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Detection and analysis of individual leaf-off tree crowns in
small footprint, high sampling density lidar data from the
eastern deciduous forest in North America
Tomas Brandtberga, Timothy A. Warnera,*, Rick E. Landenbergerb, James B. McGrawb
aDepartment of Geology and Geography, West Virginia University, Morgantown, WV 26506-6300, USAbDepartment of Biology, West Virginia University, Morgantown, WV 26506-6300, USA
Received 12 June 2002; received in revised form 6 December 2002; accepted 11 December 2002
Abstract
Leaf-off individual trees in a deciduous forest in the eastern USA are detected and analysed in small footprint, high sampling density lidar
data. The data were acquired February 1, 2001, using a SAAB TopEye laser profiling system, with a sampling density of approximately 12
returns per square meter. The sparse and complex configuration of the branches of the leaf-off forest provides sufficient returns to allow the
detection of the trees as individual objects and to analyse their vertical structures. Initially, for the detection of the individual trees only, the
lidar data are first inserted in a 2D digital image, with the height as the pixel value or brightness level. The empty pixels are interpolated, and
height outliers are removed. Gaussian smoothing at different scales is performed to create a three-dimensional scale-space structure. Blob
signatures based on second-order image derivatives are calculated, and then normalised so they can be compared at different scale-levels. The
grey-level blobs with the strongest normalised signatures are selected within the scale-space structure. The support regions of the blobs are
marked one-at-a-time in the segmentation result image with higher priority for stronger blobs. The segmentation results of six individual
hectare plots are assessed by a computerised, objective method that makes use of a ground reference data set of the individual tree crowns.
For analysis of individual trees, a subset of the original laser returns is selected within each tree crown region of the canopy reference map.
Indices based on moments of the first four orders, maximum value and number of canopy and ground returns, are estimated. The indices are
derived separately for height and laser reflectance of branches for the two echoes. Significant differences ( p < 0.05) are detected for numerous
indices for three major native species groups: oaks (Quercus spp.), red maple (Acer rubrum) and yellow poplar (Liriodendron tuliperifera).
Tree species classification results of different indices suggest a moderate to high degree of accuracy using single or multiple variables.
Furthermore, the maximum tree height is compared to ground reference tree height for 48 sample trees and a 1.1-m standard error (R2 = 68%
(adj.)) within the test-site is observed.
D 2003 Elsevier Science Inc. All rights reserved.
Keywords: Image processing; Individual tree; Lidar; Remote sensing; Species classification
1. Introduction
This paper presents, to the best of our knowledge, the
first evaluation of individual senesced (leaf-off) deciduous
trees using small footprint, high sampling density light
detection and ranging (lidar) data. Lidar and radar profiling
instruments are particularly useful for studying forest prop-
erties (Hyyppa & Hallikainen, 1996; Lefsky, Cohen, et al.,
1999; Lefsky, Harding, Cohen, Parker, & Shugart, 1999;
Magnussen & Boudewyn, 1998; Næsset, 1997; Nilsson,
1996). Most previous studies focus on the estimation of a
locally averaged forest attribute, such as the mean height per
stand or per sample plot. The increasing sophistication of
lidar sensors, especially the higher rates of sampling fre-
quencies, improves the economics of acquiring high-reso-
lution data, even from relatively high altitudes. With a laser
cross-sectional area of less than 1 m and sampling density of
multiple laser pulses per square meter, the detection and
measurement of individual trees becomes possible (e.g.,
Brandtberg, 2000; Hyyppa & Inkinen, 1999; Hyyppa, Kelle,
Lehikoinen, & Inkinen., 2001; Persson, Holmgren, &
Soderman, 2002), rather than producing only average stand
0034-4257/03/$ - see front matter D 2003 Elsevier Science Inc. All rights reserved.
doi:10.1016/S0034-4257(03)00008-7
* Corresponding author. Tel.: +1-304-293-5603x4328; fax: +1-304-
293-6522.
E-mail address: [email protected] (T.A. Warner).
www.elsevier.com/locate/rse
Remote Sensing of Environment 85 (2003) 290–303
properties. Furthermore, measurements made from high-
resolution data can be very accurate. For example, Hyyppa
and Inkinen (1999) reported a standard error for the estimate
of the height of individual, overstory coniferous trees of less
than 1 m, Persson et al. (2002) much less than 1 m, and
Brandtberg (2000) slightly more than 1 m for a test using
Norway spruce.
Individual tree-based remote sensing using lidar follows
the trend of the development over the last decade of image
processing of high spatial resolution (pixel size < 1 m)
panchromatic and multispectral imagery (e.g., Brandtberg,
1998; Gougeon, 1995; Hill & Leckie, 1999; Key, Warner,
McGraw, & Fajvan, 2001; Pinz, Zaremba, Bischof, Gugeon,
& Locas, 1993; Pollock, 1996; Wulder, Niemann, & Good-
enough, 2000). The new paradigm of individual tree-based
analysis is expected to lead to powerful remote sensing-
based forest survey and management tools for individual
overstory trees. Hyyppa and Inkinen (1999) anticipated that
single tree-based methods would be beneficial for opera-
tional activities because they are physically oriented. One
advantage of working at a fine scale is that information at
coarser scales can easily be generated. Thus, if the measure-
ment entity ‘individual tree’ is too detailed for final sum-
mary results, the information can be aggregated to mean
values per stand or hectare.
This focus on individual trees is an important develop-
ment, because it returns to the original emphasis of
traditional forest survey, in which tree numbers, sizes,
species, and percent canopy cover per unit area or stand,
were measured. However, one aspect that is distinctive in
remote sensing studies is the prominence given to the
canopy. For example, a number of authors (e.g., Lefsky,
Harding, et al., 1999; Magnussen & Boudewyn, 1998;
Næsset, 2002; Ni-Meister, Jupp, & Dubayah, 2001) have
investigated the possibility of extracting information about
the vertical structure of the canopy per stand or sample
plot, as well as aboveground biomass and basal area
estimation from lidar data. Noteworthy is that crown
shape affects the laser data (Nelson, 1997). Although this
interest in the canopy may partly reflect the nadir-view of
airborne sensors, the canopy plays an important ecological
role. The canopy is responsible for the majority of
material and energy exchanges with the atmosphere (Lef-
sky, Harding, et al., 1999). It is a critical habitat for forest
biota, and influences the microclimate of the forest
interior.
