Processing Full-Waveform Lidar Data to Extract Forest
Parameters and Digital Terrain Model: Validation in an
Alpine Coniferous Forest
Adrien Chauve, Sylvie Durrieu, Frédéric Bretar, Marc Pierrot Deseilligny,
William Puech
To cite this version:
Adrien Chauve, Sylvie Durrieu, Frédéric Bretar, Marc Pierrot Deseilligny, William Puech.
Processing Full-Waveform Lidar Data to Extract Forest Parameters and Digital Terrain
Model: Validation in an Alpine Coniferous Forest. ForestSat Conference’07, pp.5, 2007,
<http://forestsat07.teledetection.fr/>. <lirmm-00293132>
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PROCESSING FULL-WAVEFORM LIDAR DATA TO EXTRACT FOREST
PARAMETERS AND DIGITAL TERRAIN MODEL: VALIDATION IN AN
ALPINE CONIFEROUS FOREST
Adrien CHAUVE1,2,3, Sylvie DURRIEU1, Frédéric BRETAR2, Marc PIERROT-DESEILLIGNY1,
William PUECH3
1
UMR TETIS Cemagref/Cirad/ENGREF-AgroParisTech, Maison de la Télédétection
500, rue J.F. Breton, 34093 Montpellier Cedex 5, France, 04 67 54 87 32, 04 67 54 87 00,
adrien.chauve@teledetection.fr, sylvie.durrieu@teledetection.fr, pierrot@teledetection.fr
2
Laboratoire MATIS - Institut Géographique National, 2-4 Avenue Pasteur, 94165 Saint Mandé cedex,
France, 01 43 98 84 36, 01 43 98 85 81, frederic.bretar@ign.fr
3
Laboratoire LIRMM, UMR CNRS 5506, Université Montpellier II, 161, rue Ada, 34392 Montpellier
Cedex 05, France, 04 67 41 86 85, 04 67 41 85 00, william.puech@lirmm.fr
ABSTRACT
Small footprint discrete return lidar data have already proved useful for providing information on forest
areas. During the last decade, a new generation of airborne laser scanners, called full-waveform (FW) lidar
systems, has emerged. They digitize and record the entire backscattered signal of each emitted pulse. Fullwaveform data hold large potentialities. In this study, we investigated the processing of raw full-waveform
lidar data for deriving Digital Terrain Model (DTM) and Canopy Height Model (CHM). The main
objective of this work was to compare geometric information derived from full-waveform and multi-echo
data for various stands. An enhanced peak detection algorithm developed in a previous study was used to
extract target positions from full-waveform data on plots under different stand characteristics. The resulting
3D point clouds were compared to the discrete return lidar observations provided by the lidar operator.
Ground points were then identified using an original classification algorithm. They were used to derive
DTMs which were compared to ground truth. Digital Surface Models were obtained from first echoes and
canopy height models were then computed. Detecting weak echoes, when processing full-waveform data,
enabled to better describe the canopy shape and to penetrate deeper into forest cover. However DTM was
not significantly improved.
Keywords: waveform analysis, signal modelling, DTM, lidar, forest
1 INTRODUCTION
Airborne laser scanning is an active remote
sensing technique providing range measurements
between the laser scanner and the Earth topography.
Based on direct georeferencing using both GPS and
inertial measurements, such distance measurements
are mapped into 3D point clouds with high accuracy
and relevancy. Standard small footprint airborne
multi-echo laser scanner systems can detect up to
six echoes along the travel path of the laser pulse:
the first echo is associated with the top of the
canopy and the last pulse with the ground. Discrete
return lidar observations have already proved useful
for providing information on forest areas: individual
tree extraction (Brandtberg et al., 2003), height and
crown diameter measurement (Persson et al., 2002;
Naesset and Bjerknes, 2001), large scale automatic
extraction of tree tops (Nilsson et al., 2003).
Many methods and algorithms have been developed
for forest measurements (Hyyppä et al., 2004).
