OBSERVATIONS AND ANALYSIS TO DEVELOP PERFORMANCE REQUIREMENTS FOR A LIVING SNOW FENCE DURING SEVERE SNOWSTORM EVENTS
Yusuke Harada1*, Akihiro Yoshii1, Satoshi Omiya1 and Atsushi Nishimura1
1

Civil Engineering Research Institute for Cold Region, Public Works Research Institute, Sapporo, Japan

ABSTRACT: In this study, we conducted observations on trees, snowdrifts, and meteorological elements related blowing snow using a shelterbelt composed of evergreen conifers to investigate the design methodology of living snow fences in a severe snowstorm. Based on the results, we created theaverage distribution of leaf area density on the survey lines and organized the relationship between the
shelterbelt and the meteorological data in a snowstorm.
KEYWORDS: Living Snow Fences, Severe snowstorm events, Performance requirements

1. INTRODUCTION
In recent years, the cold snowy regions of Japan
have occasionally seen more incidents of vehicles being stuck in snow and road closures due
to severe snowstorms caused by rapidly developed low-pressure systems and convergencezones. Living Snow Fences (hereinafter: "LSFs")
have been installed along roadsides to mitigate
blowing snow since 1976 in Hokkaido, northernpart of Japan, which reduce road traffic problems
such as snowdrifts and poor visibility (CERI,
2011). The extent of LSFs in Hokkaido totaled approximately 100 km as of 2020 (Sakurai et al.,2023). According to “The Highway Snowstorm
Countermeasure Manual”, the standard width ofan LSF along the national highways is classified
into three types; 10 m, 20 m, and 30 m, where themaximum volume of snow settled at snowdrifts is
20 m3 m-1 or greater referring to previous experiences in Japan (CERI, 2011). However, there isno established design methodology based on theperformance requirements for LSFs considering
severe snowstorm events in Japan. Therefore,
we conducted observations on trees, snowdrifts,
and meteorological elements related blowing
snow to investigate the design methodology of
LSFs.

2. OBSERVATION METHODS
2.1 Study site
The study site was the Ishikari blowing snow test
field of the Civil Engineering Research Institutefor Cold Region, which is located in southwest
Hokkaido in the northern part of Japan (N43°12’,* Corresponding author address:
Yusuke Harada, Civil Engineering Research Institute for Cold Region, PWRI, 1-3-1-34 Hiragishi,
Toyohira-ku, Sapporo, Hokkaido 062-8602;
tel: +81-11-841-1746; fax: +81-11-841-9747;
email: harada-y@ceri.go.jp

E141°23’). The prevailing wind direction during
blowing snow in the field was WNW. We carriedout observation on trees, snowdrifts, and meteorological elements related blowing snow to investigate the design methodology, using a linear
shelterbelt of approximately 80 m long and 15-30
m wide consisting mainly of Pinaceae, locatednorth to south in the field. Here, there set threesurvey lines (in the W-E direction) orthogonal to
the shelterbelt (see Figure 1).

2.2 Tree observation
We conducted the tree observation to find out thestructure and leaf area of the shelterbelt using adrone with LiDAR and a camera (DJI
Matricce300RTK and ZemuseL1). We acquired
point cloud data and images (approximately 80 mlong and wide) on Dec. 2nd, 2022 (the early winter of 2022/23). The LiDAR measurements were
performed under three angle conditions: vertical
downward (0°), oblique (45°), and lateral (60°) using iterative and non-iterative scanning as shownin Figure 1. Using raw scan data, point cloud data
(3D XYZ data) in LAS format was created by thesoftware (DJI Terra). The LAS data including precipitation particles was manually cleaned up using point cloud editing software (CloudCompare).
Using the camera images of a wrap rate of 80 %,an ortho-image (GeoTIFF) of the observation
area was created by the method of Structurefrom-Motion/Multi-View-Stereo (SfM-MVS).
At three survey lines before the winter as shownin Figure 1, ground-level surveys were firstly conducted using automatic level and total station.
Next, tree surveys were conducted by the belttransect method (width = 10 m) to acquire treeheight, diameter at breast height, the crown width,
and the height of the crown base (see Figure 2,
Figure 3, and Figure 4). In addition, the survey
getting leaf area index (m 2 m-2) (hereinafter:"LAI") was conducted by a plant canopy analyzer(LI-COR LAI-2000, hereinafter: "PCA"), and afisheye camera (Nikon D600, and SIGMA 8mm
F3.5 EX DG Circular Fisheye). Images taken by

the fisheye camera were analyzed using the software of LIA32 (Yamamoto, 2008), and Gap Light

Analyzer (Simon Fraser University, 1999) (seeFigure 2 and Figure 4).

