Showcasing the effectiveness of GEDI footprints as first-phase sample for forest inventories based on double sampling for post-stratification

International audience

anglais. 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 thenegligible 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.