Assessing Forest Carbon Stocks
Carbon Loss from Fire
An analysis of the major drivers of deforestation in the REDD project area concluded that the most significant driver of deforestation is fire. In fact, we estimate that upwards of 60% of the project area landscape burns every year, which is mostly due to fires used to clear forest area for farmland burning out of control during the mid-to-late dry season. A model developed by our partner, the University of Edinburgh (UoE), estimates between 0.5 and 1 tonne of carbon per hectare of dry forest can be lost from fire every year.
MCDI will focus its efforts to improve carbon stocks by working with communities to effectively manage fires and avoid uncontrolled burning in community forests (learn more here). Depending on revenue earned and MCDI’s ability to expand its REDD work in south-eastern Tanzania, total emissions reductions could amount to anywhere between 520,000 – 1,850,000 tCO2e over a ten year period.
Monitoring Carbon Stocks
In order to monitor the annual carbon savings from effective community based forest management efforts, we will need to use a powerful and robust methodology for detecting carbon stock changes. Specifically, we will look at the changes in carbon stocks from avoided large tree mortality and from the regeneration of seedlings and saplings. To measure such changes over time we have selected to use the ‘GapFire’ model, which was developed by UoE to predict the response of forests to different fire regimes. The model has already been used to measure the impacts of fire in Mozambique and Zimbabwe, and we are working with UoE to adapt the model for our project.
Once the GapFire model has been adapted to meet our needs, we can predict the carbon savings from implementing community fire management initiatives. However, while the GapFire model will allow us to predict future carbon stocks in the woodlands under improved community fire management, the actual values will need to be verified using other monitoring methods, such as monitoring large size permanent sample plots, monitoring individual trees (especially large trees) and using remote sensing technology.