Developing a decision support
approach to reduce wind damage
risk – a case study on sugi
(Cryptomeria japonica (L.f.) D.Don)
forests in Japan
KANA KAMIMURA1,5*, BARRY GARDINER2, AKIO KATO3,
TAKUYA HIROSHIMA4 and NORIHIKO SHIRAISHI1
1 Laboratory
of Forest Management, Graduate School of Agricultural and Life Sciences, The University of
Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
2 Forest Research, Northern Research Station, Roslin, Midlothian EH25 9SY, Scotland
3 Toyama Forestry and Forest Products Research Center, Tateyama, Toyama 930-1362, Japan
4 The University Forest in Chiba, Graduate School of Agricultural and Life Sciences, The University of Tokyo,
770 Amatsu, Kamogawa, Chiba 299-5503, Japan
5 Present address: Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba, Ibaraki 305-8687, Japan
*Corresponding author. E-mail: [email protected]
Summary
A decision support-based approach has been developed in order to help recommend silvicultural
treatments for reducing typhoon wind damage in Japanese forests. A case study was conducted on
three management scenarios (no thinning, light thinning and heavy thinning) for sugi (Cryptomeria
japonica (L.f.) D.Don) stands in Himi, Toyama Prefecture, Japan. The decision support approach
integrated models and tools including a mechanistic/empirical wind damage risk assessment model
ForestTYPHOON, which includes a modified version of the wind damage risk model, geographical
analysis of the losses and effects of storms, and an airflow model, wind atlas analysis and application
program. A growth model Silve-no-Mori was linked with ForestTYPHOON to estimate wind
damage risk over a 50-year period. After assessing the wind damage risk, risk stands were displayed
using a geographic information system. In addition, decision tree analysis provided information
on stand characteristics related to wind damage. Approximately 90 per cent agreement was found
between the wind damage assessment using ForestTYPHOON and the outputs of the decision trees.
The decision trees showed that top height was the most important stand characteristic and provided a
critical top height at which silvicultural treatments need to be modified. If the top height exceeds the
critical height, any treatments including thinning should be avoided to minimize wind damage risk.
Introduction
Wind damage risk is a serious issue in Japanese
forests because of recent typhoon activity and
© Institute of Chartered Foresters, 2008. All rights reserved.
For Permissions, please email: [email protected]
the current condition of the forest estate. The
latest report of the Intergovernmental Panel
on Climate Change (Solomon et al., 2007)
notes that tropical cyclones (i.e. typhoons and
Forestry, Vol. 81, No. 3, 2008. doi:10.1093/forestry/cpn029
Advance Access publication date 29 May 2008
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FORESTRY
hurricanes) will tend to increase in peak wind
intensity, although the numbers of cyclones
may decrease in the future. Wind damage resulting from two historically catastrophic typhoons (the Toyamaru typhoon in 1954 and
the Isewan typhoon in 1959) is well documented. The Toyamaru typhoon destroyed
approximately 300 000 m3 of timber, and the
Isewan typhoon destroyed more than 4.1 million m3 of timber (Tamate, 1967). In terms of
catastrophic wind damage in recent decades,
the Forest Agency (1992, 2005) reported that
approximately 11 million m3 of stands in 1991
and 22 million m3 of stands in 2004 from private and public forests were damaged mainly
by typhoons. Damaged stands are often found
to be within a particular age range. Kuboyama
et al. (2003) statistically analyzed the data of
abiotic damaged stands from forest insurance
databases and found that planted forests older
than 41 years are more likely to incur wind
damage. The Forestry Agency showed that approximately 52 per cent of planted forests were
more than 36 years old in 2002 (http://www.
rinya.maff.go.jp/toukei/genkyou/jyusyu-ha/
jyusyu-ha.xls); thus, the extensive existing
semi-mature and mature stands in Japanese
forests could incur enormous damage from typhoons.
The wind climates in regions affected by typhoons vary depending on the route and size of
the typhoon. Typhoons are a form of tropical
cyclones which develop over tropical oceans
and are called hurricanes in the North Atlantic and cyclones in the region of Australia and
India (ESDU, 1987). These tropical cyclones
show similar phenomenon in the northern
hemisphere where there is an eye at the centre
with low pressure and a large counterclockwise
revolving vortex (Foster and Boose, 1995). Typhoons usually move at between 5 and 15 m
s⫺1 and are associated with strong winds and
heavy rainfall. The most destructive weather
condition in a particular location is generally
observed over a 4- to 6-h period (ESDU, 1987).
Typhoons are an unusual wind event compared
with normal wind flow. For instance, Himi
(Toyama Prefecture, Japan) suffered from catastrophic typhoon wind damage in 2004 caused
by strong winds coming from the north-east,
whereas the normal wind direction in this re-
gion is mostly west-south-west or south-west
(Japan Meteorological Agency website http://
www.jma.go.jp/jma/indexe.html).
Due to the uncertainty of typhoon events, size
and route, it is hard to develop a measure of the
wind damage risk in Japan. Consequently, such
damage has not been studied from the long-term
perspective of wind damage risk. In other words,
wind damages in forests were only observed on the
basis of location, volume and area in order to determine economic losses (Matsuzaki and Nakata,
1994) and ignored the impact of crop characteristics and local airflow patterns. This has limited the
development of future wind damage risk strategies.
