An international team of scientists from the University of Florida (USA), University of São
Paulo (Brazil), Florida A&M University (USA), USDA Forest Service (USA), Federal University of
Paraná (Brazil), University of Maryland (USA), and Bangor University (UK) worked together to
use the University of Florida’s GatorEye unmanned aerial vehicle (UAV) LiDAR system to fight
climate change. The GatorEye system uses Phoenix LiDAR cameras and software to compare
and contrast tree crowns and forest structure over time.
When storms and hurricanes change the forest canopy structure, traditional LiDAR takes too
much time and logistical planning to quickly assess the damage. Phoenix UAV LiDAR can
autonomously and efficiently get high-resolution, quality data from relatively large areas
(hundreds to thousands of hectares) to help decision makers with post-storm recovery.
The team compared aircraft-borne LiDAR surveys of the Apalachicola National Forest, USA with
single-pass GatorEye UAV LiDAR surveys. They found the following:
● Digital Terrain Models (DTM) with less than 1 m differences between airborne and UAV
LiDAR, within a 145 degree field of view.
● Canopy height models (CHM) provided reliable information from the top layer of the
forest, allowing reliable treetop detection, though tree height underestimations occurred
at 175 m from the flightline.
● Crown segmentation was reliable only within a narrower field of view, from which the
shadowing effect made it unviable.
Because UAV LiDAR is limited by onboard battery capacity, this group is researching efficient
sampling methods using single-pass surveys that focus on samples of specific locations only.
Despite the limitations, UAV LiDAR systems cost less and are more flexible, enabling rapid
planning and response, as well as more frequent data collection and higher point density data.
Single-pass surveying has not been studied for airborne LiDAR, but using LiDAR for monitoring
forest health is well studied. Airborne LiDAR data is highly dependent on the flightline, which
determines pulse density and usually includes overlapping. Phoenix UAV LiDAR is more
efficient with a single pass, but closer to the objects being surveyed, mitigating the difference in
density. Scan angle makes a large difference, and further research is required to determine
accuracy based on scan angle.
This study included sample areas in the Apalachicola National Forest, public land in the sandhills of the Florida Panhandle that covers approximately 233,000 hectares.
The image above shows the location of the study sites in the Apalachicola National Forest. The
red polygons in the satellite image are the eight GatorEye UAV LiDAR single-pass survey
locations. Each box (1 to 8) corresponds to a location and is colored with the canopy height
models (CHM). Dashed strips are each flightline and the green boxes are the plots (130 m ×
430 m), in a total of 43 plots.
The Apalachicola National Forest is located in a humid subtropical climate zone, and the
topography is flat. The forest is managed in accordance with the USDA Forest Service
management plan. Cypress, oak, and magnolia trees make up a large part of its flora, and it is
one of the few remaining homes to the longleaf pine (Pinus palustris Mill.) in the United States.
Mild impacts from Hurricane Michael in 2018 presented an opportunity to study previously
surveyed, verified undamaged plots, as a way to establish and test the UAV LiDAR single-pass
methodology on determining forest changes over time.
This image shows the LiDAR point cloud returns from the GatorEye UAV LiDAR (top) and
aircraft LiDAR (bottom). The returns are colored by height, and the visualization was generated
by the Quick Terrain Modeler software.
UAV and aircraft LiDAR datasets, separated by 1.5 years, were collected and studied.
The aircraft LiDAR dataset, collected as part of the March-May 2018 FL Panhandle LiDAR
Project, came from a Piper PA-31 Navajo outfitted with a Riegl VQ-1560i LiDAR system. The
UAV LiDAR dataset, taken in November 2019, came from the GatorEye UAV LiDAR system,
composed of the Phoenix LiDAR sensor suite, onboard the DJI Matrice 600 Pro hexacopter
multi-rotor RPAS, with L1/L2 dual-frequency GNSS (PPK mode—Post Processing Kinematic).
This system consists of a Velodyne VLP-16 dual-return laser scanner head, capable of 600,000 pulses per second, with Phoenix live and post-processing software.
In addition to the data collection, the team used data processing to compare the sensors.
This image above shows the methods workflow, from raw data to sensor comparisons.
