UAV LIDAR DATA PROCESSING: INFLUENCE OF FLIGHT HEIGHT ON GEOMETRIC ACCURACY, RADIOMETRIC INFORMATION AND PARAMETER SETTING IN DTM PRODUCTION

Abstract

This article presents the results of studies related to the impact of flight altitude of UAV equipped with lidar data on geometric and radiometric information. Experiments were conducted in two test areas by performing UAV test flight missions at different UAV Laser Scanner (ULS) altitudes. The results were compared to other parameters describing the point clouds in order to answer the questions related to their genesis and evaluation of a product from such high-resolution datasets. The accuracy of the elevation models was assessed on the basis of control points measured with GNSS RTK and Terrestrial Laser Scanning (TLS). Accuracy was assessed by statistical parameters and differential digital elevation models. The second issue raised in this work is the study of the decrease in radiometric value with an increase in platform elevation. The results of this work clearly indicate the very low impact of platform altitude on DTM vertical error. In presented works the suggestion about DTM resolution and interpolation method are provided. Moreover, the influence of flight height on the reflectance and intensity is notable, however, its impact is related more with the details and resolution of the raster than radiometric values considering the possibility of radiometric calibration of the intensity.

Full text

UAV LIDAR DATA PROCESSING: INFLUENCE OF FLIGHT HEIGHT ON
GEOMETRIC ACCURACY, RADIOMETRIC INFORMATION AND PARAMETER
SETTING IN DTM PRODUCTION
K. Bakuła 1, *, M. Pilarska 1, W. Ostrowski 1, A. Nowicki 1, Z. Kurczyński 1
1 Department of Photogrammetry, Remote Sensing and Spatial Information Systems, Faculty of Geodesy and Cartography,
Warsaw University of Technology, Warsaw, Poland
(krzysztof.bakula, magdalena.pilarska, wojciech.ostrowski, artur.nowicki.stud, zdzislaw.kurczynski)@pw.edu.pl
Commission I, WG I/2
KEY WORDS: lidar, UAV, ULS, geometric accuracy, intensity, DTM, interpolation, resolution
ABSTRACT:
This article presents the results of studies related to the impact of flight altitude of UAV equipped with lidar data on geometric and
radiometric information. Experiments were conducted in two test areas by performing UAV test flight missions at different UAV Laser
Scanner (ULS) altitudes. The results were compared to other parameters describing the point clouds in order to answer the questions
related to their genesis and evaluation of a product from such high-resolution datasets. The accuracy of the elevation models was
assessed on the basis of control points measured with GNSS RTK and Terrestrial Laser Scanning (TLS). Accuracy was assessed by
statistical parameters and differential digital elevation models. The second issue raised in this work is the study of the decrease in
radiometric value with an increase in platform elevation. The results of this work clearly indicate the very low impact of platform
altitude on DTM vertical error. In presented works the suggestion about DTM resolution and interpolation method are provided.
Moreover, the influence of flight height on the reflectance and intensity is notable, however, its impact is related more with the details
and resolution of the raster than radiometric values considering the possibility of radiometric calibration of the intensity.
1. INTRODUCTION:
In recent years, ultralight laser scanners dedicated to unmanned
aerial platforms have been developed dynamically. The first
UAV laser scanners were relatively heavy compared to today's
sensors. Lately, there is a tendency to develop lighter and smaller
laser scanners. In Lin et al. (2009), an Ibeo Lux scanner was used,
weighing 1.2 kg. Kuhnert and Kuhnert (2013) used a lightweight
Hokuyo UTM-30LX sensor, whose weight was 0.37 kg.
