Evaluating the Transferability of GEDI-Calibrated Landsat Biomass Mapping for Retrospective Post-Fire Assessment: A Churchill Case Study

Retrospective mapping of aboveground biomass (AGB) using long satellite archives offers a valuable approach for assessing post-fire biomass change, but contemporary calibration does not necessarily ensure reliable transferability to historical conditions. The Global Ecosystem Dynamics Investigation (GEDI), a spaceborne lidar mission that provides observations of forest structure and biomass estimates, offers a potential source of contemporary reference data for model calibration. We evaluated this issue using a GEDI-calibrated Landsat workflow for the landscape affected by the 2009 Churchill wildfire in southeastern Australia. Random forest models combined optical, environmental and pre-fire predictors using 4,746 GEDI-linked reference observations from 2020–2023. Evaluation included reference-provenance auditing, spatial cross-validation across nine blocks with 300 m training exclusions, forward temporal and spatiotemporal tests within 2020–2023, paired predictor ablation, conditional-error diagnostics and an external field comparison. All reported provenance checks passed, supporting reference-processing integrity. The full model outperformed the training-mean baseline and reduced configurations in every block, although improvements were modest. Predictions showed a compressed range, with a prediction-to-reference standard-deviation ratio of 0.362 and regression slope of 0.104, overpredicting low reference biomass and underpredicting high reference biomass. Temporal and spatiotemporal transfer from 2020 to 2023 failed to outperform their corresponding training-mean baselines. Annual predictions declined after the fire and subsequently increased, but biomass loss and recovery were not independently validated. These findings support using the maps as descriptive reconstructions and highlight the need for independent structural observations across disturbance stages before quantitative recovery assessment or carbon accounting.