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Remote Sens. 2016, 8(6), 528; doi:10.3390/rs8060528

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

1
Department of Forestry and Natural Resources, National Chiayi University, 300 University Road, Chiayi 60004, Taiwan
2
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
View Full-Text   |   Download PDF [1704 KB, uploaded 23 June 2016]   |  

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. 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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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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.

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