However, these sensors were of low performance (Jóźków et al.,
2016). Pilarska et al. (2016) presented a review of commercial
UAV lidar solutions with better performance, though in the year
2020 these solutions are already obsolete. UAV laser scanning
(ULS) has provided new possibilities for lidar applications and
digital terrain modelling due to the higher density of collected
data and more flexible organization of flight missions with lower
costs. Additionally, UAV flights can be conducted more often
than regular aircraft flights. Digital terrain models generated
from dense point clouds containing dozens or even over hundred
points per square metre is a product that is very detailed and
useful for the inventory of terrain surface. Such dense data can
be evaluated in a different way than typical DTM provided as a
product of sparse manual measurement or typical airborne lidar
point clouds. The quality of the lidar data from UAV platforms
is very dependent on the performance of flying missions i.e.
altitude above the ground and scanning angle, though it is mainly
associated with the scanner and platform used as a tool for
collecting data.
The accuracy of data from light UAV laser scanners differs and
depends on many factors. Vosselmann and Mass (2010)
proposed a complex formula for calculation of the final accuracy
of the lidar point cloud from aircraft laser scanners. According to
the formula, final measurement accuracy depends on the
accuracy of particular components, namely: navigational and
positional accuracy (GNSS/INS), laser scanner accuracy (range
* Corresponding author
and incidence angle accuracy), as well as scanner mounting
errors (bore-sight and lever-arm errors). In Pilarska et al. (2016),
the potential and accuracy of light laser scanners available on the
market is presented. In this article, the accuracy of UAV-
dedicated laser scanners was assessed based on the formula
presented in Vosselman and Mass (2010). The results showed
that the most important component of errors for scanning systems
dedicated to UAVs is IMU unit.
The impact of the flying height may be included in the analysis.
In the literature other measures of estimating the altitude
accuracy of lidar-based DTM can be found. In contrast to the
approach using photogrammetric images, DTM accuracy does
not depend so strongly on the altitude of the flight, though the
density of lidar points acquires importance. This is shown in the
relation proposed by Kraus (2007).
(1)
where:
mh - average elevation error of DTM,
α – slope angle,
n – density of point cloud (shown in the number of points per
parcel size).
It is worth noting that the mentioned dependency is not linear. A
density of lidar points that is four times higher causes a doubling
in the accuracy of DTM. In the empirical formula (1), there are
two constants: 6 and 120. There may be a slightly different
estimate of accuracy in the literature, expressed in the different
value of these constants, e.g. 6 and 50, which would mean less
impact of the slope (Karel, Kraus, 2006).
The density of the initial data (lidar data) determines the
resolution of the DTM generated (size of the pixel of the
ortophotomap). McCullagh (1988) suggests that the number of
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B1-2020, 2020
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GRID cells should be (approximately) equal to the number of
field points in the area. This means that the GRID cell size can
be set as in formula (2):
(2)
where:
S – cell size of GRID DTM
n – number of laser points
A – area.
In this paper, the influence of the flight height of the ULS
platform on the accuracy of the final products (point clouds and
DTM) and radiometric data quality are examined. The
parameters of the DTM generation (interpolation and resolution
of GRID) will be also discussed in this analysis.
2. METHODOLOGY AND RESULTS
The methodology section presents two test areas and describes
the data collected with an unmanned aerial system equipped with
a lidar unit and a GNSS/INS unit. Reference data is introduced
here and finally the scope of the experiment is presented with
methods used in the investigation.
2.1 Test areas
The first test area is located in Świniary, near Płock city, in
central Poland. This is a small village. The second test area is
Nietkowice, near Zielona Góra in Western Poland which is
located by the Oder River. On both test areas, there is a riverside
with levees that are linear objects, and which were successfully
mapped using UAV laser scanning.
2.2 Data tested
MiniVUX1-UAV - the lidar unit used at both test sites, was
launched on the market in 2016 (Figure 1). It has a range of
measurement of 330 m with an approximate maximum flight
height above the ground of 160 m. 360° field of view and 0.001°
angle resolution make this quite a light sensor (1.6 kg) that can
collect point cloud data with density up to several dozens.
The weight of the fixed-wing platform is almost 11 kg. This
platform can be equipped with several sensors due to its useful
capacity. A more detailed description of the platform can be
found in Bakuła et al. (2019).
