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Cellular automata with non-linear transitio rules for simulating land cover change
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  • 1. Cellular automata with non-linear transitio rules for simulating land cover change Katarzyna OSTAPOWICZ [email_address] T he 2009 Annual International Conference of the Royal Geographical Society , 2 6-28 August 2009 , Manchester Department of GIS, Cartography and Remote Sensing Institute of Geography and Spatial Management Jagiellonian University
  • 2. Aim
    • implematation of nonlinear transition rules – artificial neural networks and support vector machines to geographical cellular automata
  • 3. Cellular automata: four paradigms
    • space consituted by an array of cells
    • discretization of cells states time
    • the state of the cells (pixel) based on initial state
      • local influence neighbourhoods
      • universally applied transition rules
    Transition rules?
  • 4. Transition rules:
    • Neural networks
    • Support vector machines
    Transition rules?
  • 5. Artificial neural networks (ANN)
    • Generalized regression neural network (GRNN)
    • – one direction network
    x 1 . . x n ∑ f Y w n (x n w n ) input activation Output w n
  • 6. Suport vector machines (SVM)
    • Based on concept of decision boundaries. A decision plan is one that separatesbetween a set of obecjts having diffrent class memberships
    • Regression SVM y = f(x) + noise
  • 7. Test area
  • 8. Land cover: forest/non-forest
  • 9. Driving forces
    • Economic: agriculture (CAP), forestry (managment direction; state/private forest), tourism (development?), traditional livelihoods (support/elimination?)
    • Societal: population and demographic development (migration to cities/from mountain/from rural areas)
    • Natural: elevation/slope (limitation for agriculture)
  • 10.
    • space consituted by an array of cells:
    • 28.5 m x 28.5 m
    • discretization of cells states time:
    • 2006-2056, 5 iterrations (10 years each)
    • local influence neighbourhoods:
    • Moore’a (8 cells)
    • universally applied transition rules:
    • ANN/SVM + regional rules
    • Cells states: five states:
            • forest
            • non-forest
    Cellular automata model
  • 11. Workflow Input data Change probabilities Transition rates Natural and antropogenical variable CALIBRATION PART SIMULATION PART ANN/SVM Land cover maps Management plans cross-tabulation Land cover change simulation
  • 12. Input data Forest/non-forest: 1987, 2000, 2006 ( source: Landsat images, supervised, hierarchcal approach combining image segmentation, knowledge-based rules and likelihood decision rule ) Elevation and slope ( source: STRM DEM, spatial resolution 90 m ) Distance to artificial areas (source: land cover map 2006; distance operation) Migration, NUTS type (urban/rural), distace to urban NUTS (source: GUS) Ownership: state/private forest (source: state forest)
  • 13. Training plots (800, 200 per class) forest aforestation and natural succession non-forest deforestation
  • 14. Transition rules f(P ij , N i , R ij ) CHANGE PROBABILITIES (P ij ) NEIGHBOURHOOD (N i ) Σ n i > 6 (i – land cover type) forest f (elevation, slope, migration, NUTS type, ownership, distance to artificial areas) TRANSITION RATES (R ij ) e.g. for forest 0.25% per year TRAINING: forest change between 1987-2000-2006 SCENARIOS: 2006-2056
  • 15. Maximum accuracy for transsition rules ANN: 75% SVM: 79% 57,06 2050 53,27 2020 55,79 2040 52,16 2010 54,54 2030 50,98 2000 forest cover [%] year forest cover [%] year
  • 16. CONCLUSSION
    • ANN and SVM performed well in deriving land cover (forest/non-forest) pattern
    • SVM more stable the ANN
    • effective methods on eliminating spatial autocorrelation
  • 17. Thank you :-)