The Effects of Cell Size and Filtering Range on Automatically Extracted Tree Number and Average Tree Height Using Light Detection and Ranging Data in Fusion-LDV
Hiromi Shiota, Kazuhiro Tanaka and Keiko Nagashima
Hiromi Shiota1*, Kazuhiro Tanaka2 and Keiko Nagashima1
1Graduate School of Life and Environmental Sciences, Kyoto Prefectural
University, 1-5 Hangi-cho Shimogamo, Sakyo-ku, Kyoto, Japan
2Department of Bioenvironmental Science, Kyoto University of Advanced
Science, 1-1 Nanjo Otani, Sogabe-cho, Kameoka, Japan
*Corresponding Author :Hiromi Shiota, Graduate School of Life and Environmental
Sciences
Kyoto Prefectural University, 1-5 Hangi-cho Shimogamo, Sakyo-ku, Kyoto
606-8522, Japan Tel/Fax: +81-77-525-2075; E-mail: hshiota@msn.com
Received: December 26, 2019 Accepted: January 10, 2020 Published: January
17, 2020
Citation:Shiota H, Tanaka K, Nagashima K (2020) The Effects of Cell Size and Filtering Range on Automatically Extracted Tree Number and Average Tree
Height Using Light Detection and Ranging Data in Fusion/LDV. J Biodivers Manage Forestry 9:1. doi: 10.37532/jbmf.2020.9(1).226
Airborne light detection and ranging (LiDAR) is a technology
developed for measuring height of objects on the ground surface by
irradiating numerous lasers from the air towards the ground and
determining the laser reflection time (i.e., the return pulse). These
measurements provide three-dimensional positional information
across broad spatial ranges. In forests, LiDAR data can be used to
measure the tree height, extract the number of trees (i.e., stand
density), and estimate timber volume. For effective and efficient
management of forest resources, many studies have examined the
practical use of data obtained using airborne LiDAR [1,2]. In addition
to Japan, several studies and projects have been undertaken in the
United States, Canada, and northern Europe, since the 1990s [3-5].
There are two main methods for analyzing LiDAR data, namely,
area-based approach (ABA) and individual tree detection (ITD)
method. ABA is used to estimate area-based information, such as
average tree number and tree height.
ABA is already being applied in several locations [3,6], and
previous studies, conducted in different countries, have reported its
accuracy [6,7]. Conversely, ITD is used to extract information relating
to individual trees. ITD has been receiving increasing attention, as it
can provide more concrete information than ABA [8]. For extracting
information based on ITD, the usual process is to first develop the
digital canopy height model (DCHM), and then apply methods such
as the local maxima (LM) method, the watershed method, the valleyfollowing
method, or the hybrid method, to combine the DCHM data
with high-precision aerial photography.
Several studies have demonstrated the effectiveness of this
method in extracting tree numbers and measuring tree heights [2,5,9].
However, few studies have discussed the effects of the differences in
applied parameters (e.g., cell size, filter type, range of the method,
etc) on the results [10]. For instance, previous studies applying LM
used a cell size of 0.25 m to develop the DCHM [5,10,11], however,
few studies have examined the appropriate cell size for developing the
DCHM for extracting tree numbers.
Here, we used the LM method with a sampling density of 4 points/
m2 of LiDAR data, for two forest stands differing in age and species, i.e.,
a 0.37 ha forest of 42-year-old Japanese cedar (Cryptomeria japonica)
in a national forest at Mt. Ansyoji, Kyoto, and a 0.75 ha forest of 97-yearold
Japanese cypress (Chamaecyparis obtusa) in a national forest at Mt.
Koyama, Kyoto, in order to assess the appropriate parameters. We then
compared the results of the extracted tree numbers and automatically
calculated tree heights with those obtained from the on-site survey to
determine the most appropriate parameter combinations that resulted
in measurements close to the actual values. We further compared the
appropriate parameter combinations of the two stands, and discussed
the effects of tree species on these parameter combinations.
Materials and Methods
Study area
Mt. Ansyoji: The national forest at Mt. Ansyoji (19-21compartments, 16 ha) is located to the north of Yamashina Ward in
Kyoto (Figure 1).
