Accurate detection of tree apexes in
coniferous canopies from airborne
scanning light detection and ranging
images based on crown-extraction
filtering
Fumiki Hosoi
Hiroaki Matsugami
Kenichi Watanuki
Yo Shimizu
Kenji Omasa

Downloaded from SPIE Digital Library on 19 Apr 2012 to 66.165.46.178. Terms of Use: http://spiedl.org/terms

Accurate detection of tree apexes in coniferous canopies
from airborne scanning light detection and ranging
images based on crown-extraction filtering
Fumiki Hosoi, Hiroaki Matsugami, Kenichi Watanuki,
Yo Shimizu, and Kenji Omasa
The University of Tokyo, Graduate School of Agricultural and Life Sciences,
Bunkyo-ku, Tokyo 113-8657, Japan
E-mail: aomasa@mail.ecc.u-tokyo.ac.jp

Abstract. We describe crown-extraction (CE) filtering to accurately determine tree apex positions for various coniferous species using an airborne light detection and ranging–derived digital
canopy height model (DCHM). This method uses a square mask, with a frame at the edges, thatoverlaps pixels within the DCHM image; when no pixels touch the frame, the pixel at the centeris extracted as a tree-crown pixel. The apex of each tree is determined by choosing the pixel withmaximum height from the pixels in the crown. We compared the performance of this method andof two other methods (local-maximum filtering and canopy-segmentation method) for several
species. The CE filtering had the most accurate results for most tree species with appropriate
mask size selection. The mean omission, commission, and total errors for all tree species were8.1%, 1.6%, and 9.7%, respectively, for CE filtering. Comparing mask sizes and canopy diameters estimated from the DCHM for each species revealed that the smallest canopy diameterof each species was close to the most appropriate mask size for that species in CE filtering.
We also confirmed that the smoothing process used in the DCHM has little effect on the accuracyof CE filtering. © 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/
1.JRS.6.063502]

Keywords: airborne scanning light detection and ranging; coniferous tree; digital canopy heightmodel; tree apex.
Paper 10140 received Sep. 14, 2010; revised manuscript received Nov. 30, 2011; accepted for
publication Dec. 28, 2011; published online Mar. 12, 2012.

1 Introduction
Light detection and ranging (LIDAR), an active remote-sensing technique that uses a laser scanner, has been used to measure canopy structure. Mapping of tree apexes in a LIDAR image isvery important because spatial distribution of tree heights can be obtained from the map, so thatthe distribution is associated with spatial heterogeneity of biophysical properties of trees, such as
aboveground biomass or carbon stocks using allometric relationships between these properties
and the tree heights.1–5 Information of such spatial heterogeneity would be useful for understanding of plant functions such as photosynthesis and transpiration and determining suitability ofdifferent habitats for various species. Thus, a method for accurate mapping of tree apexes ina LIDAR image is needed.
The use of airplane-mounted scanning LIDAR for canopy measurements started in the mid1990s.6,7 Airborne LIDAR with a large footprint and a large scan width was developed forremote sensing of forests at large scales.8–12 However, the image resolution is too coarse toallow for determination of the canopy structure at the scale of individual trees. On the other
hand, commercially available small-footprint airborne LIDAR systems have been widely used
to measure canopy structure.1–5,13–25 In the 1990s, the density of laser pulses provided by ordinary small-footprint airborne scanning LIDAR systems was about 1 pulse per m2 on the ground at
flying altitude of 500 to 1000 m, with a pulse repetition frequency of 1 to 33 kHz. The resolution
0091-3286/2012/$25.00 © 2012 SPIE
Journal of Applied Remote Sensing

063502-1

Vol. 6, 2012

Downloaded from SPIE Digital Library on 19 Apr 2012 to 66.165.46.178. Terms of Use: http://spiedl.org/terms

Hosoi et al.: Accurate detection of tree apexes in coniferous canopies from airborne scanning light : : :of these systems was not sufficiently fine to produce a precise 3-D image at the scale ofindividual trees. Recent advances in LIDAR technology have increased the scanning density
to >10 pulse per m2 , with a pulse repetition frequency of >200 kHz. Moreover, the density
can be increased several-fold by using helicopters, which permit slower flight speeds than
fixed-wing aircraft, as the platform.1,13,14 A high-resolution image obtained from a LIDAR system with a high pulse density can provide images of sufficient resolution to allow measurementof the structural characteristics of individual trees.1,2,13–21 To extract tree heights from LIDAR
images, it is necessary to accurately detect the position of each tree’s apex. Several methods canbe used to calculate the positions of tree apexes for coniferous species.18,19,21–27 Local-maximum(LM) filtering was originally developed to detect tree apexes in aerial photographs,26,27 but canalso be applied to LIDAR images.23,24 In this method, local maxima within the image are identified using a search window whose size changes based on the semivariance range or slope breakcalculated for each of pixels and on the relationships between tree height and crown size derived
from field inventory data.23 The local maxima are then regarded as tree apexes. Actually, themethod is difficult to use when the goal is to provide accurate results for a range of coniferous
tree species with different canopy structures, as shown in a previous study.24 An alternative, thecanopy-segmentation method, is based on the watershed algorithm, and has become a popularmethod to delineate canopy boundaries.18,19,21,25 In this method, pixels corresponding to local
maxima are identified in a smoothed LIDAR image by searching for pixels whose height values
are greater than those of any of the eight neighboring pixels. These pixels are regarded as tree
apexes. Computation using this method is easier than in LM filtering because the search window
size is fixed at 3 × 3 pixels. However, the accuracy is sensitive to the strength of the smoothing,25
and it is often difficult to determine the most appropriate strength for stands that contain a varietyof canopy structures. Thus, it is also difficult to obtain accurate results with this method for arange of coniferous species with different canopy structures.
Thus, there is still a lack of techniques for detecting tree apexes in heterogeneous multispecies canopies from airborne LIDAR images. In the present paper, we propose an alternative formof crown-extraction (CE) filtering, which we developed to accurately identify tree apexes ofvarious coniferous species from airborne LIDAR images. We tested the method for several
coniferous species and compared its accuracy with that of the LM and canopy-segmentation(CS) methods.

