A TEST OF AIRBORNE LASER MAPPING
UNDER VARYING FOREST CANOPY
Stephen E. Reutebuch
USDA Forest Service
Pacific Northwest Research Station
Seattle, Washington
sreutebu@u.washington.edu
Kamal M. Ahmed
Department of Civil and Environmental Engineering
College of Engineering
University of Washington
Seattle, Washington
kamal@u.washington.edu
Terry A. Curtis
Dick Petermann
Michael Wellander
Michael Froslie
Photogrammetry and Mapping Department
Washington State Department of Natural Resources
Olympia, Washington
tczn490@wadnr.gov
ABSTRACT
The accuracy of two digital terrain models (DTMs) was assessed. The DTMs were generated from two
different airborne laser missions flown with the same sensor. Ground elevations from the airborne laser DTMs were
compared to 350 ground survey points in a mountainous, heavily forested test site. The RMSE of the 1999 DTM
was 2.4 ft with an average error of 0.0 ft. The RMSE of the 1998 DTM was 3.8 ft with an average error of +1.3 ft.
The 1999 airborne laser DTM was also compared to spot heights measured photogrammetrically from 1:12,000
aerial photos. A total of 992 photogrammetric heights were measured throughout the study area. The average
difference from the 1999 LIDAR DTM was 0.0 ft, with a standard deviation of 6.0 ft. In relatively open areas, the
photogrammetric heights compared very well with the LIDAR DTM elevations. Eighty-seven percent of the
photogrammetric heights were within10 ft of the corresponding LIDAR DTM elevations. In areas with very tall,
dense canopy, the photogrammetric heights occasionally had large errors.
INTRODUCTION
Airborne laser mapping technology is a remote sensing technology that is increasingly being used to map terrain
surfaces. This mapping technology utilizes a laser light detection and ranging (LIDAR) system to compute
distances from the airborne sensor to surfaces below the aircraft. LIDAR systems emit laser pulses that are reflected
by vegetation, buildings, or the ground surface. A detector in the LIDAR sensor records the time it takes for each
laser pulse to travel from the sensor to the ground and back to the sensor. This time is then used to compute the
distance from the sensor to the reflecting surface. In open areas with hard surfaces LIDAR systems often achieve
vertical accuracies of 6 inches (Pereira and Janssen 1999). However, in forested environments that are covered with
dense canopy the accuracy of LIDAR mapping has not been thoroughly examined. Kraus and Pfeifer (1998) report
on the vertical accuracy of the Optech∗ ALTM 1020 laser scanner for mapping a wooded area (91 km2) in Austria.
They report a vertical root-mean-square error (RMSE) of 1.9 ft. However, they do not provide information on the
tree cover in the study area or how dense the cover was in the area of ground checkpoints.
∗
Use of trade or firm names in this publication is for reader information and does not imply endorsement by the
U.S. Department of Agriculture of any product or service.
APPROACH
In this study a small-foot print LIDAR system was used to map the ground surface of two square miles of
heavily forested lands in Washington State. The site is mountainous with elevation varying from 500-1300 ft and
ground slopes from 0-45 degrees. Figures 1and 2 are 1997 and 1999 aerial photographs of the study area. The
forest canopy within the study area is primarily coniferous and highly variable. It includes recent clearcuts, forest
plantations ranging from recently planted to 70-year-old mature forests. As part of a forest management study, the
canopy of the 70-year-old forest stands was partially harvested in 1998 to result in six different residual canopy
densities. In the clearcut unit, the number of residual trees per acre (tpa) is zero; in the 2-age unit, 16 tpa remain; in
the thinning unit 71 tpa remain; in the patch cut unit, 53 tpa remain; in the group selection unit 45 tpa remain; and in
the control unit (not harvested) 113 tpa remain. Dominate tree height in the harvest area was approximately 160 ft.
To test the accuracy of a LIDAR mapping system, the study area was flown both prior to harvesting and after
harvesting.
Figure 1. 1997 aerial photograph of the study site (sections 22 and 23, township 16 N, range 4 W, Washington).
The study area corresponds to the outlined two square-mile sections. The different harvesting treatments
are outlined in yellow. The harvesting treatments were applied between LIDAR flights.
Figure 2. 1999 orthophotograph of the study area. The harvesting treatments have been applied. Yellow dots
indicate the location of ground survey checkpoints.