Figure 1: A) Kyoto Prefecture B) Center of Kyoto City and survey sites.
Topographically, elevation of the forest is higher on the north,
south, and west sides, i.e., the area gradually slopes to the east. The
surveyed plot [0.3 ha] (Figure 2A) is a catchment for water flowing
from the north, south, and west sides towards the east. The forest is a
42-years-old woodlot for Japanese cedar, and had been thinned twice
before the LiDAR data was acquired. A plot was installed by surveying
the site, using the boundary markers of the national forest.
Figure 2: Point data cloud image A) Mt. Ansyoji. B) Mt. Koyama C) Expansion
of Mt. Ansyoji, 0.1 m MF5: cell size 0.1 m, MF, Filter range 5×5 D) Expansion of
Mt. Ansyoji, 0.2 m MF5: cell size 0.2 m, MF, Filter range 5×5.
Mt. Koyama: The national forest at Mt. Koyama, a mountain
known as the “holy object of worship” of the Kamigamo Shrine, is
located on its southwest slope. The land southeast to the surveyed site
was bare, without any forest (Figure 2B). Topographically, the elevation
of the national forest is higher on the north side and lower on the
south side. This forest is a 97 years old woodlot for Japanese cypress,
and had been thinned twice before the LiDAR data was acquired. The
survey plot [0.75 ha] (Figure 2B) was installed by surveying the site
using coordinates whose fiducial points were artificial structures in
proximity of the plot (Figures 2A and 2B).
Fusion/LDV: Fusion/LDV (V3.8) is a freely available software
program for analyzing LiDAR data from forest resources, developed
by Robert J. McGaughey of the USDA Forest Service Pacific Northwest
Research Station. This program uses an algorithm based on the LMF
(Local Maximum Filtering) method to automatically extract tree
number, measure tree height, and record tree coordinates.
LiDAR data: LiDAR data and aerial photographs were acquired
on November 24 and 26, 2013, using a helicopter-borne laser scanner
(HARRIER56, Trimble) operated by Asia Air Survey, Japan. LiDAR
data were acquired with the following specifications: average flight
speed 20 m/s, flight altitude 550-1360 m above sea level, pulse
frequency 120 kHz, scan frequency 44.74 kHz, scan angle ± 30°, with
over 50% overlap between the courses. The LiDAR system registered
two echoes of the laser beam, i.e., the first and last pulses. The laser
sampling density of the LiDAR data was configured for acquiring greater than 4 points/m2. A Global Navigation Satellite System base
control station was located at Kyoto Sakyo 2, code EL05235466102.
Methodology
Field survey: We established one survey plot at each study site.
The boundary coordinates of each plot were obtained using GPS and
Google Earth. We evaluated the tree number, tree height, and crown
width for all trees in the plot, and then obtained the average tree
height and average crown width for each plot. Information regarding
the tree species and forest age was obtained from the forest register.
The tree height was measured using Vertex II and III, and the crown
width was obtained by measuring the distance between the stem and
the tip of the raw branch in all four directions (east, west, south, and
north of the stem), and then averaging the four distances.
LiDAR data analyses: Plot polygons were created using plot
boundary coordinates, and plot area was calculated using geographic
information system (GIS).
The density of standing trees was obtained from the plot area and
tree number. In addition, the relative spacing index (Sr), indicating
the degree of forest congestion, was calculated from the average tree
height and number of standing trees per ha (N), using the following
formula:
LAS (LASer) data corresponding to the plot was obtained by
clipping the original LAS data from LiDAR, using the plot polygons. The digital terrain model (DTM) was developed by extracting the last
pulse from the clipped LAS data.
The DCHM was developed from the LAS and DTM data, using
the “Canopy Model” command in Fusion/LDV. Once the DCHM
was obtained, we changed the parameters of cell size, filter type, and
filter range, as follows: nine cell sizes (every 0.05 m, from 0.1 m to 0.5
m), two filter types (average value filter [AF] and median value filter
[MF]), and 6 filter ranges (3 × 3, 5 × 5, 7 × 7, 9 × 9, 11 × 11, and 13 ×
13 cells), totaling 108 combinations. DCHM images are presented in
Figures 2C and 2D.