2 Materials and Methods
2.1 Study Sites
We chose two study sites for the experiments. Site 1 was a forest with nearly flat topography
located near Tazawa Lake, Akita Prefecture, Japan (39°37 0 N, 140°38 0 E1). Japanese cedar(Cryptomeria japonica) trees dominated the forest. The trees were about 50 years old. At
this site, we identified a region [Fig. 1(a), broken lines] containing 62 cedar trees. Site 2
was an urban park, the Shinjuku Gyoen National Garden, in central Tokyo (35°41 0 N,
139°42 0 E).14 About 250 species and 2000 trees grow there. Two regions within site 2[Figs. 1(b) and 1(c); broken lines] included 42 deodar cedars (Cedrus deodara), 17 Lebanon
cedars (Cedrus libani), 14 Japanese black pines (Pinus thunbergii), and 11 dawn redwoods(Metasequoia glyptostroboides).

2.2 Helicopter-Borne Scanning LIDAR Measurements
Sites 1 and 2 were scanned in May 1998 and November 2001, respectively, using a helicopterborne scanning LIDAR system (ALTM 1025 special model; Optech Co. and Aero Asahi Co.;
Table 1).1,14 The LIDAR system calculated the distance to a target by measuring the elapsed time
between the emitted and returned laser pulses (the time-of-flight method). The laser wavelength
was 1064 nm and the range accuracy was 15 cm, with a resolution of 1 cm. The system had
two receiving modes—the first-pulse (FP) and last-pulse (LP) modes—in which the first and last
returned laser pulses, respectively, were detected. Laser pulses illuminating the canopy surface
Journal of Applied Remote Sensing

063502-2Vol. 6, 2012

Downloaded from SPIE Digital Library on 19 Apr 2012 to 66.165.46.178. Terms of Use: http://spiedl.org/terms

Hosoi et al.: Accurate detection of tree apexes in coniferous canopies from airborne scanning light : : :Fig. 1 Aerial photographs of the two study sites. The regions surrounded by broken lines are themeasurement plots. (a) Site 1, located near Tazawa Lake, Akita Prefecture, Japan. (b) and (c) Site2, located in the Shinjuku Gyoen National Garden in central Tokyo, Japan.

Table 1 Specifications of the helicopter-borne scanning LIDAR system.1,14

Type
Flight height (m)Flight speed (km h−1 )Laser wavelength (nm)Range accuracy (cm)Range resolution (cm)
Repetition frequency (kHz)Scanning frequency (Hz)Scanning angle (degree)
Receiving modes
Beam divergence (mrad)
Footprint size (cm)Footprint interval (cm)Site 1Site 2

ALTM 1025 special model
160 to 200
50
1064
15
1
25
25
11.3
First and last pulse modes
1.2
20–30
Flight direction: 10–15
Scan direction: 28

ALTM 1025 special model
300
60
1064
15
1
25
20
10.0
First and last pulse modes
1.0
30
Flight direction: 17
Scan direction: 42

were received as FP-mode data, and pulses that reached the ground surface were received as
LP-mode data. During measurements, the flight speed and flight height were 50 km h−1 and160 to 200 m, respectively, at site 1 and 60 km h−1 and about 300 m at site 2.
The repetition frequency and scanning frequency were 25 kHz and 25 Hz, respectively, at site
1 and 25 kHz and 20 Hz at site 2. The maximum scanning angle off-nadir and the beam divergence were 11.3 deg and 1.2 Mrad, respectively, at site 1 and 10.0 deg and 1.0 Mrad at site 2. The
footprint sizes on the ground were 20 to 30 cm at site 1 and about 30 cm at site 2. The footprintintervals (i.e., the distance between the centers of adjacent laser beams on the ground) were 10 to
15 cm in the flight direction and 28 cm in the scan direction at site 1, versus about 17 cm andabout 42 cm, respectively, at site 2. By comparing the diameters and intervals of the footprints on
the ground, we confirmed that the laser pulses covered most of the woody canopy surface. The
geographic position of the data was determined with a helicopter-borne inertial measurement
unit (IMU) and high-resolution global positioning system (GPS) receivers both in the helicopter
Journal of Applied Remote Sensing

063502-3

Vol. 6, 2012

Downloaded from SPIE Digital Library on 19 Apr 2012 to 66.165.46.178. Terms of Use: http://spiedl.org/terms

Hosoi et al.: Accurate detection of tree apexes in coniferous canopies from airborne scanning light : : :and on the ground. The point coordinates of both the FP-mode and LP-mode data were determined from the GPS data recorded in the helicopter and on the ground.