Airborne Laser Mapping System
Several LIDAR systems are commercially available for a wide variety of topographic mapping applications
(Flood and Gutelius 1997; Baltsavias 1999). All of these systems combine passive laser ranging, GPS positioning,
and inertial motion detection technologies to collect three-dimensional surface information. For this study Aerotec
of Bessemer, Alabama, used a Saab TopEye scanning system, flown in a helicopter, to collect data over the study
site both in the spring of 1998 (prior to commencement of harvesting operations) and 1999. Table 1 lists the flight
parameters and instrument settings used during data collection.
Table 1. Flight parameters and scanning system setting.
Flying height
Flying speed
Scanning swath width
Forward tilt
Laser pulse density
Laser pulse rate
Maximum echoes per pulse
650 m
25 m/sec
70 m
8 degrees
3.5 pulses/m2
7,000 points/sec
2 in 1998, 4 in 1999
Ground- and Canopy-surface DTMs
The flight data were processed by Aerotec staff using proprietary correction and filtering software. This software first computes an XYZ coordinate for each laser pulse echo using the recorded aircraft GPS position, inertial
motion, and laser scanner data. The coordinate data were filtered to remove gross anomalies and then further
processed to identify the subset of positions most likely to have been reflected at the ground surface. The filtered
coordinates were then gridded to produce a ground-surface digital terrain model (DTM) with 15 x 15-ft spacing on
the North American Datum 1983, State Plane Washington South Zone, with elevations in the North American
Vertical Datum 1988. Both horizontal and vertical units were International Feet. Although the filtering process did
not remove all the effects of the vegetation cover, the ground 1999 DTM (Fig. 3b) appears to have relatively few
indications of errors due to canopy. The 1998 ground DTM (Fig. 3a) appears to have more surface anomalies that
remain after processing.
In addition to the ground-surface DTM, a canopy-surface DTM (grid spacing of 15 x 15ft) was produced from
the first echo of each laser pulse (Fig. 4). As can be seen in Figures 1 and 2, the study area contained a wide range
of vegetative cover, ranging from recent clearcuts to dense, closed-canopy coniferous stands. Trees in these stands
range in height from 2 to 160 feet. A related study is underway to determine the usefulness of these canopy data for
characterizing vegetation parameters such as crown closure, tree heights, and trees per acre.
LIDAR DTM ACCURACY ASSESSMENT
It is relatively easy to assess the accuracy of LIDAR data in open areas. A set of well-spaced checkpoints can
be measured with survey-grade (carrier phase) GPS. The elevations of these GPS checkpoints can then be compared
to DTM elevations for the same positions. It is much more difficult to assess the accuracy of LIDAR data in heavily
forested areas; yet in areas with heavy vegetation LIDAR holds the most promise for providing DTMs that are much
more accurate than photogrammetrically derived DTMs. Unfortunately, GPS cannot be used to obtain accurate
positions under forest canopy. For this reason, traditional ground survey methods must be employed to assess
LIDAR accuracy in forests. Such surveys are particularly difficult in mountainous terrain with thick underbrush.
Ground Surveyed Checkpoints
To assess the accuracy of the ground DTMs, an intense ground survey was conducted. A Topcon ITS-1 total
station surveying instrument was used to survey 350 checkpoint locations in the central portion of the area (Fig. 2).
Approximately 85 percent of the survey points were under forest canopy. The ground survey consisted of three
closed traverses that originated at reference points that had been established using survey-grade GPS. Horizontal
accuracy of the ground survey was 1:2840 and vertical closure was 0.10 ft. After adjustment of the ground
traverses, the horizontal and vertical accuracy of ground points were 0.5 ft and 0.10 ft, respectively. It is important
to note that the ground survey did not include areas that were covered with young forest plantations.
The elevation of each checkpoint was compared to the elevation of the same horizontal position within the 1999
and 1998 LIDAR DTMs. Because the DTMs are gridded models of the terrain surface, a bilinear interpolation was
Figure 3a. 1998 ground LIDAR DTM.
Figure 3b. 1999 ground LIDAR DTM.
Figure 3. Ground LIDAR DTMs from 1998 and 1999 flights. Grid spacing is 15 x 15 ft. Notice residual surface
anomolies in 1998 DTM. (Courtesy of F. Damian.)
Figure 4a. 1998 canopy-level LIDAR DTM (pre-harvesting).
Figure 4a. 1999 canopy-level LIDAR DTM (post-harvesting).
Figure 4. Perspective views of the 1998 and 1999 canopy-level LIDAR DTMs. (Courtesy of B. McGaughey).
applied to compute the elevation at the horizontal position of each survey checkpoint. This interpolation method
first determines which DTM grid cell contains the horizontal position of the checkpoint. The elevation of the
checkpoint is computed by interpolating between the four corners of the grid cell (Lemkow 1977).
