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

Aim implematation of nonlinear transition rules – artificial neural networks and support vector machines to geographical cellular automata

implematation of nonlinear transition rules – artificial neural networks and support vector machines to geographical cellular automata

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?

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: Neural networks Support vector machines Transition rules?

Neural networks

Support vector machines

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

Generalized regression neural network (GRNN)

– one direction network

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

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

Test area

Land cover: forest/non-forest

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)

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)

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

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

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

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)

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 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

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

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

ANN and SVM performed well in deriving land cover (forest/non-forest) pattern

SVM more stable the ANN

effective methods on eliminating spatial autocorrelation

Thank you :-)