Remarks on Geomatics Measurement Methods Focused on Forestry Inventory

This contribution focuses on a comparison of modern geomatics technologies for the derivation of growth parameters in forest management. The present text summarizes the results of our measurements over the last five years. As a case project, a mountain spruce forest with planned forest logging was selected. In this locality, terrestrial laser scanning (TLS) and terrestrial and drone close-range photogrammetry were experimentally used, as was the use of PLS mobile technology (personal laser scanning) and ALS (aerial laser scanning). Results from the data joining, usability, and economics of all technologies for forest management and ecology were discussed. ALS is expensive for small areas and the results were not suitable for a detailed parameter derivation. The RPAS (remotely piloted aircraft systems, known as "drones") method of data acquisition combines the benefits of close-range and aerial photogrammetry. If the approximate height and number of the trees are known, one can approximately calculate the extracted cubage of wood mass before forest logging. The use of conventional terrestrial close-range photogrammetry and TLS proved to be inappropriate and practically unusable in our case, and also in standard forestry practice after consultation with forestry workers. On the other hand, the use of PLS is very simple and allows you to quickly define ordered parameters and further calculate, for example, the cubic volume of wood stockpiles. The results from our research into forestry show that drones can be used to estimate quantities (wood cubature) and inspect the health status of spruce forests, However, PLS seems, nowadays, to be the best solution in forest management for deriving forest parameters. Our results are mainly oriented to practice and in no way diminish the general research in this area.

A novel post-fire method to estimate individual tree crown scorch height and volume using simple RPAS-derived data

BACKGROUND

An accurate understanding of wildfire impacts is critical to the success of any post-fire management framework. Fire severity maps are typically created from satellite-derived imagery that are capable of mapping fires across large spatial extents, but cannot detect damage to individual trees. In recent years, higher resolution fire severity maps have been created from orthomosaics collected from remotely piloted aerial systems (RPAS). Digital aerial photogrammetric (DAP) point clouds can be derived from these same systems, allowing for spectral and structural features to be collected concurrently. In this note, a methodology was developed to analyze fire impacts within individual trees using these two synergistic data types. The novel methodology presented here uses RPAS-acquired orthomosaics to classify trees based on a binary presence of fire damage. Crown scorch heights and volumes are then extracted from fire-damaged trees using RPAS-acquired DAP point clouds. Such an analysis allows for crown scorch heights and volumes to be estimated across much broader spatial scales than is possible from field data.

RESULTS

There was a distinct difference in the spectral values for burned and unburned trees, which allowed the developed methodology to correctly classify 92.1% of trees as either burned or unburned. Following a correct classification, the crown scorch heights of burned trees were extracted at high accuracies that when regressed against field-measured heights yielded a slope of 0.85, an R-squared value of 0.78, and an RMSE value of 2.2 m. When converted to crown volume scorched, 83.3% of the DAP-derived values were within ± 10% of field-measured values.

CONCLUSION

This research presents a novel post-fire methodology that utilizes cost-effective RPAS-acquired data to accurately characterize individual tree-level fire severity through an estimation of crown scorch heights and volumes. Though the results were favorable, improvements can be made. Specifically, through the addition of processing steps that would remove shadows and better calibrate the spectral data used in this study. Additionally, the utility of this approach would be made more apparent through a detailed cost analysis comparing these methods with more conventional field-based approaches.

Measured effects of workload and auditory feedback on remote pilot task performance

Absent or reduced sensory cueing can deprive pilots operating remotely piloted aircraft beyond visual line of sight (BVLOS) of vital information necessary for safe flight. The present study tested the effects of real-time auditory feedback on remote pilot perception and decision-making task performance in an automated BVLOS flight, under three levels of workload (Low, Moderate and High). Results from 36 participants revealed workload and auditory feedback influenced perception task performance in terms of error type count, with misses more frequent than wrong identifications. In terms of performance in the decision-making task, under low and moderate levels of workload, auditory feedback was found to improve performance. Conversely, under high workloads, an inflexion or tipping point occurred whereby auditory feedback became detrimental to task performance. These results correspond with the expected behavioural responses to external stressors as predicted by the Arousal and Maximal Adaptability theory, and build upon previous findings related to workload, auditory feedback and remote pilot task performance. Practitioner summary: This study tested the effect of real-time auditory feedback and dynamic workloads on remote pilots' task performance. Auditory feedback and workload each influenced the perception tasks in terms of error types committed. Auditory feedback improved decision-making task performance under low and moderate workloads, and reduced performance under high workloads. These results may benefit practitioners by considering the nuanced effects of auditory feedback on human task performance within sensory deprived working environments, including those utilising teleoperated systems.

