In Reply

Wang, Wei Shu; Lin, Jen Kou; Lin, Tzu Chen; Chen, Wei Shone; Jiang, Jeng Kai; Wang, Huann Sheng; Chiou, Tzeon Jye; Liu, Jin Hwang; Yen, Chueh Chuan; Chen, Po Min

doi:10.1634/theoncologist.12-11-1372

Cited by 1 publication

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On average, Japanese cedar DBH of 64.60 ± 13.34 cm was larger than the Taiwan red cypress DBH of 36.70 ± 7.65 cm, and the average Japanese cedar height exceeded that of the Taiwanese red cypress at 29.43 ± 2.58 and 21.39 ± 1.89 m respectively. As a consequence of previous experiments [38], the wood density, biomass expansion factor, and carbon fraction of Japanese cedar and Taiwan red cypress were determined to be 0.51, 1.23, 0.50 and 0.50, 1.24, 0.50, respectively. According to the IPCC method (Equation (1)), the AGC of the trees in the training sample can be determined using allometric formula A ground-based inventory recorded the following measurements for every tree in the two validation plots: diameter at breast height, tree height, and crown width.…”

Section: Datasets Used To Train and Validate The Agc Modelsmentioning

confidence: 66%

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

2016

View full text Add to dashboard Cite

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.

show abstract

Section: Datasets Used To Train and Validate The Agc Modelsmentioning

confidence: 66%