On the other hand, several tools for assessing wind damage risk are already available in
different countries. The tools are based on directly linking tree mechanical behaviour and
forest airflow to predict stand vulnerability
and potential damage risk. Forest Research in
Britain has developed a windthrow risk assessment model ForestGALES, which includes a
predictor of wind climate named detailed aspect method of scoring (DAMS) (Quine and
White, 1993) and a predictor of stand vulnerability named geographical analysis of the
losses and effects of storms (GALES) (Gardiner
et al., 2000). GALES is a mechanistic/empirical
model developed to calculate the critical wind
speed (CWS), leading to overturning or stem
breakage inside or on the edge of unthinned
or lightly thinned coniferous forests in Britain
(Gardiner et al., 2000). Peltola et al. (1999) developed a mechanistic model HWIND to assess
wind and snow damage at the edges of Finnish
forests. A modified version of HWIND is also
used with an airflow model, wind atlas analysis
and application program (WAsP) (Mortensen
et al., 2004) in the WINDA system (Blennow
and Sallnas, 2004) for assessing the stand risk
in Sweden.
In addition to the wind damage assessment
models, other techniques such as decision
trees could be used to supply practical information for forest management. Although the
wind damage models can indicate the most risk
stands in terms of CWS, the information has
practical limitations in its direct applicability.
However, these stands can be identified by key
characteristics (e.g. height, stem spacing, stem
taper, slope and aspect) to enable practitioners
DECISION SUPPORT APPROACH TO REDUCE WIND DAMAGE RISK
to make long-term forest management plans.
Decision tree methods including the classification and regression trees (CART) algorithm
(Breiman et al., 1984) are powerful tools for
use in forestry to help identify significant
characteristics in relation to particular phenomena (Olofsson and Blennow, 2005; Fan
et al., 2006).
The aim of this paper is to demonstrate a decision support approach to provide overall and
practical information for the development of silvicultural treatment alternatives with a special
focus on reducing the typhoon wind damage
risk in sugi (Cryptomeria japonica (L.f.) D.Don)
stands in Himi region, Toyama Prefecture,
Japan. A wind risk management (WRM) tool,
ForestTYPHOON, was developed using a modified version of GALES and the airflow model
WAsP. ForestTYPHOON has already been
developed and validated using typhoon wind
damage data from Himi by Kamimura (2007).
The decision support approach was constructed
from a growth model Silve-no-Mori, ForestTYPHOON, ArcGIS (ESRI Ltd, Redlands, CA)
and the decision tree algorithm CART. After
simulating forest growth using Silve-no-Mori,
ForestTYPHOON was used to predict the wind
damage risk for sugi stands in Himi over a 50year period using three management scenarios:
no thinning, light thinning and heavy thinning.
The predicted risk location and site characteristics (with 5-year intervals) were then used together with the estimates of damage to formulate
decision trees, which provided more detailed information on the factors related to wind damage. The original wind risk assessment done by
ForestTYPHOON was also compared with the
predictions of the decision trees to evaluate the
latter approach. Using the decision trees, the risk
of wind damage for different silvicultural management approaches was evaluated.
Material and methods
Study site
The study site was located in Himi, Toyama
Prefecture, including 42 subcompartments
of planted sugi trees (Cryptomeria japonica
(L.f.) D.Don) on private property (Figure 1).
431
(Subcompartment is the minimum forestry management unit in most cases. It is usually based on
the same ownership, tree species and tree age.)
This region suffered from wind damage caused
by Typhoon No. 23 on 20 October 2004. (This
is the number of a specific typhoon occurring in
the north-western Pacific Ocean during a particular year. All typhoons do not pass across
Japan.) Stand characteristics, such as diameter
at breast height (d.b.h.), tree height and stand
density, were obtained from 0.03 to 0.04 ha of
a field survey plots after the typhoon (Kato and
Zushi, unpublished data). The total study area
was 305.5 ha and total number of plots was 72.
This field survey data were used as basic information both to validate models and to develop
the decision support approach.
Calculation of the CWS in ForestTYPHOON
was based on subcompartment level in which the
general site information was calculated from the
field data (Table 1). Geographical data, such as
elevation, aspect and slope, were estimated at
the centre of the subcompartments using ArcGIS.
Table 1 provides the basic information for the
subcompartments. The location and size of gaps,
defined as an open area having more than 10 m
width in the wind direction, were estimated from
aerial orthophotographs.
Outlines for the WRM tool and component
models for decision support approach
ForestTYPHOON is a WRM tool containing a
modified version of the CWS model GALES and
the airflow model WAsP (Gardiner et al., 2008).