The 108 DCHM were assessed by the LMF method, applied
using the “CanopyMaxima” command in Fusion/LDV. Trees were
automatically extracted, and tree number and the median and
average values for tree height were calculated. For consistency, the
combinations are expressed in the order of cell size, filter type, and
filter range, e.g., in 0.2 m MF5, 0.2 m indicates cell size, MF indicates
filter type MF, and 5 indicate filter range of 5×5 (Table 1).
Field
Mt. Ansyoji
Mt. Koyama
Number of return points
Unclassified
91,362
2,86,492
Ground
4,379
25,337
Total
95,741
3,11,897
First return
73,270
2,12,800
Coordinate
Minimum X
-17576.02
-22609.59
Minimum Y
-110406.77
-102382.98
Maximum X
-17404.68
-22,448.90
Maximum Y
-110378.96
-102275.09
Elevation (m)
Minimum
139.22
166.97
Maximum
177.94
206.69
Table 1: LiDAR data catalog by field. Data: JGD2011_Japan_Zone_6.
Influence of parameter combinations on the estimation results:
Tree numbers: The trends in the extracted tree numbers due to
differences in cell size, filter type, and filter range were interpreted
by creating a contour graph based on the automatically extracted
tree number data. A line graph of the number of extracted trees was
created for each combination of filter type (AF, MF) and filter ranges.
For this, the cell size was plotted on the X axis and the number of
extractions was plotted on the Y axis. As the shape of the graph
was considered to approximate to an inversely proportional graph, a
power approximate curve was created for each filter type and filter
range, using Excel, for estimating the actual tree number (Figure 3).
Figure 3: Number of trees by height class (Blue: field data, Orange: data from local maxima method) (A) Mt. Ansyoji, 0.2 m MF5: cell size 0.2 m, MF, filter range 5 × 5 (B) Mt. Koyama, 0.25 m AF3: cell size 0.25 m, AF, filter range
3 × 3.
Tree height: In order to investigate the accuracy of the
automatically calculated tree heights, the distribution of tree numbers
by height class, based on the best parameter combination obtained
from tree number extraction, was compared to that obtained from the field data. Using the average (AV) and median (MV) values of the
automatically measured tree height, the differences in tree height due
to cell size, filter type, and filter range were interpreted by creating a
contour graph. A box chart was created for comparing the results of
AV and MV by filter type and filter range.
Results and Discussion
Extracted tree number
•From the extraction of tree number by cell size, filter type, and
filter range, it became evident that as the cell size decreased, the
number of extracted trees increased (Table 2). This tendency was
confirmed by contour graphs of both Mt. Ansyoji (Figure 4A) and Mt.
Koyama (Figure 5A). For small cell sizes and filter ranges of 7 × 7 and
9 × 9, the extracted value was nearly 1.5 to 2 times greater than those
extracted using other ranges. This tendency was further confirmed in
AF and MF, for both the sites.
Cell size
MF3
MF5
MF7
MF9
MF11
MF13
AF3
AF5
AF7
AF9
AF11
AF13
0.1
614
676
999
976
853
850
590
716
1110
1031
903
871
0.15
329
370
428
435
400
395
331
403
469
459
433
399
0.2
279
313
337
349
341
354
279
306
359
366
341
345
0.25
254
283
308
301
288
270
265
312
326
322
302
269
0.3
242
267
280
254
243
242
258
292
290
256
243
223
0.35
242
265
276
249
242
239
244
274
246
225
203
187
0.4
232
260
240
220
215
218
246
260
239
206
187
179
0.45
234
252
224
187
192
234
238
249
213
188
170
170
0.5
241
255
211
202
197
205
241
247
198
165
166
152
Table 2A: Number of extracted trees. Red cell: Within ± 3% of field data. Field data: Mt. Ansyoji, 317.
