AAG - 2010 Annual Meeting, Washington, DC
Comparison of Two GIS-based Land Change Modules for Constructing REDD Baselines in Bolivia
is part of the Paper Session:
Land Use and Spatial Analysis
scheduled on Sunday, 4/18/10 at 8:00 AM.
Author(s):
Oh Seok Kim* - Department of Geography, University of Southern California
Abstract:
With the increasing concerns in developing methodologies for Reducing Emissions from Deforestation and Degradation (REDD) projects, there is a need to understand the characteristics of existing Land-Use/Cover Change (LUCC) modules. This research presents a comparison of two existing approaches: GEOMOD Modeling (GM) and Land Change Modeler (LCM). The comparison uses data from a case study in ChiquitanÃa, Bolivia. Data from 1986 and 1994 are used to simulate land-cover of 2000; the resulting maps are compared to an observed land-cover map of 2000. GM and LCM simulate baseline deforestation at the pixel-level. The model structures of linear extrapolation and Markov chain are compared to review quantity of LUCC; and the model structures of empirical frequency, logistic regression and multilayer perceptron are compared to review allocation of LUCC. Relative operating characteristics, figure of merit and multiple resolution analysis are employed to assess predictive accuracy of multiple transition modeling. By design, GM lacks the potential to model multiple transitions, while LCM may produce different results for each simulation. Based on the model structure and predictive accuracy comparisons, the LCM's logistic regression seems the most suitable LUCC module to construct a REDD baseline in this case. However, it is crucial to emphasize that these results are strictly limited to this particular case. Thus, there is no obvious method that is most accurate, so if a REDD project employs predictive GIS-based LUCC modeling for its spatially-explicit baseline construction, it should include the framework employed in this research to establish the baseline in a scientific manner.
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