Deforestation Part 2: Top-down Modelling Pedro R. Andrade Münster, 2013

Representing the process How to develop a deforestation model?

Modelling and Public Policy System Ecology Economy Politics Scenarios Decision Maker Desired System State External Influences Policy Options

Top-down Land Change Models Demand submodel Transition potential submodel Change allocation submodel Land use at t Land use at t+1 Time loop How much? Where? Input data

Transition Matrix (Markov chain) Global economic model Trend analysis Building Scenarios Demand

Simple Demand Demand submodel Difference between years

Potential map Driving factors Neural Network Multivariate Statistics Mathematics Potential Transition potential submodel

Potential map Potential – CLUE like Transition potential submodel Protected Areas Ports Roads Deforestation Subtract from Deforestation

Potential map at t Landscape map at t Landscape map at t+1 Demand t+1 Rank-order Stochastic Iterative Allocation submodel Allocation

deforestation.lua Three strategies for computing potential:  Neighborhood: Based on the average deforestation of the neighbors  Regression: Based on distance to roads, ports, and protected area  Mixed: Based on these four attributes Fixed yearly demand

Models need to be Calibrated and “Validated” t p - 20 t p - 10 tptp Calibration Validation t p + 10 Scenario Source: Cláudia Almeida

Goodness-of-fit Source: Costanza, 1989

Goodness-of-fit: Multilevel Source: Costanza, 1989

Fit According to Window Size

Goodness-of-fit of Land Change Models

 Normalize the error according to the demand  Compute error instead of fit

Exercise  Use the PRODES data as yearly demand (from “total-prodes.lua”)  Compute the final real deforestation summing up the deforestation data in 2001 with the yearly PRODES until 2011  Use the multi-resolution metric to calibrate the different potential strategies by changing the weights manually (see an example of computing the goodness-of-fit in “check-fit.lua”)  Is it possible to be better than the allocation from the potential based only on the neighborhood?

Land Change Models x Cellular Automata  Grid of cells  Neighbourhood  Finite set of discrete states  Finite set of transition rules  Initial state  Discrete time  Behavior parallel in space  Read from the neighbors and write in the cell Can a land change model be considered a Cellular Automata?