Using ATHL9010 to estimate above-ground carbon stocks at the single-tree scale in subtropical forests
author: Gavin
2022-02-25
With the intensification of global warming and the greenhouse effect, the carbon cycle has become a hotspot in global climate change research. Forests have the ability to absorb and fix CO2 in the atmosphere, and are the largest carbon pools in terrestrial ecosystems. The United Nations Intergovernmental Panel on Climate Change (IPCC) has repeatedly pointed out that forests play an irreplaceable role in regulating the global carbon cycle and mitigating climate change, and have the potential to reduce carbon emissions and increase carbon sinks.
Principle: Correct estimation of forest carbon storage can accurately assess the carbon sequestration potential of forest ecosystems, which is crucial for in-depth study of regional ecological environment and global climate change. However, accurate and rapid assessment of tree carbon stocks remains a challenge. Traditionally, plot-based sampling methods can obtain accurate forest carbon storage, but they are labor-intensive and costly, making it difficult to apply to large areas. Airborne lidar can obtain accurate three-dimensional structure information of forest environment, while hyperspectral imaging can provide detailed reflectivity information. Therefore, combining airborne lidar and hyperspectral data can obtain forest biophysical and biochemical characteristics to better estimate forest aboveground biomass or carbon storage.
Solution:Acquire LiDAR data and UAV hyperspectral image data in the study area. A field survey was also conducted, tree height (H) and diameter at breast height (DBH) were measured, and the location of each tree was recorded, based on carbon coefficient (CC) and above-ground biomass (AGB) including stem biomass (stemAGB), Branch biomass (branchAGB) and leaf biomass (leafAGB), and tree carbon storage (CS) were calculated as control data.
Single trees were described by a CHM-based watershed segmentation algorithm, then structural and spectral features were extracted from UAV LiDAR and hyperspectral data, respectively, and the relationship between LiDAR features, hyperspectral features, and tree carbon storage was evaluated by Pearson correlation analysis. On this basis, features are selected for model development. Finally, a tree carbon stock estimation model was developed based on the Schumacher-Hall equation and stepwise multiple regression, using coefficient of determination (r2), root mean square error (RMSE), mean absolute error (MAE), percent RMSE (PRMSE) and root mean square Percentage Error (RMSPE) evaluates the performance of the predictive model.
Test result:
Correlation coefficients between LiDAR features, hyperspectral features and control data
Prediction Error and Error Percentage of Tree Carbon Stock Estimation Models
Principle: Correct estimation of forest carbon storage can accurately assess the carbon sequestration potential of forest ecosystems, which is crucial for in-depth study of regional ecological environment and global climate change. However, accurate and rapid assessment of tree carbon stocks remains a challenge. Traditionally, plot-based sampling methods can obtain accurate forest carbon storage, but they are labor-intensive and costly, making it difficult to apply to large areas. Airborne lidar can obtain accurate three-dimensional structure information of forest environment, while hyperspectral imaging can provide detailed reflectivity information. Therefore, combining airborne lidar and hyperspectral data can obtain forest biophysical and biochemical characteristics to better estimate forest aboveground biomass or carbon storage.
Solution:Acquire LiDAR data and UAV hyperspectral image data in the study area. A field survey was also conducted, tree height (H) and diameter at breast height (DBH) were measured, and the location of each tree was recorded, based on carbon coefficient (CC) and above-ground biomass (AGB) including stem biomass (stemAGB), Branch biomass (branchAGB) and leaf biomass (leafAGB), and tree carbon storage (CS) were calculated as control data.
Single trees were described by a CHM-based watershed segmentation algorithm, then structural and spectral features were extracted from UAV LiDAR and hyperspectral data, respectively, and the relationship between LiDAR features, hyperspectral features, and tree carbon storage was evaluated by Pearson correlation analysis. On this basis, features are selected for model development. Finally, a tree carbon stock estimation model was developed based on the Schumacher-Hall equation and stepwise multiple regression, using coefficient of determination (r2), root mean square error (RMSE), mean absolute error (MAE), percent RMSE (PRMSE) and root mean square Percentage Error (RMSPE) evaluates the performance of the predictive model.
Test result:
Correlation coefficients between LiDAR features, hyperspectral features and control data
Prediction Error and Error Percentage of Tree Carbon Stock Estimation Models
Conclusion:
This study explores the potential of combining UAV LiDAR and hyperspectral data to estimate tree carbon stocks in subtropical forests. Tree-level structural and spectral features were extracted from UAV LiDAR and hyperspectral data, respectively, and the ability to use LiDAR and hyperspectral features alone and in combination to predict above-ground carbon storage for individual trees was assessed. The results indicate that the use of LiDAR or hyperspectral features alone can yield reasonable carbon stocks (r2 of 0.74 and 0.75, respectively). As expected, combining LiDAR and hyperspectral data can improve the accuracy of tree carbon stock estimation to some extent (r2 = 0.89), and incorporate LiDAR-derived PH95 and hyperspectral-derived GI into the combined model. This improvement may be attributed to the fact that carbon storage is not only related to tree structural features extracted from LiDAR data, but also to biomass conversion factors as well as carbon coefficients reflected by hyperspectral information.
Recommand:
LiDAR Remote Sensing Multirotor Unmanned Aircraft System:ATHL9010
This study explores the potential of combining UAV LiDAR and hyperspectral data to estimate tree carbon stocks in subtropical forests. Tree-level structural and spectral features were extracted from UAV LiDAR and hyperspectral data, respectively, and the ability to use LiDAR and hyperspectral features alone and in combination to predict above-ground carbon storage for individual trees was assessed. The results indicate that the use of LiDAR or hyperspectral features alone can yield reasonable carbon stocks (r2 of 0.74 and 0.75, respectively). As expected, combining LiDAR and hyperspectral data can improve the accuracy of tree carbon stock estimation to some extent (r2 = 0.89), and incorporate LiDAR-derived PH95 and hyperspectral-derived GI into the combined model. This improvement may be attributed to the fact that carbon storage is not only related to tree structural features extracted from LiDAR data, but also to biomass conversion factors as well as carbon coefficients reflected by hyperspectral information.
Recommand:
LiDAR Remote Sensing Multirotor Unmanned Aircraft System:ATHL9010
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