Plantation based natual forests biomass estimation for REDD policies implementation in Cambodia

Forests play an important role in the climate change mitigation and regulation. Monitoring of forest biomass from local to global scale has become a challenging issue in the context of climate change. In this study, we have investigated the backscattering properties of ALOS/PALSAR data in cashew and rubber plantation area of Cambodia. The result shows that PALSAR backscattering coefficient (σ⁰) has different response for both the plantation types because of difference in their biophysical parameters. The PALSAR σ⁰ indicates a high correlation and a less saturation in cashew plants than rubber plants. A multi-linear regression (MLR) model has been generated using the cashew plants ground based biomass and PALSAR σ⁰. This MLR model has been applied to estimate natural forests biomass in Cambodia. The natural forest biomass was validated using forest inventory data of Cambodia. The validation results indicate a strong correlation (R² = 0.64) between PALSAR estimated biomass and field based biomass with RMSE = 23t/ha in deciduous forests. In high biomass region such as dense evergreen forest, this model becomes saturated because of saturation of PALSAR signal. The application of this model is to estimate biomass of deciduous forest in Cambodia for UN-REDD (United Nations Reducing Emission from Deforestation and Forest Degradation) policies implementation.