Figure 1: Description of the study site.

Figure 2: Tree observation in the shelterbelt: left, tree surveys; center, LAI using a PCA;
right, LAI using a fisheye camera.

Figure 3: Items of tree survey.

Figure 4: Ranges of belt transect (orange),and LAI measurement positions (yellow).

2.3 Snowdrift observation
LiDAR measurements were also conducted in
mid-winter of 2022/23 on Jan. 27th, 2023 to determine the blowdown geometry of the study site,
similar to the method as shown in 2.2. In addition,
snowdrift surveys were conducted at the threesurvey lines during the snow season using snow
probes with measurement scales (see Figure 1).

2.4 Meteorological observation
Meteorological observations were conducted during the 2022/23 winter. Measurement elements
were these three; the mass flux of blowing snow
using snow particle counters (SPC-95), wind velocity and direction using propeller-vane anemometers (KDC-S04), and visibility in blowing
snow using transmissometers (PWD12). Eachsensor was set at 1 windward side point and 3
leeward side points of the shelterbelt at a heightof 1.8 m. Measurement intervals were 1 minute(wind velocity and direction, and visibility) and 1
second (mass flux of blowing snow). These data
were organized as 10-minute statistics. In addition, Snow depth was measured every 10 minutes
at the windward side point using a laser snow
depth sensor (KDC-S18-L-10R) to grasp the distance between 1.8 m height of the each sensor
and snow surface (see Figure 1). In addition, airtemperature was measured using a platinum resistance thermometer, and snowfall intensity (mmh-1) was measured using a Double Fence Intercomparison Reference (DFIR: Goodison et al.,1998) in the test field. These data were organized
as 10-minute statistics.

3. OBSERVATION RESULTS
3.1 Tree observation
First, we indicated the result of LiDAR measurements (e.g., Figure 5). It was not possible to obtain the structure of the interior of the shelterbelt
under each measurement condition due to thehigh density of leaves and branches at the observation site. In this study, the internal structure of
the shelterbelt (leaves, branches, trunks, etc.)
could be determined by combining the measurement profiles taken under the conditions of vertical downward (0°), oblique (45°), and lateral (60°).
As an example, a cross-section of point clouddata on survey line No.1 is shown in Figure 6.

Figure 5: Aerial view of the study site looking
southwest using point cloud data andcamera data on Dec. 2nd, 2022.

Figure 6: Cross section of point cloud data on the survey line No.1

Next, Table 1 shows the results of tree surveys
and the LAI measured data by several methods
for three survey lines. The characteristics of eachsurvey line based on Table 1 are shown below.
No.1: The number of trees is the largest and theaverage height of the crown base (the height of
the withered lower branches) is the highest in thethree survey lines. The shelterbelt width and LAD
are similar to No.3 and larger than No.2.
No.2: The shelterbelt width and LAD are thesmallest of the three survey lines. The number oftrees is similar to No.3. The average height of thewithered lower branches is in between No.1 and
No.3.
No.3: The average height of the withered lower
branches is the smallest of the three survey lines.

3.2 Snowdrift observation

understand the effect of the shelterbelt in a snowstorm, we selected data from three points on the
downwind side at the same time as the extracted
data. Using the selected data and the extracted
data, the ratio of meteorological elements was organized, which were on the leeward side of thesurvey line No.1-3 to the windward side of the
shelterbelt (see Figure 1).
Figure 8 shows box-and-whisker plots of windspeed and logarithm visibility ratios (Takechi et al.,2009) for three survey lines in the orthogonal winddirection during the observation period, with theupper and lower limits set at 1.5 times the interquartile range. The wind speed ratios were allless than 1.0, and this indicates that the windspeed was lower on the leeward side of the shelterbelt than on the windward side. The smallest
wind speed ratio was No.3, followed by No.1 andNo.2. In addition, a one-way analysis of variance

We obtained the data of the snowdrifts around the
shelterbelt based on the different measurement
profiles between before winter and mid-winter(Figure 7).