Figure 1. MiniVUX1-UAV scanner by Riegl (www.riegle.com)
2.3 Reference data
As reference data, two types of observations were used: GNSS
RTK measurements of Ground Control Points (GCP) and cross
sections as well as Terrestrial Laser Scanning (TLS) of the
levees’ surface. GCP were signalised by 0.5 × 0.5 m black and
white chessboards printed on PCV and placed on the terrain. For
each of the chessboards, the central point was measured with
RTK GNSS and used as control points for orientation and then
for accuracy assessment of DTM interpolation. The second group
of GNSS RTK measurements was points measurement along the
levees’ cross sections which were selected in order to take into
account various types of land cover and slopes. Cross section
points were used as independent check points for accuracy
assessment of DTM. TLS measurements were acquired only for
the first test area, and TLS data were orientated in the national
projection system with an accuracy of 0.01 m. An example of
TLS data is shown in Figure 2
test area
Świniary
Nietkowice
platform
NEO-3 (fixed-wing)
lidar unit
miniVUX1-UAV by Riegl
reference data
44 control and
88 check GNSS
RTK points,
TLS
27 control and
107 check GNSS
RTK points
average density (two
strips) for flight
heights (AGL):
80 m (1)
80 m (2)
100 m
120 m
180 m
12.96 p./m2
-
10.58 p./m2
9.59 p./m2
-
15.55 p./m2
14.65 p./m2
-
8.07 p./m2
5.55 p./m2
filtering software
RiProcess
Terrasolid
Table 1. Description of tested data
a)
b)
Figure 2. Example of investigated ULS (a) data and TLS (b)
data used as reference.
2.4 Methodology of experiments
The scope of the experiment is related to ULS data processing.
First of all, the influence of data acquisition altitude on the
geometric quality of point clouds and on the intensity of the laser
beam reflection was analysed. In these analyses, the accuracy of
georeferencing of three blocks of data in the two test areas was
examined. The accuracy of alignment was analysed, as well as
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B1-2020, 2020
XXIV ISPRS Congress (2020 edition)
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deviations on signalized control planes, cross-section check
points and differences between ULS and TLS data.
3. RESULTS
The results of the experiment are divided into subsections on the
impact of height on geometric and radiometric information
Recommendations for DTM preparations referred to choose of
resolution and interpolation method can be also find here.
3.1 Flight height influence on geometric accuracy
Analysis over entire surfaces involved a comparison of the point
clouds received from ULS data with data from the TLS point
cloud. The results of the comparison with TLS data are shown in
Table 2 and visualized in Figure 3. In this comparison, 4 samples
of two parts of the embankment were analysed considering flight
elevation on the final result of using the cloud-to-cloud distance
tool. It can be seen that if a higher flight height is used, no
significant decrease in accuracy is observed. Most average
distances in the ULS point cloud to TLS are lower than 4
centimetres.
Flight
height
TLS
Sample 1
(middle of the flight line)
Sample 2
(middle of the flight line)
average
[m]
STD
[m]
average
[m]
STD
[m]
80m
0.023
0.024
0.028
0.034
100m
0.024
0.027
0.032
0.030
120m
0.020
0.020
0.028
0.025
Sample 3
(end of the flight line)
Sample 4
(end of the flight line)
80m
0.010
0.013
0.008
0.012
100m
0.011
0.013
0.010
0.013
120m
0.016
0.018
0.020
0.019
Table 2. Comparison of ULS point cloud to TLS data
Figure 3. Example of visual comparison of ULS data to TLS for
a levee.
The influence of flight height is presented also in Table 4. In this
table, considerations about type of interpolation (linear
interpolation, binning average) and cell size (0.25; 0.5 and 1m)
of GRID were included.
The results in Table 3 again confirm that regardless of the
interpolation method used, flight altitude does not significantly
affect the decrease in DTM accuracy in the analysis of control
points. Based on the results in Table 3, one effect of data
acquisition height was noted on the accuracy resulting from the
density of the point cloud. Data collected at an altitude of 80
meters are 3 times denser than those at an altitude of 180 metres.
This density should not affect the selected DTM cell size
according to formula (2).
