IJERDInternational Journal of Environmental and Rural Development (2018) 9-2

Research article
erd

Equivalency and Similarity Assessment of Forest Ecosystem
Services by a Multi-Point Field Survey in Nagoya City,
Japan
KIICHIRO HAYASHI*
Nagoya University, Nagoya, Japan
Email: maruhaya98--@imass.nagoya-u.ac.jp

WATARU KOBAYASHI
Nagoya University, Nagoya, Japan

MAKOTO OOBANational Institute for Environmental Studies, Fukushima Branch, Miharu, Japan
Received 31 January 2018

Accepted 15 October 2018(*Corresponding Author)

Abstract Urban forests provide a variety of benefits to citizens, including micro climate
regulation, soil erosion regulation, and wildlife habitats. Under the biodiversity offset system,
the equivalency and similarity of biodiversity and ecosystem services (BD/ESs) are importantbut controversial issues. The purpose of this study is to develop a simple method to assess
aspects of the equivalency and similarity of forest BD/ESs and consider their applicability tobiodiversity conservation policy. The study was conducted on a selection of 41 forests in
Nagoya City. Several combinations of forests were recognized as similar providers of
BD/ESs. Historical land-use change was also important in assessing forest equivalency andsimilarity for biodiversity conservation policy.
Keywords ecosystem service, biodiversity, forest, Japan, GIS

INTRODUCTION
Urban forests provide many benefits to citizens, including micro climate regulation, soil erosion
regulation, and habitats for wildlife. According to the Millennium Ecosystem Assessment (MA,2005), these benefits can be summarized as ecosystem services (ESs) provided to human society.
There are several types of forest, including urban parks, secondary forests, and natural forests. Eachplays a different role in providing biodiversity (BD) and ESs. Despite their proven benefits, urban
forests in Japan have been decreasing over the past several decades. Biodiversity offsets (BO) are apolicy instrument developed to compensate for the loss of BD by development activities (BBOP,
2013). Madsen et al. (2010) summarized the global state of BO systems, and reported that the USA
and Australia are experienced in this field. In contrast, there is no legal requirement to conduct BO
in Japan on the national scale, although a basic guideline of the environmental impact assessment
law recommends compensating for the impacts of a development project on BD (MOE-J, 2014).
Many issues related to the BO system have been identified, including metrics, ecologicalequivalency, and no-net loss (Maron et al., 2012; Bull et al., 2013; Koyama and Okabe, 2017).
Additionally, the positive outcomes of BO have occasionally been overestimated (Pickett, 2013;
Gibbons et al., 2017). Nevertheless, BO has been recognized as a biodiversity conservation policy
and its potential use has been studied (MOE-J, EIA Division, 2014), with a focus on important
issues such as gains and losses of biodiversity in the BO system. Many BO related assessment
methods have been implemented and studied (Quétier and Lavorel, 2011), including the Habitat
Hectares method (The State of Victoria, https://www.environment.vic.gov.au/native-vegetation/native-vegetation), and the BioBanking assessment methodology (The State of New South Wales,
http://www.environment.nsw.gov.au/biobanking/assessmethodology.htm), both in Australia.
Another study focused on the development of objective and transparent BO assessment methodsⒸ ISERD

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IJERDInternational Journal of Environmental and Rural Development (2018) 9-2(Gibbons et al., 2009). Koyama and Okabe (2017) summarized many issues related to the potential
application of the BO system in Japan. Several case studies have also touched on the applicabilityof BO methods in Japan (Hasegawa and Hayashi, 2014; Ito and Hayashi, 2014). However, not only
species, habitat, and ecosystems, but also the similarity of potential ESs needs to be considered in
the future (MOE-J, EIA-Division, 2014; Koyama and Okabe, 2017). In the study on supporting andregulating ESs, Yonekura et al. (2014) categorized forests into several types based on a simple fieldsurvey for 52 forests in Nagoya. Hayashi and Ooba (2017) analyzed the categorization of culturalESs (CESs) based on a multi-point field survey for 180 forest sites in Nagoya City. These studies
were limited both in the scope of ESs covered and in the assessment items included in the survey.
A comprehensive assessment of BD/ES potential is needed in order to develop a simple assessment
method.
OBJECTIVES
The purpose of this study is to develop a simple method, one that could be used in the developmentof biodiversity conservation policy, to assess the equivalency and similarity aspects of forest
BD/ESs. To this end, a case study was done in Nagoya City using a multi-point field survey. The
scope of this study was expanded to cover a variety of BD/ESs, in contrast with the study ofHayashi and Ooba (2017), which focused only on cultural ESs.
METHODOLOGY
Study Method
Nago a Ci (ci hall: 35.181 N, 136.906 E) i he hi d la ge ci in Japan. The d a ea ip e en ed in Fig 1. The a e age ann al empe a e and p ecipi a ion in 2017 e e 15.9 C and1701.5 mm (Japan Me eo ological Agenc , h p://.da a.jma.go.jp/obd/ a /e n/inde .php).

