The measurement of forestry trials is a costly and time-consuming process. Over the past few years, unmanned aerial vehicles (UAVs) have provided some significant developments that could improve cost and time efficiencies. However, little research has examined the accuracies of these technologies for measuring young trees. This study compared the data captured by a UAV laser scanning system (ULS), and UAV structure from motion photogrammetry (SfM), with traditional field-measured heights in a series of forestry trials in the central North Island of New Zealand. Data were captured from UAVs, and then processed into point clouds, from which heights were derived and compared to field measurements. The results show that predictions from both ULS and SfM were very strongly correlated to tree heights (R² = 0.99, RMSE = 5.91%, and R² = 0.94, RMSE = 18.5%, respectively) but that the height underprediction was markedly lower for ULS than SfM (Mean Bias Error = 0.05 vs. 0.38 m). Integration of a ULS DTM to the SfM made a minor improvement in precision (R² = 0.95, RMSE = 16.5%). Through plotting error against tree height, we identified a minimum threshold of 1 m, under which the accuracy of height measurements using ULS and SfM significantly declines. Our results show that SfM and ULS data collected from UAV remote sensing can be used to accurately measure height in young forestry trials. It is hoped that this study will give foresters and tree breeders the confidence to start to operationalise this technology for monitoring trials.

Phenotyping has been a reality for aiding the selection of optimal crops for specific environments for decades in various horticultural industries. However, until recently, phenotyping was less accessible to tree breeders due to the size of the crop, the length of the rotation and the difficulty in acquiring detailed measurements. With the advent of affordable and non-destructive technologies, such as mobile laser scanners (MLS), phenotyping of mature forests is now becoming practical. Despite the potential of MLS technology, few studies included detailed assessments of its accuracy in mature plantations. In this study, we assessed a novel, high-density MLS operated below canopy for its ability to derive phenotypic measurements from mature Pinus radiata. MLS data were co-registered with above-canopy UAV laser scanner (ULS) data and imported to a pipeline that segments individual trees from the point cloud before extracting tree-level metrics. The metrics studied include tree height, diameter at breast height (DBH), stem volume and whorl characteristics. MLS-derived tree metrics were compared to field measurements and metrics derived from ULS alone. Our pipeline was able to segment individual trees with a success rate of 90.3%. We also observed strong agreement between field measurements and MLS-derived DBH (R² = 0.99, RMSE = 5.4%) and stem volume (R² = 0.99, RMSE = 10.16%). Additionally, we proposed a new variable height method for deriving DBH to avoid swelling, with an overall accuracy of 52% for identifying the correct method for where to take the diameter measurement. A key finding of this study was that MLS data acquired from below the canopy was able to derive canopy heights with a level of accuracy comparable to a high-end ULS scanner (R² = 0.94, RMSE = 3.02%), negating the need for capturing above-canopy data to obtain accurate canopy height models. Overall, the findings of this study demonstrate that even in mature forests, MLS technology holds strong potential for advancing forest phenotyping and tree measurement.

The management of plantation forests using precision forestry requires advanced inventory methods. Unmanned aerial vehicle laser scanning (ULS) offers a cost-effective approach to accurately estimate forest structural attributes at both plot and individual tree levels. We examined the utility of ULS data collected from a radiata pine stand for tree detection and prediction of diameter at breast height (DBH) and stem volume, using data thinned to 13-point densities (ranging from 10–12,200 points/m²). These datasets were created using a DTM with the highest pulse density and DTMs that used the native decimated point clouds. Models of DBH were constructed using partial least squares (PLS) and random forest (RF) from seven classes of metrics that characterized the horizontal and vertical structure of the canopy. Individual tree segmentation was consistently accurate across the 13-point densities and was insensitive to DTM type (F1 scores > 0.96). Predictions of DBH using PLS models were consistently more accurate than RF models and accuracy was insensitive to the DTM type. Using data from the native DTMs, DBH estimation using PLS had the lowest RMSE of 1.624 cm (R² of 0.756) at a point density of 12,200 points/m². Stem volume predictions made using PLS predictions of DBH and height from the ULS had the lowest RMSE of 0.0418 m³ (R² of 0.792) at 12,200 points/m². The RMSE values for DBH and volume remained relatively stable from 12,200 to between 750 and 400 points/m², with reductions in accuracy occurring as point density declined below this threshold. Overall, these findings have significant implications, particularly for the precise estimation of DBH and stem volume at the individual tree level. They demonstrate the potential of cost-effective ULS sensors for rapid and frequent plantation forest assessment, thereby enhancing the application of light detection and ranging (LiDAR) technology in plantation forest management.

