Applied Introduction of Modeling of REDD Projects: A Strategy for Reduce the Impacts of Climate Change

Reducing Emissions from Deforestation and Forest Degradation (REDD) is a climate change mitigation strategy employed to reduce the intensity of deforestation and GHGS emissions in developing countries. In recent decades, drastic land-use changes in the Mazandaran province caused a substantial reduction in the amount of Hyrcanian forest. The present research has been conducted with the knowledge of the objectives of REDD projects. Accordingly, forest cover changes in a range of Nowshahr and Noor in the Mazandaran province using Landsat satellite images (1984, 2000, and 2014) were examined. Then, transition potential modeling using the multi-layer perceptron artificial neural network (MLP) was implemented and continuing the accuracy assessment of the model was performed. In the end, according to the Voluntary Carbon Standard (VCS) methodology and the calibration period 1984-2014, forest cover change was predicted for the 30 next years (2044) and CO2 emissions up to the 2044 was computed. The results showed that forest cover decreased by about 3413 ha during 1984-2014. Also, accuracy assessment indicates the good accuracy of the model with ROC equal to 0/95. Finally, REDD baseline modeling results showed that 827591.5 tCO2e will be released during 2014-2044 (the next 30 years), but REDD project implementation will prevent the release of 584056.38 tCO2e. The findings of this research shows that using the methodology presented, the rate of land-use changes, GHGS emissions and the impact of REDD projects to reduce emissions can be predicted and can be used for the Project Design Document (PDD) of Clean Development Mechanism (CDM) in the Iran.