Distance and Visual Angle of Line-of-Sight of a Small Drone

Mental workload assessments of aerial photography missions performed by novice unmanned aerial vehicle operators

BACKGROUND

Mental workload is one of the important variables in understanding human performance in drone operation.

OBJECTIVE

To test the effects of gender, age group, flight route, and altitude on the flight performance and mental workload of the novice drone operators.

METHODS

Ten male and 10 female participants without prior drone operating experience joined. They were split into two age groups. After attending a training, the participants operated a drone to perform photo taking missions under flight route and altitude conditions. The weighted NASA task-load-index (TLX), modified Cooper-Harper (MCH) scale, heart rate, and interbeat interval were measured to assess the mental workload of the participants. Flight time to complete the mission was adopted to indicate flight performance.

RESULTS

The effect of age group was significant (p <  0.05) on flight time, weighted TLX score, and MCH score. Flight route and altitude were not significant on the two subjective ratings and two cardiac measures.

CONCLUSION

The flight performance of younger participants was significantly better than that of their older counterpart. The effects of both the flight route and altitude on the perceived mental workload of the drone operators were insignificant. Both the weighted NASA TLX and MCH scales were appropriate in measuring the mental workload of the novice drone operators.

Perceived difficulty, flight information access, and performance of male and female novice drone operators

BACKGROUND: Unmanned aerial vehicles (UAVs) or drones are becoming popular in both commerce and private usage. Behaviors of novice drone operators are critical to UAV flight safety. OBJECTIVES: This study aims at discovering the perceived difficulty, flight information access frequency, and flight performance of novice drone operators. METHODS: Twenty adults without prior experience joined as human participants. After attending an onsite training, they operated a drone to perform three missions. The subjective rating of mission difficulty, frequency of flight information checked, and time to complete the mission were analysed. RESULTS: The results indicated significance of gender effects on the difficulty experienced by the novices. Females reported significantly higher difficulty ratings than males initially. This gender difference diminished later in their last mission. Frequency of flight information access was negatively correlated with the difficulty experienced by the operators. Operators who checked flight information more often had less difficulty in performing the flight missions. CONCLUSIONS: There were gender effects in the perceived difficulty and flight information access in drone operations for novices. Such information is helpful in drone operator training and drone design considering drone operation safety.

Improving Approaches to Mapping Seagrass within the Great Barrier Reef: From Field to Spaceborne Earth Observation

Seagrass meadows are a key ecosystem of the Great Barrier Reef World Heritage Area, providing one of the natural heritage attributes underpinning the reef’s outstanding universal value. We reviewed approaches employed to date to create maps of seagrass meadows in the optically complex waters of the Great Barrier Reef and explored enhanced mapping approaches with a focus on emerging technologies, and key considerations for future mapping. Our review showed that field-based mapping of seagrass has traditionally been the most common approach in the GBRWHA, with few attempts to adopt remote sensing approaches and emerging technologies. Using a series of case studies to harness the power of machine- and deep-learning, we mapped seagrass cover with PlanetScope and UAV-captured imagery in a variety of settings. Using a machine-learning pixel-based classification coupled with a bootstrapping process, we were able to significantly improve maps of seagrass, particularly in low cover, fragmented and complex habitats. We also used deep-learning models to derive enhanced maps from UAV imagery. Combined, these lessons and emerging technologies show that more accurate and efficient seagrass mapping approaches are possible, producing maps of higher confidence for users and enabling the upscaling of seagrass mapping into the future.