A Calibration Procedure for Field and UAV-Based Uncooled Thermal Infrared Instruments

Thermal infrared cameras provide unique information on surface temperature that can benefit a range of environmental, industrial and agricultural applications. However, the use of uncooled thermal cameras for field and unmanned aerial vehicle (UAV) based data collection is often hampered by vignette effects, sensor drift, ambient temperature influences and measurement bias. Here, we develop and apply an ambient temperature-dependent radiometric calibration function that is evaluated against three thermal infrared sensors (Apogee SI-11(Apogee Electronics, Santa Monica, CA, USA), FLIR A655sc (FLIR Systems, Wilsonville, OR, USA), TeAx 640 (TeAx Technology, Wilnsdorf, Germany)). Upon calibration, all systems demonstrated significant improvement in measured surface temperatures when compared against a temperature modulated black body target. The laboratory calibration process used a series of calibrated resistance temperature detectors to measure the temperature of a black body at different ambient temperatures to derive calibration equations for the thermal data acquired by the three sensors. As a point-collecting device, the Apogee sensor was corrected for sensor bias and ambient temperature influences. For the 2D thermal cameras, each pixel was calibrated independently, with results showing that measurement bias and vignette effects were greatly reduced for the FLIR A655sc (from a root mean squared error (RMSE) of 6.219 to 0.815 degrees Celsius (℃)) and TeAx 640 (from an RMSE of 3.438 to 1.013 ℃) cameras. This relatively straightforward approach for the radiometric calibration of infrared thermal sensors can enable more accurate surface temperature retrievals to support field and UAV-based data collection efforts.

Occupational stress in Remotely Piloted Aircraft System operators

BACKGROUND

Although there is currently little research data to support the contention, concerns have been raised about possible traumatic stressors inherent to Remotely Piloted Aircraft System (RPAS) operator roles. Factors such as exposure to visually traumatic events compounded by long working hours and blurred boundaries between military and civilian life have been cited as potential stressors. Robust research into the well-being of RPAS operators is scarce and mostly samples US personnel.

AIMS

To provide mental health and well-being data relating to UK RPAS operators.

METHODS

UK RPAS operators completed mental health questionnaires to assess levels of post-traumatic stress disorder (PTSD), anxiety and depression symptoms, alcohol use and occupational functioning. Respondents were also asked about work patterns.

RESULTS

Forty-one per cent of the sample reported potentially hazardous alcohol use. Ten per cent met psychiatric symptom criteria for moderate or severe anxiety, and 20% for moderate depressive symptoms. While there were no cases of probable PTSD, 30% of the sample reported sub-clinical PTSD symptoms likely to impair occupational functioning. Overall, 70% of the sample reported that psychological symptoms significantly impaired their functioning.

CONCLUSIONS

Compared to UK military sub-groups, RPAS operators were not at increased risk of mental health problems. However, a high proportion of the sample reported significant functional impairment, which has not been explored in other comparable studies. The most frequently highlighted work-related stressors were timing of RPAS work and operator shift patterns.

Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images

In the present study, the detection and mapping of Silybum marianum (L.) Gaertn. weed using novelty detection classifiers is reported. A multispectral camera (green-red-NIR) on board a fixed wing unmanned aerial vehicle (UAV) was employed for obtaining high-resolution images. Four novelty detection classifiers were used to identify S. marianum between other vegetation in a field. The classifiers were One Class Support Vector Machine (OC-SVM), One Class Self-Organizing Maps (OC-SOM), Autoencoders and One Class Principal Component Analysis (OC-PCA). As input features to the novelty detection classifiers, the three spectral bands and texture were used. The S. marianum identification accuracy using OC-SVM reached an overall accuracy of 96%. The results show the feasibility of effective S. marianum mapping by means of novelty detection classifiers acting on multispectral UAV imagery.

'You Wouldn't have Your Granny Using Them': Drawing Boundaries Between Acceptable and Unacceptable Applications of Civil Drones

Some industry and policy actors are concerned about public opposition to civil drones, in particular because of their association with military drones. However, very little is understood about public reactions to the technology. Strategies to 'manage public acceptance' have so far relied upon several untested assumptions. We conducted public engagement activities to explore citizens' visions of civil drones. Several insights counteracted the prevailing assumptions. Rejecting the notion of blanket support for or opposition to civil drones, we found that citizens make nuanced decisions about the acceptability of civil drones depending upon the purpose of the flight and the actors involved. The results are positioned in support for calls to strengthen the role of citizens in civil drone development and, in particular, to shift away from the current focus on citizens' acceptance of civil drone development towards the development of civil drones that are acceptable to citizens.

Domesticating the Drone: The Demilitarisation of Unmanned Aircraft for Civil Markets

Remotely piloted aviation systems (RPAS) or 'drones' are well known for their military applications, but could also be used for a range of non-military applications for state, industrial, commercial and recreational purposes. The technology is advanced and regulatory changes are underway which will allow their use in domestic airspace. As well as the functional and economic benefits of a strong civil RPAS sector, the potential benefits for the military RPAS sector are also widely recognised. Several actors have nurtured this dual-use aspect of civil RPAS development. However, concerns have been raised about the public rejecting the technology because of their association with military applications and potentially controversial applications, for example in policing and border control. In contrast with the enthusiasm for dual-use exhibited throughout the EC consultation process, the strategy for avoiding public rejection devised in its roadmap would downplay the connection between military and non-military RPAS and focus upon less controversial applications such as search and rescue. We reflect upon this contrast in the context of the European agenda of responsible research and innovation. In doing so, we do not rely upon critique of drones per se, in their neither their civil nor military guise, but explore the extent to which current strategies for managing their public acceptability are compatible with a responsible and socially beneficial development of RPAS for civil purposes.