Fig. 1 Maps of (a) Japan with Nagoya City as a star symbol, (b) Nagoya City outlined inblack and forest in green, and (c) a sample of targeted forests with 1955 land use map
Source: Forest by Nagoya green coverage GIS (Nagoya City) and 1955 Land-use maps by GSI (1955)

In this paper, forests were defined as the continuous tree crown area within a forest area ≥1 ha.
Based on the Nagoya green coverage GIS (geographic information system) data provided by
Nagoya City, around 200 forests (of around 240 total forests in the city) have been surveyed since2012. The survey items included species; the diameter at breast height (DBH 5cm ); tree height;
litter thickness; over-, mid-, and understory canopy cover (assessed visually); and naturalness. The
items, proxies, methods, and data are summarized in Tables 1 and 2. The carbon stock estimation
method is explained below. The surveys were conducted in a similar manner to Hayashi and Ooba
(2015). A second round surveys (referred to hereafter as the 2nd survey) on the same forests was
begun in 2016, including additional items and expanding the survey coverage to a 300 m2 area. In
some cases, the location of the site had to be changed to allow the extension of survey coveragearea. For several forests, new 200 m2 sites were set up in addition to the original 100 m2 sites. InⒸ ISERD

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IJERDInternational Journal of Environmental and Rural Development (2018) 9-2

this study, the results from the 2nd survey were used along with several first round results that had
enough data for this analysis. Bamboo was counted as one independent species, and the number ofindividual trees included the number of individual bamboo trees (counted only within 100 m2 andconverted to the 300 m2 value). The 2nd survey is not yet complete for all targeted forest sites. For
this study, 41 forest sites were selected.
Table 1 BD/ES items, proxies, methods and data
BD/ES

BD

RES

SES

Item

Proxy

Conservation area

C_A

Habitat size

Forest area

Species diversity

Simpson' DI

Naturalness

Naturalness

Micro climate
regulation
Soil erosion
regulation
Carbon Stock

ForestvolumeForest flow
cover
C-Stock

Soil formation

Litter(thickness)

MethodCheck special green
conservation areas designatedby Nagoya City and Aichi Pref.
Continuous tree crown areas (1ha)
Details in Table 2
Subjective judgement on a scaleof 1 to 5 scale by the author

Data
Nagoya City urban planning
information providing service, URL:
http://www.tokei-gis.city.nagoya.jp/
Nagoya green coverage GIS(Nagoya
City)
Based on field survey

Details in Table 2
Details in Table 2
Details in the main text
Three points in each 100 m2
area and averaging the results ofseveral visits

Based on field survey

Description
The diversity of species is an important indicator for explaining
the ecological community diversity. Hasegawa and Hayashi.
(2014) compared several diversity related indexes including
Simp on Di e i inde (Sim on DI) to the results of a BO
assessment methodology. In this study, Sim on DI was used
to explain within-habitat diversity in Formula 1 (Simpson,
1949; Morishita, 1996; Ohgaki, 2008). Individual species andthe number of the species were used to calculate Sim on DI.

