Research note: cost-efficient estimates of Pinus radiata wood volumes using multitemporal LiDAR data

Multitemporal airborne laser scanning (ALS) and unmanned aerial vehicles (UAV)-based light detection and ranging (LiDAR) data provide a rich source of spatiotemporal information for modelling and estimating wood volume change in commercial plantation forestry. However, model updates based on area-wide acquisitions of ALS are very cost-intensive. The purpose of this paper is to investigate how existing time series of ALS data can be used for a cost-efficient UAV-LiDAR-based update of timber yield estimates. We used two time series of ALS data (captured in 2012 and 2015) and simulated UAV data over a radiata pine forest compartment. The study area was located 10 km north-east of Millicent, South Australia, and comprised one forest compartment of approximately 21 ha with inventory plots situated inside. In total, 16 inventory plots with measured tree heights and diameter at breast height for each time series were taken as ground-truthing data. A LiDAR-processing framework was developed to derive multitemporal forest metrics. Using k-nearest neighbours modelling, wood volume was predicted based on these metrics. The paper suggests two approaches for the cost-efficient updating of timber-yield estimates using newly acquired UAV-LiDAR data at the plot level in combination with prior assimilated stand-wide ALS datasets. Both approaches produced very similar wood volume estimates. These findings support the feasibility of using multitemporal ALS and UAV-LiDAR data for cost-efficient updates of timber-yield prediction in forestry.