Cellular automata with non-linear transitio rules for simulating land cover change Katarzyna OSTAPOWICZ [email_address] T ...
Aim  <ul><li>implematation of nonlinear transition rules – artificial neural networks and support vector machines to geogr...
Cellular automata: four paradigms <ul><li>space consituted by an array of cells </li></ul><ul><li>discretization of cells ...
Transition rules: <ul><li>Neural networks </li></ul><ul><li>Support vector machines </li></ul>Transition rules?
Artificial neural networks (ANN) <ul><li>Generalized regression neural network (GRNN) </li></ul><ul><li> –  one direction ...
Suport vector machines (SVM) <ul><li>Based on concept of decision boundaries. A decision plan is one that separatesbetween...
Test area
Land cover: forest/non-forest
Driving forces <ul><li>Economic: agriculture (CAP), forestry (managment direction; state/private forest), tourism (develop...
<ul><li>space consituted by an array of cells:  </li></ul><ul><li>28.5 m x 28.5 m </li></ul><ul><li>discretization of cell...
Workflow Input data Change probabilities Transition rates Natural and antropogenical variable CALIBRATION PART SIMULATION ...
Input data Forest/non-forest: 1987, 2000, 2006 ( source: Landsat images, supervised, hierarchcal approach combining image ...
Training plots (800, 200 per class) forest aforestation and natural succession non-forest deforestation
Transition rules f(P ij , N i , R ij ) CHANGE PROBABILITIES (P ij )   NEIGHBOURHOOD (N i )   Σ  n i  > 6  (i – land cover ...
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...
CONCLUSSION <ul><li>ANN and SVM performed well in deriving land cover (forest/non-forest) pattern </li></ul><ul><li>SVM mo...
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Cellular automata with non-linear transitio rules for simulating land cover change

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Cellular automata with non-linear transitio rules for simulating land cover change

  1. 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. 2. Aim <ul><li>implematation of nonlinear transition rules – artificial neural networks and support vector machines to geographical cellular automata </li></ul>
  3. 3. Cellular automata: four paradigms <ul><li>space consituted by an array of cells </li></ul><ul><li>discretization of cells states time </li></ul><ul><li>the state of the cells (pixel) based on initial state </li></ul><ul><ul><li>local influence neighbourhoods </li></ul></ul><ul><ul><li>universally applied transition rules </li></ul></ul>Transition rules?
  4. 4. Transition rules: <ul><li>Neural networks </li></ul><ul><li>Support vector machines </li></ul>Transition rules?
  5. 5. Artificial neural networks (ANN) <ul><li>Generalized regression neural network (GRNN) </li></ul><ul><li> – one direction network </li></ul>x 1 . . x n ∑ f Y w n (x n w n ) input activation Output w n
  6. 6. Suport vector machines (SVM) <ul><li>Based on concept of decision boundaries. A decision plan is one that separatesbetween a set of obecjts having diffrent class memberships </li></ul><ul><li>Regression SVM y = f(x) + noise </li></ul>
  7. 7. Test area
  8. 8. Land cover: forest/non-forest
  9. 9. Driving forces <ul><li>Economic: agriculture (CAP), forestry (managment direction; state/private forest), tourism (development?), traditional livelihoods (support/elimination?) </li></ul><ul><li>Societal: population and demographic development (migration to cities/from mountain/from rural areas) </li></ul><ul><li>Natural: elevation/slope (limitation for agriculture) </li></ul>
  10. 10. <ul><li>space consituted by an array of cells: </li></ul><ul><li>28.5 m x 28.5 m </li></ul><ul><li>discretization of cells states time: </li></ul><ul><li>2006-2056, 5 iterrations (10 years each) </li></ul><ul><li>local influence neighbourhoods: </li></ul><ul><li>Moore’a (8 cells) </li></ul><ul><li>universally applied transition rules: </li></ul><ul><li>ANN/SVM + regional rules </li></ul><ul><li>Cells states: five states: </li></ul><ul><ul><ul><ul><ul><li>forest </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>non-forest </li></ul></ul></ul></ul></ul>Cellular automata model
  11. 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. 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. 13. Training plots (800, 200 per class) forest aforestation and natural succession non-forest deforestation
  14. 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. 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. 16. CONCLUSSION <ul><li>ANN and SVM performed well in deriving land cover (forest/non-forest) pattern </li></ul><ul><li>SVM more stable the ANN </li></ul><ul><li>effective methods on eliminating spatial autocorrelation </li></ul>
  17. 17. Thank you :-)

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