## 4 Discussion  
We have demonstrated that the ACD of oil palm plantations can be assessed accurately using airborne LiDAR and can give better results compared to estimates using satellites [18]. Our ACD estimates of 10.0 Mg C ha\({}^{-1}\) for a 8-year-old plantation and 18.3 Mg C ha\({}^{-1}\) for a 14-year-old plantation are similar to those reported in previous studies. mean ACD of oil palm in the SAFE landscape was estimated as 17.9 Mg C ha\({}^{-1}\) by [45] using optical satellite imagery (age of stands not distinguished), while elsewhere in Sabah, the ACD of a 3-year-old oil palm plantation was given as 1.1 Mg C/ha, rising to an average value of 24.4 Mg C ha\({}^{-1}\) for plantations ranging from 4 to 19 years old [18]. Area-based approaches yielded more accurate carbon stock predictions in oil palm plantations than the tree-centric approach. ACD predictions were most accurate when incorporating both TCH and canopy cover as predictors [10]. Power-law modelling incorporates basal area and wood density relating to TCH; however, our function assumes that wood density and basal area are both constant, as the field biomass equation that was used to predict individual biomass, used tree height only. The authors of [12] found that the crown area of each forest tree scales with its basal area, so the gap fraction at ground level of a plot is negatively related to the basal area of its trees. The authors of [12] showed that incorporating canopy cover in tropical forests into ACD models gave more accurate results, as ACD is more closely related to basal area than to height. However, secondary thickening does not occur in palm trees, with basal area increase instead being due to division and enlargement of parenchyma cells [46]. Thus, cover fraction is not necessarily correlated to basal area, but more dependent on tree density and height. When incorporating both TCH and CC, the model yielded better results as a result of complementarity, by taking into account the high correlations between tree height and TCH as well as with tree density and CC. The tree-centric approach was less accurate. It showed a slight tendency to over-estimate the number of trees in younger plantations. A likely reason for this oversegmentation is overlap of palm crowns of similar heights. In mature plantations, crowns were more likely to be connected to other tree crowns, making them difficult to distinguish and resulting in miscounting of trees. Other studies focussing on tropical rainforests have also found the tree-centric approach to perform slightly less well than area-based methods [12; 26], due to over-segmentation of emergent trees and incomplete detection of sub-canopy trees. Here, the problem is instead the under-segmentation of large crowns, which merge into one another, owing to their similar height. Although the tree-centric approach gives less accurate results, it can still be advantageous to segment individual trees for precision agriculture [47], particularly in oil palm plantations as individual trees are long-lived. Field-based tree counting is labour intensive, and the use of remote-sensing using high-resolution optical imagery from satellites has been suggested as a cost efficient alternative [48]. A semi-automatic technique to count oil palm trees using high-resolution airborne imaging spectrometer data has also been developed [16], and LiDAR has been used to detect and classify oil palm diseases [49]. The detection of diseases, such as the Ganoderma basal stem rot, using airborne spectral imagery [50]) and space-borne multispectral sensors [51] has also been demonstrated. The Roundtable on Sustainable Palm Oil, the major accreditation body for sustainable palm oil production, recognises the importance of developing approaches for growers to monitor emissions across their estates. However, it has been recognised that emission cannot be monitored completely, or accurately, using current methodologies [52]. Studies indicate that remote sensing missions should estimate carbon with an error within 20% of field estimates [53; 54].