Estimating aboveground biomass density using hybrid statistical inference with GEDI lidar data and Paraguay’s national forest inventory

Forests are widely recognized as critical to combating climate change due to their ability to sequester and store carbon in the form of biomass. In recent years, the combined use of data from ground-based forest inventories and remotely sensed data from light detection and ranging (lidar) has proven useful for large-scale assessment of forest biomass, but airborne lidar is expensive and data acquisition is infeasible for many countries. By contrast, the spaceborne Global Ecosystem Dynamics Investigation (GEDI) lidar instrument has collected freely available data for most of the world’s temperate and tropical forests since 2019. GEDI’s biomass products rely on models calibrated with a global network of field plots paired with GEDI waveforms simulated from airborne lidar to predict biomass. While this calibration strategy minimizes spatial and temporal offsets between field measurements and corresponding lidar returns, calibration data are sparse in many regions. Paraguay’s forests are known to be poorly represented in GEDI’s current calibration dataset, and here we demonstrate that local models calibrated opportunistically with on-orbit GEDI data and field surveys from Paraguay’s national forest inventory can be used with GEDI’s statistical estimators of aboveground biomass density (AGBD). We specify a protocol for opportunistically matching GEDI observations with field plots to calibrate a field-to-GEDI biomass model for use in GEDI’s hybrid statistical framework. Country-specific calibration using on-orbit data resulted in relatively accurate and unbiased predictions of footprint-level biomass, and importantly, supported the assumption underlying model-based inference that the model must ‘apply’ to the area of interest. Using a locally calibrated biomass model, we estimate that the mean AGBD in Paraguay is 65.55 Mg ha ^−1 , which coincides well with the design-based approach employed by the national forest inventory. The GEDI estimates for individual forest strata range from 52.34 Mg ha ^−1 to 103.88 Mg ha ^−1 . On average, the standard errors are 47% lower for estimates based on GEDI than the forest inventory, representing a significant gain in precision. Our research demonstrates that GEDI can be used by national forest inventories in countries that seek reliable estimates of AGBD, and that local calibration using existing field plots may be more appropriate in some applications than using GEDI global models, especially in regions where those models are sparsely calibrated.