Comparing yield estimates derived from LiDAR and aerial photogrammetric point-cloud data with cut-to-length harvester data in a Pinus radiata plantation in Tasmania

Abstract

ABSTRACTAccurate mapping of timber resources in commercial forestry is essential to support planning and management operations of forest growers. Over the last two decades, Light Detection and Ranging (LiDAR) systems have been successfully deployed for the collection of point-cloud data for accurate modelling of forest attributes that are traditionally obtained from plot-based inventory. In recent years, studies conducted in North America and Scandinavia have shown that three-dimensional point clouds derived from digital aerial photogrammetric (AP) data can be used to model forest attributes with a level of accuracy similar to traditional LiDAR-based approaches. A comparative analysis of the performance of the two point-cloud technologies has never been attempted in Australian plantations. In this study, we compared the performance of LiDAR-based and AP-based point clouds for estimating total recoverable volume in a Pinus radiata plantation at Springfield in north-eastern Tasmania, using volume data collected by harvesting machines as a reference. Our results showed that AP point clouds can be used for mapping total recoverable volume in P. radiata plantations with levels of accuracy that are comparable to LiDAR-based estimates. Plot-level relative root mean squared error (RMSE%) values were 23.85% for LiDAR and ranged from 22.07% to 27.10% for the three AP dense point-cloud settings evaluated. At the stand level, RMSE% decreased to 9.86% and 8.91% for LiDAR and AP, respectively. Both LiDAR-based and AP-based modelled volumes showed a close agreement with volumes measured using harvester head data, demonstrating the potential of AP technology for the management and planning of forestry operations in softwood plantations.