One distinctive aspect of our research is that we studied
a deciduous forest in winter. Laser scanners have been used
over coniferous forests, and sometimes over mixed con-
iferous and leaf-on deciduous trees. Lidar data have also
been previously acquired during the winter (e.g., Magnus-
sen & Boudewyn, 1998) over coniferous forests, but we
know of no report of leaf-off (and individual tree-based)
analysis of lidar data. One reason for this might be that, by
tradition, the summer is mostly used for remotely sensed
data acquisitions in the forest community. Nonetheless,
with modern navigational instruments, and an active sensor
using lidar, data can in principle be captured 24 h per day
all year around, unless there are unfavourable flying con-
ditions. A wintertime survey has some obvious advantages.
The absence of leaves in the canopy might facilitate the
penetration of the laser beam in a deciduous forest so that
the vertical structure of the branches is more clearly
discerned from above. This approach is utilised in Bacher
and Mayer (2000), where leaf-off trees in urban areas are
extracted from high spatial resolution aerial images. Fur-
thermore, winter is the low season with respect to avail-
ability of aircraft and personnel in the remote sensing
community, at least for lidar system operators who do
not relocate equipment to other regions when the seasons
change.
As the field of lidar analysis has matured, there has been
a gradual improvement in the accuracy of detection analy-
sis, and a trend from the application of methods from
structurally simplistic plantations to structurally complex
natural forests. These trends are somewhat contradictory
since few natural forests are sufficiently characterized to
provide a large sample of referenced data, especially of
canopy size and shape, to give good statistics on detection
and delineation accuracy. However, for our study, we have a
highly detailed reference GIS map of every overstory tree in
our plot, and consequently are able to evaluate both
detection and delineation of trees in a natural forest environ-
ment that is characteristic of a larger region.
2. Objectives
The primary objective of this paper is to evaluate the
utility of small footprint, high sampling density leaf-off laser
scanning data, acquired during the winter over a deciduous
forest in the eastern USA. The specific aims of the work are:
(1) to develop and test a robust, but relatively simple,
technique for detecting individual tree crowns in the lidar
data, (2) to develop and test an objective measure of the
segmentation result relative to ground reference tree crown
polygons, (3) to assess the accuracy of estimated individual
tree heights, (4) to analyse statistics of the vertical height
distribution and reflectance measures of individual trees for
three major species groups, and (5) to investigate the
potential to use lidar indices of individual tree character-
istics for species classification.
3. Test site, ground reference data and lidar data
acquisition
This research was carried out at the West Virginia
University Research Forest, approximately 15 km east of
Morgantown (Nellis et al., 2000). This work forms part of
a larger research project (Warner, McGraw, Dean, & Land-
enberger, 2000), for which we are gathering a comprehen-
T. Brandtberg et al. / Remote Sensing of Environment 85 (2003) 290–303 291
sive array of satellite, aerial and ground data over a six
hectare forest community. This data includes a GIS data-
base of the species of all 1526 canopy trees, referenced to a
high-resolution canopy map produced from photo-interpre-
tation and ground survey during the year 2000 (Land-
enberger, McGraw, & Warner, 2000). The canopy map has
a locational uncertainty of approximately 1 m due to
fuzziness in the definition of the tree edges and errors in
the differentially corrected global position systems (GPS)
data. The canopy crowns were delineated as non-over-
lapping polygons, reflecting the isolated nature of each
crown in this mature forest. Thus, our test-site is an ideal
location for research on identifying and characterising
individual trees.
The study site lies at an altitude of approximately 600 m,
and includes a small perennial stream. The majority of the
site is south facing, with a small portion facing north. The
site includes a deciduous mid-successional closed canopy
forest community (Table 1). Chestnut oaks (Quercus prinus)
and other xeric or dry site species dominate ridge tops and
south facing sites, and mixed mesophytic types grow in
coves and on north- and east-facing slopes, where the soils
have higher moisture and nutrient status. The mixed meso-
phytic forest is relatively diverse for these latitudes, with
dominant canopy species including native oaks (Quercus
spp.; 48%), red maple (Acer rubrum; 16%), and yellow
poplar (Liriodendron tuliperifera; 19%). In this paper, these
three major species groups are called oaks, maple and
poplar, respectively.
The laser scanning data were acquired with the Saab
TopEye system (Saab Survey Systems AB, 1997) on Feb-
ruary 1 (2001) by Aerotec LLC of Bessemer, AL, USA. The
instrument was flown on an AS350 BA helicopter, at an
average altitude of 100 m above the ground. The Saab
TopEye has a Trimble 4000 SSi GPS, with an associated
ground station for differential correction. In addition, a
Honeywell H-764 inertial navigation system (INS) is incor-
porated in the Saab TopEye, giving an estimated absolute
accuracy of location at 100 m altitude of less than 10 cm
(1r) (Saab Survey Systems AB, 1997). The Saab TopEye
system has a laser range finder (LRF) as its primary sensor,
and records laser pulses at a frequency of 7 kHz. Up to four
echoes are recorded for each laser pulse. However, only two
echoes (referred to as pulses #1 and #2, respectively) were
captured in our data set, presumably because branches are
opaque and relatively dark at the 1064 nm wavelength (Saab
Survey Systems AB, 1997) of the laser beam, and therefore
are less effective at returning multiple pulses compared to
leaves. The data include an (x,y,z)-position of each echo, and
a reflectance value. The ground cross-sectional diameter
(footprint) of the laser beam was approximately 0.1 m. The
data points are spatially distributed in an irregular Z-shaped
pattern as the instrument sweeps across a total field of view
of 40j. The average number of returns per square meter
within the test-site was approximately 12 for echo #1, and 3
for echo #2. During the lidar data acquisition on February 1,
there was no visible snow or ice in the canopies according to
a video sequence recorded during the flight in combination
with ground observations. However, there was snow on the
ground, with a depth of approximately 15 cm. Given the
uncertainty in the lidar positions, and unevenness of the
ground, a 15-cm offset in elevations is not regarded as
significant.
A random sample, totalling 48 trees, was selected from
the three major species groups (28 oaks, 6 maples and 14
poplars, respectively). The trees were distributed across the
study site, with eight trees from each 1-ha subarea in the 6-
ha plot. The trees were randomly chosen based on the local
proportional probability of the crown area of the species for
that hectare in our canopy ground reference map. The height
of each of the 48 trees was measured in the field in March
2002 using a clinometer and a laser rangefinder. No meas-
urable change in tree height for this mature forest is likely
during the interval of the single growing season between the
acquisition of the lidar data and the field height measure-
ments.
