REDD implementation for greenhouse gas reduction and climate change mitigation in Hyrcanian forests: a case study of the Kojoor Watershed, Northern Iran

Abstract

Reducing emissions from deforestation and forest degradation (REDD) is a specific strategy for combating deforestation and forest degradation to alleviate the effects of climate change. In this study, the potential greenhouse gas (GHG) emission reduction resulting from the implementation of a REDD project is estimated. Changes in forest cover throughout the years 1985, 1990, 1995, 2000, 2010, 2015, and 2020 were analyzed using time-series Landsat imagery (TM, ETM + , and OLI) and a random forest algorithm. Multilayer perceptron neural networks were used to model the transition potential of the forest cover, which were then predicted via Markov chain analysis. The change detection analysis revealed two discernible patterns in forest cover dynamics. Between 1985 and 2000, a notable decrease in forest cover was seen, whereas from 2000 to 2020, it significantly increased. The results suggested that the absence of REDD implementation would result in the deforestation of approximately 199,569 hectares of forest cover between 2020 and 2050, leading to the release of 1,995,695 tCO2e of emissions into the atmosphere. However, with the implementation of REDD, these emissions would be reduced to 405,512 tCO2e, effectively preventing the release of 1,590,183 tCO2e of emissions into the upper atmosphere. This study demonstrates that the implementation of REDD projects can be an effective strategy for reducing GHG emissions and mitigating climate change in the Hyrcanian forests.