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
IPAM – INSTITUTO DE PESQUISA
AMBIENTAL DA AMAZÔNIA
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.
Analytical and heuristic methods
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.
Variables need to be spatially independent:
pair-wise tests, such as Crammer’s V coefficient or Joint Uncertainty
breaking points for this graph are determined by applying an line-generalizing algorithm
What does it imply in terms of model accuracy?
Need to define a map comparison method:
fuzzy similarity using maps of differences and comparing only changed areas
Simulations run on DINAMICA
Distance to deforested
Removing two variables
Removing one variable
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
until n=50 or gain does not increase
Select the best from the best-so-far of all generations
What does it mean?
GAIN: 0.309458 Similarity 1x1: 0.313331
1.24%, 2.41%, 2.68%
observed x simulated
two patch sizes
two patch sizes
DINAMICA’s transition functions to form patches at various sizes
Not only spatial accuracy but similar landscape structure
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