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Article

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

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Department of Forestry and Natural Resources, National Chiayi University, 300 University Road, Chiayi 60004, Taiwan
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Department of Applied Foreign Languages, National Formosa University, 64 Wunhua Road, Huwei Township, Yunlin County 63201, Taiwan
3
Department of Ecosystem Science and Management, Texas A&M University, 1500 Research Parkway Suite B 217, College Station, TX 77843, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Alfredo R. Huete and Prasad S. Thenkabail
Received: 16 April 2016 / Revised: 29 May 2016 / Accepted: 14 June 2016 / Published: 22 June 2016
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. View Full-Text
Keywords: MMAC; LiDAR; competition index; above-ground biomass; forest carbon stock MMAC; LiDAR; competition index; above-ground biomass; forest carbon stock
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MDPI and ACS Style

Lin, C.; Thomson, G.; Popescu, S.C. An IPCC-Compliant Technique for Forest Carbon Stock Assessment Using Airborne LiDAR-Derived Tree Metrics and Competition Index. Remote Sens. 2016, 8, 528. https://0-doi-org.brum.beds.ac.uk/10.3390/rs8060528

AMA Style

Lin C, Thomson G, Popescu SC. An IPCC-Compliant Technique for Forest Carbon Stock Assessment Using Airborne LiDAR-Derived Tree Metrics and Competition Index. Remote Sensing. 2016; 8(6):528. https://0-doi-org.brum.beds.ac.uk/10.3390/rs8060528

Chicago/Turabian Style

Lin, Chinsu, Gavin Thomson, and Sorin C. Popescu. 2016. "An IPCC-Compliant Technique for Forest Carbon Stock Assessment Using Airborne LiDAR-Derived Tree Metrics and Competition Index" Remote Sensing 8, no. 6: 528. https://0-doi-org.brum.beds.ac.uk/10.3390/rs8060528

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