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CHANGE DETECTION IN THE TWIN CITIES
By: Katie Blake and Paul Walters
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
Counties Classified TWIN CITIES METO
AREA:
Anoka Carver Dakota Hennepin Ramsey Scott Washington
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
Temporal
1984
2005
Supervised Classification
1984 2005
Color Classification
= Urban
= Water
= Vegetation
= Agriculture
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
Image Difference
20% Threshold Value 10% Threshold Value
Thematic Change
Agriculture Percent (%) Hectares (ha)
Water to agriculture .74 977.04
Urban to agriculture 22.14 29183.8
Vegetation to agriculture
38.46 50693.8
Thematic Change
Urban Percent (%)
Hectares (ha)
Water to Urban 1.79 3151.8
Vegetation to Urban 27.18 47944
Agriculture to Urban 7.98 14076.2
Thematic Change
Water Percent (%)
Hectares (ha)
Agriculture to water 4.94 1773.9
Urban to water 12.14 4356.27
Vegetation to water 32.16 11537.8
Thematic Change
Vegetation Percent (%)
Hectares (ha)
Water to vegetation 2.40 10085
Urban to vegetation 23.33 98027.2
Agriculture to vegetation
23.00 96606.5
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
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
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
Questions?