2.3 Generation of Canopy and Site Models from the LIDAR Dataand Ground Measurements
We reconstructed the canopy surfaces at both sites as digital elevation models (DEMs) generated
from the FP-mode data. We reconstructed the ground surfaces as digital terrain models (DTMs)
by interpolating ground-level points extracted from the LP-mode data. To extract ground level
points, LP-mode data was divided by square meshes with a certain size and lowest points withineach mesh were selected as candidates of ground points. Then, slopes between each of the candidates and the neighboring ones were calculated and the ground points were selected based on
the threshold value of the slope. We generated digital canopy-height models (DCHMs), which
express the net canopy height after accounting for undulation of the ground surface, by subtracting the DTM elevations from the FP-mode DEM elevations at each site.1,14 These models had amesh size of 10 × 10 cm at site 1 and 33 × 33 cm at site 2, and were produced using a software
made by TopScan, ERDAS IMAGINE (Leica Geosystems GIS & Mapping). The correct positions of the tree apexes in the generated DCHMs were determined using ground measurements in
December 1998 for site 1 and December 2005 for site 2.
Before performing the ground measurements, we estimated the positions of the tree apexes
from the DCHMs by manually identifying the local maxima and creating a position map for thetree apexes. The resulting map included both the true tree apexes and local maxima at different
locations from the tree apexes. These local maxima represented unevenness in the canopy surface. We then identified the true positions of the tree apexes on the ground by measuring the
distance between stems using a tape measure; this step allowed us to remove the local maxima
different from tree apexes from the map. The accuracy of tree apex positions on the map based on
the ground measurement was estimated to between 10 and 40 cm. Together with the tree apex
identification, we also identified the species of each tree on the ground and recorded it on themap. We used this map subsequently for validation of the accuracy of the tree apex detection
methods.

2.4 Determination of Tree Apexes
Figure 2 shows the sequence we followed for determination of the tree apexes by means of
CE filtering. During the pretreatment process, we first removed the spike noise included in
the original DCHM image at each site using a median filter with a mask size of 3 × 3 pixels,
followed by smoothing of the image using a Gaussian filter. We then applied CE filtering to
the images to extract the tree crowns. We defined a tree crown as the top portion of a tree’s
canopy that included an apex. The extracted tree crowns overlapped in the non-smoothed
DCHM image, and the pixel with the highest value in each tree crown was identified as thetree apex.
In CE filtering, a distinctive feature of a conifer’s canopy shape is utilized to extract each tree
crown. When a coniferous canopy is cut by a horizontal plane at certain height and projected
onto the horizontal plane, the projection forms a roughly circular or elliptical region (i.e., aregion defined by a certain radius, or by a semi-major and semi-minor radius, from the center).

Fig. 2 The sequence followed to determine tree apex position by means of CE filtering.
Journal of Applied Remote Sensing

063502-4

Vol. 6, 2012

Downloaded from SPIE Digital Library on 19 Apr 2012 to 66.165.46.178. Terms of Use: http://spiedl.org/terms

Hosoi et al.: Accurate detection of tree apexes in coniferous canopies from airborne scanning light : : :A tree crown can therefore be projected around the center. In the DCHM image, the projection isequivalent to the pixels whose height values are equal to or higher than a certain height; in Fig. 3(parts I and II), this height is expressed as a-a’ to d-d’. It is possible to prepare a mask whose
pixel values are 1 at the center and edges and 0 at other points (Fig. 3, part VI); that is, the edgesof the mask form a square frame. The mask can be overlapped on a certain part of the projection,and if the border of the projection does not touch the frame of the mask, the mask’s center isconsidered to be located around the center of the projection, and a pixel corresponding to themask center is identified as part of the tree’s crown [parts (b) to (d) in Fig. 3, parts III and IV].
The actual determination process is as follows: First, pixel values in the DCHM image are
multiplied by the values of the mask at positions corresponding to those pixels. When the multiplication simultaneously yields non-zero values at the center of the mask and zero values at theedges, the pixel corresponding to the center of the mask is considered to be one of the pixels thatcompose the tree’s crown. This process starts at lower heights and is repeated at progressively
greater heights, step by step, using a height interval of 10 cm [Fig. 3(a) to 3(d), in sequence].
Moving from Fig. 3(a) to 3(d), zero, one, five, and one pixels, respectively, are identified as the
pixels that comprise the tree’s crown. Next, the logical disjunction of the pixels is calculated andthe pixels that compose a tree’s crown are determined based on the results of this calculation(Fig. 3, step V). Next, the mask is moved horizontally by one pixel within the DCHM image andthese processes are repeated for all pixels within the DCHM image, so that each set of pixels thatcompose a single tree crown is determined. Each tree apex is then derived as the highest point in
the identified crowns.