4. Lidar analysis methods
The majority of the computer analysis of the lidar data
was carried out with custom programs written in IDL
(Research Systems, 1999). However, the conversion of
the lidar coordinate system from geographic to UTM
values was performed by a program written in C, and the
statistical analyses were performed in Minitab (Minitab,
1998). The detection of the tree crowns, and the analysis of
the lidar data within individual tree crowns, was carried out
independently. The detection of the tree crowns was based
on Gaussian smoothing of rasterized data at multiple
scales, in order to identify single blobs that represent the
individual trees. The analysis of the laser returns from each
tree was based on the original lidar point data and on our
ground reference polygon layer. Using the vertically dis-
tributed original laser point data, rather than rasterized
image data, ensures that all collected information can be
employed in the analysis. The use of the reference poly-
gons instead of the automatic delineated tree crowns will
Table 1
Forest statistics for the study site canopy trees
Mean canopy
trees per
hectare
Mean stand
basal area
(m2)
Mean canopy
tree height
(m)
DBH
(cm)
Mean canopy
tree volume
(m3)a
260 27.4 27.2 44.3 8.9
a Biological volume, calculated by estimating heights and diameters of
all canopy trees on two sample plots within the 6-ha site. Biological volume
is defined as the volume of stem with branches trimmed at the junction with
the stem, but excluding irregularities that are not part of the natural growth
habit (e.g. malformation due to insects, fungi, fire, and mechanical
damage).
T. Brandtberg et al. / Remote Sensing of Environment 85 (2003) 290–303292
result in more reliable results, unaffected by segmentation
errors.
4.1. Transformation and visualisation of the raw lidar data
The laser scanning data were recorded in geographic
(latitude, longitude) coordinates. Therefore, the first step
was to re-project the data to a UTM coordinate system. The
re-projection was based on second-order regression analysis
of nine equally distributed control points. The regression
function pair explained 100.0% of the variances of the (x,y)
control point coordinates.
In this paper, we illustrate the processing of the lidar data
with a 100� 100-m test area (subarea 2) within our 6-ha
study site, which was scanned on nine overlapping flight
lines. Fig. 1 is a 10� 100-m cross section clearly showing
the ground and the canopy of the deciduous trees. Note the
sparse distribution of returns in the area between the ground
and the canopy, reflecting the relative openness of the
understory of this mature forest.
4.2. Interpolation of laser points and Gaussian smoothing
A two-dimensional grid, with a 25-cm pixel size, was
specified over the 6-ha study area. The maximum laser
height within each grid cell was written out to a new image.
To provide the potential for scaling over large data sets,
irrespective of computer memory limitations, the analysis
was carried out in subareas. The subareas comprise over-
lapping 110� 110 m (440� 440 pixels) regions, each of
which has a 5-m buffer zone on all sides in order to
minimize image edge effects in the spatial analysis. The
maximum height of the raw laser data for the example given
is shown in Fig. 2, together with a digital aerial photograph
of exactly the same area (subarea 2). Some of the lidar
image pixels are zero, because the complex scanning pattern
Fig. 1. A 100-m-long north–south section, 10 m wide, of the raw lidar data of the leaf-off deciduous forest (50% of all echoes within the section are shown).
Fig. 2. Left: Raw laser data points, with bright tones representing higher elevations. Right: Digital aerial photograph of the same area (subarea 2).
T. Brandtberg et al. / Remote Sensing of Environment 85 (2003) 290–303 293
of the laser data does not result in data for all cells.
Individual deciduous trees can easily be identified as blobs
with relatively high values (bright pixels).
The initial lidar image (Fig. 2, left) was interpolated prior
to further image processing. Zero values were replaced with
the mean of non-zero values in a 3� 3 local window. This
operation was repeated until all pixel values were non-zero,
in order to gradually fill in holes greater than 3� 3 pixels.
The pixels closest to the image edge were defined by one-
sided estimates.
An inherent property of the structures in images is that
they only exist as meaningful entities over certain ranges of
spatial scales. Scale-space theory is a framework for visual
operations developed by the computer vision community to
handle this multi-scale nature of image data (a tutorial
overview of scale-space theory is given in Lindeberg,
1996). A multi-scale representation of an image can be
derived by convolution of the image with Gaussian kernels
of different variances (scale parameter t = r2). The 2D
Gaussian kernel at scale level t is given by Lindeberg
(1993):
gðx; y; tÞ ¼ 1
2ptexpð�ðx2 þ y2Þ=2tÞ ð1Þ
where x and y are the cell coordinates of the kernel centred
at the origin (0,0).
The laser beam often penetrated between the branches to
the ground below, giving a very rough surface to the canopy.
Therefore, in order to provide a more consistent estimate of
the canopy surface, laser point values that are statistical
outliers from a lightly Gaussian smoothed 2D height sur-
face, derived at a fine scale (t= 4.0), are deleted. For this
study, laser points with original values more than 10.0 m
below the corresponding height of the smoothed image were
treated as non-existing when the interpolation, as described
above, was repeated. However, it is not absolutely necessary
to remove these outliers because finer details (e.g., isolated
penetrations) are gradually removed at coarser levels of
scales and will not affect the detection of the trees very
much. Fig. 3 shows the interpolated image before and after
the adjustment.
4.3. Automatic scale selection and blob detection
An important issue in scale-space theory is how to select
appropriate scale levels for further analysis. In this work, we
used a scale-selection tool that is based on local extrema
over scales of different combinations of normalised scale
invariant derivatives (Bn =MtBx) (Lindeberg, 1993). At
these scale levels, distinctive structures can be detected
and analysed further. It can be shown that an ideal Gaussian
blob with characteristic radius Mt0 assumes a maximum of
its scale-space signature at a scale (i.e., at scale t0) propor-
tional to the radius of the blob (Lindeberg, 1993). The
scale-space signature of a blob is given by the normalised
Laplacian Atj2LA= tALxx + LyyA, where Lxx and Lyy are the
second-order image derivatives along the x- and y-axes,
respectively, computed at the spatial maximum (local max-
imum) of the blob.
Appropriate scale intervals must be selected using this
scale-space technique. In an operational system, this scale
interval can be selected in relation to the mean blob
signature, which typically has a maximum at a certain scale
level. For all six different 1-ha subareas in this study, the
maximum occurred at a scale corresponding to rc 4.5
pixels (i.e., log(t)c 3.0). This particular scale-level was
dominated by small tree crowns and sub-crowns in our data.
However, in this paper we draw on the canopy map to
estimate the scale parameters empirically, based on the
histogram of the tree crown radius of the 6-ha study area
(Fig. 4). The first two test intervals were selected as the 5th
and 95th percentiles, and the 25th and 75th percentiles of
the histogram, respectively. The third test interval was
obtained by selecting an arbitrary range on either side of
the modal tree size, represented by the peak in Fig. 4. The
Fig. 3. Left: Interpolated image of raw laser points. Right: Adjusted and interpolated image.
