Estimating Aboveground Biomass and Carbon Sequestration in Afforestation Areas Using Optical/SAR Data Fusion and Machine Learning

The growing population and the impacts of climate change present a major challenge to forests, which play a crucial role in regulating the carbon cycle. Pakistan, as a Kyoto Protocol signatory, has implemented afforestation initiatives such as the Khyber Pakhtunkhwa (KP) government’s Billion Tree Afforestation Project (BTAP). Quantifying the environmental impacts of such initiatives is very important; however, carbon pool data for BTAP plantation regions remain unavailable and are underexplored. This study aims to quantify aboveground biomass (AGB) and carbon sequestration potential (CSP) in the BTAP plantation regions using remote sensing and field data. Random sampling of 310 circular plots (17.84 m radius) provided measurements for tree height and diameter, from which AGB was calculated using allometric equations. Remote sensing data from Sentinel-1 and Sentinel-2, combined with polarization rasters and vegetation indices, were used to train and evaluate multiple regression models including multiple linear regression (MLR), support vector regression (SVR), and random forest regression (RFR). The RFR model outperformed the others (R2 = 0.766) when using combined optical and radar data, yielding a mean AGB of 4.77 t/ha, carbon stock of 2.24 t/ha, and CO2 equivalent of 10.36 t/ha. For BTAP plantations, the total biomass reached 1.19 million tons, with 2.06 million tons of CO2 equivalent sequestered, corresponding to an annual sequestration of 0.47 tC/ha/yr and a potential of 99.18 ± 15 tC/ha. This research introduces innovative predictive models and a comprehensive carbon assessment framework for afforestation projects, providing critical insights for policymakers and climate change mitigation efforts.