3D-SAR Tomography and Machine Learning for High-Resolution Tree Height Estimation
This work addresses the need for precise forest biomass estimation to support global carbon cycle modelling and climate change mitigation, representing an incremental advancement in remote sensing techniques.
This study tackled the problem of accurately estimating tree height for forest biomass calculations by applying machine learning to Synthetic Aperture Radar (SAR) data, achieving a mean absolute error of 2.82m for canopies around 30m.
Accurately estimating forest biomass is crucial for global carbon cycle modelling and climate change mitigation. Tree height, a key factor in biomass calculations, can be measured using Synthetic Aperture Radar (SAR) technology. This study applies machine learning to extract forest height data from two SAR products: Single Look Complex (SLC) images and tomographic cubes, in preparation for the ESA Biomass Satellite mission. We use the TomoSense dataset, containing SAR and LiDAR data from Germany's Eifel National Park, to develop and evaluate height estimation models. Our approach includes classical methods, deep learning with a 3D U-Net, and Bayesian-optimized techniques. By testing various SAR frequencies and polarimetries, we establish a baseline for future height and biomass modelling. Best-performing models predict forest height to be within 2.82m mean absolute error for canopies around 30m, advancing our ability to measure global carbon stocks and support climate action.