Above ground carbon stock estimation of agroforestry systems in Balung plantation, Tawau, Sabah using airborne Lidar Data - Daniel James (2017)
Agroforestry systems have a promising potential in climate change mitigation by storing carbon in the multistorey planted trees and crops. However, there has been very few research on quantifying aboveground carbon (AGC) stock of agroforestry systems in Malaysia. Thus, this study was conducted in Balung Plantation, Tawau, Sabah, with the aims to determine the AGC stock potential and also to evaluate the performance of LiDAR data in assisting AGC stock estimation of the agroforestry systems. Three types of teak-based agroforestry systems combination were studied mainly polyculture system 1 (teak + agarwood + snake fruit), polyculture system 2 (teak + coffee) and polyculture system 3 (teak + agarwood). In addition, teak monoculture plantation and natural forest reserve (Tawau Hill FR) was treated as controls. A total of 20 square plots (50 m x 50 m) was established in the agroforestry systems while 3 square plots (50 m x 50 m) was established in the teak monoculture plantation and 6 square plots (30 m x 30 m) in the natural forest reserve. Aboveground biomass (AGB) was calculated from the field measured DBH and height using allometric equations and converted into AGC stock using a conversion factor of 0.50. The results showed that the accumulation of AGC stock is in the following order: natural forest reserve (213.84 t C/ha) > polyculture system 3 (69.94 t C/ha) > polyculture system 2 (37.75 t C/ha) > polyculture system 1 (37.34 t C/ha) > teak monoculture plantation (34.53 t C/ha). The findings have demonstrated that the agroforestry systems are capable to store about a quarter percent of the total AGC stock of a natural forest reserve which is relatively better than the monoculture plantation. For the AGC estimation using airborne LiDAR data, two estimation approaches were used (Approach 1: AGC estimation based on the layering of species-specific AGC models developed through vertical canopy stratification; and Approach 2: AGC modelling through the combination of all sample plots). LiDAR metrics such as height metrics, cover density metrics, strata density metrics and canopy cover percentage metrics was extracted from the LiDAR point clouds data (all returns) and regressed with field AGC to establish the AGC estimation models. Through the layering of the best AGC estimation models for teak trees (Adj-R2 cv= 0.92, %RMSE01 = 12.65 %), agarwood trees (*2 outlier removed; Adj-R2cv= 0.86, %RMSE01 = 44.21 %) and understorey crops (Adj-R2cv = 0.40, %RMSEcv = 15.88 %), the approach 1 method were able to explain 81 % (%RMSEcv = 17.65 %) of the AGC variance in the agroforestry systems. Through linear regression model without transformation, the approach 2 method has improved the estimation by 3 % with AGC estimation performance of 84 % (%RMSEcv = 13.45 %). Overall, this study showed that the teak trees have a great potential in transforming a low biomass land cover into a carbon-rich tree based agroforestry systems, with the capability to store more than 60 % of the total AGC stock in the agroforestry systems. This study also demonstrated that airborne LiDAR data was capable in estimating AGC of agroforestry systems at the plot level with high accuracy.