Accurate estimation of above ground biomass (AGB) is required to better understand the variability and dynamics of tropical peat swamp forest (PSF) ecosystem function and resilience to disturbance events. The objective of this work is to examine the relationship between tropical PSF AGB and small-footprint airborne Light Detection and ranging (LiDAR) discrete return (DR) and full waveform (FW) derived metrics, with a view to establishing the optimal use of this technology in this environment. The study was undertaken in North Selangor peat swamp forest (NSPSF) reserve, Peninsular Malaysia. Plot-based multiple regression analysis was performed to established the strongest predictive models of PSF AGB using DR metrics (only), FW metrics (only), and a combination of DR and FW metrics. Overall, the results demonstrate that a Combination-model, coupling the benefits derived from both DR and FW metrics, had the best performance in modelling AGB for tropical PSF (R2 = 0.77, RMSE = 36.4, rRMSE = 10.8%); however, no statistical difference was found between the rRMSE of this model and the best models using only DR and FW metrics. We conclude that the optimal approach to using airborne LiDAR for the estimation of PSF AGB is to use LiDAR metrics that relate to the description of the mid-canopy. This should inform the use of remote sensing in this ecosystem and how innovation in LiDAR-based technology could be usefully deployed.
|Early online date||25 Apr 2018|
|Publication status||E-pub ahead of print - 25 Apr 2018|
- tropical peat swamp
- discrete return LiDAR
- full waveform LiDAR
- above ground biomass