The GEDI spaceborne lidar system was specifically designed to study forest ecosystems. Inference on forest attributes using GEDI data was mostly addressed through model-assisted, model-based and hybrid approaches. In this study, we applied a double sampling for post-stratification (DSPS) design-based approach to combine GEDI and national forest inventory data. Although widely used in the field of forest inventories, the use of such a design-based approach relying on GEDI data has not yet been investigated. This method is advantageous because it requires neither precise geolocation nor co-location between GEDI footprints and inventory plots. We evaluated the impact of the bridge variable and the impact of GEDI’s spatial sampling pattern on the results of the DSPS approach by comparing our GEDI-based results to reference airborne-laser-based results. We employed maximum tree height as the bridge variable and chose a complex study area in northeastern France with relief and highly diverse forest stands. We used 202,808 GEDI footprints as the first-phase sample and 476 National Forest Inventory (NFI) plots as the second-phase sample to estimate the growing stock volume (GSV). Compared with estimates based solely on NFI field plots, the DSPS approach reduced the GSV variance by up to 54% without any additional cost, aside from the negligible additional time required to download and process the GEDI data. GEDI can thus be considered as an effective data source to post-stratify NFI data and provide forest attribute estimates with a greater precision or at a finer spatial scale.

GEDI (Global Ecosystem Dynamics Investigation) is a lidar system on-board the International Space Station designed to study forest ecosystems. However, GEDI footprint low accuracy geolocation is a major impediment to the optimal benefit of the data. We thus proposed a geolocation correction method, GeoGEDI, only based on high-resolution digital terrain models (DTMs) and GEDI derived ground elevations. For each footprint, an error map between GEDI ground estimates and reference DTM was computed, and a flow accumulation algorithm was used to retrieve the optimal footprint position. GeoGEDI was tested on 150 000 footprints in Landes and Vosges, two French forests with various stands and topographic conditions. It was applied to GEDI versions 1 (v1) and 2 (v2), by either a single or four full-power laser beam tracks. GeoGEDI output accuracy was evaluated by analyzing shift distributions and comparing GEDI ground elevations and surface heights to reference data. GeoGEDI corrections were greater for v1 than for v2 and agreed with errors announced by NASA. Within forests, GeoGEDI improved the RMSE of ground elevation in Landes by 26.8 % (0.34 m) and by 13.3 % (0.14 m) for v1 and v2, respectively. For Vosges, ground elevation RMSE improved by 59.6 % (3.82 m) and 36.2 % (1.41 m), for v1 and v2, respectively. Regarding surface heights, except for v2 in Landes, where insufficient variations in topography combined to GEDI ground detection issues might have penalized the adjustment, GeoGEDI improved GEDI estimates. Using GeoGEDI showed efficient to improve positioning bias and precision.