Optimizing remotely acquired, dense point cloud data for plantation inventory. M Watt, C Stone (eds)

This collaborative “Trans-Tasman” research project evaluated a series of novel, advanced remote sensing systems that capture accurate, 3D dense point cloud data in order to assess their potential for delivering operational plantation resource assessment tools. The project brought together internationally recognised expertise in remote sensing, Airborne Laser Scanning (ALS) and UAV technologies and robotics located within the TerraLuma group at the University of Tasmania, the New Zealand forest research agency Scion, the Australian Centre for Field Robotics, University of Sydney and NSW Department of Primary Industries. An important aspect of this project was also the collaborative engagement with the participating companies including Interpine and Indufor Asia Pacific.

The emerging diversity of platforms, sensors, algorithms and efficient processing workflows presents multiple opportunities across the forestry sector for more accurate and reliable resource information. This project represents the most recent FWPA and grower investment in a series of R&D projects focused on the evaluation of the advancing developments in remote sensing applications for the Australian forestry sector. We demonstrate that the improvements in the quality and density of data captured by these systems can be harnessed to significantly improve stand and tree-level assessment.

The overall aim of this project was to evaluate the acquisition, processing and analysis of dense point cloud data for the extraction of meaningful resource information acquired from light aircraft and UAV platforms.

Specifically the key tasks were to:

1) Evaluate whether UAV acquired ultra-high density point cloud datasets are suitable for treelevel on-screen visual assessment and 3D construction modelling for accurate estimation of stem attributes i.e. virtual plot inventory.

2) Develop efficient workflow processing pipelines for the analysis of dense point cloud data suitable for integration into operational LiDAR modelling systems for wood volumes and product prediction.

3) Develop and evaluate novel metrics extracted from dense ALS point clouds acquired by small aircraft and their impact on the recently implemented spatial plot imputation process for estimating resource volume and product mix.

In order to undertake these tasks and fulfil the proposed project deliverables, three Work Packages were developed by the project team, i.e.; i) 3D visualisation for interactive assessment of individual tree stems; ii) UAS LiDAR for dense point cloud acquisition and iii) Individual tree detection, 3D tree reconstruction, and automated extraction of improved point cloud metrics for forest inventory (e.g. use of voxelised metrics). These Work Packages represented the structure of the specific sub-projects that were undertaken, either individually or in combination to address the objectives and deliverables identified for this project.