Changes in forest canopy structure due to natural disturbances, such as storms and hurricanes, can be monitored with high accuracy from active remote sensors such as LiDAR. The most common LiDAR data, able to perform large area ecosystem characterization, are acquired from aircraft platforms that demand a high operating cost and dependence on specialized companies and require medium-term planning, which often leads to long delays in data becoming available to decision makers.
These shortcomings considerably burden its efficacy for rapid response to natural disasters, which is a significant drawback for practical applications of airborne LiDAR. In contrast, unmanned aerial vehicles (UAV) are autonomous alternatives that allow rapid logistical planning and data collection over relatively large areas (hundreds to thousands of hectares). One of the main limitations of battery-powered UAV systems is the low flight efficiency due to their dependence on battery capacity.
It is, thus, of critical importance to develop sampling methods based on UAV single-pass surveys, which allow rapid large area assessment by concentrating in sampling locations of timely interest only. While airborne LiDAR sampling has been widely developed for very large area assessments, no study has yet tested the efficiency of single-pass flight sampling designs, more suited for UAVs.
