Deforestation:
Top-down Modelling
Pedro R. Andrade
Modelling and Public Policy
External
Influences
System
Ecology
Economy
Politics
Scenarios
Policy
Options
Decision
Maker
Desired
System
State
Brazilian Amazonia
Source: PRODES (INPE)
PRODES - Deforestation in Amazonia
What drives deforestation?
What drives deforestation?
Cellular Spaces
Protected Areas
Ports
Deforestation
Roads
Top-down Land Change Models
Input data
Demand submodel
How much?
Where?
Transition potential
submodel
Change allocation
submodel
Land use at t
Time loop
Land use at t+1
Demand
Trend analysis
Global economic model
Building Scenarios
Transition Matrix (Markov chain)
Simple Demand
Difference between years
Demand submodel
Potential
Driving factors
Potential map
Transition potential
submodel
Neural Network
Multivariate
Statistics
Mathematics
Potential – CLUE like
Deforestation
Protected Areas
Ports
Subtract
from
Potential map
Roads
Deforestation
Transition potential
submodel
Statistics: Humans as clouds
y=a0 + a1x1 + a2x2 + ... +aixi +E
Establishes statistical relationship with variables that
are related to the phenomena under study
Basic hypothesis: stationary processes
Fonte: Verburg et al, Env. Man., Vol. 30, No. 3, pp. 391–405
Amazônia x Variables of 1996/2006
26 Variáveis
R-squared
10 Years
0.4501
Beta
p-level
FERTIL_ALTA
0.2513421
<2e-16
log(DIST_MIN_MAD_96 + 1)
-0.2239365
<2e-16
log(DIST_MIN_ROD_PAV_96 + 1)
-0.1830264
<2e-16
DIST_MIN_PORTOS
0.1816154
<2e-16
A_UC_1996
-0.1559962
<2e-16
log(POP_RUR_1996 + 1)
0.1365780
<2e-16
A_ASSENT_96_NUNFAM
0.1346037
<2e-16
log(PREC_INV + 1)
0.1238640
<2e-16
A_NU_AGR_MEDIUM_96
-0.1221922
<2e-16
25Km
R-squared
0.5148
Beta
p-level
FERTIL_ALTA
0.3639212
<2e-16
log(DIST_MIN_MAD + 1)
-0.2887361
<2e-16
log(PREC_INV + 1)
0.1689600
<2e-16
TI_2006
-0.1600799
<2e-16
ASSENT_06_NUNFAM
0.1607236
<2e-16
DIST_MIN_PORTOS
0.1604278
<2e-16
FERTIL_MUITOBAIXA
0.1037998
<2e-16
log(DIST_MIN_ROD_PAV + 1)
-0.1030752
<2e-16
UC_2006
-0.0995451
<2e-16
Allocation
Demand
t+1
Allocation
submodel
Landscape map at t
Potential map at t

Rank-order

Stochastic

Iterative
Landscape map at t+1
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 areas
 Mixed: Based on these four attributes
Fixed yearly demand
Models need to be Calibrated and “Validated”
Scenario
tp - 20
tp - 10
tp
Calibration
Source: Cláudia Almeida
Validation
tp + 10
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
Land Change Models x Cellular Automata
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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 itself
Can a land change model be considered a Cellular
Automata?