An Assessment of Deforestation Models for Reducing Emissions from Deforestation and Forest Degradation (REDD)


Oh Seok Kim, Geography Doctoral Program, University of Southern California, 3620 S Vermont Ave, Los Angeles, CA 90089-2534, USA. E-mail:


With the increasing concerns in developing methodologies for Reducing Emissions from Deforestation and forest Degradation (REDD) projects, there is a need to understand the characteristics of existing Land-Use/Cover Change (LUCC) modules. This research presents a modular framework for assessing predictive accuracy of business-as-usual deforestation in the future by comparing two existing approaches: GEOMOD Modeling (GM) and Land Change Modeler (LCM). The comparison uses data from a case study in Chiquitanía, Bolivia. Data from 1986 and 1994 are used to simulate land-cover of 2000; the resulting maps are compared with an observed land-cover map of 2000. GM and LCM simulate business-as-usual deforestations at the pixel level. The model structures of GM's linear extrapolation and LCM's Markov Chain are compared to review quantity of LUCC; and the model structures of GM's empirical frequency, LCM's logistic regression, and LCM's multilayer perceptron are compared to review (spatial) 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, and the LCM's multilayer perceptron may produce different results for each simulation due to its stochastic element. Based on the model structure and predictive accuracy comparisons, the LCM seems more suitable than the GM for a REDD application. When a project is to employ a predictive method for its spatially explicit baseline setting, then it is highly recommended to use the proposed framework to assess accuracy of the baseline as part of a project design document.