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CHANGE DETECTION IN THE TWIN CITIES

This study examines land cover changes in the Twin Cities Metro Area from 1984 to 2005 using satellite images and classification techniques. The results highlight the conversion of vegetation to agriculture and urban areas, with potential implications for environmental effects and city planning.

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CHANGE DETECTION IN THE TWIN CITIES

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  1. CHANGE DETECTION IN THE TWIN CITIES By: Katie Blake and Paul Walters

  2. Objectives • 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. Counties Classified • TWIN CITIES METO AREA: • Anoka • Carver • Dakota • Hennepin • Ramsey • Scott • Washington

  4. Data/Programs Used 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. Temporal 1984 2005

  6. Supervised Classification 1984 2005

  7. Classification • 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. Image Difference 20% Threshold Value 10% Threshold Value

  9. Thematic Change

  10. Thematic Change

  11. Thematic Change

  12. Thematic Change

  13. Results • 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. Accuracy Assessment • 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

  15. Conclusion/Project Improvement • 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

  16. Questions?

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