3.3 Meteorological observation
First, about the meteorological observation dataof the windward side of the shelterbelt, the winddirection was classified as orthogonal (±67.5 to90°), oblique (±22.5 to ±67.5°), and parallel (0 to±22.5°) to the shelterbelt. From the orthogonaldata, the data of wind speeds of 5 m s-1 and visibility of less than 100 m were extracted. Next, toFigure 7: Snowdrift heights around the shelterbelt in mid-winter on Jan. 27th, 2023.

Table 1: Results of tree survey based on the belt transect method and several LAI measurements
for survey lines No. 1-3 (TH, tree heights; BH, diameter at breast height; CW, crown width;
HCB, height of crown base)
SurveyLine No.

Width of the
shelterbelt

Numberof trees

Ave.
TH

Ave.
DBH

PCA

Mdn. LAI
LIA32

GLA

Ave.
CW

Ave.
HCB

1
2
3

19m
11m
19m

34
15
16

9.8m
10.4m
10.8m

14.3cm15.9cm18.9cm

2.95
2.30
2.91

1.50
1.35
1.77

1.75
1.68
2.84

1.67m
1.87m
2.18m

4.02m
3.37m
3.52m

2.00

Logarithm visibility ratio

Wind speed ratio

1.00
0.75

0.50
0.25
0.00
Line1
(n=1310)Line2(n=1310)Line3(n=1310)

1.75
1.50
1.25

1.00
Line1
(n=264)Line2(n=201)Line3(n=380)Figure 8: The ratio of meteorological elements on the leeward side on the survey line No.1-3 to thewindward side of the shelterbelt, using the orthogonal wind direction, wind speeds of 5 m s-1
and visibility of less than 100 m data during the observation period from Dec.10th, 2022 toMar. 10th, 2023: left, wind speed; right, logarithm of visibility.

Figure 3: Cross section of point cloud data on the measurement line No.1

revealed significant differences among the lines(P < 0.01). Next, The 25% tile value of the logarithm visibility ratio exceeded 1.35 for all lines,
and the median value was the highest in No.3.
This indicates that the shelterbelt reduces blowing snow from the windward side, resulting in
greater visibility on the leeward side. A one-way
analysis of variance revealed significant differences among the lines (P < 0.01).

4. ANALYSIS
4.1 Setting leaf area density using point
cloud data obtained by a drone with LiDAR
The Voxel-based Canopy Profiling method (hereinafter: "VCP method") has been used to calculate leaf area density (m2 m-3) (hereinafter: "LAD")
from point cloud data obtained by a ground-based
LiDAR (Hosoi and Omasa, 2006, 2014). In the
VCP method, it estimates the LAD using the number of voxels containing the laser beam trajectory,
the number of voxels containing the point where
the laser beam is reflected, distribution of the leaf
inclination angle. At the time, voxels that the laser
beam has not passed through are excluded to improve the estimation accuracy.
The point cloud data for the shelterbelt in thisstudy were measured from several angles by adrone moving in the sky, combined to create thedata (see Figure 1 and Figure 6). In this case, the
VCP method could not be applied because thetrajectory of the laser beam was unknown, andthe distribution of leaf inclination angle in coniferdominated shelterbelt could not be measured.
We considered that there were few areas where
the laser beam did not pass through in the shelterbelt from coupled the scanning data (see Figure 6). Therefore, we set LAD using point clouddata by a drone LiDAR without using the laser
beam trajectory in this study. The flow of setting
is shown in Figure 9, referring to the VCP method.
In this study, leaf area density (m 2 m-3) (hereinafter: "LAD") was calculated by dividing LAI by theaverage tree height (see Figure 3 and Table 1).
In Figure 9, (i) the Coordinate transformation ofpoint cloud data at 10 m width centered on thesurvey line No.1 is shown in Figure 10. (ii) The
space containing the shelterbelt (e.g., in the yellow frame in Figure 10) was divided into voxels,
and the number of voxels containing point clouds(hereinafter: "tree voxels") was calculated foreach 1-meter cubic grid (m-3) so that the internal
structure of the shelterbelt could be represented.
(iii) Oshio et al., (2013) reported that the LAD estimated by the VCP method depends on voxel
size. In this study, the number of tree voxels wasorganized by seven different voxel sizes: 1, 5, 10,
20, 50, 100, and 200 mm. Appropriate voxel size
was investigated using the relationship between
the different voxel sizes and the number of tree

Figure 9: The flow of the setting LAD using point cloud data in this study.

voxels in a 1 m grid near the center of the shelterbelt and a grid of the edge near the ground surface. The results showed that 10 mm was themost suitable voxel size (Figure 11).