Fig. 3 Illustration of the process used in CE filtering. (a) to (d) represent different heights within the
canopy, and represent different cross-sections of the canopy (a-a’, b-b’, c-c’, and d-d’, respectively). (I) Side views of the canopy in a DCHM. (II) Selected pixels at each height level. (III) The
mask used in the CE filtering is overlapped on the selected pixels at each height level. (IV) The
result of this overlapping is the group of pixels at a given height that belong to the tree’s crown.
(V) A logical disjunction is calculated for the pixels identified in (IV). (VI) An example of a typicalmask used in CE filtering.
Journal of Applied Remote Sensing

063502-5

Vol. 6, 2012

Downloaded from SPIE Digital Library on 19 Apr 2012 to 66.165.46.178. Terms of Use: http://spiedl.org/terms

Hosoi et al.: Accurate detection of tree apexes in coniferous canopies from airborne scanning light : : :In this paper, we used square masks of dimensions L × L, where L is measured in meters. For
example, in Fig. 3, the masks are 7 × 7 squares, so a pixel size of 0.1 m would mean that
L ¼ 0.7 m. At the beginning of our analysis, we did not know the most appropriate mask
size to use in the CE filtering. To determine this size, we performed CE filtering with mask
sizes ranging from L ¼ 0.7 to 2.9 m, with a pixel size of 0.1 m × 0.1 m, using a DCHM
image of site 1 after smoothing using a weak Gaussian filter, with a standard deviation (σ)
of 1∕π and a radius of 1 pixel (¼ the integer that is closest to the value of 2σ). We estimatedthe errors in the detected apexes by comparing the detected apex positions with the positions in
the field survey map described in Sec. 2.3. We determined the most appropriate mask size bycomparing the errors in tree apex detection among all mask sizes.
Because the effect of smoothing of the DCHM image on the accuracy of tree apex detection
was also unknown, we first applied CE filtering with the most appropriate mask size to thenonsmoothed DCHM image of site 1 (i.e., the image after only applying the median filter).
We compared this result with the results of smoothing using Gaussian filters of different strength(i.e., with σ ¼ 1∕π, 4∕π, 6∕π, and 8∕π and with the radius of the integer that is closest to the valueof 2σ). The strength of the Gaussian filter increases as σ increases. We investigated the effect of
this smoothing by comparing the errors of tree apex detection among the images created using
different levels of smoothing. Tree apex positions were also detected for the DCHMs for site 2 bymeans of CE filtering. As was done for site 1, we determined the most appropriate mask sizes forsite 2 for each tree species by evaluating the accuracy of apex detection using different mask sizes.
We selected the strength of the Gaussian filters for site 2 based on the results for site 1.
We also used the nonsmoothed and smoothed DCHM images of site 1 for tree apex detection
by means of LM filtering.26 In this method, the windows were overlain on a DCHM image and apixel corresponding to the center of the window was regarded as the local-maximum point andtherefore as a tree apex if the pixel had the highest value of all pixels within the window. We
varied the window size based on the semivariance range in each of the pixels (see Ref. 26 formore details about the calculation). We applied this method to site 1 using different strengths ofGaussian filter (i.e., with σ ¼ 1∕π, 4∕π, 6∕π, and 8∕π and with the radius of the integer that isclosest to the value of 2σ), and investigated the effect of smoothing by comparing the errors oftree apex detection among images with different levels of smoothing. We also performed tree
apex detection in the DCHMs for site 2 using Gaussian filters whose strength was determinedbased on the results for site 1.
In the CS method,18 pixels corresponding to local maxima were identified from a smoothed
LIDAR image by searching for pixels whose height values were greater than those of the eightneighboring pixels. These local maxima were regarded as tree apexes. We applied this method tosite 1, after changing the strength of the Gaussian filters used in the smoothing (i.e., withσ ¼ 1∕π, 4∕π, 6∕π, and 8∕π and with the radius of the integer that is closest to the value of2σ), and compared the errors among the results obtained using different filter strengths. We
also applied this method to the DCHMs for site 2 using the filter strength determined basedon the results for site 1.
Based on the tree apex detection results for sites 1 and 2, we compared the performance of thethree methods for each tree species in the sample images. To provide a guideline for determining
the most appropriate mask size to use in CE filtering, we investigated the relationship between
the most appropriate mask size and the canopy diameter (i.e., the diameter of the canopy’s
vertical projection onto the ground, which was estimated manually using the DCHM images)
for each species.

3 Results
Figure 4 shows the images of site 1 resulting from tree apex detection by means of CE filtering.
Figure 4(a) shows a DCHM image of site 1 after the median filtering and smoothing using a
Gaussian filter with σ ¼ 1∕π. This process removed the spike noise from the image, althoughsome line noise remained at the right edge of the image. Figure 4(b) represents the tree crowns
extracted by means of CE filtering with a mask size of L ¼ 1.9 m. Note that each tree crownincluded several pixels and that the crowns were distributed almost evenly throughout the image.
Journal of Applied Remote Sensing

063502-6

Vol. 6, 2012

Downloaded from SPIE Digital Library on 19 Apr 2012 to 66.165.46.178. Terms of Use: http://spiedl.org/terms

Hosoi et al.: Accurate detection of tree apexes in coniferous canopies from airborne scanning light : : :Fig. 4 Canopy images of site 1 resulting from the tree apex detection process by means of CE
filtering. (a) A DCHM image of site 1 after the median filtering and smoothing using a Gaussian
filter with σ ¼ 1∕π. (b) Tree crowns extracted by means of CE filtering with a mask size of
L ¼ 1.9 m. and (c) The result of tree apex detection mapped on the nonsmoothed DCHM image.

