Estimation of forest biomass dynamics in subtropical forests using multi-temporal airborne LiDAR data

Estimating the change in the magnitude and distribution of forest biomass is important for monitoring carbon dynamics and understanding the implications on the terrestrial carbon cycle. In this study, we assessed the capacity of multi-temporal airborne Light Detection and Ranging (LiDAR) data to estimate forest biomass dynamics at a mixed subtropical forest of southeast China, and analyzed the magnitude and spatial patterns of the above ground biomass (AGB) change over a 6-year period. To do so, we evaluated different approaches to estimate change in AGB, specifically direct (i.e., predicting biomass change using the differences in LiDAR metrics) and indirect (i.e., modeling biomass for each point in time and predicting the change as their differences) approaches. Once models were developed, the change in AGB was mapped across the study site and examined in relation to forest type and age. Our results demonstrated that the direct approach (CV-R2=0.63, CV-rRMSE=25.64%) produced more accurate estimates in change of AGB than the indirect approach (CV-R2=0.59, CV-rRMSE=28.35%). Canopy height metrics of delta mean height (∆hmean) and a number of upper percentile heights (i.e., ∆h75 and ∆h95), calculated from the LiDAR first returns, were found to be most sensitive to changes in biomass, whereas metrics based on maximum canopy height or canopy density had less predictive power. Furthermore, differences in point cloud densities did not significantly impact the estimations of AGB and change of AGB at the plot-scale (30×30m). Spatial extrapolation of the AGB change indicated that, in general, most of the forest area had an overall gain in biomass. The middle-aged stands (typically dominated by Quercus acutissima between 41 and 60years old) show marked growth of biomass (median ∆AGB=2.29Mgha−1year−1), followed by mature (typically dominated by Pinus massoniana between 31 and 60years old) (median ∆AGB=1.72Mgha−1year−1) and young stands (typically consisting of regenerated broadleaved trees younger than 40years old) (median ∆AGB=1.48 Mg ha−1year−1), whereas over-mature stands (typically dominated by Cunninghamia lanceolata older than 35years) show the lowest growth (median ∆AGB=0.76Mgha−1year−1). This study demonstrates benefits of using multi-temporal LiDAR data to help identify specific areas for management interventions, to enhance forest productivity, maintain biomass stocks and optimize carbon sequestration.