GALES is a mechanistic model used for wind risk
assessment, which calculates the CWS at a height
of the zero-plane displacement (≈tree height ×
0.8) + 10 m. By conducting tree-pulling experiment in the experimental forest of the University of Tokyo, Chichibu, in Saitama prefecture
(Kamimura, 2007), GALES was modified for tree
species in Japan, including sugi. The main parameters derived for sugi trees on brown earth soils
are the regression slope between the maximum
measured turning moment at the stem base and
the stem weight (229 Nm kg⫺1) and the modulus
of rupture (MOR) calculated from broken trees
(42.5 × 106 N m⫺2). Modified versions of GALES
have been applied in several countries including
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FORESTRY
Figure 1. Location of the study site in Himi, Toyama Prefecture, Japan. Subcompartments and elevation are
also indicated.
Table 1: Basic information of the study site (subcompartments) in Himi based on the field survey conducted by
Kato et al. (unpublished data)
Mean
Minimum
Maximum
Area
(ha)
Stand age
(years)
d.b.h.
(cm)
Height
(m)
Stem spacing
(m)
Elevation
(m)
Slope
(degrees)
7.3
3.0
34.8
32
18
74
25.9
18.1
43.5
18.5
13.1
30.6
3.1
2.1
5.0
368
338
450
14.6
5.3
25.3
Stand age, d.b.h. and tree height were observed by field survey and area; elevation, slope and aspect were
estimated by using ArcGIS Spatial Analyst. Average slope aspect is south-east. Stem spacing was calculated from
the stand density.
New Zealand (Moore and Quine, 2000), France
(Cucchi et al., 2005) and Canada (Ruel et al.,
2000; Achim et al., 2005).
To calculate the CWS, GALES requires basic
information on the stand such as tree species,
d.b.h., top height, spacing and soil type (Gardiner
et al., 2000). GALES also gives values of CWS
for overturning and breakage due to the different mechanism of failure. CWS for overturning
is mostly based on the relationship between the
maximum measured turning moment at the stem
base and the stem weight and that for breakage
is based on the stem strength (e.g. MOR and
d.b.h.).
WAsP was used to estimate the variation in
wind speeds during typhoons. WAsP is a PCbased program developed by Risø National Laboratory in Denmark (Mortensen et al., 2004) to
DECISION SUPPORT APPROACH TO REDUCE WIND DAMAGE RISK
estimate the local wind climate. The calculation is
based on a linearized airflow model for low hills
developed by Jackson and Hunt (1975). Time series wind climate data and surface information
(e.g. roughness length and terrain conditions) are
required to generate a ‘wind atlas’ consisting of
the estimated wind speed (EWS) for particular
azimuths. Weibull distributions were then used in
the analysis to indicate the probability of occurrence of different wind speeds. WAsP is designed
to provide a measure of the normal wind climate
variation in moderate terrain. The strong spatial
and temporal wind speed variation in typhoons
means that there are uncertainties about the accuracy of our wind speed predictions. Future
work will hopefully be able to make use of mesoscale airflow models specifically developed for
typhoons (e.g. Yoshida et al., 2006).
Validation of ForestTYPHOON was performed by comparing actual wind damage and
predicted wind damage using ForestTYPHOON
(Kamimura, 2007). This process was based on
the typhoon damage in 2004 in Himi, Toyama
Prefecture. The basic information of stand and
damage was obtained from 0.03 to 0.04 ha of a
field survey plots after the typhoon (Table 1) and
included d.b.h., tree height, number of trees and
failure type. Then, the CWS and local EWS were
calculated using ForestTYPHOON with the stand
information and wind climate data measured at
the meteorological station in Himi. Next, the CWS
was compared with EWS and used to estimate the
likelihood of wind damage in each plot. This was
followed by actual damage and predicted damage.
Finally, since the accuracy of the prediction was
likely to be less than 70 per cent, fuzzy and sensitivity analyses were used to find the most reliable
factor to improve prediction accuracy. (Gardiner
et al. (2008) validated GALES using stand data
in Britain and concluded that the accuracy of the
prediction of wind damage was at best approximately 70 per cent.) We primarily conducted a
fuzzy analysis for the EWS, based on the hourly
mean speed, because it was likely to be lower than
the extreme wind speed during the typhoon. The
EWS was increased in 0.5 m s⫺1 steps and the improvement of accuracy was examined. In addition,
sensitivity analysis for the CWS was performed.
Table 2 shows the final results of the validation.
To achieve more than 70 per cent of accuracy with
the minimum change of the EWS and CWS, the
433
Table 2: Final result of validation for
ForestTYPHOON in order to achieve 70 per cent
of accuracy due to comparison between actual wind
damage and predicted wind damage in Himi, Toyama
Prefecture
Numbers of expected
damaged plots
Numbers of expected
undamaged plots
Numbers of
observed
damaged
plots
Numbers of
observed
undamaged
plots
34
12
8
19
The validation included a fussy analysis for the EWS
and a sensitivity analysis for the CWS. The result was
based on the EWS × 1.3 and ±1 m s⫺1 of the CWS
range.
EWS should be increased up to 30 per cent and
the CWS needs to be considered within a ±1 m s⫺1
band (e.g. a CWS of 15 m s⫺1 should be regarded
as ranging from 14 to 15 m s⫺1). The final accuracy was 72.6 per cent, which was calculated from
the correctly predicted damaged and undamaged
plots divided by total number of plots.
