Integrating Pigeon-Inspired Optimization and Support Vector Machines for Forest Aboveground Biomass Estimation

Accurate estimation of aboveground biomass (AGB) in mountainous forest ecosystem remains a significant challenge due to complex terrain, the high cost and limited applicability of traditional field-based methods. To address this issue, a remote sensing–based AGB estimation framework integrating intelligent optimization and machine learning was developed for Mount Tai in eastern China. Sentinel-2 multispectral data were selected to derive 105 candidate variables, including spectral bands, vegetation indices, texture features, and topographic factors, from which 17 key variables were selected using Pearson correlation analysis for model construction. A Support Vector Machine (SVM) optimized by the Pigeon-inspired optimization (PIO) algorithm was developed to adaptively determine optimal hyperparameters, and its performance was compared with that of Random Forest (RF) and standard SVM models. The results demonstrate that the PIO-SVM model achieved the best overall performance. For the training dataset, the model obtained an R² of 0.85. For the test dataset, the R² reached 0.73, outperforming RF (0.70) and standard SVM (0.72). The spatial distribution of AGB derived from the optimal model shows higher AGB values in the central and northern regions characterized by dense forest cover, in close agreement with field observations. These findings indicate that the PIO algorithm effectively enhances SVM hyperparameter optimization in complex parameter spaces, significantly improving the accuracy and stability of AGB estimation in mountainous forest. This study provides a reliable and efficient framework for regional-scale monitoring of forest biomass and carbon sink dynamics.