Wall-to-Wall Above-Ground Biomass Estimation with Alos-2 Palsar-2 L-Band SAR Data and GEDI

Under the impact of climate change, monitoring forest carbon stock becomes an important task to evaluate the changes in carbon sequestrated from the atmosphere. Forest carbon stock estimation is still a challenging task, due to limited data sources that have a high correlation with above-ground biomass. With the help of the NASA Global Ecosystem Dynamics Investigation (GEDI) mission, above-ground biomass (AGB) can be measured by using the LiDAR data provided. However, GEDI data is sparse since it only samples about 4% of the Earth's land surface between 51.6° N&S. Previous studies demonstrated L-Band SAR's promising ability in retrieving forest stem volumes and estimating above-ground biomass. In this work, we propose a Deep Learning based workflow which utilizes PALSAR-2 L-Band images and GEDI to generate wall-to-wall above-ground biomass maps of North America. The workflow uses Convolutional Neural Network as the DL model and leverages both PALSAR-2 L-Band images and GEDI Relative Heights data to estimate the dense above-ground biomass maps. The results show that, by fusing GEDI Level 2 Relative Heights data with PALSAR-2 L-Band SAR data, it is possible to achieve a significantly high correlation with GEDI level 4 AGB data, as the final R-squared score of our model is as high as 0.83.