Towards using GEDI in the French NFI-based AGB estimations: Systematic Assessment of Plot-level Simulations Using Nationwide Airborne LiDAR HD

Accurate estimation of aboveground biomass (AGB) is essential for quantifying forest carbon stocks. Missions such as NASA’s Global Ecosystem Dynamics Investigation (GEDI) provide valuable forest structure data that can be converted into AGB estimates. The most robust approach relies on calibrating these metrics against National Forest Inventory (NFI) plots. However, GEDI’s sampling remains sparse in both space and time, limiting opportunities for local calibration and validation of biomass models [1]. To address these limitations, the forest and remote sensing community increasingly uses simulators to generate GEDI-like measurements at NFI locations. Among the available tools for emulating GEDI waveforms, the simulator developed by Steven Hancock [2] has been widely adopted. Yet, its accuracy and biases have not been systematically assessed beyond the initial test areas or against real GEDI observations. 

By evaluating the Hancock simulator across diverse French forests using high-density national airborne LiDAR data (LiDAR HD), this work investigates the validity of a globally developed tool when applied at the local scale. We quantify discrepancies between simulated and actual GEDI data with a focus on bias metric due to its potential propagation into downstream biomass models. Such errors may lead to significant over- or underestimation of carbon stocks. 

Our approach focuses on a bottom-up, empirical evaluation of GEDI-simulated metrics to diagnose local biases and their drivers. It does not provide a comprehensive review of the simulator's theoretical framework. Results reveal systematic structural biases of up to 1 m in RH metrics. We investigate these errors in relation to pulse shape, algorithms and beam energy differences, canopy cover, forest type, seasonal effects and topography. Finally, we propose correction strategies in which a multi-layer perceptron (MLP) is trained to adjust simulated RH metrics to better match real GEDI observations. Our findings provide practical recommendations for simulator users, implications for improving GEDI-based biomass estimation and insights for the design of future LiDAR missions.

 

[1] N. Besic, et al., “Using structural class pairing to address the spatial mismatch between GEDI measurements and NFI plots,” IEEE JSTARS, vol. 17, pp. 12854–12867, 2024. DOI: 10.1109/JSTARS.2024.3425431  

[2] S. Hancock, et al., “The GEDI simulator: A large-footprint waveform lidar simulator for calibration and validation of spaceborne missions,” Earth Space Sci., vol. 6, no. 2, pp. 294–310, 2019. DOI: 10.1029/2018EA000506