During the last decade, a new generation of airborne
laser scanners, called full-waveform (FW) lidar
systems, has appeared. They digitize and record the
entire backscattered signal of each emitted pulse
(see figure 1). Full-waveform data hold large
potentialities. In addition to an improvement of
range measurements, physical properties of the
targets included in the diffraction cone are likely to
be derived from waveform analysis. Studies have
been carried out on forestry applications to measure
the canopy height (Lefsky et al., 1999), and vertical
distribution of canopy material (Dubayah and Blair,
2000) using data acquired with large footprint
experimental lidar systems. Modelling of raw lidar
signal recorded by recent small footprint industrial
systems has already proved efficient in increasing
the number of detected targets in comparison with
data provided by multi-echo lidar systems for which
real-time point extraction method is unknown to the
end user (Persson et al., 2005; Chauve et al., 2007).
Detecting weak echoes allows to better describe 3D
vegetation structure and ground.. As a consequence,
Digital Terrain Model (DTM), Digital Surface
Model (DSM), and the derived Canopy Height
Model (CHM) are expected to be significantly
improved.
bare soil area; 9 m difference in height; around
66 stem/ha;
plot
2: (32 m x 22 m) old dense old Black pine
plantation on sloping terrain; 12 m difference in
height; around 440 stem/ha;
plot
3: (35 m x 21 m) very dense old Sylvester
pine plantation; around 449 stem/ha.
Figure 1. Principle of full-waveform lidar system. Laser
emitted pulse (in blue) and backscattered signal (in red).
However real potentialities of small footprint
full-waveform
lidar
systems
for
forest
characterization has been little studied until now. In
this study, we investigated the processing of raw
full-waveform lidar data for extracting more points
than classical multi-echo data, and studied the
influence on resulting DTMs and CHMs.
2 AVAILABLE DATA
2.1 AREA OF INTEREST
In this study, the surveyed area was an alpine
coniferous forest near Digne-les-Bains, France.
2.2 FULL-WAVEFORM LIDAR DATA
The data acquisition was performed in April 2007
using a RIEGL LMS-Q560 system. The main
technical characteristics of this sensor are presented
in (Wagner et al., 2006). The lidar system operated
at a pulse rate of 111 kHz. The flight height was
around 500 m leading to a footprint size of about
0.25 m. The system temporal sampling is 1 ns
(0.30 m). The point density was about 5 pts/m2.
Each return waveform was made of one or two
sequences of 80 samples corresponding to an
altimetric profile of 24 or 48 m. For each emitted
pulse, both emitted and return waveforms as well as
the 3D point cloud computed by the lidar operator
were provided.
2.3 FIELD DATA
In order to evaluate the potential of full-waveform
lidar data in various stand conditions, 3 plots were
selected with different characteristics:
plot
1: (71 m x 47 m) low-density Black pine
stand originating from a seed cutting, including a
Accurate positions, diameters at breast height
(DBH), total heights and crown dimensions (heights
and diameters) were measured for all the trees of the
arboreal strata. The underlayer vegetation was also
described. For each plot, ground coordinates,
measured using tacheometers and DGPS, were
available for a set of points. Unfortunately, because
of georeferencing issues, we could only process the
data of the first two plots for DTM quality
assessment.
3 METHODOLOGY
3.1 WAVEFORM PROCESSING
Waveform processing consists in decomposing the
waveform into a sum of components or echoes,
where each component characterizes the
contribution of a target to the backscattered signal.
Many studies have already been carried out to
perform full-waveform lidar data processing and
analysis. Non-linear least-squares (NLS) methods
(Hofton et al., 2000, Reitberger et al., 2006) or
maximum likelihood estimation using the
Expectation Maximization (EM) algorithm (Persson
et al., 2005) are typically used to fit the signal to a
mixture of Gaussian functions to parametrize the
peaks. It was found that small-footprint lidar
waveforms could be generally well modelled with a
sum of Gaussian pulses (Wagner et al., 2006).
We focused in this study on maximising the
number of peaks detected from the waveforms: the
issue is to extract as much information as possible
above the noise level while limiting erroneous peak
detection. The optimization step is well-known and
efficient and the critical step relies on the
assessment of the right number of components. The
main sources of ill-detections are both the noise and
the ringing effect. They are taken into account as
follows: (1) the background noise is thresholded;
(2) only one peak is kept when two very close
echoes are detected under the lidar system
resolution; (3) and finally the peaks due to the
ringing effect are removed based on an amplitude
ratio criterion.