To simulate forest growth, a PC-based forest
growth and yield model Silve-no-Mori (Tanaka,
1991) was used to obtain the change in forest
characteristics for long time. The input datasets
are stand age, field survey plot area, the number
of stems per d.b.h. class per hectare and mean
tree height in each d.b.h. class. The outputs are
d.b.h., mean tree height and volume per hectare
for every 5 years up to 50 years (Toyama Forestry
and Forest Products Research Center, 2005).
Silve-no-Mori was originally developed for the
indigenous sugi species in Toyama Prefecture,
Japan. The model is a probability model that predicts future d.b.h., mean tree height and the number of stems in each d.b.h. class per hectare by
using stem density distribution and height curves.
Silve-no-Mori allows for the simulation of several thinning scenarios by choosing the number
of removed stems in each d.b.h. class per hectare
(Toyama Forestry and Forest Products Research
Center, 2005). Mortality is not included in the
current version of Silve-no-Mori.
The main purpose of developing decision
trees is to divide large sets of data into smaller
subsets (nodes) in order to understand what
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variables influence specific phenomena. Thus,
decision trees not only show accurate classifiers
but also provide an understanding of the data
structure, which can be used to predict the same
phenomena for other datasets (Breiman et al.,
1984). Several algorithms have been proposed for
creating decision trees, such as CART (Breiman
et al., 1984), chi-squared automatic interaction
detection (CHAID) (Kass, 1980) and quick, unbiased, efficient, statistical tree (QUEST) (Loh
and Shih, 1997). These algorithms are available
in the Classification Trees extension in SPSS 14.0
(SPSS, Chicago). CART and QUEST are based on
a binary-splitting node method, while CHAID is
based on a multiple-splitting node method. In this
analysis, CART was used because of its simplified
output trees and self-pruning algorithm, which
can avoid overgrowth of decision trees (Berry and
Linoff, 2004).
CART is a non-parametric and binary system to split datasets to subsets (nodes) on a tree
(Breiman et al., 1984). CART includes two components of the classification tree for qualitative
variables and the regression tree for quantitative variables. The growth of CART is based on
three steps: selecting the split, deciding whether
the node is terminal or if splitting should continue and assigning terminal nodes into a class.
Throughout the simulation, CART selects the
split so that the descendent nodes (child nodes)
contain purer data than the previous nodes (parent nodes). There are two splitting rules in CART:
the Gini index of diversity and the two-ing rule.
In this study, the Gini index, which is the most
popular splitting rule, was selected since the twoing rule is not related to the impurity measurement in nodes. The Gini index sums the square of
the proportion of data classes in a node; the Gini
index of the perfectly pure node is one (Berry and
Linoff, 2004). In addition to the splitting rule,
misclassification cost is used when one previously
has information of importance in each data class.
The misclassification cost sets the penalty associated with misclassification (SPSS, 2004), so that
the important data class is purer in a node than
the other unimportant data class.
Scenario setting
The decision support approach for this study
site was conducted depending on three silvicul-
tural scenarios in order to examine how silvicultural treatment, in particular thinning, and
stand condition would affect wind damage occurrence. These scenarios over 50 years were
set up for different thinning regimes in terms
of stand density control (Drew and Flewelling,
1979). The timing and intensity of thinning
were based on the relative yield index (Ry),
which is the ratio of stand volume to the maximum stand volume. Ry is commonly used for
Japanese forestry to identify stand conditions
(in particular stand density) in order to achieve
stand conditions that are suitable for harvesting
(Forest Agency, 1999). In general, Ry in Himi
is set to 0.7 as a final goal for timber production. There were three scenarios; one no thinning scenario and two thinning scenarios based
on Ry = 0.7. Scenario 1 was no thinning over 50
years from the survey year. Stem spacing did not
change over the periods due to a lack of mortality in Silve-no-Mori. Scenario 2 was light and
frequent thinning. If the stand condition exceeds
Ry = 0.7, thinning was carried out by felling 20
per cent of the stems per hectare. There is no interval restriction. Scenario 3 was heavy and infrequent thinning. If the current stand condition
exceeds Ry = 0.7, thinning was carried out until
Ry = 0.6. Total removed stand volume has to be
within Ry = 0.15 to avoid overcutting. Thinning
is not conducted within 10 years of the previous
thinning.
Flow of the decision support approach
The decision support approach consists of five
steps such as simulating forest growth, calculating the relative wind speeds (CWS and EWS),
calculating wind damage risk, visualizing wind
damage risk using the results of the risk assessment and analysing the significant stand characteristics related to wind damage risk to provide
information for risk management of forests. The
framework of the decision support approach is
presented in Figure 2.
A growth model, Silve-no-Mori, was applied
for this study site to simulate change of forest
growth. The rotation period was set to 50 years,
which was the maximum period of Silve-no-Mori
simulation. Input data were derived from the subcompartment information (Table 1).
DECISION SUPPORT APPROACH TO REDUCE WIND DAMAGE RISK
435
Figure 2. Framework of the decision support approach consists of growth model (Silve-no-Mori), ForestTYPHOON (modified GALES and WAsP), ArcGIS and decision trees (CART).