Figure 5 provides a histogram of the differences between the 1999 LIDAR DTM and the ground-surveyed
checkpoints. The average of the differences between the 1999 LIDAR DTM and the 350 checkpoints was 0.0 ft.
The RMSE was 2.4 ft. The range of the elevation differences was +5.8 to – 7.6 ft. Considering that the LIDAR
DTM is a 15 x 15-ft grid, much of the difference in elevation might be attributed to changes in microtopography
within a grid cell that are not modeled by the gridded DTM.
Figure 6 provides a histogram of the differences between the 1998 LIDAR DTM and the ground-surveyed
checkpoints. The average of the differences was +1.3 ft. The RMSE was 3.8 ft. The range of the elevation
differences was +10.7 to –26.8 ft. From this analysis, it appears that the 1999 LIDAR DTM is more accurate. This
is not surprising for several reasons. The laser system was upgraded in 1999 to capture four, rather than two, echoes
per laser pulse. Part of the area was logged between the time of the flights; therefore, more of the area was open in
1999. Finally, by 1999 the contractor had gained more experience working in mountainous and forested areas
resulting in better coverage of the area.
Comparison of LIDAR DTM and Photogrammetric Spot Height Measurements
Comparison of the LIDAR DTM elevations to photogrammetric heights was also conducted. In 1999 1:12,000
color aerial photographs of the area were flown with a Wild RC-30 equipped with a 12-in lens. This photography
was controlled using ground targets that were surveyed with carrier phase GPS. The photo block was adjusted using
the L/H Systems DPW softcopy photogrammetry system. The horizontal and vertical RMSEs of the photo block
adjustment were 0.3 and 0.2 ft, respectively.
Sixteen polygons were delineated on the photos (Fig. 7) covering the range of forest canopy conditions within
the study area. A Zeiss P3 analytical stereoplotter was used to collect at least 50 spot heights in each of these
polygons. In addition, spot heights were collected along open sections of forest roads within the study area. The
stereoplotter operator had many years of experience mapping in similar forest conditions. Differences between the
photogrammetric heights and the 1999 LIDAR DTM are shown in Table 2.
Table 2. Difference between the 1999 LIDAR DTM and photogrammetric height measurements. Average dominate
tree height varied from 5-160 ft.
0
LIDAR DTM minus Photogr. Height
(ft)
Range of
Average
STDEV
Differences
+4.9
-7.0
-1.6
2.3
0
0
+6.8
-4.7
-0.3
2.3
77
160
16
+7.5
-6.7
-1.6
2.2
66
160
71
+15.1
-15.4
-2.0
4.3
50
160
113
+62.2
-16.2
+7.5
12.5
992
0~160
0~400
+62.2
-45.8
0.0
6.0
Open Road
99
Tree Ht
(ft)
0
Clearcut
60
Heavily Thinned
Lightly Thinned
N
No Thinning
*
All photogr. Points
Trees/ac
*
Includes photogrammetric heights from forest roads and all 16 timber units, i.e. both untreated and units that
were harvested in 1998.
DISCUSSION OF RESULTS
Overall, the 1999 LIDAR data appear to be quite accurate. The 2.4 ft vertical RMSE of the 1999 DTM
computed from ground checkpoints is very similar to that obtained by Pereira and Janssen (1998) for a wooded area
in Austria (RMSE=1.9 ft). In their study, a fixed-wing aircraft was employed, resulting in an average point spacing
of 10 ft. They chose to produce a 10 x 10-ft gridded DTM from these data. In our study, the LIDAR sensor was
1999 LIDAR DTM minus
Ground Survey Elevations
Frequency
100
80
60
40
20
0
-7 -6 -5 -4 -3 -2 -1
0
1
2
3
4
5
6
Elevation Difference (ft)
Figure 5. Histogram of the elevation differences between ground checkpoints and the 1999 ground LIDAR DTM.
1998 LIDAR DTM minus
Ground Survey Elevations
Frequency
250
200
150
100
50
0
-25
-20
-15
-10
-5
0
5
10
15
Elevation Difference (ft)
Figure 6. Histogram of the elevation differences between ground checkpoints and the 1998 ground LIDAR DTM.
Figure 7. 1999 orthophotograph showing location of 16 timber units in which photogrammetric spot heights were
collected. Yellow dots represent spot heights in timber units. Red crosses represent spot heights on roads.
(Courtesy of F. Damian).
flown on a slow-moving helicopter. The average ground spacing of raw LIDAR data varied from 1-10 ft, averaging
approximately 2.5 ft. This was due to the extremely tall trees and the mountainous terrain that can cause gaps in
coverage. For this reason, we chose to conservatively grid our data at a spacing of 15 x 15 ft. Given the
mountainous, dissected nature of the terrain, it is possible that a finer grid spacing might produce a slightly lower
vertical RMSE. This assumes that the finer grid would better model microtopography.