T. Brandtberg et al. / Remote Sensing of Environment 85 (2003) 290–303294
start and stop positions along the log(t)-axis for these three
test intervals were [2.8, 5.8], [3.6, 5.1] and [2.8, 4.55],
respectively, and the step-length was 0.25. Subsequently, the
scale intervals are denoted as Intervals #1, #2 and #3,
respectively.
A problem in scale-space is that some blobs, which are
distinctive at a fine scale, merge with other blobs at coarser
scales. This problem was overcome to some extent by
sorting all blob strength measures from all selected discrete
scales (within a scale-interval) in descending order, and
writing them in the output image in this order, as long as the
overlap with other marked segments was insignificant
( < 20% of the current blob support area). Thus, the output
image represents objects identified at a range of discrete
scales.
The procedure so far represents the detection of the tree
crowns. Usually, the segments at this stage are slightly
smaller than the ground reference polygons, due to erosion
by the Gaussian kernel prior to the detection procedure.
Therefore, a region-growing operation was performed on
the selected and labelled segments within a binary support
area. The latter region-of-interest (ROI) was defined as
those pixels in a lightly Gaussian smoothed image
(t= 2.25) where the estimated canopy height was greater
than 5 m above the ground. The region-growing operation
was performed using the 3–4 distance transform (3–4 DT)
(Borgefors, 1986) in combination with an image which kept
track of the closest segment label number in each back-
ground pixel. The 3–4 DT is an approximation of the
Euclidean distance between pixels in order to avoid floating
point operations: horizontal and vertical steps are defined as
three distance units, while a diagonal step is defined as four
distance units.
4.3.1. A fuzzy method for objective assessment of segmen-
tation results
Manually comparing a segmentation result in an image
with the corresponding ground reference polygons is a very
difficult task because of the ambiguity of the overlapping
segments. A human interpreter would use terms such as
‘‘partly correctly segmented’’, in recognition of the fuzzy
and subjective nature of the problem. We have therefore
developed a relatively simple fuzzy image processing tech-
nique to quantify the accuracy of the segmentation results.
The ground reference polygon layer defines as accurately
as possible the true extent of each tree crown. However, the
confidence associated with each polygon is much higher in
the centre compared to the edges, where there is consid-
erable uncertainty. Therefore, weighting was given to each
pixel, based on its relative location with respect to the
nearest edge of the individual crown within which it falls.
This weighting was specified with the 3–4 DT (Borgefors,
1986). The 3–4 DT values within each tree crown were
summed, and the value of each pixel was normalised using
this sum so that the new total sum per individual was always
one. The new values (all pixels < 1) specified the relative
spatial location of pixels within each segment, with higher
weights for internal pixels.
The same relative spatial weighting procedure was
applied to the polygons identified in the segmentation
procedure. Then, the two derived fuzzy images (Dref and
Dtest) were compared to each other. When two segments
match each other very well they have similar distributions,
regarding extent, and value for each pixel. The opposite is
true if they do not match each other very well. Two simple
measures that summarize the differences between the two
images, Dref and Dtest, are the minimum and maximum
operations, which return the lowest and highest value,
respectively, in each pixel position when the two images
Dref and Dtest are overlain. An overall measure of the
performance in the whole image pair is the ratio of the total
sums of the minimum and maximum images. For a perfect
match, the minimum and maximum images are identical,
which results in a segmentation assessment value A= 1
(alternatively, it corresponds to a 100% correct match). All
other cases result in A < 1.
Three complementary variables were also defined that
quantified the omission (OmSum), commission (ComSum),
and the ratio of the total number of segments in the test
image to the ground reference image (Overseg). The latter
ratio is the mean over- or under-segmentation per hectare,
which is >1 for over-segmentation and < 1 for under-
segmentation. The omission measure was found by sum-
ming all non-zero pixels within the image Dref, if the
corresponding pixels were zero in the image Dtest. The
commission measure is the opposite procedure. The seg-
mentation result assessment method is exemplified by two
simple but common examples during tree crown delinea-
tion (Fig. 5). The two-part reference object (first column)
and its corresponding single-part test-segment (third col-
umn) in Fig. 5 have a segmentation assessment value
A= 0.49 (i.e., 49% of the ideal match). The three-part
example reference object and its test-object (second row)
has A= 0.29 (i.e., 29%). It is important to note that seg-
Fig. 4. A histogram of the ground reference tree crown radius (0.25-m
pixels) of all individuals on the 6-ha study site.
T. Brandtberg et al. / Remote Sensing of Environment 85 (2003) 290–303 295
ments that are overly dissected reduce the overall A value
in a manner identical to the overly aggregated example
given, because the operations are commutative. Thus, the
measure is particularly effective in producing a single value
for the overall degree of similarity between the segmenta-
tion of the two images.
4.4. Ground height estimation procedure
Fig. 1 suggests that the data can be viewed as comprising
two different sets of points: returns from the ground and
returns from the canopy (Brandtberg, 2000). It is note-
worthy that there is no exact delimiter between the sets,
so the notion of fuzzy sets is also appropriate here. The
algorithm for estimating the ground surface starts with the
problem of finding a good delimiter between the two subsets
of points within each one-hectare subarea. Initially, a single
two-dimensional third-order regression surface was fitted to
the data set. The whole set of laser points for the subarea
was split in two approximately ‘‘parallel’’ sets, using the
initial regression surface as the delimiter. Two new inde-
pendent third-order regression surfaces were fitted one-at-a-
time to the corresponding subsets, and a new delimiter
between the surfaces was calculated. During this operation,
some points can thus move from one subset to the other.
There is also a small adjustment of the delimiter for the
different height variances (Z-axis) of the two fuzzy subsets.
In particular, the canopy points are more spread out along
the Z-axis than the ground points (Fig. 1). The ground
subset also initially included points that were relatively high
above the actual ground surface, and potentially a small
number of anomalously low values. Therefore, the final
ground subset selected excludes the outliers (defined from
the percentiles of the histogram of the values as less than
0.5% and greater than 99%, respectively). The ground
image was created by a simple interpolation of the selected
ground points. The interpolation made use of eight principal
directions to reduce the influence of the irregular density of
the points. The results from the 6 ha were combined and
slightly Gaussian smoothed (r = 2.0). A perspective view of
the test-site is shown in Fig. 6. The highest elevations have a
mining-influenced topography, and the whole surface has
some minor artefacts that can be eroded by further Gaussian
smoothing.
4.5. Statistical analyses of individual trees-based character-
istics
4.5.1. Segmentation performance
The segmentation technique was evaluated using the
method described in Section 4.3.1. However, the perform-
ance was further quantified using the mean diameter
(MDiam) and tree crown area weighted mean diameter
(WDiam) per each 1-ha subarea. The latter measure gives
higher weights to the larger tree crowns. The polygon area
in pixel units was treated as a circle when the diameter
(pixels) was calculated for these two measures.
