Abstract
This study developed an IPCC (Intergovernmental Panel on Climate Change) compliant method for the estimation of above-ground carbon (AGC) in forest stands using remote sensing technology. A multi-level morphological active contour (MMAC) algorithm was employed to obtain tree-level metrics (tree height (LH), crown radius (LCR), competition index (LCI), and stem diameter (LDBH)) from an airborne LiDAR-derived canopy height model. Seven biomass-based AGC models and 13 volume-based AGC models were developed using a training dataset and validated using a separate validation dataset. Four accuracy measures, mean absolute error (MAE), root-mean-square error (RMSE), percentage RMSE (PRMSE), and root-mean-square percentage error (RMSPE) were calculated for each of the 20 models. These measures were transformed into a new index, accuracy improvement percentage (AIP), for post hoc testing of model performance in estimating forest stand AGC stock. Results showed that the tree-level AGC models explained 84% to 91% of the variance in tree-level AGC within the training dataset. Prediction errors (RMSEs) for these models ranged between 15 ton/ha and 210 ton/ha in mature forest stands, which is equal to an error percentage in the range 6% to 86%. At the stand-level, several models achieved accurate and reliable predictions of AGC stock. Some models achieved 90% to 95% accuracy, which was equal to or superior to the R-squared of the tree-level AGC models. The first recommended model was a biomass-based model using the metrics LDBH, LH, and LCI and the others were volume-based models using LH, LCI, and LCR and LDBH and LH. One metric, LCI, played a critical role in upgrading model performance when banded together with LH and LCR or LDBH and LCR. We conclude by proposing an IPCC-compatible method that is suitable for calculating tree-level AGC and predicting AGC stock of forest stands from airborne LiDAR data.
Highlights
Estimating above-ground carbon (AGC) stock in dense forest normally involves conducting a ground-based inventory and logging sample trees from multiple forest plots
The amount of above-ground carbon storage in individual trees in a forest stand varies considerably due to variations in tree size, which occurs naturally in space and time
Airborne LiDAR data facilitates the construction of a three-dimensional forest model at sufficiently high spatial resolution to capture specific metrics
Summary
Estimating above-ground carbon (AGC) stock in dense forest normally involves conducting a ground-based inventory and logging sample trees from multiple forest plots. Tree parameters (such as diameter and height) are correlated in an allometric model to estimate individual tree volume/biomass or carbon stock. Calculations for stand-level AGC are based on a stand structure model of tree diameter and height distribution. Parameters are fitted into a probability distribution function in order to estimate the AGC for an entire forest stand [1,2]. 2016, 8, 528 techniques provide reasonably accurate estimates of volume, biomass, and forest stand carbon stocks, field-inventory-based methods are often labor intensive and can require time-consuming measurements and inspections.
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