematic based on pixel values of other land cover types and training sites.

7-County Metro, MN 1984 Minneapolis / St. Paul area easily identified. Residential intermixed with vegetation. More realistic looking than Supervised classification, except loss of road networks. Unsupervised Classification

7-County Metro, MN years of urban growth very apparent. Vegetation is more dispersed, thought to follow development patterns, and seasonal fluctuation. Taken in fall months. Temporal, and seasonal variability. Unsupervised Classification

7-County Metro Change Dense urban development caused significant change surrounding downtown areas. Surrounding Agricultural land shows lower change levels.

7-County Metro Change Top: Shakopee Bottom: Stillwater Dense development of growing suburbs is evident by close-up. Vegetation change to agriculture only accounted for low levels of change.

7-County Metro, MN Met Council GIS map identifying Development Extent through 2006 (3). For Visual Comparison of Urban Growth. Qualitative accuracy assessment

7-County Metro Stats Classification Stats (84’-05’) 17.5% total agriculture turned into Residential and Urban/ Impervious classes both accounted for 11% total land cover 2005 both accounted for nearly 24% total land cover. 13% growth in 20 years. 60% Vegetation Changed. Seasonality Development Golf courses grew 51% golf courses in MN

7-County Metro Stats Census Statistics for MN Metro area Population Growth of 7-County metro: 207,514 increase 7.85 % increase based on current growth rate Populations of St. Paul and Minneapolis have stagnated

Scott County, MN 1984 Developed urban area to the north visible, along with surrounding residential. Riparian vegetation visible. Dominated by agriculture Early urban expansion into rural south metro Unsupervised Classification

Scott County, MN 2005 Growth of Shakopee area in North Scott County is very apparent. Cloud interference affected classification results. This made Agriculture, and vegetation class change inaccurate. Water, and Dense Urban still distinguishable. Unsupervised Classification

Scott County, MN Met Council GIS map identifying Development Extent through 2006 (3). For Visual Comparison of Urban Growth done by professionals against our novice supervised and unsupervised classification using ERDAS Imagine.

Scott County Stats Cloud interference, differences in water level (flood plain border) between years, and slight geometric error (discontinuity between years). Fastest growing County in MN. 45% increase (1). 40, 430 new residents (2). Unsupervised Change Data (84’- 05’): 37% Vegetation converted to agriculture. 26% Vegetation converted to Residential. 10% vegetation converted Urban/Impervious 28% of Agriculture converted to Residential and Urban / Impervious

Scott County Stats Cloud interference, differences in water level (flood plain border) between years, and slight geometric error (discontinuity between years). Scott County has grown an estimated % from 1980 – Majority of this growth has taken place in the city of Shakopee. 28.5% of Scott County population 16,508 new residents Passenger vehicles emit: 423 grams CO2/ Mile Savage to Minneapolis: 17.6 miles St. Paul: 24.3 miles Shakopee to Minneapolis 25 mi. St. Paul: 31.5 mi.

Results 1984 & 2005 Thematic Urban, and Residential growth easily distinguishable Vegetation change (partially developed, and seasonal) Original Landsat acquisition dates: 8/15/1984 & 9/10/2005 Unsupervised Classification

Results 2005 & Reference Visual Comparison between all images. Above: Unsupervised Classification Top Right: Shows development trend Bottom Right: Landsat Imagery maps.google.com

Results: Stillwater Area Left: 7-County Classification for Stillwater residential apparent. Highway 36 apparent. Earlier years of Anderson Windows campus noticed. Right: 7-County Classification for Oak Park Heights development apparent. Loss of road distinction. Extremely Developed Anderson campus visible.

Results: Stillwater Area Top Left: 2005 Unsupervised Classification was able to identify vegetation down to yards, boulevard, parks, and riparian edges in many areas, but looses road networks. Below: 2005 Landsat 5 Imagery 2013 maps.google.com

Results: Shakopee Area Right: 7-County 1984 – zoom More water in flood plain areas. Shakopee is still a smaller community. Heavier vegetation. Left: 7-County 2005 – zoom. Much more developed. Loss of vegetation. Increase in golf courses / parks. Much less water.

Results: Shakopee Area 2005 & google Above: Landsat 5 imagery Below: Hi-Res reference in true color. Left: Unsupervised Classification for 2005 was able to identify drastic urban / residential change, but misses riparian vegetation maps.google.com

Conclusion Another effort fallen. Cloud interference dominated final possibility. Given time, and availability, results of previous Unsupervised classifications had to be accepted. Smaller AOI would have been more manageable from the start. Area could have been found with lower atmospheric attenuation, and land cover variability, (ex. Bare ground vs. urban pixel values.

Conclusion ………..Time And Resources……….. Slight geometric difference (error) between images, not noticed until post classification accuracy assessment. Non-parametric geometric correction could have been helpful to correct the unknown source of distortion. New GCP – Bring into geometric coincidence Resample – Nearest Neighbor Contrast Stretch could have also been helpful in hindsight to help distinguish between Urban and Bare Soil land cover types. High Pass filter could have made edge detection easier between spectrally similar areas.

Conclusion Unsupervised classification has limitations that can not be overcome by thorough pixel cluster identification. Supervised classification can produce superior results, but requires much more time upfront (learning curve). Pixel values similar or common between multiple land cover classes become indiscernible using unsupervised classification. More access on campus. Access to software off campus would result in a greater understanding, and better project results. A strong understanding of the ERDAS Imagine capabilities gained through this challenging experience.

Questions? Thank You.

References: 1- Census Viewer. (2012). Population of Scott County, Minnesota: census 2010 and 2000 interactive map, demographics, statistics, graphs, quick facts. Retrieved from 2- United States Census Bureau. (2010). State and county quickfacts. Retrieved from 3- "Metropolitan Council - Map Gallery." Giswebsite.metc.state.mn.us. Metropolitan Council, n.d. Web. 14 Nov