Figure 4(c) shows the results of the tree apex detection. Here and in the other methods of apex
detection, there were two types of error of tree apex detection: omission errors, in which a tree
apex was not detected, and commission errors, in which a pixel was incorrectly classified as atree apex. The CE filtering detected most tree apexes correctly, and we observed no commission
errors in the results. The total error in tree apex detection was 9.7%.
Figure 5 shows the results of CE filtering of the DCHM image shown in Fig. 4(a) using mask
sizes ranging from L ¼ 0.7 to 2.9 m. The number of commission errors increased drastically asmask size decreased below L ¼ 1.7 m, whereas the number of omission errors increased slightly
as the mask size increased beyond L ¼ 2.1 m. Consequently, the minimum total number of
errors occurred at a mask size of L ¼ 1.9 m, which we therefore chose as the most appropriate
mask size for the DCHM for site 1.

Fig. 5 Errors in tree apex detection at site 1 using CE filtering at various mask sizes LðmÞ.
Journal of Applied Remote Sensing

063502-7

Vol. 6, 2012

Downloaded from SPIE Digital Library on 19 Apr 2012 to 66.165.46.178. Terms of Use: http://spiedl.org/terms

Hosoi et al.: Accurate detection of tree apexes in coniferous canopies from airborne scanning light : : :Fig. 6 Errors of tree apex detection using the DCHM image of site 1 as a function of the strength of
the Gaussian filtering (standard deviation, σ) for the three detection methods. CE: crown-extraction
filtering; LM: local-maximum method; CS: canopy-segmentation method.

Figure 6 summarizes the errors in tree apex detection using the DCHM image of site 1 foreach strength of Gaussian filtering in the three detection methods. In the LM method, theomission error was lowest at σ ¼ 4∕π but remained similar as σ increased beyond thispoint, whereas the commission error was lowest at σ ¼ 1∕π and gradually increased at higher
values. Consequently, the total error was lowest (63.0%) at σ ¼ 4∕π. Although smoothing by theGaussian filter reduced the error in the LM method, large errors (greater than 60%) were found at
all strengths of the Gaussian filter. In the CS method, the commission errors were extremely large
at σ ¼ 4∕π or lower, and decreased drastically as the strength of the Gaussian filter increased,
whereas omission errors were small at all strengths of the Gaussian filter. Consequently, the total
error was dominated by the commission error, and reached its minimum value (14.5%) at thestrongest Gaussian filter we tested (σ ¼ 8∕π). These results demonstrate that this method washighly sensitive to the strength of the Gaussian filter. In the CE filtering, commission errors
decreased to zero at σ ¼ 1∕π or greater, and omission errors slightly increased as the strengthof the Gaussian filter increased; thus, the total error was only slightly affected by the smoothing
strength, and remained small at all filter strengths that we tested. The minimum total error was
9.7% at σ ¼ 1∕π.
Figure 7 shows the best tree apex detection results using the LM and CS methods, mappedusing the non-smoothed DCHM of site 1. In the LM method, omission errors were frequent andFig. 7 Errors of tree apex detection using (a) the local-maximum method and (b) the canopysegmentation method, mapped using the non-smoothed DCHM for site 1.
Journal of Applied Remote Sensing

063502-8

Vol. 6, 2012

Downloaded from SPIE Digital Library on 19 Apr 2012 to 66.165.46.178. Terms of Use: http://spiedl.org/terms

Hosoi et al.: Accurate detection of tree apexes in coniferous canopies from airborne scanning light : : :were distributed almost evenly throughout the image, whereas most commission errors were
distributed along the lower and right edges of the image. In the CS method, most commission
errors also occurred along the edges of the map, although there were fewer total errors than in the
LM method. The omission, commission, and total errors in the best results for site 1 were 32.0%,
31.0%, and 63.0%, respectively, for the LM method and 6.0%, 8.0%, and 14.0% for the CS
method.
Figure 8 shows the tree apex detection results for site 2 using the three detection methods,
mapped using the nonsmoothed DCHMs for the site. Although many errors were observed in the
LM method, fewer errors were observed in the other methods. In particular, most tree apexes
were detected correctly by the CE filtering, so this method was the most accurate method overall.
The mean omission, commission, and total errors at site 2 were 7.7%, 2.1%, and 9.8%, respectively, for the CE filtering, versus 46.3%, 9.0%, and 55.3% for the LM method and 12.0%, 6.5%,
and 18.5% for the CS method.
Figure 9 summarizes the tree apex detection errors in the three methods for all species at sites
1 and 2. In the CE filtering, there were relatively few errors (an error of less than 20%) for all tree
species. The method performed particularly well for dawn redwoods, for which we observed noerrors. We observed more omission errors than commission errors for all species in this method.

Fig. 8 Errors of tree apex detection using (a) crown-extraction (CE) filtering, (b) the localmaximum method, and (c) the canopy-segmentation method, mapped using the non-smoothed
DCHMs for site 2.
Journal of Applied Remote Sensing

063502-9Vol. 6, 2012

Downloaded from SPIE Digital Library on 19 Apr 2012 to 66.165.46.178. Terms of Use: http://spiedl.org/terms

Hosoi et al.: Accurate detection of tree apexes in coniferous canopies from airborne scanning light : : :Fig. 9 Errors of tree apex detection using CE filtering, the local-maximum (LM) method, and the
CS method for all tree species at sites 1 and 2. JC: Japanese cedar; DC: deodar cedar; LC: Lebanon cedar; JBP: Japanese black pine; DR: dawn redwood.