In this study waveforms were decomposed into
sums of Gaussian functions and the optimization
method was a non-linear least-squares algorithm. To
detect the number of components, we used an
improved peak detection method developed in a
previous study (Chauve et al., 2007). The main steps
are:
3.3 DTM AND CHM COMPUTATION
A
DTMs are triangulated from lidar ground points and
finally re-sampled on a 0.5 m resolution grid, in
agreement with the spatial resolution of the lidar
acquisition (4-5 pts/m2).
Using
DSMs are computed from first echoes using the
Inverse Distance Weighting (IDW) interpolation
technique. CHMs are obtained by subtracting DTM
from DSM.
basic detection method, based on zero
crossings of the first derivative, is used at first to
estimate the number and the position of the
components;
these values as initialisation values, a first
estimation of the signal is computed;
iterative process is performed to find
missing peaks by detecting echoes on the
difference between the modelled and initial
signals. If new peaks are detected, the fit is
performed again. This process is repeated until no
improvement is found.
4 RESULTS AND DISCUSSION
An
This enhanced peak detection method is useful to
model complex waveforms with overlapping echoes
and also to extract weak echoes which are not found
by multi-echo systems. Both cases often occur in
vegetated areas.
3.2 GROUND POINTS CLASSIFICATION
The classification process is based on a previous
work described in (Bretar et al.,2004). From an initial location within the point cloud, the filtering algorithm propagates following the steepest local
slope over a grid topology. A neighborhood of lidar
points is extracted at each grid location. The neighborhood extension is set so that the overlapping ratio between two adjacent locations should be at least
50 %. An initial estimate of the terrain elevation is
performed by calculating an average value of laser
point height belonging to a rank filtered subset. The
filtering algorithm is based on a bipartite voting process.
Lidar points will be classified as ground or offground points depending on their height difference
to the local terrain estimate (mean value of lidar
points classified as ground points). Considering the
overlapping ratio of the neighborhoods, laser points
are classified several times either as ground or offground points. At the end of the propagation, a label
corresponding to the most representative votes is
affected to each lidar point.
A post-processing step is performed to detect
under-terrain outliers. Such points mainly come
from under-ground erroneous echoes that were
extracted during waveform processing. The filter is
based on a robust local plane estimation of ground
points. Points located above a given threshold are
removed.
4.1 POINT DETECTION
Lidar waveform post-processing allows to improve
the density of the final point cloud up to more than
130 % on very dense vegetated areas (see table 1).
Table 1 shows that on large ground areas with only
sparse trees (like in plot 1), only few additional
echoes are detected. The number of detected points
increases when the vegetation is getting denser in
both overstory and understory vegetation.
Table 1. Statistics on the point extraction over different
plots. Plot 1: sparse Black pine stand; plot 2: dense Black
pine stand; plot 3: dense Sylvester Pine stand.
Area
Plot 1
Plot 2
Plot 3
Nb multi-echo points
19863
1729
1566
Nb fullwave points
25769
3305
3712
% additional points
30 %
91 %
137 %
Analyzing the differences between the fitted
waveforms and the multi-echo point cloud, one can
notice that the additional points come from weak
and overlapping echoes that are now detected. These
points are located near the tree canopy and in the
understory. Only few additional points are detected
on the ground due to the fact that pine crowns,
although thin, are very dense and the laser beam can
hardly get through them.
Figure 2. Histograms of 3D point cloud altitudes with
1 m bin size: Plot 1 (left), plot 2 (middle), plot 3 (right).
Red lines correspond to multi-echo point cloud and black
lines to the additional points extracted from fullwaveform data.
Histograms on figure 2 show the altimetric
distribution of multi-echo points (red lines) and of
additional points detected by processing lidar
waveforms (black lines). In plot 1 (left subfigure),
the landscape is hilly and as a consequence there is
only one wide peak corresponding to ground points
and low vegetation. Due to a very low tree density
the overstory peak is reduced and hardly
distinguishable. In plot 2 (middle subfigure)
overstory and understory can be clearly separated.
The ground peak is also quite large because the
slope of the plot is very high. Most of the additional
points are here located in the tree canopy. In the
third plot (right subfigure), which is relatively flat,
both understory and overstory are very dense and
almost continuous. Additional points are here
located in the canopy as well as in the low
vegetation.