RMS errors for control points / check points
GRID
resolution
1 m
0.5 m
0.25 m
Świniary
Linear triangulation
80 m
0.026 / 0.027
0.016 / 0.027
0.015 / 0.025
100 m
0.021 / 0.028
0.019 / 0.026
0.015 / 0.025
120 m
0.015 / 0.048
0.017 / 0.048
0.011 / 0.048
Świniary
Binning average
80 m
0.023 / 0.028
0.013 / 0.031
0.017 / 0.030
100 m
0.021 / 0.028
0.020 / 0.023
0.022 / 0.027
120 m
0.018 / 0.050
0.015 / 0.048
0.015 / 0.047
Nietkowice
Linear triangulation
80 m (1)
0.070 / 0.094
0.062 / 0.087
0.062 / 0.086
80 m (2)
0.078 / 0.090
0.067 / 0.094
0.067 / 0.093
120 m
0.060 / 0.114
0.056 / 0.111
0.059 / 0.111
180 m
0.070 / 0.103
0.061 / 0.103
0.060 / 0.102
Nietkowice
Binning average
80 m (1)
0.068 / 0.092
0.059 / 0.087
0.055/ 0.081
80 m (2)
0.069 / 0.094
0.063 / 0.093
0.055 / 0.089
120 m
0.054 / 0.113
0.052 / 0.108
0.058 / 0.108
180 m
0.062 / 0.102
0.060 / 0.101
0.059 / 0.100
Table 3. RMS errors on check points in comparison to different
GRID size and interpolation method of DTM generated from
ULS data
3.2 Flight height influence on radiometric information
Referring to intensity information and its relation to flight height,
the miniVUX-UAV1 scanner allows recording echo intensity
information at three different attributes. The first is intensity
value (Amplitude) which is the integer representation of the pulse
return magnitude. The second is Riegl amplitude (_Amplitude)
which is the logarithms of ratio given in the units of decibel of
optical input power and minimum detectable input power. The
third is reflectance (_Reflectance) that includes calibration using
ratio of the actual amplitude of that target to the amplitude of a
white flat target at the same range, orientated orthonormal to the
beam axis, and with a size in excess of the laser footprint. (Riegl,
2017).
All three rasters of radiometric information are presented in
Figure 4. It shows the influence of flight height on the radiometry
value for intensity, reflectance and amplitude. For other rasters,
values for intensity and reflectance were different due to the
lower resolution of the point cloud.
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Figure 4. Intensity, amplitude and reflectance of ULS data for
three flight heights (Nietkowice test area).
Analysing Figure 4 it can be noticed that the lower the image
sharpness, the higher the flight altitude. This is related to the
decrease in the density of the point cloud. This shows the
undeniable effect of height on the detail of the intensity images.
It is worth noting that there are also differences in the intensity
value extremely visible for different altitudes in case of
_Amplitude raster. To examine it thoroughly, polygon areas of
low grass vegetation (grass) and uncovered bare ground were
selected, point clouds from two strips were separated and
histograms of a radiometric value were counted and analysed.
Basic statistics such as mean value and standard deviations are
included in Table 4. These values are also shown in Figure 5.
Figure 5. Intensity, amplitude and reflectance of ULS data for
three flight heights for bare ground and grass polygon
(Nietkowice test area).
Figure 6. Percent value change for intensity, amplitude and
reflectance of ULS data for three flight heights for bare ground
and grass polygon (Nietkowice test area).
The charts in Figure 5 show changes in the absolute values for all
intensity rasters obtained from the lidar scanner. Changes in the
raster value for the bare ground and grass polygon are clearly
discernible, however, in the case of _Reflectance and Amplitude
the range of these changes is very limited - less than 5% for
Amplitude and less than 15% for _Reflectance when the values
for the highest and the lowest flight altitude are considered. The
changes for _Amplitude raster are much more significant and
decrease in value by more than 40%. It is clearly seen in Figure 6
presenting percent of intensity value change with reference to the
lowest flight altitude.