The most appropriate mask sizes in the CE filtering were L ¼ 1.9 m for Japanese cedar, 4.3 m for
Deodar cedar, 3.6 m for Lebanon cedar, and 4.3 m for Japanese black pine. By comparing thesemask sizes with the canopy diameters estimated from the DCHMs, we confirmed that the mostappropriate mask sizes were nearly equal to the smallest canopy diameter of each species estimated from the DCHM (i.e., 1.9, 4.3, 3.4, and 4.0 m, respectively). For the dawn redwoods, wefound no errors at mask sizes ranging from L ¼ 2.3 to 6.9 m; thus, any mask size would beappropriate for this species. The LM method only worked well for Japanese black pine,with large errors (>40%) for all other species. Omission errors were the largest proportionof the errors in this method. In the CS method, we observed small errors for Japanese cedar
and dawn redwood, but large errors (20%) for the other species. In addition, commission errors
were greatest for some of the species and omission errors were greatest for other species. Thisshows that the performance of the CS method depends on the tree species. The mean values ofomission, commission, and total errors for all species combined were 8.1%, 1.6%, and 9.7%,
respectively, for CE filtering versus 43.5%, 10.1%, and 53.6% for the LM method and 10.9%,
10.1%, and 21.0% for the CS method.

4 Discussion
Appropriate selection of mask size is important in CE filtering because the mask size determines
the sensitivity of tree crown detection. CE filtering with a small mask size allows detection ofboth large and small tree crowns. However, adopting a too-small mask size can lead to incorrect
identification of a region around local maxima that are caused by unevenness of the canopy
surface as tree crowns, resulting in commission errors. This explains the results in Fig. 5,
where many commission errors were observed at the smallest mask sizes. Thus, commission
errors should decrease as the mask size increases, and Fig. 5 supports this hypothesis; few commission errors were observed at a mask size of L ¼ 1.9 m or more. However, small tree crowns
are less likely to be detected as the mask size increases because these crowns may be regarded aspart of a larger tree’s crown, particularly when a small tree is close to a large tree. This explains
the gradual increase in omission errors as the mask size increases beyond L ¼ 1.9 m in Fig. 5. As
a result of these two offsetting changes, the total error was minimized at a mask size of
L ¼ 1.9 m, which corresponded to the smallest canopy diameter of Japanese cedar estimatedfrom the DCHM for site 1. The most appropriate mask sizes for the other species were also
close to the smallest canopy diameters of each species. Thus, the smallest canopy diameterof each species estimated from the DCHM can be used as a criterion to select an appropriate
mask size in CE filtering.
Our comparison of the three detection methods revealed that CE filtering offered the mostaccurate results for most of the species. The LM method worked poorly for most of the tree
Journal of Applied Remote Sensing

063502-10

Vol. 6, 2012

Downloaded from SPIE Digital Library on 19 Apr 2012 to 66.165.46.178. Terms of Use: http://spiedl.org/terms

Hosoi et al.: Accurate detection of tree apexes in coniferous canopies from airborne scanning light : : :species at all strengths of Gaussian smoothing. The variable window size, based on the semivariance range of each of pixels, was not effective for the canopy structures at sites 1 and 2. Alsoin a previous study,24 this method did not work well in canopies of mixed species with structuralvariability. Those results show that it is difficult in this method to change the mask (window) size
appropriately to account for variations in canopy structure. In the CS method, all local-maxima
were identified as tree apex candidates. These points included both true tree apexes and local
maxima that resulted from canopy unevenness. Thus, commission errors occurred when the
canopy was uneven. Fortunately, these errors can be reduced by increasing the strength ofsmoothing (Fig. 6). Consequently, the number of errors strongly depended on the strengthof the smoothing. The relationship between the number of errors and smoothing strength
has also been investigated in a previous study conducted in a natural forest reserve dominatedby Norway spruce [Picea abies (L.) Karst.].25 In this study, similar results to the present study
were reported, i.e., commission errors in the CS method decreased drastically as smoothing(Gaussian) strength increased. Those results show that the selection of an appropriate smoothing
strength is important for improving the accuracy of this method.
Currently, in the CS method, there is no clear criterion that can be used to select the mostsuitable smoothing strength for each tree species. This is a disadvantage of the CS method
because the results will vary for different coniferous species. In contrast, the CE filtering method
excludes a local maximum that is not a tree apex when the frame of the mask touches points
around that local maximum. This is affected by whether the mask size was appropriately selected
rather than by the smoothing strength that is applied; as a result, the error frequency in CE filtering is insensitive to the smoothing strength (Fig. 6). In practice, a moderate level of smoothing isenough to obtain accurate results in CE filtering. Although the smoothing strength is not important in CE filtering, it is still necessary to select a mask size that is appropriate for each coniferous
species. However, a criterion to select the mask size appropriate for each coniferous species has
been demonstrated above. Thus, CE filtering has advantages over the other two methods for arange of coniferous species.

