Machine Learning and Remote Sensing for Monitoring Tree Biomass

Tree-based ecosystems play a crucial role in climate change mitigation by sequestering atmospheric CO₂. However, tree resource monitoring practices are often inconsistent, biased, and fail to account for trees outside forests, limiting the effectiveness of carbon credit systems and restoration strategies. This talk presents recent advances in large-scale tree ecosystem monitoring enabled by machine learning and remote sensing [1]. We demonstrate methods for estimating tree biomass and carbon stocks at continental and national scales based on high-resolution satellite imagery and LiDAR data using deep neural networks. Case studies include mapping 9.9 billion trees across African drylands [5], nationwide tree mapping and carbon stock estimation in Rwanda supporting efforts to achieve net-zero emissions [3], and assessing the overlooked contribution of trees outside forests in Europe [2]. We present an application of 3D point cloud deep neural networks to predicting vegetation biomass from airborne LiDAR [4]. Furthermore, we introduce an approach for predicting vertical vegetation structure from Sentinel-2 and spaceborne LiDAR (GEDI) data at 10 meter resolution, potentially providing insights into biodiversity, biomass, and human interventions [6]. These developments pave the way for accurate, high-resolution, and unbiased monitoring of tree biomass, supporting carbon cycle modelling and informing carbon market policies.

 

[1] Brandt et al. High-resolution sensors and deep learning models for tree resource monitoring. Nature Reviews Electrical Engineering, 2025

[2] Liu et al. The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe. Science Advances, 2023

[3] Mugabowindekwe et al. Trees on smallholder farms and forest restoration are critical for Rwanda to achieve net zero emissions. Communications Earth & Environment , 2024

[4] Oehmcke et al. Deep point cloud regression for above-ground forest biomass estimation from airborne LiDAR. Remote Sensing of Environment, 2024

[5] Tucker et al. Towards continental scale monitoring of carbon stocks of individual trees in African dryland. Nature, 2023

[6] Zhang et al. A Vertical Vegetation Structure Model of Europe. Advances in Representation Learning for Earth Observation at EURIPS, 2025