Assessing Optimal Flight and Capture Parameters for Post-Thinning Quality Control in Radiata Pine Forest Stands using Unmanned Aerial Vehicles. NZGRC 2022.
Creators: name: Dhruva, Atman
name: Hartley, Robin
name: Redpath, Todd
Publisher: name: figshare
Year: 2022
Subjects: subject: 90903 Geospatial Information Systems
subject: FOS: Environmental engineering

description: Unmanned Aerial Vehicle (UAV) operations are being increasingly rolled out for forestry applications. Some of the main operations are carrying out pre-thinning and post-thinning drone flights. Pre-thinning flights are carried out to determine the tree height, which gives foresters an indication of whether the stand has reached full canopy closure and needs to be thinned. Post-thinning flights are carried out for providing foresters with the best indication about the quality of their thinning operations as it becomes possible to assess stocking density in the thinned forest stand. Assessing stocking density of the stand and gathering forest inventory following a thinning operation is crucial, and counting trees has historically been a difficult task. However, there are very few resources to inform foresters about the exact photogrammetry parameters they need in order to carry out post-thinning quality control and quality assurance flight operations effectively. This leads to a lot of trial and error, frustration, loss of time, and often proves to be costly. This abstract is part of the research proposal of an MAppSc GIS workplace-based research project. This MAppSc GIS research will aim at assessing three photogrammetry parameters – image overlap (side overlap: forward overlap), Ground Sampling Distance (GSD) and flight altitude. RGB imagery of a 50-hectare thinned stand of a radiata pine forest in New Zealand’s North Island will be captured with a DJI Phantom 4 Pro/Phantom 4 RTK and processed using a Structure-from-Motion (SfM)-Multi View Stereo (MVS) photogrammetry workflow. Canopy Surface Models will be reconstructed using different combinations of the parameters mentioned above. Tree tops will be detected and quantified on each of the Canopy Surface Models using the YOLOv5 algorithm. YOLOv5 (You Only Look Once) is a state-of-the-art object detection neural network. This deep learning approach was first developed by Redmon in 2015 and the latest version was published in 2020 by Glenn Jocher on Github. In a forest stand, certain features and elements in the multi-layered vegetation profile of the understorey, especially those that are close to the ground, are likely to be occluded in the 3-dimensional Canopy Surface Models recreated using Structure-from-Motion photogrammetry. LiDAR (Light Detection and Ranging) has been observed to outperform SfM photogrammetry in forest inventory applications owing to the completeness and richness of detail of the 3-dimensional point cloud, as noted from several studies over the last decade. For ground-truthing, a Canopy Height Model will be reconstructed with data from airborne LiDAR or terrestrial-borne LiDAR to fully characterize the stand, including the understorey. A segment-based stem detection or an improved RANSAC algorithm will be performed on the coloured point cloud generated from the LiDAR to count individual stems. The results of the tree top counts from Canopy Surface Models will be compared to stem counts produced by the LiDAR data to assess the sensitivity and accuracy of the different models. These results are expected to inform us of the optimal photogrammetry parameters required for data capture in thinned stands of radiata pine. It will be interesting to also assess how environmental factors like wind and varying elevation influence the analysis.