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Google Earth for Land Use assessmet
 

Google Earth for Land Use assessmet

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    Google Earth for Land Use assessmet Google Earth for Land Use assessmet Presentation Transcript

    • MSc Thesis by :HRVOJE UJLAKI k1046761
    • The aim of this research project is:To identify potential of Google Earth in land use changedetection and detect disadvantages and advantages of standardland use/ land cover change detection.The objectives of this research project are:• To collect land use data from Google history imagery in 2001 and 2011.• To compare and evaluate two different remote sensing methods.• To compare the land use for different years.• To identify the most appropriate remote sensing method to use for the classification of land use zones.
    • GOOGLE WORKFLOW LANDSAT EARTH IMAGERY 2001 IMAGERY 2011 IMAGERY IMAGEDIGITALIZATION DIGITALIZATION PREPARATION SUPERVISED KML FILE CLASSIFICATION ACCURACY ASSESMENT GIS ANALYSIS WITH ArcGIS 10 LAND LAND RESULTS OF ANALYSIS IN COVER USE MAPS TABULAR AND GRAFHIC MAPS FORMAT
    • 2001 20111 . Digitizing polygons with Google Earth PANORAMIO PHOTO polygon tool for 20112. Using Google Earth database for land use detection (photos , 3d models, street view...)3. Detecting changes for each polygon in 2001 and changing class if necessary 3D MODEL 2D IMAGE4. Reshaping polygons whose land use is changed5. KML export to ESRI SHP file
    • RA - single Mixed family RA - multi RA - Sport Unmaintaine Construction Agricultural - Agricultural - agricultural- Parks - green houses family houses features Industrial d sites croplands unmaintained urban areas Rangeland Forest land-0.75(km²) 2.25(km²) -0.27(km²) 3.27(km²) 0.03(km²) 0.04(km²) -5.64(km²) 0.06(km²) 0.14(km²) 1.49(km²) -0.67(km²) -0.09(km²)
    • 4:3:2 BAND COMBINATION• Deciding on Land cover classes• Image pre - processing SUPERVISED CLASSIFICATION• Training stage• Supervised Classification - ACCURACY ASSESMENT Maximum Likelihood Classifier ACCURACY ASSESMENT• Accuracy Assessment – Error matrix
    • User Accuracy 2001 User accuracy 2011Accuracy % % Class %Agricultural 93 Urban 90Forest 93 Agricultural 95Urban 85 Forest 100Water 75 Water 100Overall 89 Overall 93
    • Supervised classification (2001-2011) Land use/cover class Change (km2) % -Change Urban or built up 25.3 7.21 Agricultural -11.31 -3.22 Forest -12.87 -3.67 Water -1.12 -0.328.00 % -Diffrence Landsat6.00 %- Diffrence Google4.002.000.00-2.00-4.00 Google Earth (2001-2011)Land use/cover class Difference (km2) %- ChangeUrban or built up 4.59 1.34Agricultural -4.63 -1.32Forest -0.09 -0.02Water 0 0
    • DISCUSSION & CONCLUSION• Results show a similar pattern in both analyses• Urban areas are increasing and agricultural areas are decreasing• The increase is evident, but Zagreb is still not over developed City Google Earth Analysis Landsat Imagery Supervised Classification• Very useful tool for Land use • Much faster process, but less analysis, but with minor bugs detailed when converted to SHP files• Google database (photos, 3D • Misplaced pixels of water in models, street view...) can be of forested and shadow areas great help for land use determination• Time consuming, not suitable for • Combination of both methods larger areas would give even better results
    • QUESTIONS?Reference:Lillesand, T.M.,Kiefer, R.W. and Chipman, J. (2008)Remote Sensing and Image Interpretation, Sixth Edition, Wiley, Chichester.Google Earth Blog http://www.gearthblog.com/