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UF G. Universidade Federal de Minas Gerais. Woods Hole Research Center. IPAM – INSTITUTO DE PESQUISA AMBIENTAL DA AMAZÔNIA. Spatial determinants of deforestation in Amazonia: an automated calibration procedure for simulation models. Britaldo Silveira Soares-Filho

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Universidade Federal de Minas Gerais

Woods Hole Research Center



Spatial determinants of deforestation in Amazonia:an automated calibration procedure for simulation models

Britaldo Silveira Soares-Filho

Hermann Rodrigues, Gustavo Cerqueira

Daniel Nepstad, Ane Alencar, Eliane Voll

Spatially explicit simulation models rely on the calculation of probability (favorability) maps, which attempt to quantify and integrate the influences of variables, representing biophysical, infrastructure, and territorial features - such as topography, rivers, vegetation, soils, climate, proximity to roads, towns and markets, and land use zoning -, on the spatial prediction of deforestation.

analyzing the effects of spatial variables on the location of deforestation by applying:

Analytical and heuristic methods

  • Weights of Evidence

  • Genetic Algorithm

The method is being tested in 12 case study regions representative of different types of Amazonian colonization frontier, each one comprising a Landsat scene.

Database for the selected regions include:

INPE/PRODES deforestation maps from 1997 to 2000, at 250 meter resolution, and cartographic layers of road and urban networks, soils, vegetation, topography, rivers, settlement and protected areas, and distance to previously deforested land.

The analytical method



Modeling the occurrence of an event based on weights of evidence

Variables need to be spatially independent:

pair-wise tests, such as Crammer’s V coefficient or Joint Uncertainty

Assigning weights to produce transition probability maps

Optimizing Weights of Evidence

  • calculates ranges according to the data structure

  • interpolation between the ranges

  • quantization using an exponential function

defined ranges

breaking points for this graph are determined by applying an line-generalizing algorithm

Spatial determinants of deforestation

Statistically significant

What does it imply in terms of model accuracy?

Validation method

Need to define a map comparison method:

  • Costanza (1989)

  • Power et al. (2001)

  • Pontius (2002)

  • Hagen (2003): Fuzzy similarity,

    And Kfuzzy

  • Soares-Filho et al. (forthcoming)

    fuzzy similarity using maps of differences and comparing only changed areas

Simulation software


Simulations run on DINAMICA

  • Calibrator

  • Simulator


Fitness for the WEO models

Combined effect of analytical WOE on model fitness

Distance to deforested

Removing two variables

Removing one variable

The heuristic method

The GA method takes advantage of the weights of evidence technique using its resulting coefficients as initial inputs for the same formula that calculates probability surface of deforestation

Haploid representation of the WOE chromosome



weights can be mapped one to one or using a bezier function

  • stochastic structure


Initial individual

1 k

population n

calculate probability

calculate GAIN





cross-over, mutation

until n=50 or gain does not increase

The GA mechanism

Select the best from the best-so-far of all generations

GA evolution



What does it mean?

Fitness results










GAIN: 0.309458 Similarity 1x1: 0.313331

1.24%, 2.41%, 2.68%

Fuzzy location comparison


observed x simulated



deforestation 1997-200

pattern comparison

two patch sizes


simulated 1997-2000

two patch sizes

Simulation employing

DINAMICA’s transition functions to form patches at various sizes

Not only spatial accuracy but similar landscape structure

Final conclusions

  • Analytical methods such as WEO are useful to analyze the effects of spatial variables on deforestation separately.

  • WEO provides a reasonable and quick method to calibrate spatial simulation models, especially when improved through range definition using data natural breaks and exponential quantization. Up to 10%.

  • Simulation models calibrated through GA show superior performance. Up to 40%, considering non-optimized WOE models.

  • All methods are limited by data availability and their capacity in explaining the phenomenon under study.

WEO still needs to analyze the interaction between variables. GA only presents a combined solution and demands high computer performance and long execution time (over 8 hours). It can parallelized. The gain function must be specific for a simulation approach. DINAMICA is a constrained CA

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