Estimating aboveground carbon of teak-based agroforestry systems in Sabah Malaysia using airborne LiDAR

James Daniel, . and Normah Awang Besar, . and Mazlin Mokhtar, . and Phua Mui How, . Estimating aboveground carbon of teak-based agroforestry systems in Sabah Malaysia using airborne LiDAR. pp. 85-99. ISSN 2672-7226

As a sustainable land use system agroforestry can potentially mitigate climate change mitigation by sequestering carbon and reducing greenhouse gasses (GHGs) emissions. Since the implementation of the Kyoto Protocol agroforestry has been recognized as a GHGs mitigation strategy that requires accurate estimation of the carbon storage. Focusing on teak-based agroforestry systems in Sabah Malaysia this study examined the use of airborne Light Detection and Ranging (LiDAR) data for aboveground carbon (AGC) estimation. Field inventory data were collected at the agroforestry systems with different intercropping crops to calculate the field AGC. We derived height and canopy density metrics from the LiDAR data to correlate and regress with the field AGC. Stepwise multiple linear regression analyses resulted in a multivariate model that explains 88 of the AGC variance in the agroforestry systems. With the 25th and 55th height percentiles as predictors the model had a cross-validated root-mean-square error (RMSEcv) of 6.12 Mg C ha-1 (Relative RMSEcv: 13.45). As teak is one of the major plantation species in Southeast Asia accurate LiDAR-based AGC estimation could assist in developing teakbased agroforestry systems for climate change mitigation in the region.

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Abstract

As a sustainable land use system agroforestry can potentially mitigate climate change mitigation by sequestering carbon and reducing greenhouse gasses (GHGs) emissions. Since the implementation of the Kyoto Protocol agroforestry has been recognized as a GHGs mitigation strategy that requires accurate estimation of the carbon storage. Focusing on teak-based agroforestry systems in Sabah Malaysia this study examined the use of airborne Light Detection and Ranging (LiDAR) data for aboveground carbon (AGC) estimation. Field inventory data were collected at the agroforestry systems with different intercropping crops to calculate the field AGC. We derived height and canopy density metrics from the LiDAR data to correlate and regress with the field AGC. Stepwise multiple linear regression analyses resulted in a multivariate model that explains 88 of the AGC variance in the agroforestry systems. With the 25th and 55th height percentiles as predictors the model had a cross-validated root-mean-square error (RMSEcv) of 6.12 Mg C ha-1 (Relative RMSEcv: 13.45). As teak is one of the major plantation species in Southeast Asia accurate LiDAR-based AGC estimation could assist in developing teakbased agroforestry systems for climate change mitigation in the region.

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Item Type:	Article
AGROVOC Term:	Agroforestry
AGROVOC Term:	Agricultural systems
AGROVOC Term:	Aboveground parts
AGROVOC Term:	Forest plantations
AGROVOC Term:	Planting density
AGROVOC Term:	Aerial application
AGROVOC Term:	Climate change
Depositing User:	Mr. AFANDI ABDUL MALEK
Last Modified:	24 Apr 2025 00:55
URI:	http://webagris.upm.edu.my/id/eprint/10719

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