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Altitude
Mean
_Amplitude
STD
_Amplitude
Mean
_Reflectance
STD
_Reflectance
Mean
Amplitude
STD
Amplitude
Grass
1st strip
80m
16.141
0.759
-4.233
0.533
35280.363
1748.012
120m
11.349
0.631
-3.902
0.528
36363.297
1730.026
180m
9.051
0.631
-3.901
0.582
36366.991
1907.303
Grass
2nd strip
80m
15.372
1.169
-4.37
0.733
35225.011
2402.256
120m
10.87
0.663
-3.801
0.526
36694.731
1723.107
180m
8.789
0.825
-3.936
0.724
36255.081
2371.146
Bare-
ground
1st strip
80m
14.938
1.577
-4.43
1.142
33666.732
3741.573
120m
10.856
0.786
-4.065
0.69
35831.852
2259.979
180m
8.719
0.877
-3.863
0.717
36492.202
2350.968
Bare
ground
2nd strip
80m
15.611
1.154
-4.385
0.822
34781.318
2692.993
120m
10.878
0.79
-4.109
0.691
35687.694
2264.004
180m
8.789
0.825
-3.936
0.724
36255.081
2371.146
Table 4. Intensity histogram statistics for bare ground and grass polygon in the Nietkowice test area
It should be also noted that while analysing intensity values from
two separate lidar strips, these values are quite consistent for all
rasters, with a difference of less than 5% for _Amplitude, less
than 3% for _Reflectance and less than 1% for Amplitude. The
small difference in intensity images in the last raster is due to the
fact that all corrections related to signal propagation and
radiometric calibration have already been included in this
intensity raster.
4. DISUSSION AND CONCLUSION
In this paper, a simple investigation considering the influence of
the flight height of UAV equipped with a lidar unit was carried
out. According to the results it was confirmed that flight height
does not have a significant impact on the accuracy of ULS data
which is also the conclusion coming from Kraus (2007) for
typical high-altitude airborne laser scanning. However, flight
height has an influence on the point cloud density, which
determines the accuracy and detail of the digital terrain models
and intensity rasters.
Regarding the analysed data for the two test areas in which the
data were acquired from different heights, there was no
significant difference in the accuracy of the digital elevation
models. In experiments conducted for flight height from 80 to
180 metres, it was confirmed that ULS can provide DTM in the
accuracy within a 3 to 10 cm range depending on model
resolution and interpolation method. These results are
comparable with those investigations where flight altitudes were
much lower - mostly less than 50 m (Salach et al, 2018; Lin et
al., 2019; Resop et al., 2019).
Low differences of vertical error of DTM between parameter
settings may also result from slight differences in flight heights.
The limitation is the range of the scanners, which prevents
significant height differences in the compared data. Therefore, it
can be assumed that in the case of low-altitude ULS data, the
laser scanning height has no significant impact on the accuracy
of the final point cloud and DTM, however it does affect the data
detail represented by the density of the point cloud and the
possible GRID of DTM. Analysing the influence of data
acquisition altitude on radiometric information, the expected
decrease in amplitude can be observed, however, the reflectance
values are quite constant, proving that these values are free from
the range influence what can be useful in works using intensity
information in detection of selected objects (Lin et al., 2019).
While processing ULS data with densities higher than a few
points per square metre, adequate resolutions of the DTMs
should be applied for the given density of a point cloud. Height
accuracy of the DTM product will not matter as it is limited by
the parameters of the scanner and IMU. For point clouds with a
density of several points per square metre, the higher spatial
resolution of DTM was able to improve the accuracy of the
resulting product with an increase of up to 1-2 cm, however this
model resolution is limited by point density.
ACKNOWLEDGMENT
The presented results were obtained within the framework of the
project “Advanced technologies in the prevention of flood hazard
(SAFEDAM)”, financed by the National Centre for Research and
Development in Defence, Security Programme. The authors
would like to thank MSP Innotech for their co-operation with
photogrammetric works and for providing the UAS images and
laser scanning data used in the presented study.
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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLIII-B1-2020, 2020
XXIV ISPRS Congress (2020 edition)
This contribution has been peer-reviewed.
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