Forest Aboveground Biomass Estimation in Küre Mountains National Park Using Multifrequency SAR and Multispectral Optical Data with Machine-Learning Regression Models

Abstract

Aboveground biomass (AGB) is crucial in forest ecosystems and is intricately linked to the carbon cycle and global climate change dynamics. This study investigates the efficacy of synthetic aperture radar (SAR) data from the X, C, and L bands, combined with Sentinel-2 optical imagery, vegetation indices, gray-level co-occurrence matrix (GLCM) texture metrics, and topographical variables in estimating AGB in the K & uuml;re Mountains National Park, T & uuml;rkiye. Four machine-learning regression models were employed: partial least squares (PLS), least absolute shrinkage and selection operator (LASSO), multivariate linear, and ridge regression. Among these, the PLS regression (PLSR) model demonstrated the highest accuracy in AGB estimation, achieving an R2 of 0.74, a mean absolute error (MAE) of 28.22 t/ha, and a root mean square error (RMSE) of 30.77 t/ha. An analysis across twelve models revealed that integrating ALOS-2 PALSAR-2 and SAOCOM L-band satellite data, particularly the SAOCOM HV and ALOS-2 PALSAR-2 HH polarizations with optical imagery, significantly enhances the precision and reliability of AGB estimations.