Accurate, high-resolution forest canopy height information is critical for monitoring forest structure, estimating aboveground biomass, and informing land management and climate resilience strategies. In particular, remotely-sensed canopy height estimates can provide an estimate of fuel volume for wildland fire effects analyses, including in the Wildland Urban Interface where fuels in the home ignition zone are influential. This study presents a scalable machine learning framework to estimate canopy height across California by integrating Global Ecosystem Dynamics Investigation (GEDI) LiDAR Relative Height measurements with a suite of multi-sensor remote sensing data. The model was trained on more than 5.6 million rigorously filtered GEDI footprints from California and Arizona to minimize noise and ensure robust performance across ecologically diverse and topographically complex landscapes. Predictor variables included optical (Sentinel-2), radar (Sentinel-1, PALSAR-2), topographic, land cover, and a range of derived spectral indices and spatial-textural features. Model validation using an independent GEDI hold-out set demonstrated strong performance, with an R2 of 0.72, RMSE of 6.05 m, MAE of 4.31 m, and a slight positive bias of 0.80 m, closely matching the training metrics, suggesting a well-calibrated model. Compared to existing global canopy height products, the proposed regional approach shows improved agreement with GEDI observations in complex terrain and dense vegetation areas within California, while reflecting differences in training scope and objectives between regional and global models. A key outcome of this study is a 10-m resolution biennial canopy height time series for California (2016, 2018, 2020, 2022, and 2024) suitable for stand-scale and multi-epoch structural assessment. The resulting 10-m, biennial California canopy-height series for 2016–2024 is used to assess regrowth trajectories of canopy height and biomass in four major wildfire areas.
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