Change Detection in the Metro Area Michelle Cummings Laura Cossette

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Text of Change Detection in the Metro Area Michelle Cummings Laura Cossette

  • Change Detection in the Metro AreaMichelle CummingsLaura Cossette

  • ObjectivesTo 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 applicationTo apply our knowledge from lecture and lab and improve our skills with ERDAS Imagine and data analysis

  • RelevanceCan be used by scientists, policy-makers, and educatorsScience:Data for future usePolicy:City planning, population dynamics, land management, hydrological pathways and concerns, soil degradationEducation:To convey general trends in land cover/use

  • Study AreaTwin Cities and surrounding metro areaIncludes parts of Hennepin, Ramsey, Anoka, and Dakota counties652,000 acres or 1,020 square miles

  • ProcessData CollectionImage PreparationClassificationChange DetectionAnalysisAccuracy AssessmentReflections

  • Data CollectionCropped images using AOI and Subset toolsUsed Inquire Box to find area of studyPreparationData was provided by Professor KnightLandsat 5, TM sensor, path 27, row 29Four years: 1984, 1991, 2000, 2005Images taken in August or September

  • ClassificationDelineate Training Areas5 classesUrbanAgricultureGrassland/ Bare soilForestWater20-25 per class for each imageMerge SignaturesRun supervised ClassificationMaximum Likelihood

  • 1984UrbanAgricultureForestGrassland/SoilWater

  • 2005UrbanAgricultureForestGrassland/SoilWater

  • 19842005

  • 1984

  • 2005?!?!

  • Reasons for ErrorChoosing bad training areasNot representativeMisclassificationIncluding bad pixels/edgesBad class schemeUrban and suburban are very differentSplit up urban to urban and sub-urbanAg fields were split into 2 classesUse cultivated instead of Ag. and Bare soil classHaze and cloud coverAlgae on water?

    Yellow streak matches haze on original image

  • Change DetectionPost-Classification Change DetectionMatrix Union Summary ReportUse change detection image for visual aid Use summary report for statistical data

  • Data . . .

  • Change from 1984 to 1991Urban71% stayed Urban 8% to Agriculture 7% to Forest11% to Grass/Soil 2% to Water

    Agriculture29% stayed Agriculture27% to Urban18% to Forest20% to Grass/Soil 5% to Water

  • Change from 1991 to 2000Urban86% stayed Urban 8% to Agriculture 3% to Forest 2% to Grass/Soil 0.6% to Water

    Agriculture48% stayed Agriculture22% to Urban13% to Forest13% to Grass/Soil 0.8% to Water

  • Change from 2000 to 2005Urban88% stayed Urban 1% to Agriculture 5% to Forest 4% to Grass/Soil 1% to Water

    Agriculture29% stayed Agriculture29% to Urban27% to Forest15% to Grass/Soil 0.7% to Water

  • Change from 1984 to 2005Urban77% stayed Urban 3% to Agriculture12% to Forest 5% to Grass/Soil 2% to Water

    Agriculture17% stayed Agriculture39% to Urban24% to Forest14% to Grass/Soil 6% to Water

  • AnalysisTrends & FindingsAgricultural land is being converted to Urban developmentFrom 1984 to 2005 (21 years) 39% of Ag. land (66mi2) was converted to Urban landThis 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 AssessmentReference data from Minnesota Geospatial Image Server (Web Map Service)We used MnGeos 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 pointsStratified Random 50 points per class/ 250 total3 of the 4 yearsArcMapImported sample pointsClassified reference points using aerial imagery

  • Accuracy assessment sampling pointsZoom in on pointAccuracy assessment table (ERDAS)Used ArcMap and ERDAS Imagine to assess sample points

  • Accuracy ResultsTotal % Accuracy:

    69.6%2005

    1991: 65.6%

    2000: 66.8%

    Reference DataClassified DataAgricultureUrbanForestGrassland/SoilWater Row TotalsAgriculture10009019Urban21133222142Forest1102215250Grassland/Soil53114023Water00101516Column Totals18126276019

    ClassOmissionCommissionAgriculture56%53%Urban90%80%Forest81%44%Grassland/Soil23%61%Water79%94%

  • Accuracy of AccuracyUrban 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

  • ApplicationMain 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

  • ReflectionsClassification schemeCorrecting for haze and cloud coverRecodeERDAS Imagine is frustrating and finicky at timesLots of wasted time Calculation of areas

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