DeepBioFusion: Multi-modal deep learning based above ground biomass estimation using SAR and optical satellite images

Abstract

Accurate estimation of forest above-ground biomass (AGB) is essential for ecosystem conservation, sustainable forest management, and mitigating climate change and wildfire risks. Traditional methods, such as manual field surveys, are labor-intensive and limited in scope. This study presents DeepBioFusion, a multi-modal deep learning framework that first estimates AGB for validation as ground truth generation by using LiDAR-derived tree heights and a Tree Species map, employing allometry equations to relate tree height to Diameter at Breast Height (DBH). After this initial estimation, the framework is trained to predict AGB using high-resolution optical imagery and multiple bands of Synthetic Aperture Radar (SAR), including X, C, and L bands. The use of SAR bands enables improved canopy penetration, particularly in dense and cloud-covered forests. DeepBioFusion leverages the complementary strengths of SAR and optical data to enhance the accuracy of biomass predictions. Benchmarking against models like ResNet50 and Transformer, the proposed model demonstrates superior performance in AGB estimation across diverse forest environments. This study offers a scalable, cutting-edge approach to biomass monitoring, advancing efforts in climate change mitigation and sustainable forest management.