4.2 DIGITAL TERRAIN MODELS
Table 2 summarizes the results of the
comparison between 0.5 m resolution raster DTMs
derived from multi-echo and full-waveform lidar
data, and from terrain measurements. Means and
RMSs were computed on the difference images.
Results are homogeneous for all comparisons: less
than 0.2 m in RMS, except for the comparison
between field measurements and lidar point cloud in
the first plot. In these cases (RMS = 0.57 m) errors
are mainly due to an insufficient field measurement
sample for describing the hilly topography of the
first plot.
Table 2. Statistics on DTM (in m).
Area
Plot 1: difference multi-echo – fullwave
Mean
RMS
(m)
(m)
-0,02
0,15
Plot 1: difference field – multi-echo
0,08
0,57
Plot 1: difference field – fullwave
0,05
0,57
Plot 2: difference multi-echo – fullwave
0,06
0,20
-0,04
0,16
0,01
0,20
Plot 2: difference field – multi-echo
Plot 2: difference field – fullwave
Waveform processing did not improve the DTM
on these two plots, because very few additional
points were detected on the ground (around 6 %) for
the first plot and because the classification between
ground and low vegetation is still an issue in dense
and near-ground vegetation (0.3 to 1 m) for the
second plot.
4.3 CANOPY HEIGHT MODELS
Figures 3 and 4 show results of the comparison
between multi-echo and fullwave CHMs for plots 1
and 2. On the left the CHM is computed from multiecho point cloud and on the right the difference
between CHM is computed from multi-echo and
full-waveform point clouds. The range of
differences in height are similar in both plots: from
-3 m to about 7 to 9 m. The few negative values are
located around the trees and correspond to
additional points detected in the low part of the
canopy that are not in the multi-echo point cloud.
Figure 3. Plot 1: (left) CHM computed from multi-echo
point cloud; (right) difference between CHM computed
from fullwave and from multi-echo point clouds.
Figure 4. Plot 2: (left) CHM computed from multi-echo
point cloud; (right) difference between CHM computed
from fullwave and from multi-echo point clouds.
Figure 5. Histograms of CHM differences between.
Plot 1 (left, 0.22 m mean difference; Plot 2 (right, 1.7 m
mean difference).
Histograms of CHM differences are plotted on
figure 5. These histograms are linked to the
vegetation density and cannot be directly compared.
Nevertheless, what is noticeable is that on a dense
forest area processing waveforms significantly
changes the description of the canopy: volume,
height and 3D structure are expected to be
improved. Detailed validation of the canopy shape
with field measurements is in progress.
5 CONCLUSIONS AND FUTURE
WORK
Processing lidar waveforms has been investigated in
this paper in order to extract more echoes than
equivalent multi-echo data. We studied the
altimetric distribution of additional points and
evaluated the potential of processing waveforms to
improve DTM and CHM. DTMs were finally
compared to field measurements.
Improving peak detection was shown in this
paper to be very successful to extract additional
targets in the return waveforms. Depending on
vegetation density, we detected from 30 % to 130 %
additional points. These points are located mainly
within the canopy and in highly dense understory.
Very few additional points were detected on the
ground, which explains why the DTMs were not
significantly improved. On the contrary, CHM
really benefited from waveform processing as the
number of echoes were doubled in the overstory and
inside the canopy. The 3D structure of the
vegetation is thus expected to be significantly
improved, and detailed field measurements are in
progress to confirm this result.
Modeling raw lidar signal also enables to extract,
beyond target position, additional parameters which
are of interest to study the geometry and the
radiometry of the targets: both echo intensity and
width, and also shape parameters when complex
models, such as generalized Gaussian model, are
used to decompose lidar waveforms into a sum of
target contributions. This information is promising
to improve the classification of ground and low
vegetation points, and also to identify tree species.
ACKNOWLEDGMENTS
We thank Laurent Albrech for his help during the
field data measurement campaigns and Nicolas
David for his help to georeference the lidar data. We
also thank the GIS Draix and the Cemagref of
Grenoble for providing the full-waveform lidar data.
Field measurements were realized thanks to the
financial support of CNES.
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