## 1 Introduction  
Southeast Asia has been the epicentre of the oil palm industry for over 50 years [1]. Oil palm is one of the most profitable land uses in the humid tropics [2]. Malaysia has increased its planted area by 150% over the last decade [3] and, along with Indonesia, currently represents over 80% of global palm oil production [3; 4]. The planted area increased from 6 to 16 million hectares between 1990 and 2010, an area which now accounts for about 10 percent of the world's permanent cropland [5]. Conversion of forests to plantation agriculture represents a substantial source of greenhouse gas (GHG) emissions, especially in tropical peatlands [6], generating 10-20% of net global emissions [7]. Although oil palm plantations continue to expand, the government of Malaysia has pledged to reduce their projected GHG emissions by 45% by 2030 [8].  
High-resolution forest biomass maps can provide detailed and spatially explicit estimates of aboveground carbon density (ACD, units of Mg C ha\({}^{-1}\)) to assist natural resource management and assess emissions from deforestation. LiDAR (light detection and ranging) has become a commonly used technology to remotely predict ACD in many forest types [9; 10; 11; 12]. Carbon mapping by airborne LiDAR is significantly more accurate than approaches based on radar or passive optical measurements from space [13; 14; 15]. Oil palm trees have been counted using airborne imaging spectrometry [16] and their biomass estimated from satellite images [17; 18; 19; 20], but nobody has yet developed equations to estimate carbon density in oil palm plantations from airborne LiDAR data. The development of carbon prediction models provides opportunities to assess the aboveground carbon density of oil palm plantations, which is essential for assessments of the environmental sustainability of human modified tropical landscapes.  
Airborne LiDAR provides detailed information about forest structure within scanned areas, which can extend over hundreds of square kilometres [21; 22; 23], but it also poses the challenge of how best to use these data to estimate aboveground carbon [24]. It has been demonstrated that top of canopy height (TCH), as measured by LiDAR, is a useful metric for estimating ACD of natural tropical forests and is relatively insensitive to sensor and flight specifications [10]. However, the generality of TCH-based approaches to plot-aggregate carbon stock estimation has not been examined for oil palm plantations. There is current interest in developing individual-tree-based approaches to make greater use of the 3D information contained in airborne LiDAR data [25; 26; 27]. The tree-centric method is fundamentally similar to field-based approaches based on inventory plots [28; 29], so individual-based modelling has a strong theoretical basis [30]. Nevertheless, over- or under-segmentation of trees can lead to biases in biomass estimation [12]. To our knowledge, no study has used tree-centric approaches to map carbon in tropical plantations.  
This paper develops methods for mapping the aboveground carbon density of oil palm plantations using LiDAR. Sabah has a larger percentage of oil palm plantation than any other state in the world [5], so has become an important testbed to examine the effects of oil palm plantations within human modified tropical landscapes. We compare the [PERSON] and [PERSON] (2016) tree-centric approach with area-based methods [10; 12; 31], and critically evaluate whether the advantages of working with individual trees outweigh any disadvantages associated with the accuracy of tree detection. We also explore the relationship between ACD and canopy gap fraction measured by LiDAR, and then use this finding to refine the area-based approach. Our study site contained plots with oil palm plantation that varied in age from 8 to 14 years at the time of sampling, providing an opportunity to test the generality of the approaches used.