Soil erosion regulation

Micro climate regulation

ESs

Species diversity

Table 2 BD/ES estimation methods
FormulaHiruta and Ishikawa (2012) studied the heat-reduction effect bygreen spaces, which compared three indicators: forest canopy
coverage, tree volume, and ground coverage. They concluded
that tree volume had the largest effect on the heat-reduction,
especially for daytime in summer. Our study focused on the
micro climate regulation in a summer daytime. Forest volume
was picked out as a proxy in this study by Formula 2 using
fo e co e and a e age ee heigh (DBH 5 cm) (Kobayashi
et al., 2016). The height of bamboo trees (only 5 selectedbamboo trees were measured in the bamboo forest) were
included in the average tree heights and forest cover.
For soil erosion regulation, Chu et al. (2010) studied therelationship between forest floor cover and soil erosion. They
concluded that the total forest floor cover (understory and litter
cover) was correlated with soil erosion rate in the case of
Tanzawa mountain area, Japan. In this study, forest floor cover
was selected as a proxy for soil erosion regulation. Forest floor
cover was the totaled score of understory tree (0 to 1 m height),
grassland, bamboo grass and fern coverages with litter coverage
averaged by several visits through the field survey in 100 m2
i e b he a hoi al j dgemen .

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𝑆𝑖𝑚𝑝𝑠𝑜𝑛 𝐷𝐼

1

𝛴

(1)

N : number of individuals
ni : number of each species

Vi = 104 * (CovHov - (1 - Cov)CmidHmid)+(1 Cov)(1 - Cmid)CundHund}(2)
Vi : forest volume in 100 m2 site i (m3/ha)
Cov : overstory cover (10 m<)
Cmid : midstory cover (10 m , >5 m)
Cund : understory cover (5 m , > 1m)
Hov : average height of overstory trees (m)Hmid : average height of midstory trees (m)Hund : average height of understory trees (m)Cforest = Cund + {(1-Cund)Clit}(3)

Cforest : forest floor cover(%)
Clit : litter cover(%)
Cund : understory cover (%, understory tree (01m), grassland, bamboo grass and fern)

IJERDInternational Journal of Environmental and Rural Development (2018) 9-2

In Hayashi and Ooba (2017), CES forest use in Nagoya was evaluated subjectively (n=180).
Seven CES items (CES7) were developed, taking into account four basic parameters: forest area,
scenic districts (S_D), shrine or temple (S_T), and major attraction facilities listed by the city
(A_F). Then a clustering analysis was conducted to categorize forests into several types. The
results of the clustering analysis were used as the CES values in this study.
For the carbon stock survey, DBH ( 5 cm) and the tree height (H) were measured by a 15 m
tree height pole (SK measurement pole AT-15, Senshin Industry Co.) supported by a Laser range
finder (Nikon Laser 550A S) to calculate above ground biomass (AGB) as in Kobayashi et al.
(2019). First, DBH and H were measured for each tree in a 100 m2 area. H was only measured for
100 m2 sites, so the H of the 300 m2 sites were estimated by Formula 4 (Matsumura, 2003). The
values of a and b were estimated by using the DBH and H values for conifer-, deciduous- andevergreen broad leaf trees measured in the 100 m2 area. The values of DBH and H used to estimate
a and b were taken from the 41 forests for which a 100 m2 area had been surveyed, along withvalues from additional four sites in the city (in total 45 sites). Yamamoto (1985) and Matsumura(2003) compared several tree height curves including Näslund, Allometry referring to Nishizawa(1972). In this study, the Näslund equation was used for coniferous, deciduous, and evergreen
forests based on Matsumura (2003). These results are summarized in Table 3. By using DBH and H,
the AGBs of each tree were estimated based on Tadaki et al. (2004) in Formulas 5 and 6.
Underground biomass was assumed to be one fourth of AGB and the carbon content of the biomass
was assumed to be 4/9 of the total biomass as in Tadaki et al. (2004). According to Tadaki et al.
(2004), the AGBcon was estimated to apply for Japanese red pine (Pinus densiflora). This wastentatively used for other conifer trees in this study.
For the AGB of bamboo trees, Okuda et al. (2006) developed a simple estimate (Formulas 7 to
9). Averaged DBH of bamboo trees for 5 selected individuals were used for the estimation,
multiplied by the number of bamboo trees in each 100 m2 site with 4/9 of carbon content. In thecase of no bamboo tree in a 100 m2 site, a 100-300 m2 converted figure was used as a 100 m2 value.
Lastly, the number of dead trees was
Table 3 Estimated equation parametersroughly estimated by Formula 5 and 7 on the 100abm2 sites by visual estimation of the DBH ( 5Evergreen broad leaf tree (n=586) 1.4089 0.2109
cm) and length of dead trees, and using the
average H of bamboo trees. After that, all the
Deciduous broad leaf tree (n=210) 1.2622 0.2017
carbon contents including tree carbon, tree AGB
Conifer tree (n=16)
1.2092 0.2161
of bamboo and dead trees were summed to Note: R2 values 0.918, 0.965 and 0.937, respectively.
obtain carbon stock per ha.
𝐵𝑎 𝑏 ∗ 𝐷𝐵𝐻
H : tree height
AGB br = 0.0601*(DBH2 * H)0.901
AGBcon = 0.0278*(DBH2 * H)0.990
a, b : parameters ; AGBbr : AGB of broad leaf tree
AGBcon : AGB of Japanese red pine (Pinus densiflora)
AGBbam-cul = 0.0845 * DBH2.2275
AGMbam-bra = 0.047 * DBH1.5989
AGMbam-le = 0.0097 * DBH1.8388
AGBbam-cul : AGB of bamboo culm; AGMbam-bra : AGB of bamboo branch
AGMbam-le : AGB of bamboo leaf
√

