An IPCC-Compliant Technique for Forest Carbon Stock Assessment Using Airborne LiDAR-Derived Tree Metrics and Competition Index

Abstract

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.

Prediction of Competition Indices in a Norway Spruce and Silver Fir-Dominated Forest Using Lidar Data

“…Few previous studies were found in the literature that combined lidar and competition indices. Among the ones present in the literature, the ones of Lo et al [42], Lin et al [43], and Ma et al [44] are the only ones slightly related to this work. In all these studies, competition indices were computed using ITCs automatically delineated on lidar data, in a similar way to our computation of the lidar metrics CI_DBH_ITC and CI_H_ITC.…”

“…The effectiveness of lidar metrics in predicting both AGB and competition indices was also found in the study conducted by Lin et al [43]. In particular, Lin et al [43] showed that the height competition index estimated by lidar, especially when combined with other lidar metrics (crown radius and height) of the trees, is capable of effectively estimating above-ground carbon (AGC) at both the stand and tree level. In our case, the competition indices were used to assess the influence that high or low competition values have on biomass.…”

A Generalized Logistic-Gaussian-Complex Signal Model for the Restoration of Canopy SWIR Hyperspectral Reflectance

“…The performance of the GLGCM method in deriving the SWIR spectra was evaluated using the accuracy measure RMSE, i.e., the root mean square error [39]. The measure was calculated based on the bias of the front-edge signal base (1030-1049 nm), end-edge signal base (1411-1430 nm), and the central segment or in-between the signal bases (1050-1410 nm) where the physical meaning of this measure is the average bias of reflectance per unit wavelength, nm.…”

Adaptive Window-Based Constrained Energy Minimization for Detection of Newly Grown Tree Leaves

“…Trees start to sprout once they sense the growing signals in early spring. After the leaf initiation stage, the newly sprouted leaflet will gradually develop and further facilitate tree growth in crown width, height, diameter, and carbon storage [3]. As a result, the newly grown leaves (NGL) can be seen as the first objects of trees in response to a change in temperature, and can therefore provide critical information for the early detection of climate changes [4].…”