Using multisource satellite products to estimate forest aboveground biomass in Oita prefecture: a novel approach with improved accuracy and computational efficiency

Abstract

Accurate estimation of forest aboveground biomass (AGB) using satellite information is crucial for quantitatively evaluating forest carbon stock for climate change mitigation. However, with the emergence of an enormous number of identical data, selecting the most valuable dataset for estimation is essential to improve model efficiency and accuracy. Here, we present a novel framework using principal component analysis (PCA)-derived textures to tackle redundant variables and the integration of multisource satellite images using the random forest regression algorithm with recursive feature elimination to map the AGB of deciduous broadleaf forests in Oita prefecture, Japan. Our results highlighted that the model using PCA and derivative features had the best performance [R2 = 0.69, root mean square error (RMSE) = 33.59 Mg/ha, mean absolute error = 25.01 Mg/ha, relative RMSE = 0.27]. In this case, of the 34 variables selected by RFE for the model, 19 were derived from principal components and corresponding textures, indicating a considerable improvement in using our novel framework to estimate AGB. Together, this study provides a valuable strategy for feature selection to lessen multicollinearity and redundancy.