Cell size
MF3
MF5
MF7
MF9
MF11
MF13
AF3
AF5
AF7
AF9
AF11
AF13
0.1
1260
1857
2779
2736
2137
1644
1277
2005
2938
2760
2125
1581
0.15
426
607
903
895
685
523
447
861
1167
991
710
536
0.2
324
395
488
458
379
363
339
541
633
499
396
340
0.25
327
405
428
366
322
304
333
470
454
377
334
289
0.3
315
388
348
302
269
251
337
397
344
292
257
217
0.35
313
348
316
265
232
245
327
354
302
251
209
201
0.4
296
318
273
225
229
222
308
311
271
215
187
184
0.45
286
293
243
224
206
248
301
288
229
189
177
171
0.5
293
293
226
223
255
241
303
279
213
192
177
173
Table 2B: Number of extracted trees. Red cell: Within ± 3% of field data; Mt. Koyama, 331.
Figure 4: (A) Results of tree extraction at Mt. Ansyoji. Number of extracted
trees by cell size, filter type, and filter range. Red ellipse: Near cell size 0.2
m (B) Results of tree extraction at Mt. Ansyoji (C) Expanded version near field
data, red line: field data.
Based on the linear graph (Figure 4B and Figure 5B) and the tree
number extraction data, the optimal cell size, i.e., the cell size for
which the extracted tree number was within ± 3% of the actual tree
number, was 0.2-0.25 m at Mt. Ansyoji (317); (Figure 5 and Table 2),
and 0.2-0.35 m at Mt. Koyama (331); (Figure 6 and Table 2).
Figure 5: (A) Results of tree extraction at Mt. Koyama. Number of extracted
trees by cell size, filter type, and filter range. Red ellipse: Near cell size 0.25
m (B) Results of tree extraction at Mt. Koyama (C) Expanded version near field
data, red line: field data.
Figure 6: Results of tree height measurement at Mt. Ansyoji (A) AV for tree
height (B) MV for tree height, Black line: field data (average 23.6 m).
The forest stand at Mt. Ansyoji is a cedar plantation, and cedar has
a more obvious treetop than cypress, therefore, smaller cell sizes can
be used for cedar forests. In addition, as evident from the tree density
of both forests, Mt. Ansyoji was more crowded (1,056 trees/ha, Sr:13)
than Mt. Koyama (441 trees/ha, Sr:22.1); and the average crown width
at Mt. Ansyoji (4 m); was about 1 m less than that at Mt. Koyama (4.95
m); (Table 3).
Field
Mt. Ansyoji
Mt. Koyama
Field data
Elevation (m)
146–150
170-180
Area (ha)
0.3
0.75
Species
Japanese cedar
Japanese Cypress
Tree age
42
97
No. of tree
317
331
Stand Density
1,056
441
number/ha
Average
4
4.95
crown width (m)
Sr
13
22.1
Tree height (m)
Average
23.6
21.6
Median
23.6
22.6
LMF
Tree height (m)
23.7
21.1
Cell size (m)
0.2
0.25
Filter type
MF
AF
Filter range
5 × 5
3 × 3
Number of extracted trees by upper parameter
313
333
Survey month
Oct, Nov, 2014
Mar, Apr, 2015
Table 3: Field data and automatically extracted data.
As crowding increases in a stand, the crown size of individual trees
decreases. The cell size, which is a unit for detecting the maximum
height point to extract tree numbers by LMF, strongly relates to the
crown size. This is because treetops of small crowns cannot be detected
by large cell sizes, as only the treetop of the largest tree, among the
several trees included in the huge cell, will be detected. Therefore, a
smaller appropriate cell size at Mt. Ansyoji than that at Mt Koyama
would be suitable.
The linear graph further indicated that the optimal cell size for
each combination of filter type and filter range was around the concave
conversion point (Figure 4B and Figure 5B). For example, extracted
tree numbers closest to the actual tree numbers were obtained with the
parameter combination of 0.2 m MF5 at Mt. Ansyoji (313 extracted
trees), and 0.25 m AF5 at Mt. Koyama (333 extracted trees).
The calculated power approximated curve was strongly correlated
to the linear graph (correlation coefficient: 0.8692 at Mt. Ansyoji,
0.7593 at Mt Koyama). This might indicate the suitability of the
power approximated curve in determining the appropriate cell size
for estimating the number of trees, if the curve could be applied to
other stands with the same tree species and tree density. Further
research is required to confirm if the curve would be the same for such
stands, and to calculate the curve for different stands and different
tree densities.
