1. Change Detection in the Metro Area Michelle Cummings Laura Cossette

2. Objectives To provide statistical data on land cover change in the metro area over a 21 year period (1984- 2005) To create visuals that aid in the understanding of this land cover change To provide some basic analysis of this data to strengthen understanding and application To apply our knowledge from lecture and lab and improve our skills with ERDAS Imagine and data analysis

3. Relevance Can be used by scientists, policy-makers, and educators Science: –Data for future use Policy: –City planning, population dynamics, land management, hydrological pathways and concerns, soil degradation Education: –To convey general trends in land cover/use

4. Study Area Twin Cities and surrounding metro area Includes parts of Hennepin, Ramsey, Anoka, and Dakota counties 652,000 acres or 1,020 square miles

5. Process  Data Collection  Image Preparation  Classification  Change Detection  Analysis  Accuracy Assessment  Reflections

6. Data Collection Cropped images using AOI and Subset tools Used Inquire Box to find area of study Preparation Data was provided by Professor Knight Landsat 5, TM sensor, path 27, row 29 Four years: 1984, 1991, 2000, 2005 Images taken in August or September

7. Classification Delineate Training Areas –5 classes Urban Agriculture Grassland/ Bare soil Forest Water –20-25 per class for each image Merge Signatures Run supervised Classification –Maximum Likelihood

8. 1984 Urban Agriculture Forest Grassland/Soil Water

9. 2005 Urban Agriculture Forest Grassland/Soil Water

10. 1984 2005

11. 1984

12. 2005 ?!?!

13. Reasons for Error Choosing bad training areas –Not representative –Misclassification –Including bad pixels/edges Bad class scheme –Urban and suburban are very different Split up urban to urban and sub- urban –Ag fields were split into 2 classes Use ‘cultivated’ instead of ‘Ag.’ and ‘Bare soil’ class Haze and cloud cover Algae on water? Yellow streak matches haze on original image

14. Change Detection Post-Classification Change Detection –Matrix Union –Summary Report Use change detection image for visual aid Use summary report for statistical data

15. Data...

16. Change from 1984 to 1991 Urban 71% stayed Urban 8% to Agriculture 7% to Forest 11% to Grass/Soil 2% to Water Agriculture 29% stayed Agriculture 27% to Urban 18% to Forest 20% to Grass/Soil 5% to Water

17. Change from 1991 to 2000 Urban 86% stayed Urban 8% to Agriculture 3% to Forest 2% to Grass/Soil 0.6% to Water Agriculture 48% stayed Agriculture 22% to Urban 13% to Forest 13% to Grass/Soil 0.8% to Water

18. Change from 2000 to 2005 Urban 88% stayed Urban 1% to Agriculture 5% to Forest 4% to Grass/Soil 1% to Water Agriculture 29% stayed Agriculture 29% to Urban 27% to Forest 15% to Grass/Soil 0.7% to Water

19. Change from 1984 to 2005 Urban 77% stayed Urban 3% to Agriculture 12% to Forest 5% to Grass/Soil 2% to Water Agriculture 17% stayed Agriculture 39% to Urban 24% to Forest 14% to Grass/Soil 6% to Water

20. Analysis Trends & Findings –Agricultural land is being converted to Urban development –From 1984 to 2005 (21 years) 39% of Ag. land (66mi 2 ) was converted to Urban land –This may be off because some Ag. was considered Grassland/Soil. –Data is not accurate enough for good analysis of Grassland/Soil and Forest classes –Water did not change much (Duh!)

21. Accuracy Assessment Reference data from Minnesota Geospatial Image Server (Web Map Service) We used MnGeo’s WMS image server to get digital orthophotography. –1991 data: USGS. Statewide. B&W. 1m res. Spring 1991. –2000 data: Met Council. Twin cities. B&W. 0.6m res. Spring 2000 –2005 data: 1. USGS. Color. 0.3m res. Spring 2006. 2. USDA. Color. 2m res. Summer 2006. ERDAS Imagine –Got sample points Stratified Random 50 points per class/ 250 total 3 of the 4 years ArcMap –Imported sample points –Classified reference points using aerial imagery

22. Accuracy assessment sampling points Zoom in on point Accuracy assessment table (ERDAS) Used ArcMap and ERDAS Imagine to assess sample points

23. Accuracy Results Reference Data Classified DataAgricultureUrbanForestGrassland/SoilWater Row Totals Agriculture10009019 Urban21133222142 Forest1102215250 Grassland/Soil53114023 Water00101516 Column Totals18126276019 ClassOmissionCommission Agriculture56%53% Urban90%80% Forest81%44% Grassland/Soil23%61% Water79%94% Total % Accuracy: 69.6% 2005 1991: 65.6% 2000: 66.8%

24. Accuracy of Accuracy “Urban” or “Forest”??? Points that fall on or close to borders/edges Unidentifiable areas due to poor image quality or analyst ignorance Multiple Analysts with different interpretive skills and judgment Typing error (recording wrong #, in wrong field on table) Ag field are identified as ‘Ag’ instead of ‘Ag’ or ‘Bare Soil’

25. Application Main Use: –Learning tool for us! –To see general trends Would be cautious to suggest use for specific projects because of poor class choices and low accuracy

26. Reflections Classification scheme Correcting for haze and cloud cover Recode ERDAS Imagine is frustrating and finicky at times Lots of wasted time Calculation of areas