4.5.2. Test-site characteristics
Variables that to some extent were related to the specific
test-site include the laser-based tree height distribution
within each species group (ZMax), the number of tree
crowns (Ni) of each species group i, and the overall tree
crown size distribution (radius in 0.25 m pixels) on the test-
site (Fig. 4). The crown size distribution influenced our
choices of scale-intervals in scale-space (Section 4.3). In
this paper, one-way analysis of variance (ANOVA) was
used to detect species differences for the variables. Further-
more, the Tukey’s method (also called Tukey–Kramer
method) was used to detect significant (experimentwise
error rate 0.05) pairwise differences between level means
of the species groups.
4.5.3. Leaf-off individual tree-based characteristics
Height distribution indices, such as mean and maximum
canopy surface height, have been estimated for stands of
Fig. 5. Two examples of ground reference objects (first column), their normalised 3–4 distance transform (second column), two corresponding examples of test
objects (third column) and their normalised 3–4 distance transform (fourth column).
T. Brandtberg et al. / Remote Sensing of Environment 85 (2003) 290–303296
trees from large footprint (e.g., Lefsky, Cohen, et al., 1999;
Lefsky, Harding, et al., 1999) and small footprint (e.g.,
Magnussen & Boudewyn, 1998; Næsset, 2002) lidar data.
Blair and Hofton (1999) identified a 1-m cross-sectional
diameter as the threshold between small and large lidar
footprint sizes. In this work, we defined indices based on the
height and reflectance distributions of the lidar returns,
which, in contrast to previous studies, are calculated for
individual tree crowns based on a prior segmentation from
the crown canopy map. In addition, we also use the canopy
map to compare the differences in lidar returns from
individuals of different species.
Each tree crown reflected numerous laser beam pulses,
and each pulse up to two echoes, that could be utilised in
the analysis of the vertical distribution of branches. A laser
point reflected within each polygon from the crown canopy
map was counted as a canopy point (Nc) for the specific
individual tree if it was at least 2 m above the interpolated
ground surface (Section 4.4). This threshold (2 m) was
based on the value selected by Næsset and Økland (2002)
for a conifer forest. The remaining points within the tree
crown polygon that were below 2 m were defined as
ground points (Ng) for that specific individual polygon.
Subsequently, the ratio of Nc and Ng is analysed, and a
high value is assumed to indicate a relatively opaque
canopy.
The canopy points (Nc) for each individual tree formed a
height (Z) histogram, the main features of which were
characterised by a number of common statistical measures.
The values for each statistical measure were calculated
separately for each echo (echoes #1 and #2). Common
statistical measures have the advantage of being well
known, and with properties that have been well character-
ized. The statistical measures also provide a good summary
of distribution of the laser returns, which is likely to be
influenced by the vertical distribution and configuration of
leaf-off branches of each individual tree. Thus, these stat-
istical measures may allow species differentiation.
The statistical measures used include the four first
moments of the height values: mean (ZMean), variance
converted to standard deviation (ZStdDev), skewness
(ZSkew) and kurtosis (ZKurt). Skewness is a measure of
distribution asymmetry, and kurtosis is a measure of the
peakedness of a distribution compared to that of a normal
distribution (Minitab, 1998). The modal (ZModal) and
median (ZMedian) values were also calculated, even though
they are closely related to the mean value (ZMean). Fur-
thermore, the lidar height estimate for the 48 sample trees
was compared to the field height data. The reflectance
values for each tree were summarized with similar param-
eters, as well as the maximum reflectance value (RMax) per
ground reference polygon. The laser reflectance indices
depend mainly on branch thickness and the reflectance
properties of the bark.
4.5.4. Tree species classification
In Section 4.5.3, we argued that the vertical distribution,
configuration and features of the leaf-off branches within
each tree crown could be species dependent. Therefore, a
simple tree species classification test was performed on the
individual tree-based indices using linear discriminant
analysis (LDA). Two hundred individuals of each species
group (oaks, maple and poplar, respectively) were ran-
domly selected and classified using LDA with cross-vali-
dation. A simple test of a combination of several
complementary variables was also performed, even though
the introduction of several variables makes the species
classification more complex (e.g., Brandtberg, 2002; Key
et al., 2001).
5. Results
In this section all results including the results from the
ANOVA’s are presented. Significant p-values ( p < 0.05) and
p-values close to be significant (trends; 0.05 < p < 0.10) are
Fig. 6. A 3D view of the lidar data estimated ground surface on the study site, with the river visible in the valley. Subarea 2 is located along the river and in the
middle of the view.
T. Brandtberg et al. / Remote Sensing of Environment 85 (2003) 290–303 297
shown. All non-significant p-values are omitted (marked
with a ‘–’ in the tables). Means followed by the same letter
in the tables are not significantly different, as determined by
Tukey’s test.
Of the approximately 697,000 first echoes recorded
within the 6-ha study site, 78% were classified as part of
the canopy group (i.e., z 2 m above the estimated ground
surface). This compares to 22% of the total 153,000 #2
echoes, which were identified as canopy points. A total of
32% of the combined #1 and #2 echoes were classified as
part of the ground points—i.e., within 2 m of the ground
surface. Furthermore, the mean laser reflectance percen-
tages (R-values) for echoes #1 and #2 were 12.9 and 8.0,
respectively, for the ground points (affected by snow). The
mean R-values for the canopy points (no snow or ice in the
canopy) for echoes #1 and #2 were 2.6 and 3.4, respec-
tively.
5.1. Analysis of tree crown detection and segmentation
results
Individual trees in the lidar data were detected and the
segmentation results were analysed for the three different
scale intervals. Fig. 7 shows the ground reference data and
the corresponding segmentation result for the 1-ha example
area (subarea 2). Table 2 shows a summary of the objective
assessments, including the complementary variables of the
different segmentation performances. The corresponding
ground reference values of mean polygon diameter
(MDiam) for subarea one to six were 24, 24, 21, 26, 25
and 24 pixels (0.25-m pixels), respectively. The ground
reference values for the weighted mean diameter (WDiam)
were 33, 34, 29, 33, 33 and 32 pixels, respectively, for the
six subareas.
The overall best scale interval was Interval #2, with the
highest relative assessment values (A, ranging from 0.23 to
0.35) and without severe over- or under-segmentation.
Much of the omission and commission error was caused
by non-overlapping reference and test polygons.
Fig. 7. Left: Ground reference polygons of the example area (subarea 2). Right: Segmentation result (scale interval 2).
