Improving deforestation scenarios for REDD and biodiversity co-benefits.

Authors: Florencia Sangermano*, Graduate School of Geography - Clark University
Topics: Land Use and Land Cover Change, Spatial Analysis & Modeling
Keywords: Land cover change, REDD, Biodiveristy, co-benefits
Session Type: Paper
Day: 4/13/2018
Start / End Time: 8:00 AM / 9:40 AM
Room: Southdown, Sheraton, 4th Floor
Presentation File: No File Uploaded

REDD+ methodologies involve the use of land cover change models for the identification of deforestation scenarios under business as usual practices. Variations in predicted deforestation patterns can have important implications for the accounting of Carbon emissions and biodiversity co-benefits. REDD+ methodologies present guidelines for the evaluation of models, but do not account for predicted deforestation patterns. This work evaluates land cover change modeling methods based on their capability of predicting deforestation pattern and their impacts on carbon and biodiversity. Using Bolivia as a case study, the potential for deforestation was predicted using 7 transition potential modeling algorithms. The pattern of deforestation was predicted using two allocation methods based on uniform and stratified quantification of demand for land. The capacity of models to predict the real pattern of change, as well as carbon and biodiversity implications were evaluated. Differences across scenarios were more related to the allocation method than the transition potential algorithm. The deforestation pattern, carbon, and biodiversity benefits under the stratified allocation methods were more similar to reality. The selection of land change scenarios with patterns similar to reality is essential for the success of REDD+ projects. Therefore, REDD+ methodologies need to include a comparison of multiple empirical modeling methods, as well as the establishment of a stratified allocation process. The final selection of best deforestation scenarios, should be based on the results of a multi-resolution validation, as well as the assessment of the capacity of the models to simulate the impacts on Carbon and biodiversity.

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