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FORESTRY
Silve-no-Mori generally gives the mean tree
height, whereas ForestTYPHOON requires top
tree height. From the field data in Himi, the mean
tree height of a plot is strongly correlated with
the maximum tree height of a plot (R2 = 0.971)
and we assumed that the maximum tree height
could be substituted for top height in ForestTYPHOON with top height calculated as follows:
H top =
H mean
+ 1.077,
1.02
(1)
where Htop is top tree height (m) and Hmean is
mean height of a stand (m).
Next, ForestTYPHOON was used to calculate
CWS using the modified version of GALES for
Japan and EWS was calculated using WAsP. Input
data were obtained from the output of Silve-noMori and from geographical information. GALES
and WAsP were simulated 11 times (every 5 years
for the 50-year rotation) with changing stand
(above-ground) conditions. The simulation was
targeted at the centre of subcompartments.
WAsP required geographical and wind climate
data for Himi to estimate the local wind speed.
The geographical data were prepared using a
50 × 50m digital terrain model. Roughness lengths
used for this simulation were 0.0002 m for the
ocean, 0.03 m for open areas or grass fields, 0.75
m for deciduous and coniferous forests and 1 m
for urban areas (Suárez et al., 1999; Venäläinen
et al., 2004; KNMI, 2005). Input 10-year wind
climate data (from 1995 to 2004) was measured
at Himi AMeDAS station located on average
9 km from the subcompartments, which is the
only available station measuring wind climate
in this region. (AMeDAS is an abbreviation of
the automated meteorological data acquisition
system operated by the Japan Meteorological
Agency. Hourly data can be downloaded from
the Japan Meteorological Agency website http://
www.data.kishou.go.jp/etrn/index.html.) Subsequently, the wind speed data were divided into
four directions: north (315–360°, 0–45°), east
(45–135°), south (135–225°) and west (225–
315°). Because typhoons vary in intensity spatially and follow different routes, it is difficult to
determine the wind direction associated with the
peak wind speed which is directly related to the
damage to trees. In this study, the probability of
wind occurrence was assumed to be equal in all
sectors.
The wind speed was estimated at the same
height as the CWS estimation in GALES. In addition, the wind speed related to the damage during
a typhoon was calculated using the formula
RWSstand = WSmean,stand ×
24.6
× 1.3,
3.72
(2)
where RWSstand (m s⫺1) is the relative wind
speed during the typhoon and WSmean,stand is the
mean wind speed at the centre of a stand for the
10-year period as calculated by WAsP. The maximum wind speed recorded was 24.6 m s⫺1 at
10 m height at Himi AMeDAS station during the
typhoon event (20 October 2004) and 3.72 m s⫺1
was the hourly mean wind speed over the 10-year
period at Himi AMeDAS station. The conversion
factor of 1.3 (i.e. adding 30 per cent to the EWS)
was obtained from the validation of ForestTYPHOON using the datasets in Himi (Kamimura,
2007) and suggests that wind speeds over the
damaged stands were 30 per cent higher than that
predicted by WAsP.
The CWS and the EWS were then compared
to find the wind damage risk at each target point
(stands). If the EWS exceeds the CWS, wind damage should occur during the 50-year period. Here,
a binary rule was applied to indicate the risk; ‘1’
indicates the existence of high wind damage risk
and ‘0’ no wind damage risk.
Since the EWS depends on four azimuths,
there are four values for both overturning and
breakage. To simplify the risk determination
and show degrees of risk, the risk values were
averaged and classified into the following five
classes: I (0 per cent risk of wind damage), II (25
per cent), III (50 per cent), IV (75 per cent) and
V (100 per cent). However, if there is evidence
for a particular frequency of strong winds from
a particular direction, it would be recommended
to apply a weighting rule to average the risk
rather than using the simple arithmetic mean applied here.
Finally, ArcGIS Spatial Analyst was used to illustrate the highest wind risk in the targeted area
over particular periods. The raster operation was
convenient to describe the highest risk or average
risk during a certain period of time using the cell
calculator function. Eleven raster layers including risk of wind damage were created for every
5-year simulation over a total period of 50 years,
which were on the basis of the Silve-no-Mori
DECISION SUPPORT APPROACH TO REDUCE WIND DAMAGE RISK
simulation. This GIS operation was also based
on the assumption that the wind damage risk
was the same throughout a subcompartment.
To simplify the final output, all the wind damage risk (all directions and failure types) were
averaged to show a single value of wind damage
risk.
Decision trees were created for overturning
and breakage. The values of wind damage risk
were used as dependent variables; independent
variables were selected from various geographical and tree characteristics. There were two kinds
of independent variables: constant and temporal. The constant, independent variables were
based on geographical characteristics that rarely
change, such as aspect, slope and elevation. The
temporal independent variables are found in
the above-ground characteristics that change
with tree growth, such as top tree height, d.b.h.,
(d.b.h.)2 multiplied by tree height (d.b.h.)2h,
height/d.b.h. and stem spacing.
