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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).


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.


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.


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).


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.


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.


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


group.


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


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


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


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.


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.


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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