Table 2
Summary of the objective segmentation result assessments (A) for the six
different 1-ha subareas and for each tested scale interval
Subarea # Interv. # MDiam WDiam OmSum ComSum Overseg A
1 1 17.3 24.0 46 82 1.5 0.23
1 2 21.5 27.9 43 48 0.9 0.26
1 3 17.0 22.3 42 84 1.6 0.23
2 1 16.6 22.5 40 106 1.7 0.21
2 2 20.6 26.4 40 61 1.0 0.24
2 3 16.4 21.8 40 106 1.7 0.21
3 1 17.8 25.7 36 102 1.3 0.23
3 2 22.1 28.1 36 64 0.8 0.23
3 3 17.1 21.9 36 105 1.3 0.23
4 1 17.5 23.5 9 52 1.6 0.31
4 2 22.3 27.5 9 27 0.9 0.35
4 3 17.0 21.5 9 53 1.6 0.30
5 1 18.0 22.8 14 62 1.5 0.30
5 2 22.5 26.7 13 35 0.9 0.32
5 3 17.9 22.1 13 61 1.5 0.30
6 1 17.7 23.9 25 85 1.6 0.25
6 2 22.0 27.8 25 54 0.9 0.26
6 3 17.1 22.2 25 86 1.6 0.24
MDiam and WDiam are in pixel units. OmSum and ComSum correspond to
the number of individuals. Overseg >1 corresponds to over-segmentation.
Table 3
Means and standard deviations for ZMax (maximum laser height) and
RMax (maximum laser reflectance percentage) for the two echoes
Species Echo # ZMax RMax
Mean SD Mean SD
Oaks 1 26.9b 2.0 9.4b 2.2
Maple 1 26.6b 2.0 8.1a 2.3
Poplar 1 29.0a 2.1 9.0b 2.4
Oaks 2 21.9b 3.6 6.6a 2.1
Maple 2 20.7c 4.3 5.9b 2.2
Poplar 2 24.0a 4.1 6.3ab 2.2
Significant means are marked with different letters. N = 200 in each species
group.
T. Brandtberg et al. / Remote Sensing of Environment 85 (2003) 290–303298
5.2. Analysis results of test site related features
An important question is whether the species signifi-
cantly differed in tree crown area (Area) and height. This
could indirectly affect the segmentation results within each
species group. Oaks (48%) dominate the test-site. The total
numbers of individuals (Ni) completely within the 6-ha area
were 569 oaks, 225 maples and 255 poplars. The variable
Area showed significant differences between the species
(F = 27.58, p < 0.001). The means and standard deviations
(s) of the variable Area (0.25-m pixels) for oaks, maple, and
poplar were 646 (s = 467), 369 (s = 272) and 589 (s = 415)
pixels, respectively. The corresponding analysis of the laser-
based maximum height (ZMax), also showed significant
differences for both echoes (F = 81.86, p < 0.001 and
F = 34.59, p < 0.001). The mean values and standard devia-
tions of ZMax within each species group and for the two
echoes are shown in Table 3.
The correlation between the tree crown area (Area) and
the maximum tree height (ZMax for echo #1) based on the
laser measurements was moderate (r= 0.41, p < 0.001). This
relationship could potentially be used in the segmentation
process to suppress lower trees (smaller areas) and increase
the priority (i.e., blob signature) for higher trees (larger
areas). The principle was tested but it showed mixed results,
and therefore was not pursued further.
5.3. Analysis results of leaf-off individual tree-based
features
The laser-based tree heights (ZMax for echo #1) for the
48 sample trees were extracted and compared to the ground
measured tree heights. The depth of the snow on the ground
was ignored. Fig. 8, which shows a plot of the relationship
and the regression line ( y = 0.612x + 10.6), shows there is a
tendency for the height of tall trees to be underestimated and
those of shorter trees to be overestimated. This bias is
reduced however, if the single outlier, an unusually low
tree, is excluded. The mean standard error was 1.1 m, and
the coefficient of determination was 69% (68.4% adj.).
Fig. 8. A plot of the laser-based tree height values against the reference tree height values measured on the ground of the 48 sample trees.
Table 4
F- and p-values of one-way ANOVA’s for eight different variables based on
moments (Z and R) for the two echoes
Variable Echo #1 Echo #2
F p F p
ZMean 4.26 0.015 2.13 –
ZStdDev 23.57 < 0.001 17.67 < 0.001
ZSkew 2.54 < 0.001 1.05 –
ZKurt 7.34 0.001 0.50 –
RMean 11.47 < 0.001 5.61 0.004
RStdDev 4.38 0.013 4.39 0.013
RSkew 20.79 < 0.001 4.92 0.008
RKurt 13.71 < 0.001 6.20 0.002
N= 200 in each species group.
Table 5
Means and standard deviations for Z-values (percentage of each individual
tree height) of the two echoes
Species Echo # ZMean ZStdDev ZSkew ZKurt
Mean SD Mean SD Mean SD Mean SD
Oaks 1 76.4b 4.8 19.0b 3.6 � 1.32a 0.48 1.49b 2.12
Maple 1 77.8a 5.3 17.0a 4.1 � 1.44a 0.59 2.50a 3.08
Poplar 1 76.5b 5.2 19.5b 4.1 � 1.39a 0.54 1.78b 2.07
Oaks 2 53.4a 13.6 17.6b 7.0 � 0.24a 0.50 � 0.98a 0.95
Maple 2 50.7a 17.4 15.6c 7.9 � 0.19a 0.51 � 1.06a 0.98
Poplar 2 53.2a 13.6 19.8a 6.6 � 0.27a 0.56 � 0.97a 1.00
Significant means are marked with different letters. N = 200 in each species
group.
T. Brandtberg et al. / Remote Sensing of Environment 85 (2003) 290–303 299
There was not even a trend ( p>0.10) in the difference in the
measurement errors for the three species groups according
to the ANOVA.
The three central tendency variables of Mean, Median
and Mode for both normalised height (Z) and laser reflec-
tance (R) values for each of the two echoes were found to be
significantly correlated ( p < 0.001). For the first and second
echoes, ZMean and ZMedian were highly correlated (echo
#1: r= 0.84 and echo #2: r = 0.96). The Modal value showed
moderate correlation with the Mean and Median for the first
echo and of the height data Z (r = 0.47 and r = 0.51) and the
reflectance data R (r = 0.37 and r = 0.46). The Modal values
(Z and R) of the second echo were highly correlated with the
corresponding Mean and Median values (Z: r = 0.68 and
r = 0.67, R: r = 0.61 and r= 0.62). Therefore, subsequently
only the Mean per data type (Z and R) for echo numbers #1
and #2 are analysed.