Dtv10 is the density of 10 mm tree voxels in a 1 m
cubic grid (number m-3).
(v) The distribution of LAD was estimated fromthe equation (1) and Dtv10. As an example, theCrossing direction of the average distribution of
LAD on the survey line No.1 (width = 10 m, see
Figure 10) is shown in Figure 13. The height from
0 m to 1 m was excluded due to the influence ofthe ground surface.

4.2 Relationship between the shelterbelt andthe meteorological data in a snowstorm
To understand the relationship between the shelterbelt and meteorological data in a snowstorm,
we show Table 2. It shows the correlation coefficients obtained relationship between the medianvalue of the ratio of wind speed and logarithm visibility for lines 1-3 in the orthogonal wind direction(see Figure 8), and items of the shelterbelt basedon the tree observation in Section 3.1 and theanalysis results in Section 4.1.
The results show that Ave. LAD all (m2 m-3), W(m), Ave. TH (m), WTH (m2), and WTH_LAD (m4m-3) have a negative correlation of about 0.7 ormore with the wind speed ratio and a positive correlation of about 0.7 or more with the logarithm
visibility ratio. These indicate that wind speed was
reduced and visibility was greater on the leeward
side by the shelterbelt. In addition, the correlation
coefficient of WTH_LAD was greater than WTH.
This indicates the LAD contributed to the reduction of a snowstorm.
The height of the crown base (the height of thewithered lower branches) shows a positive correlation of about 0.6 for the wind speed ratio and apositive correlation of about 0.6 for the logarithm

Number of the tree voxels(1)

Number of the tree voxels

LAD = 0.00014 * Dtv10

Figure 10: Coordinate transformation of point
cloud data at 10 m width centered on thesurvey line No.1.
100

Line1, The gridaround thecenter of the
shelterbelt

80
60

40
20
0

10
0

100
200
Voxel sizes (mm)

300

6000

Line1, The gridof thewindward sideedge near the
ground surface

5000
4000

3000
2000
1000
0

10
0

100
200
Voxel sizes (mm)300

Figure 11: Relationship between the different
voxel sizes and the number of tree
voxels at the two different grids.
0.50

0.40
LAD (m² m⁻³)

Figure 9 shows (iv) the relationship between the
average density of tree voxels of 10 mm (numberm-3) at a 5 m radius from LAI measurement pointand LAD calculated from PCA, LIA32, and GLA in
the shelterbelt Interior (remove 5 m from the shelterbelt edge) is shown in Figure 12. The resultshows the correlation coefficient of LAD calculated from LIA32 and calculated from GLA wereabout 0.6, which was statistically significant (P <0.01). In this study, using the above statisticallysignificant data, a single regression equation
through the zero point was obtained.

0.30
0.20
0.10

0.00

y = 4E-05x + 0.28
R = 0.17
y = 0.0002x - 0.05
R = 0.60
y = 9E-05x + 0.04
R = 0.63

0
500
1000
1500
2000
Density of tree voxels of 10 mm (number m³)
PCA
Fisheye camera_LIA32
Fisheye camera_GLA

Figure 12: Relationship between the Density oftree voxels of 10 mm and the LAD calculated from PCA, LIA32, and GLA.
visibility ratio. It indicates that the wind speed washigher and the visibility was lower when the heightof the crown base was higher.
On the other hand, no correlation was found forthe height of 1.8 m LAD. This can be attributedthat the height and width of the shelterbelt were
not taken into account in this item.

