A public release of global vegetation height map, based on data from spaceborne lidar GLAS, was validated using forest management inventory (FI) data and airborne laser (ALS) data from two 15 × 15 km test sites in Estonia: the first one in Aegviidu and the second one in Laeva. For each global vegetation height (GVH) map pixel located in the test sites we calculated forest height based on the FI data and on ALS data. Linear regression analysis was then used to evaluate the relationships between GVH map values (H_GVH), FI forest height (H_FI) and ALS-based Lorey’s forest height (H_ALS). In the second test H_GVH and H_ALS were evaluated for estimating forest biomass using regression analysis.

   The biomass was calculated for each GVH pixel using FI data and allometric regression models. The correlation between H_GVH and HFI or H_ALS in both test sites was weak – in Aegviidu r < 0.25 and in Laeva r < 0.15; and, the relationship was not statistically significant in Laeva. The airborne lidar based H_ALS had a strong positive correlation with forest biomass and the determination coefficient of linear regression was R² > 0.6 (p < 0.01) in both test sites. The relationship between H_GVH and biomass was scattered and determination coefficient for linear model was small (R² < 0.15, p < 0.01). Although in this study only weak correlation between measured forest heights (H_FI and H_ALS) and spaceborne lidar based H_GVH was found, the GVH type estimates are essential for the areas, where forest inventory data or airborne lidar data is not available. The obtained results show that forest height estimates from ALS or spaceborne lidar could be used directly for estimating biomass in managed hemiboreal forests at coarse spatial resolution.

Keywords: hemiboreal forests, airborne laser data, forest inventory data, global vegetation height map, forest biomass.