Modeling deforestation baselines using GEOMOD - Instituto ...

Finalizing Avoided Deforestation Baselines
I. INTRODUCTION
The Spatial Modeling Approach
Spatial modeling, as we define it (M. Hall et al. 2000) is the application of a FORTRAN (or other computer
language)-based model that uses spatially distributed data to simulate landscape dynamics. In the case of
land use change modeling it implies that the spatial distribution of various factors, such as topography,
plays an important role in determining where humans exploit the landscape. We apply GEOMOD2,
developed by researchers at SUNY College of Environmental Science and Forestry (Hall, et al. 1995a;
1995b with funding from the US Department of Energy, Carbon Dioxide Research Program, Atmospheric
and Climatic Change Division), to estimate the business-as-usual baseline for deforestation in two states of
Mexico—the Calakmul region of Campeche and the Meseta Purépecha region of Michoacan.
Based on the “maximum power principle” (Odum 1983), the model assumes and tests whether those
factors which are most likely to impact an individual farmer’s energy return on investment (EROI) are
statistically significant (Hall, Cleveland and Kaufmann, 1986). These factors include topographic position
(elevation and steepness of slope), distance from rivers, roads and already established settlements, and
socio-economic factors such as infrastructure. The model allows for regional stratification to capture
different government policies in different political units that would affect land clearing and land tenure. It
also includes the option to impose, or not, the contiguity rule in simulation. This rule limits land clearing to
cells that are adjacent to cells already cleared. The search window distance used to impose contiguity
around already-developed land can be adjusted to capture the difference between clearing for shifting
cultivation, and clearing for industrialized agriculture. The model is calibrated by assigning weights to
candidate map cells based on the empirical importance of each driver in explaining the spatial pattern of
land use change at one point in time. It then simulates that change between that point in time and a later
time. Change can be from forest to non-forest, from non-forest to forest, from forest to degraded forest,
pasture to urban; in other words any pathway of change can be analyzed by the model. The model then
compares these preliminary simulation results against a validation (real) data set (i.e. a map of time two)
which is not used in weighting the pattern drivers. The test of goodness of fit for each simulated map
produced is based on both the percent of cells simulated correctly and the Kappa Index of Agreement
(Pontius et al. 2001). We use the ‘kappa-for-location’ index, rather than the ‘kappa-for-quantity’ because in
all cases for validation we already know from the satellite imagery the quantity of cells that have been
deforested or degraded between the two time periods.
II. HYPOTHESIS
We test three hypotheses. (1) GEOMOD should generate a more accurate estimate of the carbon benefits
derived from a region than a non-spatial approach because the vegetation types, and hence carbon
content, varies across a landscape and GEOMOD has the ability to specify WHERE deforestation is “most
likely” to occur. (2) We can predict regional or project-specific results in the same model simulations. And
(3) the use of GEOMOD, in particular, removes some of the “counterfactual uncertainty” (Moura-Costa,
2001; Kerr, 2001) inherent in other methods and other spatial models used to estimate the baseline
scenarios because GEOMOD employs a standardized procedure of assessment of this “counterfactual
uncertainty (‘Kappa-for-location’ statistic, Pontius, 2000).
III. How GEOMOD works
III. A. Data Collection and Preparation
The model requires as input the following mapped information:
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