Accurate 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.

Extensive research and operational trials over the past 20 years have led to the operational implementation of airborne laser scanning (ALS)-based forest inventory becoming increasingly common. More recently, digital aerial photography (AP), processed using Structure from Motion Multiview Stereopsis (SfM-MVS) photogrammetry, is emerging as an alternative to ALS. Aerial photography may provide some advantages compared with ALS, including lower deployment and data collection costs, easier access to a variety of platforms and sensors, and the opportunity for forest managers to capture and process the data in-house. This study presents an analysis of point-cloud data derived from airborne small format digital AP, and a comparison with ALS data for a Pinus radiata plantation located in north-east Tasmania. The AP was processed using commercially available photogrammetric software and three different processing strategies. The influence of processing strategy, terrain slope, canopy occlusion, canopy cover, photo-overlap and camera location are investigated in order to characterise the point clouds generated using these methods, and to assess the robustness of the photogrammetric solution to these variables. Our analysis provides strong evidence of the robustness of small format AP-based point clouds in this type of forest: characteristics of the dense point cloud are shown to be largely robust to different photogrammetric processing strategies. Observations regarding the influence of terrain slope, photo-overlap, canopy occlusions, canopy cover and camera location can be used to optimise flight planning and photo-acquisition.