Accurate estimation of aboveground carbon stock for individual trees is important for evaluating forest carbon sequestration potential and maintaining ecosystem carbon balance. Airborne light detection and ranging (LiDAR) data has been widely used to estimate tree-level carbon stock. However, few studies have explored the potential of combining LiDAR and hyperspectral data to estimate tree-level carbon stock. The objective of this study is to explore the potential of integrating unmanned aerial vehicle (UAV) LiDAR with hyperspectral data for tree-level aboveground carbon stock estimation. To achieve this goal, we first delineated individual trees by a CHM-based watershed segmentation algorithm. We then extracted structural and spectral features from UAV LiDAR and hyperspectral data respectively. Then, Pearson correlation analysis was conducted to assess the correlation between LiDAR features, hyperspectral features, and tree-level carbon stock, based on which, features were selected for model development. Finally, we developed tree-level carbon stock estimation models based on the Schumacher–Hall formula and stepwise multiple regression. Results showed that both LiDAR and hyperspectral features were strongly correlated to tree-level carbon stock. Both tree height (H, r = 0.75) and Green index (GI, r = 0.83) had the highest correlation coefficients with tree-level carbon stock in LiDAR and hyperspectral features, respectively. The best model using LiDAR features alone includes the metrics of H, the 10th height percentile of points (PH10), and mean height of points (Hmean), and can explain 74% of the variations in tree-level carbon stock. Similarly, the best model using hyperspectral data includes GI and modified normalized differential vegetation index (mNDVI), and has similar explanatory power (r2 = 0.75). The model that integrates predictors, namely, GI and the 95th height percentile of points (PH95) from hyperspectral and LiDAR data, substantially improves the explanatory power (r2 = 0.89). These results indicated that while either LiDAR data or hyperspectral data alone can estimate tree-level carbon stock with reasonable accuracy, combining LiDAR and hyperspectral features can substantially improve the explanatory power of the model. Such results suggested that tree-level carbon stock estimation can greatly benefit from the complementary nature of LiDAR-detected structural characteristics and hyperspectral-captured spectral information of vegetation.


  • With the increase in global warming and Greenhouse Effect, carbon cycle has become a hot topic in global climate change research [1,2,3,4,5,6,7]

  • For all the light detection and ranging (LiDAR) features, tree height had the highest correlation with control data (r = 0.75), followed by PH95 (r = 0.70) and PH90 (r = 0.68)

  • The higher estimation accuracy of tree-level carbon stocks reported in our study owes to the introduction of hyperspectral features. This can be explained by the fact that carbon stock is related to tree-level structural features that can be extracted from LiDAR data, and to biomass conversion factor and carbon coefficient which can be reflected in hyperspectral information [37]

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With the increase in global warming and Greenhouse Effect, carbon cycle has become a hot topic in global climate change research [1,2,3,4,5,6,7]. The United Nations Intergovernmental Panel on Climate Change (IPCC) has Remote Sens. 2021, 13, 4969 repeatedly pointed out that forests play an irreplaceable role in regulating the global carbon cycle and mitigating climate change, and have great potential to reduce carbon emissions and increase carbon sinks [10,11]. Proper estimation of forest carbon stocks can accurately evaluate the carbon sequestration potential of forest ecosystems, which is of great significance for in-depth studies of regional ecological environments and global climate change [12,13]. Traditional plot-based sampling methods can acquire accurate forest carbon stocks It is still a challenging task to estimate tree-level carbon stocks accurately and rapidly.


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