(4)
(5)
(6)
(7)
(8)
(9)

The proxies defined above were normalized by dividing each proxy value by the maximum
value of that proxy, and then the equivalency and similarity of several aspects of the forest BD/ESs
were evaluated. First, correlations among the proxies were investigated by the Pearson correlationand he Spea man ho. Second, the 41 sites were classified by forest type. Conservation areas
were treated as a separate category. Third, for each forest type, forests were ordered by BD scoresand categorized into four groups (A to D). Fourth, within each group forests in the same CES7
category defined by Hayashi and Ooba (2017) were compared to each other. Finally, the land-useⒸ ISERD

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IJERDInternational Journal of Environmental and Rural Development (2018) 9-2

changes from 1955 to 2017 were taken into consideration in conjunction with the distance betweensites. The land-use maps of 1955 provided by the Geospatial Information Authority of Japan (GSI)
were digitized for analysis on the 10 m grid scale approximately as in Kobayashi et al. (2016,2019). Four land-use maps were used, namely Nagoya north, Nagoya south, Koromo, and Seto.
The statistical analysis was conducted using Excel version 2010 (Microsoft) and SPSS
statistics v.22 (IBM). ArcGIS 10.4.1 (ESRI Japan Inc.) was used for the spatial analysis.
RESULTS AND DISCUSSION
The results of the correlations among the proxies are presented in the Table 4. BD (C_A) was wellcorrelated with RES (Forest_volume) and SES (C-stock and litter). RES (Forest_volume) was
significantly correlated with SES (C-stock). Generally, some of the BD proxies were positively
correlated with some of RES and SES proxies. BD (C_A) showed a positive correlation with CES(shrine/temple and cultural heritage value; 0.471** and 0.473**, respectively) and a negative
correlation with CES (daily recreation, holiday recreation and education; -0.343*, -0.499** and 0.387*, respectively b he Spea man ho). In Nagoya, conservation areas were located mostly inshrine/temple and recreational activities were not common in these as a whole.
The most frequent forest type classification was forest type 2 (secondary forest composed of
deciduous broad leaf tree), with 31 sites, followed by type 5 (evergreen broad leaf forest, 8 sites).
Four type 2 sites were conservation areas, which are important for the protection of natural habitats
and were thus treated differently. In total, 31 type 2 sites were used for further analysis.
After ordering by BD score, these 31 sites were categorized into 4 groups: A (BD <0.30); B(0.30≦ BD <0.45); C (0.45≦ BD <0.60); and D (0.60≦ BD). Fig. 2 shows the total score of each
forest ordered by total score of BD, RES and SES. Within each group, these total scores weresimilar. However, the CES scores of the groups were very different. Fig. 3 compares the averagescores in each group from A to D. The RES and SES scores of each group were similar except for
BD (C_A and forest area).
Table 4 Proxy correlations
Next, we employed the CES7
Forest_Forest
C-Stock
Littercategories from Hayashi and Ooba (2017)
volumefloor cover
to divide group B into sub-categories (B1
C_A
0.310 *
-0.1080.419 **
0.353 *
to B5). Fig. 4 presents the results of thisForest area
-0.001
0.079
0.243
0.175
categorization. The characteristics of the
Simpson' DI
0.047
0.301
0.242
0.305
BD, RES, and SES scores were similar
Forest_ volume
1.000
0.062
0.532 **
0.147*: Pearson Correlation is significant at the 0.05 level (2-tailed).
within sub-categories (Fig. 4 (a)).
**: Pearson Correlation is significant at the 0.01 level (2-tailed).
However, the CES proxy scores differedby sub-category (Fig. 4 (b)).
Finally, the relative site distanceswere investigated using GIS, and sites
within 100 m and 500 m of each otherwere selected for further analysis. Afterchecking the shrine and temple location
factor and the cultural heritage factor,
which were traditional aspects, land-use
Fig. 2 BD, RES, SES and CES scores by sitechanges from 1955 to 2017 were
C_A
considered. The results are shown in
A: BD <0.30
1.00
Forest0.80
B: 0.30≦ BD <0.45
LitterTable 5. Six site combinations were
area
0.60
C: 0.45≦ BD <0.60
0.40
recognized as being potentially similar.
0.20
D: 0.60≦ BD
Simpson'
C-Stock
0.00
Referring to the old land-use maps, inDIseveral of the 100 m distance
ForestNaturalncombinations, both sites were located
floor
ess
cover
within the same or neighboring forests in
Forest_v
olume
the past. This means that from the
Fig.
3
Radar
chart
of BD, RES and SES for A to D
perspective of this analysis, these siteⒸ ISERD