Significant differences between species were detected
using ANOVA. The first set of individual tree-based vari-
ables was derived from moments of the height (Z) histogram
(echoes #1 and #2 separately) formed by all laser points
within the individual canopy. In order to suppress variation
due to differences of individual tree heights, the laser
heights were first normalised to the maximum height per
individual tree, resulting in measurement units of ‘percent-
age’. The variables calculated from echo #1 were all
significantly different between species (Table 4). Likewise,
the corresponding variables for the reflectance percentage
(R) values of the first echo were also significantly different
between species (Table 4). For Z-variables of echo #2, a
significant difference between species was found only for
ZStdDev (Table 4), but for the reflectance values (R) of echo
#2, all moment-based variables were significant (Table 4).
The means and standard deviations for the three species
groups of the normalised laser height data variables (Z) are
shown in Table 5 for each of the two echoes. The means and
standard deviations of the laser reflectance percentage
variables (R) are shown in Table 6.
The maximum height and maximum laser reflectance
percentage are associated with a single laser return per tree
crown. ZMax is described in Section 5.2 and RMax also
showed significant differences ( p < 0.001) between the
species for both echoes. Table 3 shows means and standard
deviations for both ZMax and RMax for the two echoes.
Another ANOVA was performed on the ratio (variable-
name NRatio subscripted by the echo #) of number of points
in the individual canopy (z 2 m above ground) and under-
neath ( < 2 m) the individual canopy (Nc and Ng, respec-
tively). The species differences of NRatio1 and NRatio2 were
both significant (F = 27.90, p < 0.001 andF = 3.46, p = 0.032,
respectively), and their means and standard deviations are
shown in Table 7 for each species group. Furthermore, the
ratio of the number of echoes #1 and #2 in the canopy for each
individual tree showed no significant species difference
(F = 1.94, p>0.10). On the other hand, the same ratio for
the ground points was significant (F = 9.17, p < 0.001).
Table 8 shows the means and standard deviations for the
ratios within the canopy and on the ground, respectively.
Table 6
Means and standard deviations for R-values (laser reflectance percentage)
of the two echoes
Species Echo # RMean RStdDev RSkew RKurt
Mean SD Mean SD Mean SD Mean SD
Oaks 1 2.63b 0.24 1.28a 0.25 1.83b 0.45 4.77b 2.98
Maple 1 2.60b 0.29 1.19a 0.29 1.57a 0.48 3.48a 2.84
Poplar 1 2.51a 0.26 1.23a 0.29 1.85b 0.51 4.96b 3.39
Oaks 2 3.39a 0.90 1.38a 0.57 0.56ab 0.47 � 0.52ab 1.26
Maple 2 3.11b 1.06 1.21b 0.59 0.47b 0.49 � 0.71b 1.22
Poplar 2 3.14b 0.78 1.31ab 0.56 0.63a 0.62 � 0.13a 2.31
Significant means are marked with different letters. N = 200 in each species
group.
Table 7
Means and standard deviations for NRatio (ratio of the number of returns
from within and underneath the individual canopy) of the two echoes
Species Echo # Mean SD
Oaks 1 0.94b 0.42
Maple 1 1.21a 0.57
Poplar 1 0.89b 0.35
Oaks 2 0.06b 0.04
Maple 2 0.07ab 0.06
Poplar 2 0.08a 0.08
Significant means are marked with different letters. N = 200 in each species
group.
Table 8
Means and standard deviations for the ratio of the number of echoes #1 and
#2 returns within two levels: canopy (z 2 m) and on the ground ( < 2 m),
respectively
Species Level (m) Mean SD
Oaks z 2 70a 226
Maple z 2 41a 84
Poplar z 2 64a 113
Oaks < 2 2.5b 3.7
Maple < 2 1.5a 2.3
Poplar < 2 3.1b 4.7
Significant means are marked with different letters. N = 200 in each species
group.
Table 9
Correlation r (Pearson) with p-value within each species group of the four
major individual tree-based variables of echo #1
Species ZMean RMean ZMax
r p r p r p
Oaks RMean � 0.35 < 0.001
Maple � 0.30 < 0.001
Poplar � 0.48 < 0.001
Oaks ZMax 0.11 – � 0.11 –
Maple 0.032 – � 0.09 –
Poplar � 0.21 0.003 � 0.003 –
Oaks RMax � 0.23 0.001 0.41 < 0.001 0.28 < 0.001
Maple � 0.26 < 0.001 0.65 < 0.001 0.13 0.06
Poplar � 0.47 < 0.001 0.52 < 0.001 0.30 < 0.001
N = 200 in each species group.
T. Brandtberg et al. / Remote Sensing of Environment 85 (2003) 290–303300
Finally, it is interesting to study the correlations within
each species group of the four major individual tree-based
variables in this data set (Table 9). Generally, ZMean and
RMean were negatively correlated, as well as ZMean and
RMax. RMean and RMax were positively correlated.
5.4. Results of tree species classification
The results of using LDA on the individual tree-based
variables suggested that most variables could be used for
this purpose. Table 10 shows the six best single variables for
species group classification (PropCorrect) (echo #1). The
remaining variables (echo #1) were between 38% and 41%
(PropCorrect). The best single measure was ZMax, though it
is related to the height distribution between species groups
on the test-site and not directly linked to a feature of a
particular species. Note that a random variable would result
in 33% of trees classified correctly. Generally, the variables
of echo #2 showed lower accuracy values. When several
single variables were combined using LDA with cross-
validation it was possible to have an overall classification
accuracy of 60% (e.g., ZMean, ZStdDev, ZSkew, ZKurt,
RMean, RStdDev, RSkew, RKurt, ZMax and RMax for
echo #1). In this case, the numbers of correctly classified
individuals were 100 oaks, 119 maples, and 139 poplars.
6. Discussion
We regard the segmentation results in this paper as
encouraging, because recent results from our segmentation
of leaf-on digital optical data have produced lower accu-
racies, a consequence of the great complexity of this type of
deciduous forest canopy. The relative detection success
probably depends on the fact that there are many small,
thin branches in the canopy, giving rise to the canopy
returns. We show here that the reflectance, size and distri-
bution of small branches within the canopy are probably
species-dependent. Very small trees with few laser returns,
especially next to bigger trees, are difficult to detect as
single individuals. In particular, it is difficult to detect very
small and very big trees at the same time, even with a multi-
scale approach. A trade-off between size classes must be
achieved. However, improvements might be possible if
more information (e.g., laser reflectance percentage) is
introduced in the decision process. The correlation between
the maximum height and the tree crown area is an example
of additional piece of information that could assign higher
priorities to bigger trees (Section 5.2). From an economic
point of view, it is more important to delineate the latter
correctly. However, some of the segmentation error is
caused by the slight discrepancies in the co-registration of
the ground reference polygons and the lidar data, mostly due
to the estimated 1-m uncertainty in the crown canopy map.