To find the most significant characteristics
causing wind damage by using CART, higher
damage risk classes (classes IV and V) were focused on in particular. Therefore, misclassification costs were set for the risk classes with the
least acceptable classification accuracy of classes
IV and V set to 80 per cent. Although the misclassification process decreased total accuracy,
it increased the accuracy of the targeted classes
(SPSS, 2004). At the end of the analysis, the terminal nodes were selected in which the risk categories IV and V occupied more than 80 per cent
of the total risk classes. In addition, nodes were
selected where the total risk percentage (classes
II, III, IV and V) was greater than class I (i.e. no
risk of wind damage) to avoid underestimation
of the overall risk.
After creating the decision trees, they were validated and analysed for sensitivity compared with
the original values from the ForestTYPHOON
simulation. In addition, since intricate splitting
rules can confuse decision makers, rounded values of the splitting rules were applied to classify
the datasets. The new scores were then compared
with those in the original decision trees. Subsequently, important splitting rules and nodes directly related to wind damage risk were extracted
to simplify the decision trees because the original
decision trees include the information for all risk
classes.
437
Results
Wind damage risk assessments
The results of the wind damage risk assessment
using ForestTYPHOON and Silve-no-Mori are
presented in Figure 3. Wind damage risk appears
to increase with stand age and intensity and timing of thinning treatments. Although thinning
has a lot of advantages including economic benefits, thinning changes the wind turbulence above
and within the canopy. Stands just after thinning
are susceptible to wind damage until they adapt
through canopy closure, stronger root–soil anchorage and higher stem strength. Comparing
Scenarios 2 and 3, the change of the risk classes in
Scenario 2 was more moderate than that in Scenario 3 and suggests that heavy thinning could
sometimes create a high risk of wind damage at
this study site. It was found that in 27 subcompartments, the possibility of wind damage increased following thinning, whereas it decreased
only in two subcompartments. According to
Figure 3, there were few differences in the risk in
13 subcompartments between Scenarios 1 and 2
or between Scenarios 1 and 3. In the subcompartments with an increased possibility of wind damage risk over 50 years, 24 subcompartments had
increased risk in Scenario 2 and 26 subcompartments had increased risk in Scenario 3. We were
not able to perform statistical evaluation of any
differences between thinning scenarios due to the
limited number of samples.
Evaluation of decision tree approach
Six types of decision trees were created to cover
the silvicultural scenarios and failure types (overturning and breakage). The average accuracy of
classification for classes IV and V was 89 per cent
for overturning and 83 per cent for breakage
in Scenario 1, 82 per cent for overturning and
85 per cent for breakage in Scenario 2 and 85 per
cent for overturning and 86 per cent for breakage
in Scenario 3.
The validation of the splitting rules indicated
if the rules and the independent variables suitably classified subcompartments into whether or
not damage would be expected. Table 3 provides
the scores for expected wind damage according
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Figure 3. Change in the number of subcompartments in terms of wind damage risk classes (risk of wind
damage is increasing from class I to class V) for the three scenarios over 50 years.
Table 3: Comparison between wind damage risk scores from the mechanistic method (i) and from the decision
trees (ii) and between (i) and scores from the modified decision trees using rounded values of the splitting
rules (iii)
Scenario
1
2
3
Failure
type
Overturn
Breakage
Overturn
Breakage
Overturn
Breakage
(i) vs (ii).
(iii) Modified (i) vs (iii).
(i) Mechanistic (ii) Decision Total agreed (i) vs (ii). decision trees Total agreed (i) vs (iii).
method wind trees wind scores (risk Agreement wind risk
scores (risk Agreement
risk scores
risk scores and no risk) (per cent)
scores
and no risk) (per cent)
75
133
150
178
155
178
112
142
169
180
170
193
397
427
429
412
413
411
86
92
93
89
89
89
82
143
155
170
171
189
401
422
425
402
412
409
87
91
92
87
89
89
Wind damage risk scores (i and ii) consist of the number of data in risk classes IV and V. The total agreed scores
include all data in the risk and no risk classes with a maximum number for perfect agreement of 462. This
represents 42 subcompartments multiplied by 11 steps of the growth simulation (every 5 years during 50 years).
Agreement (per cent) was calculated from the total agreed scores divided by 462.
to ForestTYPHOON and according to the decision trees. The scores were approximately 90 per
cent in agreement. The decision trees tended to
overclassify the damage because some terminal
nodes included other risk classes in addition to
classes IV and V. The agreement on overturning
DECISION SUPPORT APPROACH TO REDUCE WIND DAMAGE RISK
in Scenario 1 was lower than that for other situations. This might result from the fact that the
total scores of risk classes IV and V were not
large enough to develop a suitable classification
trees (i.e. only 75 stands).
Subsequently, the values of the node splitting
rules were rounded down to whole integers (top
height, d.b.h., (d.b.h.)2h, elevation and slope) or
one decimal point (stem spacing) because decision
makers are more likely to prefer rounded values.
This makes the decision trees more practical tools
in the field and in calculations. However, sensitivity analysis was required to test the suitability of
the decision trees using rounded numbers for forest management. Although some matched scores
were slightly less than the scores with the exact
values (Table 2), there was approximately 89 per
cent consistency between the wind risk estimation
between the original scores from the mechanistic
method and the modified scores from the decision trees using rounded numbers. Consequently,
rounded values would be helpful in simplifying
the decision trees.