Average tree height
The actual average tree height at Mt. Ansyoji was 23.6 m, while
that obtained by the LMF method was 23.7 m (parameter combination
0.2 m MF5); (Table 3), which was 0.1 m greater than the actual height.
The tree height distribution of this parameter combination was very
similar to that observed during the field survey (Figure 3A); however,
it shifted towards a relatively higher height, greater by 1 m. According
to the contour graph, the measured tree height was lower than the
actual tree height when the cell size was small, and it exhibited a
tendency to increase with increasing cell size, for both AV and MV
(Figure 6). Outliers (more than 1.5 times the difference between the
first and third quartiles) were detected in the box graph, in the lower
tree height for both AV and MV. The difference in tree height between
AF and MF was less than 0.1 m. The average tree height obtained
by LMF was about 0.2-0.5 m higher than the actual tree height. In
addition, when the filter range was narrow, the difference in the
measured average tree height was small (Figure 6).
The average tree height obtained by the LMF method at Mt.
Koyama (parameter combinations shown in Table 3[cell size: 0.25,
AF3)] was 21.1 m, which was 0.5 m less than the actual height of 21.6
m. The distribution of tree height for this parameter combination
was different from the actual tree height distribution. Over- and
underestimation were observed for low tree heights. The contour graph revealed that the measurements tended to be slightly higher for
small cell size and filter range.
For cell sizes ranging from 0.2 to 0.3 m, both AV and MV were
underestimated (Figure 7). No outliers were observed in the box
graphs of AV and MV. Measured MV were about 0.8 m higher than
the actual values in all filter types and filter ranges (Figure 8), while
measured AV were found to be similar to the actual values (Figure 7
and 8).
Figure 7: Results of tree height measurement at Mt. Koyama (A) AV for tree
height (B) MV for tree height, Black line: field data (average 21.6 m).
Figure 8: Distribution of LMF tree height by filter type and filter size (A) Mt.
Ansyoji (B) Mt. Koyama. Circle: outlier (greater than 1.5 times the difference
between first and third quartiles). Field data: red line: average tree height,
blue line: median tree height includes all cell size data.
The exact reason for the different tendencies observed at Mt.
Ansyoji and Mt. Koyama is not known. However, tree species could
be one of the factors, as Mt. Ansyoji is a Japanese cedar stand and
Mt. Koyama is a Japanese cypress stand. As cypress has a gentler
crown than cedar, the height differences might be smaller even if the
extracted treetops were different from the actual ones. This might be
the reason for no outliers being detected in the cypress box charts.
The age of the forest and tree density could be other factors
influencing the tendency observed for tree height measurements.
Further studies comparing the age and density of stands of the same
species are required to conclusively determine the reason behind the
different tree height measurement tendencies observed in this study.
Overall, the measured average tree height was within ± 1 m of
the actual tree height for all parameter combinations used, suggesting that it can be used for forest resource analysis. Furthermore, our
results indicated that AV and MV could be used for measuring cedar
tree height; however, AV might be more suitable for cypress stands.
Conclusion
This study investigated different parameter combinations for
extracting tree numbers and automatically calculating tree heights,
for developing DCHM. Smaller cell sizes resulted in greater number
of trees being extracted, and cell size was the most important factor
influencing the extracted tree numbers. The most appropriate cell size
for Mt. Ansyoji was smaller than that for Mt. Koyama, which might
be attributed to the differences in tree density and crown size of these
forests. The power approximated curve, which was highly corelated
with the linear graph, could be used to determine the appropriate cell
size for estimating the tree number, if the curve could be applied to
other stands with the same tree species and density.
The estimated tree heights were lower than the actual values
for smaller cell sizes. However, the measured average tree height
was within ± 1 m of the actual tree height, and we concluded that
all parameter combinations might be suitable for estimating cedar
tree height; however, AV is recommended for use in cypress stands.
Further research is required to verify if the same tendencies occur
in other stands with the same tree species and tree density, and in
different stands with different tree densities.