Change Detection in the

Metro AreaMichelle Cummings

Laura Cossette

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

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

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

Process

Data Collection Image Preparation Classification Change Detection Analysis Accuracy Assessment Reflections

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

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

1984Urban

Agriculture

Forest

Grassland/Soil

Water

2005Urban

Agriculture

Forest

Grassland/Soil

Water

1984

2005

1984

2005

?!?!

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

Change Detection

• Post-Classification Change Detection– Matrix Union – Summary Report

• Use change detection image for visual aid

• Use summary report for statistical data

Data . . .

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

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

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

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

Analysis• Trends & Findings

– Agricultural land is being converted to Urban development

– From 1984 to 2005 (21 years) 39% of Ag. land (66mi2) 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!)

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

Accuracy assessment

sampling points

Zoom in on point

Accuracy assessment table (ERDAS)

Used ArcMap and ERDAS Imagine to assess sample points

Accuracy Results

Reference Data

Classified Data Agriculture Urban Forest Grassland/Soil Water Row Totals

Agriculture 10 0 0 9 0 19

Urban 2 113 3 22 2 142

Forest 1 10 22 15 2 50

Grassland/Soil 5 3 1 14 0 23

Water 0 0 1 0 15 16

Column Totals 18 126 27 60 19

Class Omission Commission

Agriculture 56% 53%

Urban 90% 80%

Forest 81% 44%

Grassland/Soil 23% 61%

Water 79% 94%

Total % Accuracy:

69.6%

2005

1991: 65.6%

2000: 66.8%

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’

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

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