Airborne Lidar Estimation of Aboveground Forest Biomass in the Absence of Field Inventory

摘要: The scientific community involved in the UN-REDD program is still reporting large uncertainties about amount and spatial variability of CO2 stored forests. main limitation has been lack field samplings over space time needed to calibrate convert remote sensing measurements into aboveground biomass (AGB). As an alternative costly inventories, we examine reliability state-of-the-art lidar methods provide direct retrieval many forest metrics that are commonly collected through sampling techniques (e.g., tree density, individual height, crown cover). AGB estimated using existing allometric equations fed by lidar-derived at either tree- or layer-level (for overstory underneath layers, respectively). Results 40 plots a multilayered located northwest Portugal show method provides estimates with relatively small random error (RMSE = 17.1%) bias (of 4.6%). It local baselines meet requirements terms accuracy satellite upcoming GEDI (Global Ecosystem Dynamics Investigation), Synthetic Aperture Radar (SAR) missions NISAR (National Aeronautics Space Administration Indian Research Organization SAR) BIOMASS from European Agency, ESA) for mapping purposes. development similar variety types would be significant improvement quantifying stocks changes comply policies.