5 Conclusions
Our results demonstrate that tree apexes can be detected accurately using DCHMs for coniferous
trees derived using airborne LIDAR data combined with the newly developed CE filtering
method. In this method, a square mask is used, with a frame at the edges. The mask overlaps
the DCHM image, and pixels corresponding to tree crowns are extracted from the image bychecking whether any pixels touch the mask’s frame. Each tree apex is identified by choosing
the pixel with the maximum height from the group of pixels that comprise a tree crown. We
tested this method using a range of mask sizes in stands of Japanese cedar and of several coniferous species, and confirmed that the method provides accurate results when the most appropriate mask size is chosen. Our results also revealed that the most appropriate mask size wascomparable to the smallest canopy diameter for a species (estimated manually and interactively
from the DCHM). The smoothing strength chosen for the DCHM had little effect on the accuracyof the CE filtering.
Our comparison of the three methods revealed that the other two methods of tree apex detection were less accurate than CE filtering for most of the tree species that we studied. Our results
suggest that CE filtering would be suitable for other coniferous canopies, but this should be
confirmed by studying more coniferous species at more study sites. Although canopy surface
was well reproduced in the DCHM images at the survey configuration of this study, fidelity of
canopy surface reproduction could vary depending on LIDAR measurement conditions suchas scan angle or laser footprint spacing on canopy surface. This may affect accuracy of tree
apex detection by CE filtering. Therefore the robustness of CE filtering at different measurement
conditions should be also investigated by additional works. Moreover, it is helpful to know tree
species a priori for better performance of CE filtering, so combination between CE filtering andother remote sensing techniques that enable tree species classification (e.g., multi or hyper
spectral imaging, etc.) is an interesting research subject. The present method is applicable
only for confers and is difficult to be applied to nonconiferous canopy (e.g., deciduous canopy).
Journal of Applied Remote Sensing

063502-11

Vol. 6, 2012

Downloaded from SPIE Digital Library on 19 Apr 2012 to 66.165.46.178. Terms of Use: http://spiedl.org/terms

Hosoi et al.: Accurate detection of tree apexes in coniferous canopies from airborne scanning light : : :To cope with the limitation, it is required to modify the present method, extracting and utilizing
features of shapes of nonconiferous canopy.

References
1. K. Omasa et al., “Accurate estimation of forest carbon stocks by 3-D remote sensing ofindividual trees,” Environ. Sci. Tech. 37, 1198–1201 (2003), http://dx.doi.org/10.1021/
es0259887.
2. Z. J. Bortolot and R. H. Wynne, “Estimating forest biomass using small footprint
LIDAR data: an individual tree-based approach that incorporates training data,” ISPRS
J. Photogramm. Rem. Sens. 59, 342–360 (2005), http://dx.doi.org/10.1016/j.isprsjprs.2005.07.001.
3. K. Lim et al., “LIDAR remote sensing of biophysical properties of tolerant northern hardwood forests,” Can. J. Rem. Sens. 29, 658–678 (2003), http://dx.doi.org/10.5589/m03-025.
4. Y. Nakai, F. Hosoi, and K. Omasa, “Estimating carbon stocks of coniferous woody canopy
trees using airborne LIDAR and passive optical sensor,” in Proc. ISPRS Laser scanning
2009, XXXVIII, Part 3/W8 congress, ISPRS, Paris, pp. 289–292 (2009).
5. G. Patenaude et al., “Quantifying forest above ground carbon content using LIDAR remotesensing,” Rem. Sens. Environ. 93, 368–380 (2004), http://dx.doi.org/10.1016/j.rse.2004.07.016.
6. E. Næsset, “Determination of mean tree height of forest stands using airborne laser scanner
data,” ISPRS J. Photogramm. Rem. Sens. 52(2), 49–56 (1997), http://dx.doi.org/10.1016/
S0924-2716(97)83000-6.
7. M. Nilsson, “Estimation of tree heights and stand volume using an airborne LIDAR
system,” Rem. Sens. Environ. 56, 1–7 (1996), http://dx.doi.org/10.1016/0034-4257(95)00224-3.
8. J. B. Drake et al., “Estimation of tropical forest structural characteristics using largefootprint LIDAR,” Rem. Sens. Environ. 79, 305–319 (2002), http://dx.doi.org/10.1016/
S0034-4257(01)00281-4.
9. D. J. Harding et al., “Laser altimeter canopy height profiles: methods and validation for
closed-canopy, broadleaf forests,” Rem. Sens. Environ. 76, 283–297 (2001), http://dx.doi.org/10.1016/S0034-4257(00)00210-8.
10. M. A. Hofton et al., “Validation of vegetation canopy LIDAR sub-canopy topographymeasurements for a dense tropical forest,” J. Geodyn. 34, 491–502 (2002), http://dx.doi.org/10.1016/S0264-3707(02)00046-7.
11. M. A. Lefsky et al., “LIDAR remote sensing for ecosystem studies,” BioSci. 52, 19–30(2002).
12. J. E. Means et al., “Use of large-footprint scanning airborne LIDAR to estimate forest standcharacteristics in the Western Cascades of Oregon,” Rem. Sens. Environ. 67, 298–308(1999), http://dx.doi.org/10.1016/S0034-4257(98)00091-1.
13. K. Omasa et al., “3-D remote sensing of woody canopy heights using a scanning helicopterborne LIDAR system with high spatial resolution,” J. Rem. Sens. Soc. Jpn. 20, 394–406(2000), http://dx.doi.org/10.1037/a0020824.
14. K. Omasa et al., “Three-dimensional modelling of an urban park and trees by combined
airborne and portable on-ground scanning LIDAR remote sensing,” Environ. Model. Assess.
13, 473–481 (2008), doi: http://dx.doi.org/10.1007/s10666-007-9115-5.
15. T. Brandtberg et al., “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,” Rem. Sens. Environ. 85, 290–303 (2003), http://dx.doi.org/10.1016/
S0034-4257(03)00008-7.
16. J. Holmgren and Å. Persson, “Identifying species of individual trees using airborne laser
scanner,” Rem. Sens. Environ. 90, 415–423 (2004), http://dx.doi.org/10.1016/S0034-4257(03)00140-8.
17. F. Hosoi, Y. Nakai, and K. Omasa, “Estimation and error analysis of wood canopy leaf area
density profiles using 3-D airborne and ground-based scanning LIDAR remote-sensing
Journal of Applied Remote Sensing

