1. By: Katie Blake and Paul Walters

2.  To analyze land cover changes in the Twin Cities Metro Area from 1984 to 2005 Image difference and Thematic Change  This type of information can be used in city planning, to evaluate the impact of land cover change on water quality, and other environmental effects

3.  TWIN CITIES METO AREA:  Anoka  Carver  Dakota  Hennepin  Ramsey  Scott  Washington

4.  We used the provided Landsat images from 1984 and 2005  We used MN Data Deli and ArcMap to clip the 7 county Metro Area  We used ERDAS to perform a supervised classification of both images  We used ERDAS for change detection and from-to classification

5. 1984 2005

6. 1984 2005 ColorClassification = Urban = Water = Vegetation = Agriculture

7.  We used Supervised classification because we were unable to identify the classes with unsupervised classification  We used 20 training sites to identify 4 classes: Urban, Agriculture, Water, and Vegetation

8. 20% Threshold Value10% Threshold Value

9. AgriculturePercent (%)Hectares (ha) Water to agriculture.74977.04 Urban to agriculture22.1429183.8 Vegetation to agriculture 38.4650693.8

10. UrbanPercent (%) Hectares (ha) Water to Urban1.793151.8 Vegetation to Urban27.1847944 Agriculture to Urban7.9814076.2

11. WaterPercent (%) Hectares (ha) Agriculture to water4.941773.9 Urban to water12.144356.27 Vegetation to water32.1611537.8

12. VegetationPercent (%) Hectares (ha) Water to vegetation2.4010085 Urban to vegetation23.3398027.2 Agriculture to vegetation23.0096606.5

13.  Had some issues with our classification Will discuss in our accuracy assessment  Vegetation was converted to Agriculture 38.46% 50,693.8 ha  Vegetation was converted to Urban 27.18% 47,944 ha  Agriculture was converted to Urban 7.98% 14,076.2 ha

14.  Unable to perform accuracy assessment because we had no reference photo  The thematic change matrix union summary doesn’t make sense in some categories due to misclassification and other problems Cloud in the 2005 Landsat Image was classified as Urban Our supervised classification isn’t entirely accurate despite our best efforts to select training sites

16.  More skill is needed to perform supervised classification accurately  Unsupervised classification requires more knowledge of the area to be used effectively  A reference photo is needed for accuracy assessment  Cloud cover from Landsat image influences classification and accuracy