Change Detection in the Metro Area Michelle Cummings Laura Cossette

Objectives To provide statistical data on land cover change in the metro area over a 21 year period ( ) 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

1984 Urban Agriculture Forest Grassland/Soil Water

2005 Urban Agriculture Forest Grassland/Soil Water

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 (66mi 2 ) 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 –2000 data: Met Council. Twin cities. B&W. 0.6m res. Spring 2000 –2005 data: 1. USGS. Color. 0.3m res. Spring USDA. Color. 2m res. Summer 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 DataAgricultureUrbanForestGrassland/SoilWater Row Totals Agriculture Urban Forest Grassland/Soil Water Column Totals ClassOmissionCommission Agriculture56%53% Urban90%80% Forest81%44% Grassland/Soil23%61% Water79%94% Total % Accuracy: 69.6% : 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