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?