The risk of wind damage in relation to stand
characteristics
Figures 4 and 5 show the critical locations
and stand characteristics related to wind damage over a 50-year period. After the top height
reaches a critical value, the possibility of wind
damage risk was more closely related to other
above-ground characteristics. As the stands mature, the subcompartments in the risk nodes in
both the overturning and the breakage decision
trees would move to other risk nodes and then
reach the final risk nodes 1O1, 1B1, 3O1 and
3B1. There are few ways to reduce wind damage risk for the subcompartments in the final
highest risk nodes. Therefore, once the subcompartments reach critical tree height, it would be
advisable to start making harvesting plans for
the subcompartments in these risk nodes or try
to ensure no change in stand conditions. On the
other hand, before reaching critical height, the
key characteristics were mainly related to geographical condition. This indicates that silvicultural operations could be safely carried out
before reaching this height so long as high-risk
locations are avoided.
439
Discussion and conclusions
Alternatives for forest management
The decision support approach revealed differences of susceptibility to wind depending on
silvicultural treatments. Comparing the three
scenarios, more subcompartments were found at
risk of breakage than of overturning in the decision trees (see the mechanistic method scores in
Table 2). This fits the findings of the previous
study (Kamimura et al., 2007) in which the likelihood of breakage increased with the age of the
stands. Furthermore, thinning practices appear to
decrease stand stability, which is in contrast to
Kamimura et al. (2007) who calculated the CWS
as a function of stand density and concluded that
dense stands were more likely to be unstable than
less dense stands. In dense stands, neighbouring
trees could support each other, which may partly
compensate the fact that in dense stands trees are
generally more slender and are potentially more
liable to be damaged. Moreover, thinning against
wind risk was only effective when the top height
was lower than 12 m for sugi (Kamimura et al.,
2007). Their finding indicates that the timings of
thinning may be a key to reducing wind damage
risk. The top height in this study site was already
more than 12 m; therefore, there would be few
advantages from thinning in Himi due to the increase in stand risk and the creation of gaps.
Using the rounded values of the node definition from the previous analysis, Figure 5 presents
the modified decision trees in terms of the failure types in the three scenarios. In these decision
trees, only relative nodes associated with classes
IV and V are presented. Hence, the subcompartments included in the terminal nodes (1O1,
1B1, etc.) had a very high risk of wind damage.
The most crucial independent variable was top
height, which controlled the first split. In CART,
significant independent variables were selected
to reduce impurity of nodes and to sequentially
split dataset to create the purest node (Breiman
et al., 1984). Thus, the first splitting rule could be
defined as the independent variable related to all
datasets. Here, the top height of the first splitting
rule can be defined as a critical tree height (Miller,
1985). The taller a tree, the larger the turning moment for the same above-canopy wind speed. The
observation of a critical tree height confirms the
440
FORESTRY
Figure 4. Hazard maps illustrating maximum possibility of wind damage (integrated overturning and breakage likelihood) according to three scenarios over 50 years. Risk of wind damage is increasing from class I
to class V.
relationship between wind damage risk and stand
age. In general, height growth is strongly related
to stand age compared with other tree character-
istics such as d.b.h. (Avery and Burkhart, 2002),
which may partly explain the reason why stand
stability decreases with age (Quine, 1995).
DECISION SUPPORT APPROACH TO REDUCE WIND DAMAGE RISK
441
Figure 5. Decision trees (CART) of Scenario 1 (no thinning), Scenario 2 (light and frequent thinning) and
Scenario 3 (heavy and infrequent thinning) simulated over 50 years. Every terminal (risk) node was coded
using scenario number + failure type (overturning or breakage) + node numbered from right to left side of
a decision tree.
Using CART with WINDA, Olofsson and
Blennow (2005) found that height/d.b.h. (stem
taper), in addition to tree height, is the most important tree characteristics at the stand edge for predicting wind damage. However, stem taper was less
important than other above-ground characteristics
such as spacing in our analysis, focusing on the inside of subcompartments. This may be due to differences in the CWS models used in WINDA and
ForestTYPHOON. WINDA uses HWIND, which
calculates the wind loading on edge trees from
the wind profile. ForestTYPHOON uses GALES,
442
FORESTRY
which predicts the stress distribution on trees
within the stand using the ‘roughness method’.
GALES is more sensitive to spacing between the
trees than HWIND, whereas HWIND is more
sensitive to d.b.h. (Gardiner et al., 2000). Our
results suggest that management effects may be
different for trees on the forest edge compared
with trees inside the stand, so that silvicultural
approaches are required that also account for
tree location.
Any silviculture that would result in a subcompartment entering a higher risk node through a
change in stand conditions should be avoided. At
the same time, because top height is the primary
filter in the decision trees, most subcompartments will move to higher risk with time. The
suggestions based on the scenario simulations are
as follows:
Scenario 1: After top height reaches 23 m, foresters need to prepare harvesting because the trees
have an increased risk of, in particular, breakage.
Specifically, subcompartments in 1B1 need to be
harvested as soon as possible. In Figure 4, the
highest possibility of wind damage was found in
Area 3. Thus, the management plan should be designed primarily with this area in mind.