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IJERDInternational Journal of Environmental and Rural Development (2018) 9-2

pairs had high similarity. Over the past several decades, these forest sites were fragmented byurbanization. Two site pairs within 500 m of each other were cemeteries in the past, according to
the land-use data. More data on past forest locations (such as old aerial photographs) is needed.
The equivalency and similarity assessment presented here could be used for a BO assessment oncethe scope of the BD/ES items has been extended.
CONCLUSION
This study focused on the(a)(b)
development of a simple method
for evaluating the equivalency
and similarity aspects of forest
BD/ESs through a multi-point
field survey in Nagoya City. Thismethod is potentially applicabletobiodiversity conservation
policy. Eight items from BD,
RES, and SES, along with several
Fig. 4 Radar charts of (a) BD, RES and SES and (b) CES
CES items from Hayashi andOoba (2017), were selected forTable 5 Equivalency and similarity aspects in forest type 2
assessment.
Finally,
severalcombinations were identified that Distanc Cate
SubSite ID
Old land-useegory
category
were recognized as providing
BB3
163,165
Same mixed forest
similar BD/ESs. Historical landB
B3
163,206
Neighboring mixed and conifer forest
use change was found to be ≦100m
B
B4
121,131
Same conifer forest
crucial to allow better judgement
BB2
134,139
Same cemeteryof forest equivalency andBB2
137,139
Same cemeterysimilarity in carrying out ≦500m
One was conifer forest with cleared area.
B
B3
166,167
The other was mixed forest. Both wereassessments for biodiversity
separated by agricultural landconservation policy, which havebeen pointed out from the context
of ES conservation in Ooba et al.
(2015).
The remaining issues can be summarized as follows. First, this study only includes the survey
results at 41 forests. Further field surveys covering the remainder of the forests in Nagoya City areneeded. Second, in this study 8 BD/ESs items were used. However, after completion of the forest
surveys, a wider variety of items should be considered. For example, fish-eye cameras, vegetation
coverage by 50 m transect survey, and variable species distribution would be useful.
1.00

Litter

C_A

0.80

B1

Forest area

0.60
0.40
0.20

C-Stock

0.00

Simpson'DI

B2

Socialrelations

Scenicdistricts
1.00
0.80

B3
CulturalB4
heritage

0.60

B5

0.00

0.40
0.20

Spiritual/relig
ious

Forest floor
cover

Naturalness

Forest_volume

B1

Shrine/temple

Major
attractions

B2
B3
B4
B5

Aesthetic
value

Daily
recreation

Education

Holiday
recreation

ACKNOWLEDGEMENTS
We hank he Nago a Ci G eeni ca ion and P blic Wo k B ea and he man fo e o ne
who gave us permission for the surveys. We thank N. Yoshino, N. Kawaguchi, J. Okata, H. Takagi,
T. Matsumoto, H. Sumi, T. Souphihalath and others for their assistance. This was supported by
KAKENHI15K00622 from JSPS, Japan and the Environment Research and Technology
Development Fund1-1401 from MOE-J, Japan. We also thank Editage for English language editing.
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