AGBUNet: an enhanced CNN-UNET architecture for the prediction of above ground biomass using deep learning

Accurate prediction of above ground biomass (AGB) is critical for monitoring forest health and carbon cycling. It is crucial for understanding and managing forest ecosystems. In this paper, we propose an enhanced framework combining convolutional neural network (CNN) and UNet, termed AGBUNet, specifically designed for predicting AGB using remote sensing data. The framework consists of separate CNNs for processing each type of image, whose outputs are subsequently fused in a UNet architecture to enhance prediction accuracy. These modifications include customized convolutional layers, advanced preprocessing techniques, and a novel integration of data prompts from separate Sentinel-1 and Sentinel-2 image processing streams. The AGBUNet integrates Sentinel-1 and Sentinel-2 images to leverage complementary information from synthetic aperture radar (SAR) and optical sensors. This study underscores the potential of the AGBUNet model for enhancing biomass estimation from remote sensing data, contributing to better forest management and ecological monitoring. The performance measures obtained are compared with the other models, and the following results are obtained as follows for MSE value of 298.25, RMSE value of 15.27 and MAE value of 12.21, and the values are satisfying compared with earlier benchmarks.