Forest emissions reduction assessment using airborne LiDAR for biomass estimation

Given its large mitigation potential and predictable cost, forest carbon sequestration is crucial to reducing anthropogenic CO2 emissions to achieve climate mitigation targets. However, forest-based mitigation activities have been constrained owing to the absence of a timely and cost-effective approach to assess emissions reduction, thereby limiting its financial support. This study presented a more economically viable approach to forest emissions reduction assessment using airborne light detection and ranging (LiDAR) technology. Using a forest-based emissions reduction project in China as an on-site experiment subject, we developed species-specific LiDAR-based biomass estimation models to assess emissions reduction. It was observed that LiDAR-derived feature parameters could accurately predict forest biomass measurements estimated by the traditional ground-based method (R2 = 0.93). Interestingly, we found emissions reduction through field measurement (22.2 ktCO2e) or LiDAR observations (22.4 ktCO2e) was about two times higher than that estimated by the project developer before its implementation (10.5 ktCO2e). Furthermore, we found airborne LiDAR technology could significantly reduce monitoring cost ($/tCO2e), which would only account for a small share of the total implementation cost of a forest emissions reduction project. Based on these results, we summarized the advantages of applying airborne LiDAR technologies in developing forest-based emissions reduction projects and provided policy recommendations.