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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

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Universidade Federal de Minas Gerais

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  1. 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

  2. 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.

  3. analyzing the effects of spatial variables on the location of deforestation by applying: Analytical and heuristic methods • Weights of Evidence • Genetic Algorithm

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

  5. 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.

  6. The analytical method

  7. 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

  8. Assigning weights to produce transition probability maps

  9. 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

  10. Spatial determinants of deforestation Statistically significant What does it imply in terms of model accuracy?

  11. 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

  12. Simulation software DINAMICA Simulations run on DINAMICA • Calibrator • Simulator www.csr.ufmg.br/dinamica

  13. Fitness for the WEO models

  14. Combined effect of analytical WOE on model fitness Distance to deforested Removing two variables Removing one variable

  15. The heuristic method

  16. 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

  17. 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

  18. GA evolution best-so-far 22565 What does it mean?

  19. Fitness results 14% -2% 20% 15% 4% 24% 21% 18% 34% GAIN: 0.309458 Similarity 1x1: 0.313331 1.24%, 2.41%, 2.68%

  20. Fuzzy location comparison 22565 observed x simulated 1 0

  21. deforestation 1997-200 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

  22. 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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