Aboveground Biomass Estimation of Individual Trees in a Coastal Planted Forest Using Full-Waveform Airborne Laser Scanning Data

摘要

The accurate estimation of individual tree level aboveground biomass (AGB) is critical for understanding the carbon cycle, detecting potential biofuels and managing forest ecosystems. In this study, we assessed the capability of the metrics of point clouds, extracted from the full-waveform Airborne Laser Scanning (ALS) data, and of composite waveforms, calculated based on a voxel-based approach, for estimating tree level AGB individually and in combination, over a planted forest in the coastal region of east China. To do so, we investigated the importance of point cloud and waveform metrics for estimating tree-level AGB by all subsets models and relative weight indices. We also assessed the capability of the point cloud and waveform metrics based models and combo model (including the combination of both point cloud and waveform metrics) for tree-level AGB estimation and evaluated the accuracies of these models. The results demonstrated that most of the waveform metrics have relatively low correlation coefficients (<0.60) with other metrics. The combo models (Adjusted R 2 = 0.78–0.89), including both point cloud and waveform metrics, have a relatively higher performance than the models fitted by point cloud metrics-only (Adjusted R 2 = 0.74–0.86) and waveform metrics-only (Adjusted R 2 = 0.72–0.84), with the mostly selected metrics of the 95th percentile height ( H 95 ), mean of height of median energy ( HOME μ ) and mean of the height/median ratio ( HTMR μ ). Based on the relative weights (i.e., the percentage of contribution for R 2 ) of the mostly selected metrics for all subsets, the metric of 95th percentile height ( H 95 ) has the highest relative importance for AGB estimation (19.23%), followed by 75th percentile height ( H 75 ) (18.02%) and coefficient of variation of heights ( H cv ) (15.18%) in the point cloud metrics based models. For the waveform metrics based models, the metric of mean of height of median energy ( HOME μ ) has the highest relative importance for AGB estimation (17.86%), followed by mean of the height/median ratio ( HTMR μ ) (16.23%) and standard deviation of height of median energy ( HOME σ ) (14.78%). This study demonstrated benefits of using full-waveform ALS data for estimating biomass at tree level, for sustainable forest management and mitigating climate change by planted forest, as China has the largest area of planted forest in the world, and these forests contribute to a large amount of carbon sequestration in terrestrial ecosystems.