The development and proliferation of unmanned aerial vehicles (UAV) in recent years presents a new data collection opportunity for invasive alien plant (IAP) research. The flexibility and cost‐efficiency of these craft offers a valuable solution where high‐spatial or high‐temporal resolution remotely sensed data are required. In this paper, we review all published studies using UAV for remote data collection in IAP research. We have systematically identified the taxonomy and habitat characteristics of the system studied, classified the UAV configuration, analytical methods and the limitations of each study. We used this synthesis to identify research gaps, suggest directions for future research, and identify opportunities for practical application of the technology.

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.

Unmanned Aerial Vehicles (UAVs) and remote image sensing cameras have considerable potential for use in pest control operations. UAVs equipped with remote sensing cameras could be flown over forests and remnant bush sites, particularly those not currently receiving any pest control, to record the unique spectral signature of the vegetation and to detect the presence of possums (Trichosurus vulpecula) and the damage they cause. UAVs could then be deployed to precisely distribute either toxins or kill traps to these identified locations. Predator-free 2050 is an ambitious policy announced by the New Zealand Government where several pests, including possums, are to be eradicated by the year 2050. In order to achieve this goal, pests must be identified, targeted and controlled, requiring creative and novel ideas. UAVs provide flexibility, can fly in remote and difficult terrain, and are considerably cheaper to purchase and operate than the planes and helicopters currently used in conventional aerial pest control operations. Current challenges associated with UAVs include payload capacity, battery limitations, weather, and flying restrictions. However, these issues are rapidly being resolved with sophisticated technological advances and improved regulations. A directed and targeted approach using UAVs is an additional and novel tool in the pest management toolbox that could significantly reduce pest control costs, cover inaccessible areas not receiving any pest management, and will help New Zealand advance towards its predator-free aspiration by 2050.

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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.