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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

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Universidade federal de minas gerais

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

Hermann Rodrigues, Gustavo Cerqueira

Daniel Nepstad, Ane Alencar, Eliane Voll


Universidade federal de minas gerais

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

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

Analytical and heuristic methods

  • Weights of Evidence

  • Genetic Algorithm


Universidade federal de minas gerais

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


Universidade federal de minas gerais

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

The analytical method


Modeling the occurrence of an event based on weights of evidence

D

B

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

Assigning weights to produce transition probability maps


Optimizing weights of evidence

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

Spatial determinants of deforestation

Statistically significant

What does it imply in terms of model accuracy?


Validation method

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

Simulation software

DINAMICA

Simulations run on DINAMICA

  • Calibrator

  • Simulator

www.csr.ufmg.br/dinamica


Fitness for the weo models

Fitness for the WEO models


Combined effect of analytical woe on model fitness

Combined effect of analytical WOE on model fitness

Distance to deforested

Removing two variables

Removing one variable


The heuristic method

The heuristic method


Universidade federal de minas gerais

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

gene

allele

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


The ga mechanism

  • stochastic structure

    DINAMICA

Initial individual

1 k

population n

calculate probability

calculate GAIN

iterate

selection

det.tournament

reproduction

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

GA evolution

best-so-far

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What does it mean?


Fitness results

Fitness results

14%

-2%

20%

15%

4%

24%

21%

18%

34%

GAIN: 0.309458 Similarity 1x1: 0.313331

1.24%, 2.41%, 2.68%


Fuzzy location comparison

Fuzzy location comparison

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observed x simulated

1

0


Pattern comparison

deforestation 1997-200

pattern comparison

two patch sizes

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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

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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