Estimate Forest Biomass Dynamics Using Multi-Temporal Lidar And Single-Date Inventory Data

Estimating change in forest biomass is important for monitoring carbon dynamics and understanding the global carbon cycle. Multi-temporal airborne lidar data has been recently used to accurately predict change in forest attributes such as aboveground biomass (AGB). In this study, we assessed the ability of multi-temporal airborne lidar (2008 and 2016) and single-date inventory data to estimate forest biomass dynamics. To do so, we compared different imputation approaches to predict biomass, specifically direct (i.e., a model trained by the biomass variable or AGB) and indirect (i.e., a model trained by structure variables - basal area, tree volume and stem density) approaches. We also evaluated the ability of the selected model in temporally estimating biomass by relating biomass predictions with forest disturbance data. Our results demonstrated that AGB can be better predicted using an indirect imputation method in which lidar metrics were trained by a structure variable (basal area, RMSE = 95.09, R
²
= 0.89). While the model was developed for the date of inventory measurements (2016), the model was successfully applied to predict biomass for a historical date (2008). For both years, biomass predictions were highly consistent with disturbance history. This study further informs the benefits of multi-temporal lidar data to estimate forest biomass dynamics in instances when only single-date inventory data are available. The work thus can support forest researchers and managers in improving their scientific and practical tasks in forest management.