Silvicultural operations such as planting, pruning, and thinning are vital for the forest value chain, requiring efficient monitoring to prevent value loss. While effective, traditional field plots are time-consuming, costly, spatially limited, and rely on assumptions that they adequately represent a wider area. Alternatively, unmanned aerial vehicles (UAVs) can cover large areas while keeping operators safe from hazards including steep terrain. Despite their utility, optimal flight parameters to ensure flight efficiency and data quality remain under-researched. This study evaluated the impact of forward and side overlap and flight altitude on the quality of two- and three-dimensional spatial data products from UAV photogrammetry (UAV-SfM) for assessing stand density in a recently thinned Pinus radiata D. Don plantation. A contemporaneously acquired UAV laser scanner (ULS) point cloud provided reference data. The results indicate that the optimal UAV-SfM flight parameters are 90% forward and 85% side overlap at a 120 m altitude. Flights at an 80 m altitude offered marginal resolution improvement (2.2 cm compared to 3.2 cm ground sample distance/GSD) but took longer and were more error-prone. Individual tree detection (ITD) for stand density assessment was then applied to both UAV-SfM and ULS canopy height models (CHMs). Manual cleaning of the detected ULS tree peaks provided ground truth for both methods. UAV-SfM had a lower recall (0.85 vs. 0.94) but a higher precision (0.97 vs. 0.95) compared to ULS. Overall, the F-score indicated no significant difference between a prosumer-grade photogrammetric UAV and an industrial-grade ULS for stand density assessments, demonstrating the efficacy of affordable, off-the-shelf UAV technology for forest managers. Furthermore, in addressing the knowledge gap regarding optimal UAV flight parameters for conducting operational forestry assessments, this study provides valuable insights into the importance of side overlap for orthomosaic quality in forest environments.

The classification and quantification of fuel is traditionally a labour-intensive, costly and often subjective operation, especially in hazardous vegetation types, such as gorse (Ulex europaeus L.) scrub. In this study, unmanned aerial vehicle (UAV) technologies were assessed as an alternative to traditional field methodologies for fuel characterisation. UAV laser scanning (ULS) point clouds were captured, and a variety of spatial and intensity metrics were extracted from these data. These data were used as predictor variables in models describing destructively and non-destructively sampled field measurements of total above ground biomass (TAGB) and above ground available fuel (AGAF). Multiple regression of the structural predictor variables yielded correlations of R² = 0.89 and 0.87 for destructively sampled measurements of TAGB and AGAF, respectively, with relative root mean square error (RMSE) values of 18.6% and 11.3%, respectively. The best metrics for non-destructive field-measurements yielded correlations of R² = 0.50 and 0.49, with RMSE values of 40% and 30.8%, for predicting TAGB and AGAF, respectively, indicating that ULS-derived structural metrics offer higher levels of precision. UAV-derived versions of the field metrics (overstory height and cover) predicted TAGB and AGAF with R² = 0.44 and 0.41, respectively, and RMSE values of 34.5% and 21.7%, demonstrating that even simple metrics from a UAV can still generate moderate correlations. In further analyses, UAV photogrammetric data were captured and automatically processed using deep learning in order to classify vegetation into different fuel categories. The results yielded overall high levels of precision, recall and F1 score (0.83 for each), with minimum and maximum levels per class of F1 = 0.70 and 0.91. In conclusion, these ULS-derived metrics can be used to precisely estimate fuel type components and fuel load at fine spatial resolutions over moderate-sized areas, which will be useful for research, wildfire risk assessment and fuel management operations.