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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

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: representative of different types of Amazonian colonization frontier, each one comprising a Landsat scene.

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 representative of different types of Amazonian colonization frontier, each one comprising a Landsat scene.


Modeling the occurrence of an event based on weights of evidence

D representative of different types of Amazonian colonization frontier, each one comprising a Landsat scene.

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 representative of different types of Amazonian colonization frontier, each one comprising a Landsat scene.


Optimizing weights of evidence
Optimizing Weights of Evidence representative of different types of Amazonian colonization frontier, each one comprising a Landsat scene.

  • 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 representative of different types of Amazonian colonization frontier, each one comprising a Landsat scene.

Statistically significant

What does it imply in terms of model accuracy?


Validation method
Validation method representative of different types of Amazonian colonization frontier, each one comprising a Landsat scene.

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 representative of different types of Amazonian colonization frontier, each one comprising a Landsat scene.

DINAMICA

Simulations run on DINAMICA

  • Calibrator

  • Simulator

www.csr.ufmg.br/dinamica


Fitness for the weo models
Fitness for the WEO models representative of different types of Amazonian colonization frontier, each one comprising a Landsat scene.


Combined effect of analytical woe on model fitness
Combined effect of analytical WOE on model fitness representative of different types of Amazonian colonization frontier, each one comprising a Landsat scene.

Distance to deforested

Removing two variables

Removing one variable


The heuristic method
The heuristic method representative of different types of Amazonian colonization frontier, each one comprising a Landsat scene.


Universidade federal de minas gerais

The representative of different types of Amazonian colonization frontier, each one comprising a Landsat scene. 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 representative of different types of Amazonian colonization frontier, each one comprising a Landsat scene.

    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 representative of different types of Amazonian colonization frontier, each one comprising a Landsat scene.

best-so-far

22565

What does it mean?


Fitness results
Fitness results representative of different types of Amazonian colonization frontier, each one comprising a Landsat scene.

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 representative of different types of Amazonian colonization frontier, each one comprising a Landsat scene.

22565

observed x simulated

1

0


Pattern comparison

deforestation 1997-200 representative of different types of Amazonian colonization frontier, each one comprising a Landsat scene.

pattern comparison

two patch sizes

22565

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 representative of different types of Amazonian colonization frontier, each one comprising a Landsat scene.

  • 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