GatorEye data is shown in red. Aircraft LiDAR data pre-processing is shown in blue.
To process the data, the researchers compared the sensors, plots, and LiDAR data between the
two datasets. They re-projected the data sets to the same projection to make it easier to
compare them. They derived eight “LiDAR Missions” from the data.
The images above show that as the distance increases from the flightline, the ground classifier
algorithm for the UAV LiDAR classified tree returns as ground. This pattern can be seen clearly
in flat topography such as that in the Apalachicola National Forest. The GatorEye UAV LiDAR is
shown in red, and aircraft LiDAR in blue. Figure A shows the return density, and Figure B shows
the ground returns.
Figure A above is an example of one rectangle plot (130 m × 430 m) showing GatorEye
UAV-LiDAR and aircraft LiDAR ground returns along the flightline distance, and misclassification
for GatorEye in wide angles (highlighted by the black circle).
Figure B above shows ground return profile analysis (Red = UAV, Blue = aircraft), and the
abrupt increase in the GatorEye ground elevation return after 200 m from the flightline.
The rectangle plots, scales, and profile analysis charts were generated by the Quick Terrain
The image above shows the returns density profile behavior per flightline distance.
Figure A shows the GatorEye UAV LiDAR profile in a red color palette and the aircraft LiDAR in
a blue dashed palette. The red curves grow from left to right, with a respective distance of 210
m to 30 m.
Figure B shows the magnified aircraft system profile of the blue dashed line from the left chart.
The image above shows the relative return density along the distance from the flightline. The
GatorEye UAV LiDAR data is shown in red and the aircraft LiDAR data in blue.
Because the area experienced damage by Hurricane Michael, the amount of tree detection was
expected to show differences. The single-pass survey captured these differences well.
Figure A above shows the GatorEye UAV LiDAR post-hurricane Michael, and Figure B shows
the aircraft LiDAR pre-hurricane.
The results of this research shed light on which LiDAR products, and at which distance, UAV
single-pass LiDAR provides similar quality data compared with the whole area and grid-pass
aircraft LiDAR. It included digital terrain and canopy height models (DTM and CHM), a detailed
representation of forest structure, and individual tree detection and crown delineation (ITDD).
Measuring the impacts of a hurricane on a forest is essential for managers and scientists to
manage the forest effectively.
Remote sensing can provide this information in difficult conditions, and single-pass UAV surveys
can be more than ten times faster than a grid-pass using an airplane, allowing rapid response
after natural disaster. A single-pass UAV survey provides enough accuracy to enable forest
operations such as timber salvage, fuel hazards reduction, etc. Using this method for
post-disaster management appears possible as well. Flight altitude is the most important
variable affecting point cloud density, and other variables affect the accuracy of single-pass
In our research, we measured the efficiency of single-pass UAV LiDAR derived products in
relation to flightline distance and scan angle, and, according to our flight parameters and for a
predominant longleaf pine environment, the quality the LiDAR products achieve at wide angles.
● The single-pass UAV survey can still achieve more dense point clouds compared to
aircraft, up to 175 m from the flightline.
● Accurate ground returns are achieved up to 170 m from the flightline, where only around
0.5% of the scanned area is not covered by ground returns.
● Both sensors showed similar behavior patterns for the returns profile, besides the
differences in point cloud density
● DTM showed overestimated mean value, with less than 1 m differences, compared to
the aircraft-derived DTM, up to 195 m from the flightline.
● CHM showed underestimated mean value, with less than 1 m differences,compared to
the aircraft-derived CHM, up to 95 m from the flightline (110◦ field of view). UAV LiDAR
(single-pass) could detect reliable tree heights up to 140◦ field of view, but as it gets
farther from the flightline, tree crowns appeared smaller than the actual size. This
suggests that the single-pass approach is not recommended for obtaining crown metrics,
except when using very narrow angles, which means less than 60◦ field of view, or
approximately less than 60 m from the flightline.
These results demonstrate capabilities for rapid assessment, but also show the benefits of more
efficient flight plans in grid mode, by covering larger areas in the same mission. This method is a
valuable tool for comparing the results from older LiDAR missions.