Scenario 2: Before the top height reaches 24
m, elevation is a key factor for management. For
instance, subcompartments located at more than
310 m elevation should not be disturbed by management actions (e.g. road construction) except
for thinning. However, they might be modified for
other management purposes such as to protect an
area from run-off or avalanches. Most of the subcompartments in Areas 1 and 2 are located above
300 m elevation but it would be safe to conduct
silvicultural treatments in the subcompartments
of Area 3 (Figure 3). After the top height reaches
24 m, subcompartments with less than 450 stems
ha⫺1 need to be immediately harvested. Subcompartments with 450 to 540 stems ha⫺1 are the safest for long-term retentions because their critical
tree height is 35 m.
Scenario 3: When heavy and infrequent thinning practices are planned, the critical tree height
is 27 m. Before the top height reaches 27 m,
elevation is also a key factor for management.
Figure 3 shows that most of the subcompartments in Areas 1 and 2 are located above 300
m elevation; thus, only the subcompartments
in Area 3 can be safely managed. After the top
height reaches 27 m, harvesting should be immediately conducted.
Conclusions
The aim of this study was to demonstrate a decision support approach that could provide
practical information on silvicultural treatment
alternatives for reducing wind damage risk in
long-term forest management. Through this approach, decision makers can recognize which
stands are at critical risk during different periods.
They also can perceive what kinds of stand characteristics are related to wind damage risk. Subsequently, decision makers can make plans for
how and when silvicultural treatments such as
thinning should be carried out in response to the
wind damage risk. The case study indicated the
significant stand characteristics and the location
of high wind damage risk within the forest. The
validation and sensitivity analysis confirmed that
the decision support approach could be safely
and easily used for decision makers in the region
of the study site.
Two critical issues were found from the case
study. First, the procedure relies heavily on the
growth and yield model. For example, the Silveno-Mori model in the case study did not include
mortality function and did not calculate top height;
therefore, simulated stand and tree characteristics
such as top height might be overestimated. This
would influence model outputs including the CWS
and EWS. Second, the splitting rules and node
definition values in the decision support approach
were strongly dependent on the decision tree algorithms. Moisen and Frescino (2002) found better
agreement with a linear model than with CART for
identifying forest characteristics using field survey
data and satellite-based information. Ture et al.
(2005) recommend testing several algorithms (including decision trees, logistic regression and neural network) before dealing with datasets. In this
study, CART was applied because of its simplicity
compared with other decision tree algorithms and
also because of its ability to deal with large datasets all at once. However, different approaches
have not been well studied in the area of forestry
and other approaches need to be compared with
CART. In particular, logistic regression, which is
more appropriate for binary classification, should
DECISION SUPPORT APPROACH TO REDUCE WIND DAMAGE RISK
be compared with CART before considering the
decision support approach.
In terms of the decision support approach presented in this paper, further developments are
required for both the framework and the individual models. Firstly, the approach should be a
user-friendly system for any stakeholders including forest owners. For instance, using computer
programming to develop decision support system would be helpful to link the multiple models
and to simulate several scenarios (Bunnell and
Boyland, 2003; Seely et al., 2004; Zeng et al.,
2007). It can also reduce time-consuming tasks
such as transferring output data from one model
to the input of another model. Secondly, the decision support approach needs to include other
possibilities of risk such as snow damage because these risks are often interrelated in several regions in Japan. Managing both wind and
snow damage risks sometimes creates conflicts.
Snow damage tends to decrease with stand age,
while wind damage is more likely to increase
with age (Slodičák, 1995; Kuboyama and Oka,
2000). Moreover, because the thinning procedures for the scenarios were not changeable, it
would be difficult to directly assess the overall
impact on thinning methods. For further study,
flexible scenario setting would be helpful to create management alternatives over long periods.
Therefore, to find optimal alternatives for forest
management it is necessary to consider various
situations relating to different risks in forests.
Thirdly, stand composition should be analysed
in a more spatial manner in order to find the optimum condition for the targeted area. If highrisk stands are harvested to avoid wind damage,
the new gap could influence the risk to neighbouring stands. Fourthly, economic constraints
also need to be incorporated in the decision support approach so as to ensure successful implementation of management alternatives. Without
economic assurance it may be difficult to appropriately implement the necessary silvicultural
treatments.
Although improvement of the decision support
approach is necessary, it still provides a potentially
powerful tool for long-term forest management
and planning. A decision support approach has
allowed us to link complex phenomena to simple
factors within the forest or to prioritize silvicultural treatments for reducing wind damage risk.
443
Such simplification and prioritization are very important because decisions in forest management
vary on both temporal and spatial scales.
Funding
9th Academic Research Grant of the Japan Forest
Technology Association (2005).
Acknowledgements
We are grateful to Dr Bruce Nicoll and colleagues at
Forest Research, UK, for providing us with many helpful suggestions. We would like to express our thanks to
Dr Satoshi Tatsuhara, the University of Tokyo. Thanks
also to the researchers in Toyama Forestry and Forest Products Research Center who conducted the hard
work required for in the field survey following the typhoon.
Conflict of Interest Statement
None declared.
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