063502-12

Vol. 6, 2012

Downloaded from SPIE Digital Library on 19 Apr 2012 to 66.165.46.178. Terms of Use: http://spiedl.org/terms

Hosoi et al.: Accurate detection of tree apexes in coniferous canopies from airborne scanning light : : :techniques,” IEEE Trans. Geosci. Rem. Sens. 48, 2215–2222 (2010), http://dx.doi.org/10.1109/TGRS.2009.2038372.
18. J. Hyyppä et al., “A segmentation-based method to retrieve stem volume estimates from
3-D tree height models produced by laser scanners,” IEEE Trans. Geosci. Rem. Sens.
39, 969–975 (2001), http://dx.doi.org/10.1109/36.921414.
19. E. Næsset et al., “Laser scanning of forest resources: the Nordic experience,” Scand. J. For.
Res. 19, 482–499 (2004), http://dx.doi.org/10.1080/02827580410019553.
20. H. O. Ørka, E. Næsset, and O. M. Bollandsås, “Classifying species of individual trees byintensity and structure features derived from airborne laser scanner data,” Rem. Sens.
Environ. 113, 1163–1174 (2009), http://dx.doi.org/10.1016/j.rse.2009.02.002.
21. X. W. Yu et al., “Automatic detection of harvested trees and determination of forest growthusing airborne laser scanning,” Rem. Sens. Environ. 90, 451–462 (2004), http://dx.doi.org/
10.1016/j.rse.2004.02.001.
22. Å. Persson, J. Holmgren, and U. Söderman, “Detecting and measuring individual trees
using an airborne laser scanner,” Photogramm. Eng. Rem. Sens. 68, 925–932 (2002).
23. S. C. Popescu and R. H. Wynne, “Seeing the trees in the forest: using LIDAR and multispectral data fusion with local filtering and variable window size for estimating tree height,”
Photogramm. Eng. Rem. Sens. 70, 589–604 (2004).
24. H. Huang et al., “Improving measurement of forest structural parameters by co-registeringof high resolution aerial imagery and low density LIDAR data,” Sensors 9, 1541–1558(2009), http://dx.doi.org/10.3390/s90301541.
25. S. Solberg, E. Næsset, and O. M. Bollandsas, “Single tree segmentation using airborne laser
scanner data in a structurally heterogeneous spruce forest,” Photogramm. Eng. Rem. Sens.
72, 1369–1378 (2006).
26. M. Wulder, K. O. Niemann, and D. G. Goodenough, “Local maximum filtering for the
extraction of tree locations and basal area from high spatial resolution imagery,” Rem.
Sens. Environ. 73, 103–114 (2000), http://dx.doi.org/10.1016/S0034-4257(00)00101-2.
27. M. Wulder, K. O. Niemann, and D. G. Goodenough, “Error reduction methods for local
maximum filtering of high spatial resolution imagery for locating trees,” Can. J. Rem. Sens.
28, 621–628 (2002).
Fumiki Hosoi received his ME and PhD degrees from the University of Tokyo, Tokyo, Japan, in
1995 and 2008, respectively. In 1995, he joined the Opto-Technology Laboratory, Furukawa
Electric Company, Ltd., Ichihara, Chiba, Japan. He is currently with the Graduate School of
Agricultural and Life Sciences, University of Tokyo.
Hiroaki Matsugami received an ME degree in 2006 from the University of Tokyo, Tokyo,
Japan, in the Department of Agricultural and Life Sciences.
Kenichi Watanuki received an ME degree in 2002 from the University of Tokyo, Tokyo, Japan,
Department of Agricultural and Life Sciences.
Yo Shimizu is currently with the Graduate School of Agricultural and Life Sciences, Universityof Tokyo.
Kenji Omasa received his ME degree from Ehime University, Ehime, Japan, in 1975 and his
PhD degree in engineering from the University of Tokyo, Tokyo, Japan. He is currently a professor with the Department of Agricultural and Life Sciences, University of Tokyo. His researchinterests include imaging at scales ranging from the cellular level to the whole-plant level, remotesensing, modeling of ecosystems, analysis of the effects of global change on ecosystems, andinformation engineering for biological and environmental systems.

Journal of Applied Remote Sensing

063502-13

Vol. 6, 2012

Downloaded from SPIE Digital Library on 19 Apr 2012 to 66.165.46.178. Terms of Use: http://spiedl.org/terms