Additional analysis is needed to determine how understory vegetation affects LIDAR accuracy. Understory
vegetation height was recorded at each ground checkpoint, but these data have not yet been analyzed. More
checkpoints are needed in other forest cover types within the study area. For instance, much of the area is covered
by young plantations that have closed canopies and tree heights varying from 10-30 ft. In these areas, LIDAR
pulses may not be reaching the ground surface. Also, multi-echo LIDAR systems require a minimum distance
between successive echoes. In these young, dense stands, the last pulse echo may often be on the near-ground
vegetation, 3-6 ft above the ground. It may be possible to photogrammetrically collect checkpoints from older
1:12,000 aerial photographs that show these young plantations in bare-ground condition around the time they were
planted.
The 1998 LIDAR data (RMSE=3.8 ft) appears to be less accurate than the 1999 data (RMSE=2.4 ft). There are
several possible reasons for this difference. The 1998 flight was the first forestry project in mountainous terrain
flown by the contractor. An inspection of the distribution of the 1998 raw data indicated that there were narrow
gaps between several flightlines. The TopEye scanner was upgraded before the 1999 flight so that it could record
four, rather than two, echoes per pulse. Finally, one would suspect that the proprietary LIDAR data filtering and
processing algorithms have improved in the last couple of years.
When the 1999 LIDAR DTM is compared to photogrammetric spot heights, it appears that in relatively open
areas, the photogrammetric heights are reasonably close. However, to get a more meaningful comparison, a
photogrammetric DTM of similar grid spacing should be generated and then compared to the LIDAR DTM.
As expected, the photogrammetric heighting accuracy decreased as the tree density per acre increased.
However, even in areas with relatively dense overstory (lightly thinned unit with 71 tpa, 160-ft tall trees), the
photogrammetric measurements were quite good. Although the range in differences was quite large (-45.8 to +62.2
ft) when all photogrammetric spot heights in the study area were compared to the LIDAR DTM, only 18 out of 992
points differed by more than 20 ft from the corresponding LIDAR elevations. Eighty-seven percent of the
photogrammetric heights were within 10 ft of the 1999 DTM surface.
CONCLUSIONS
In this study we found that a LIDAR ground DTM in mature forested areas can be extremely accurate. The
LIDAR DTM appears to be considerably more accurate than photogrammetric spot heights. However, more
analysis is needed to determine the accuracy of LIDAR data in young, dense forest plantations.
It was costly and laborious to conduct this accuracy test. There is a great need for better methods for verifying
LIDAR data accuracy, particularly in forested areas where GPS does not function.
Although the LIDAR DTM was more accurate, a well-trained stereoplotter operator, using large-scale photos
taken with a 12-in lens, can map reasonably well in forested areas. Given this, one would need to carefully weight
the much higher cost of LIDAR mapping if project accuracy needs were not extreme. As LIDAR technology
improves, i.e. higher pulse rates that would allow higher flying heights and speeds, this cost differential will
diminish. In situations where extremely high accuracy is needed, i.e. to locate small streams and gullies in forests
and to plan forest roads, LIDAR mapping would be of great benefit.
REFERENCES
Baltsavias, E. 1999. Airborne laser scanning: existing systems and firms and other resources. ISPRS J.
Photogramm. Remote Sensing 54(1999) 164-198.
Flood, M.; B. Gutelius. 1997. Commercial implications of topographic terrain mapping using scanning airborne
laser radar. Photogrammetric Engineering and Remote Sensing, 63(4): 327-366.
Kraus, K.; N. Pfeifer. 1998. Determination of terrain models in wooded areas with airborne laser scanner data.
ISPRS J. Photogramm. Remote Sensing 53(1998) 193-203.
Lemkow, D. Z. 1977. Development of a digital terrain simulator for short-term forest resource planning. M.S.
Thesis. Vancouver, B.C.: University of British Columbia: 207p.
Pereira, L.; L. Janssen. 1999. Suitability of laser data for DTM generation: a case study in the context of road
planning and design. ISPRS J. Photogramm. Remote Sensing 54(1999) 244-253.
ACKNOWLEDGMENTS
The authors would like to thank Bob McGaughey of the USDA Forest Service and Florentiu Damian from the
University of Washington for their assistance with GIS and DTM data processing and graphics. We also wish to
thank the Washington DNR surveyors and USDA Forest Service forestry technicians who assisted us with the
extensive ground survey work associated with this project.