Unit：m2 m-3Survey Line1

Crossing direction from windward side to leeward side (m)
0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

16
15
14
13
12

TH:
Tree
Height(m)

110.00 0.00 0.00 0.07 0.23 0.29 0.45 0.34 0.34 0.06 0.00 0.18 0.01 0.03 0.20 0.30 0.90 0.43 0.01 0.00 0.00 0.00

10

0.00 0.00 0.00 0.36 0.48 0.43 0.61 0.34 0.33 0.22 0.08 0.15 0.05 0.19 0.36 0.21 0.73 0.79 0.12 0.00 0.00 0.00

9

0.00 0.00 0.06 0.48 0.52 0.40 0.36 0.35 0.29 0.34 0.17 0.15 0.11 0.25 0.26 0.24 0.41 0.69 0.38 0.03 0.00 0.00

8

0.00 0.00 0.01 0.47 0.42 0.23 0.17 0.25 0.19 0.30 0.20 0.19 0.13 0.27 0.17 0.23 0.18 0.53 0.60 0.12 0.00 0.00

7

0.00 0.00 0.07 0.44 0.31 0.12 0.10 0.09 0.13 0.18 0.31 0.18 0.14 0.22 0.11 0.11 0.09 0.25 0.55 0.23 0.00 0.00

6

0.00 0.00 0.07 0.42 0.24 0.13 0.07 0.08 0.09 0.08 0.13 0.10 0.12 0.12 0.11 0.04 0.04 0.11 0.41 0.56 0.12 0.00

5

0.00 0.00 0.13 0.36 0.14 0.08 0.05 0.06 0.07 0.08 0.05 0.06 0.08 0.02 0.02 0.02 0.02 0.04 0.16 0.44 0.13 0.00

4

0.00 0.03 0.27 0.32 0.12 0.08 0.04 0.04 0.03 0.02 0.03 0.06 0.03 0.02 0.01 0.01 0.04 0.04 0.08 0.35 0.29 0.00

3

0.00 0.04 0.28 0.27 0.12 0.05 0.02 0.04 0.03 0.01 0.01 0.04 0.04 0.01 0.01 0.02 0.03 0.02 0.06 0.27 0.11 0.00

2

0.00 0.08 0.18 0.15 0.02 0.04 0.02 0.03 0.01 0.01 0.01 0.03 0.01 0.00 0.01 0.01 0.03 0.01 0.04 0.19 0.57 0.04

1

0.00 0.63 0.49 0.24 0.17 0.25 0.21 0.24 0.17 0.18 0.19 0.17 0.20 0.21 0.19 0.18 0.16 0.15 0.17 0.11 0.00 0.00

00.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Figure 13: Crossing direction of the distribution of average LAD at 10 m width centered on the survey line No.1 (see Figure 10)
Table 2: The correlation coefficient relationship between the median value of the ratio of wind speed
and logarithm visibility and items of the shelterbelt.
Items of the shelterbelt
Metrological data
Mdn. The ratio of
Items
Description in the survey line No.1-No.3
Wind
logarithm
speedof visibility
Ave. LAD all (m2 m-3) Average of the distribution of LAD
-0.96
0.97
Averageof
thedistribution
of
LAD
in
the
Ave. LAD 1.8(m2 m-3)
-0.17
0.19
height of 1 m to 3 m
W (m)Width of the shelterbelt
-0.69
0.68
Ave. TH (m)Average tree heights
-0.70
0.71
Average height of crown base
Ave. BCH (m)(Average of the height of the withered
0.58
-0.60
lower branches)
Area of the survey lines
WTH (m2)-0.82
0.81
W * Ave.TH (m2)
Total leaf area of the survey lines
WTH_LAD (m4 m-3)
-0.88
0.87
W * Ave.TH *Ave. LAD all (m4 m-3)

5. SUMMARY AND FUTURE ISSUESIn this study, we conducted observations on trees,
snowdrifts, and meteorological elements related
blowing snow using a shelterbelt composed of evergreen conifers to investigate the design methodology of living snow fences in a severe snowstorm. Tree, snowdrift, and meteorological observation were conducted on three survey lines orthogonal to a linear shelterbelt in a windy andsnowy area of the northern part of Japan. Basedon the results, we created the average distributionof leaf area density on the survey lines and organized the relationship between the shelterbelt andthe meteorological data in a snowstorm.

Going forward, we will continue the field observation in the 2023/24 winter, and analyze the fieldobservation data for the 2022/23 and 2023/24
winters to create benchmark data for using numerical simulation of blowing and drifting snow.
Based on the benchmark data, the optimal calculation conditions (boundary conditions, models,etc.) for snowstorm simulations will be discussed.
We will also conduct numerical simulation of
blowing and drifting snow to present an optimized
configuration of living snow fences that can meet
any required performance during severe snowstorm events.

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