This will also affect some indices more than others, depend-
ing on whether the polygon region itself is related to the
variable (e.g., number of returns within the polygon).
A number of the large ground reference polygons are
coppice individuals, and have multiple stems. From a bio-
logical point of view, these are single individuals. Thus, the
ground reference data contain some ambiguity where the
stems are links between a single ground reference object and
multiple distinct canopy objects, i.e., clearly visible bright
blobs (sub-crowns) in the interpolated lidar image (Fig. 3).
This makes it hard to detect some individuals as single trees
using the scale-space approach evaluated in this paper.
In Table 2, the commission errors are quite high, which is
partly caused by the mismatch of the ground reference
polygons with the derived support area (>5 m above
ground) for which the segments were calculated. The
ground reference polygons were manually delineated in
aerial photographs, with small gaps between each polygon.
These polygons would not necessary be identical to the
polygons that would be delineated manually from the
interpolated lidar data (Fig. 3).
The results presented in this paper indicate the potential
for leaf-off laser scanning data for tree crown detection, tree
height measurements and species classification of individual
tree crowns. The individual tree-based indices derived from
the height (Z) and reflectance (R) data, where the histogram-
based Z-indices were normalised to the maximum height of
each individual, often showed significant tree species differ-
ences, even for the second echo, which comprises only a
small minority of all the laser points. The differences in the
vertical structure and distribution of the branches of different
species may cause the different statistical parameters
observed. Although differences in age will have some effect
on the observed tree architecture, the Z-normalisation min-
imizes purely height-related differences between the species
groups.
Interestingly, the reflectance differences of the species
are partly due to the bark of the branches. On the ground, it
is possible to discriminate light and dark shades of grey of
the bark on the branches, which is probably related to its
capability to reflect the laser beam. The laser reflectance
value (i.e., laser return strength) itself is reported to be
useful to some extent for tree species classification of
coniferous (Scots pine and Norway spruce) forests (Brandt-
berg, 2000). Whether the leaf-on reflectance value can be
Table 10
A summary of the results of LDA with cross-validation for the six best
single individual tree-based variables (echo #1)
Variable N Oaks N Maple N Poplar PropCorrect
ZMax 55 96 150 0.50
RKurt 26 145 85 0.43
ZStdDev 29 124 99 0.42
ZKurt 136 95 20 0.42
RSkew 25 129 94 0.41
RMax 102 121 21 0.41
The columns titled ‘N species’ show the number of correctly classified trees
per species group and PropCorrect is the corresponding proportion correct.
N= 200 in each species group.
T. Brandtberg et al. / Remote Sensing of Environment 85 (2003) 290–303 301
used directly for tree species classification of deciduous
forests will be investigated in later research.
The laser-based tree height measurements of the leaf-off
deciduous forest are encouraging as well. We anticipated
that the laser would under-estimate the tree heights because
it would not be able to hit the maximum height of the
crown. However, our lidar-based height estimates are on
average very similar to the field data, although there is a
tendency for the heights of low trees to be overestimated,
and those of tall trees to be underestimated (Fig. 8). In
interpreting these height results, it should be borne in mind
that the field height measurements have considerable uncer-
tainty. For example, it has been reported (Hyyppa & Ink-
inen, 1999) that the tree heights measured manually on the
ground can be affected by random errors introduced by the
field personnel. Thus, it might be the case that the ground
reference heights caused a large proportion of the variance
of the differences between ground reference and laser-based
tree heights. Furthermore, irregularities of the physical
ground surface in combination with numerical inaccuracies
of the selected ground points subset might affect the tree
height estimate. On our test-site the small stream and its
neighbourhood were sometimes problematic with sharp
height edges caused by erosion (Fig. 6). Trees next to the
stream are likely to have more inaccuracies than other trees.
A similar problem is the measurements of (detected) small
trees next to big ones, potentially causing severe over-
estimation of the tree heights (i.e., outliers) (Brandtberg,
2000). Naturally, a flat terrain, without boulders or other
surface irregularities, and homogeneous, sparse forests with-
out dense undergrowth, observed under leaf-on conditions
should result in much higher accuracy of both the field
measurements and the lidar-derived height estimates. The
fact that we obtain promising results in a rugged terrain,
with a relatively broad range of tree sizes, is promising.
In this work, data from multiple flight lines were used in
the analysis, but in a practical forest survey only data from a
single flight might be acquired. This will of course decrease
the number of data points from which to generate the
images. On the other hand, the laser instrument and the
flight speed can be adjusted to fulfil the requirements of the
resolution and the laser point sampling density. In the future,
the sampling frequency of lidar instruments is likely to
continue to increase, which will facilitate the individual tree
detection and classification processes.
An important problem is the view-angle of the scanning
laser where off-nadir views of the trees occur (e.g., Mag-
nussen & Boudewyn, 1998; Næsset, 1997). Theoretically,
because of the 40j sweep angle of the sensor, a laser beam
might go through one big tree crown and result in a point
position (x,y,z) that is located within the polygon of a
smaller neighbouring tree crown. Our analysis assumes a
vertical path for the laser beam into a single canopy, and
does not consider the possibility of passing through multiple
trees. Understory trees are therefore also a potential prob-
lem. Future improvements of the laser scanner instruments
(higher frequencies and higher altitudes) could make it
possible to capture the data from a more vertical perspec-
tive, so that laser paths that pass through multiple trees can
be minimized.
7. Conclusions
This paper demonstrates that there is great potential for
analysis of individual trees using leaf-off laser scanning
data. In particular, this work provides the foundation for
further research on the detection, delineation, the measure-
ment of the height and classification of the species of
individual trees using leaf-off data. The potential for leaf-
off lidar analysis for this purpose is apparently facilitated by
the return of a portion of the laser beam by not just major
branches, but also by the small, thin branches in the upper
canopy. Consequently, the majority of all returns on our 6-
ha study site come from within the canopy, and only a small
proportion are returns from the forest floor. Snow, which has
a high reflectance for near-infrared wavelengths, is a very
good reflector of the laser beam and thus the presence of
snow on the ground did not appear to cause problems in our
analysis. The simple scale-space technique used in this
paper is appropriate and effective for detecting the trees in
the laser scanning data.
Acknowledgements
Aerotec LLC, Bessemer, AL, is thanked for the leaf-off
lidar data. Financial support from the National Science
Foundation grant number DBI-9808312, NASA EPSCoR,
and the West Virginia University Eberly College of Arts and
Sciences, is gratefully acknowledged.
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