Estimation of Forest Aboveground Biomass of Two Major Conifers in Ibaraki Prefecture, Japan, from PALSAR-2 and Sentinel-2 Data

Abstract

Forest biomass is a crucial component of the global carbon budget in climate change studies. Therefore, it is essential to develop a credible way to estimate forest biomass as carbon stock. Our study used PALSAR-2 (ALOS-2) and Sentinel-2 images to drive the Random Forest regression model, which we trained with airborne lidar data. We used the model to estimate forest aboveground biomass (AGB) of two significant coniferous trees, Japanese cedar and Japanese cypress, in Ibaraki Prefecture, Japan. We used 48 variables derived from the two remote sensing datasets to predict forest AGB under the Random Forest algorithm, and found that the model that combined the two datasets performed better than models based on only one dataset, with R2 = 0.31, root-mean-square error (RMSE) = 54.38 Mg ha−1, mean absolute error (MAE) = 40.98 Mg ha−1, and relative RMSE (rRMSE) of 0.35 for Japanese cedar, and R2 = 0.37, RMSE = 98.63 Mg ha−1, MAE = 76.97 Mg ha−1, and rRMSE of 0.33 for Japanese cypress, over the whole AGB range. In the satellite AGB map, the total AGB of Japanese cedar in 17 targeted cities in Ibaraki Prefecture was 5.27 Pg, with a mean of 146.50 Mg ha−1 and a standard deviation of 44.37 Mg ha−1. The total AGB of Japanese cypress was 3.56 Pg, with a mean of 293.12 Mg ha−1 and a standard deviation of 78.48 Mg ha−1. We also found a strong linear relationship with between the model estimates and Japanese government data, with R2 = 0.99 for both species and found the government information underestimates the AGB for cypress but overestimates it for cedar. Our results reveal that combining information from multiple sensors can predict forest AGB with increased accuracy and robustness.