Organizer(s): Robert Pontius; Rebecca Dickson This session focuses on methods for quantifying the reduction of greenhouse gas (GHG) emissions stemming from deforestation and forest degradation (REDD). REDD projects aim to reduce GHG emissions by preserving forest ecosystem services via prevention of future deforestation and forest degradation. The REDD framework allows carbon emitters to compensate for (offset) their own GHG emissions by funding forest conservation projects. The success and credibility of REDD projects depends upon their implementation and the accounting system, which can take a variety of forms. Remote Sensing and GIS-based predictive simulation models are important tools to help estimate the amount of deforestation, and the ensuing GHG emissions, that would occur in the absence of the project - the project’s baseline or business-as-usual scenario - and, thereby, determine the number of carbon credits that the REDD project can claim to achieve. This session will shed light on various issues of the REDD paradigm, especially concerning the establishment of baselines land change mapping, and modeling.