161

Habitat Analyses of Six Threatened Plant Species in a Moist Tall
Grassland Based on Hyperspectral-Remotely Sensed Data
Jun ISHII* and Izumi WASHITANI
Graduate School of Agricultural and Life Sciences, The University of Tokyo
1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japane-mail: aishijun@mail.ecc.u-tokyo.ac.jp*corresponding author

Abstract
The Watarase Wetland is the largest lowland wetland (approximately 3,300 ha) remaining on the
Honshu mainland of Japan, mainly composed of moist tall grasslands dominated by Phragmites australis(Cav.) Trin. ex Steud. and Miscanthus sacchariflorus (Maxim.) Benth., with the under-layer inhabited bymore than 650 vascular plant species, including more than 50 threatened species listed in the national Red
List. We analyzed the effectiveness of two sets of hyperspectral-remote sensing parameters ‘shoot density ofeach of P. australis and M. saccahriflorus’ and ‘summed shoot density and shoot density ratio of both
species’ to map potential habitats of six threatened plant species. Analysis by generalized linear models(GLMs) showed that the parameters related to shoot densities of P. australis and M. sacchariflorus had
significant effects on abundances of all species. Accuracy values evaluated by adj. R2 were the same
between the models for the two parameter sets in the cases of all threatened species. The models for two
species with high occurrence and abundance, Galium tokyoense and Ophioglossum namegatae, yielded highaccuracy (adj. R2 > 0.7). We also found an autocovariate term related to the spatial autocorrelation of species abundance to be important in all the species examined. These results suggest the effectiveness of
hyperspectral-remote sensing of shoot densities of P. australis and M. sacchariflorus in habitat modeling of
locally abundant threatened species in moist tall grasslands.
Key words: floodplain wetland, hyperspectral image, Miscanthus sacchariflorus, Phragmites australis,potential habitats, shoot density, spatial autocorrelation, threatened species

1. Introduction
Floodplains or lowland wetlands are valued for many
functions, or ecological services, such as flood prevention, water use, recreation and tourism, as well as
biodiversity (Costanza et al., 1997). After extensive loss
and degradation (Millennium Ecosystem Assessment,
2005), the importance of conservation of existing wetland ecosystems with many threatened species inhabiting them has become increasingly appreciated.
In Japanese lowland floodplains, moist tall grassland
dominated by Phragmites australis (Cav.) Trin. ex Steud.
and/or Miscanthus sacchariflorus (Maxim.) Benth. is amajor type of vegetation, but it has largely been lost as aresult of various types of development (Washitani,2001). Consequently, many of indigenous plant species
associated with the habitat are now listed in the national
Red List (Ministry of the Environment of Japan, 2007).
For conservation planning of these threatened species
and their habitats, what is needed is ecological characterization and mapping of potential habitats of the
Global Environmental Research
11: 161-169 (2007)
printed in Japan

species.
The Watarase Wetland is the largest lowland wetland(approximately 3,300 ha, 36°11’ to 36°17’ north and
139°40’ to 139°42’ east) remaining on the Honshu
mainland, mainly composed of typical moist tall grasslands dominated by P. australis and M. sacchariflorus,
with the under-layer inhabited by more than 650 vascular plant species (Ohwada & Ogura, 1996), including
more than 50 threatened species listed in the national
Red List (Ministry of the Environment of Japan, 2007).
The use of hyperspectral remote sensing to increase
the number of possibilities of analyzing cover types andpercentages can provide explicit and timely spatial
information for habitat mapping of such plant species
over large areas (Turner et al., 2003). In our previous
studies (Ishii et al., submitted), we demonstrated that
hyperspectral imagery provides a promising basis formapping habitat types of under-layer plants, including
threatened species in moist tall grasslands. We also
revealed the effectiveness of hyperspectral-remote sensing of the shoot density ratio of P. australis and©2007 AIRIES162

J. ISHII and I. WASHITANI

M. sacchariflorus as an index for distinguishing those
habitat types of under-layer vascular plant species
groups, which are discerned by multivariate analysis.
Potential habitats of several threatened species were also
identified based on the relationships between thesehabitat types and the distribution of threatened species.
However, insufficient data points (only 9-83 for eightindividual threatened species examined) did not allow
accurate enough mapping of the potential habitats of
those species. In addition, an indirect method of mapping via the relationships with the habitat types of
species groups mapped based on hyperspectral-remotesensed shoot density ratio inevitably created further
inaccuracy.
In the present study, based on a newly collected
2,000-point data record of species abundance, we
attempt to extend the previous work by analyzing theeffectiveness of hyperspectral-remote sensing of shootdensities of P. australis and M. sacchariflorus in direct
mapping of potential habitats of threatened species.
Since these dominant grass species have high aboveground biomass in the upper layers (Haslam, 1972;
Mutoh et al., 1968), their abundance would serve as anindex of light availability or availability of otherresources for under-layer plants. On the other hand, theirrelative abundance is thought to be determined primarily
by groundwater level (Yamasaki & Tange, 1981;
Yamasaki, 1990). It would also be influenced by naturaland anthropogenic disturbances with direct damage toplant cover or other habitat alteration. Therefore, weconstructed models for two sets of parameters: ‘shootdensity of each of P. australis and M. sacchariflorus’and ‘summed shoot density and shoot density ratio of
both species.’
We analyzed the relationships between the spatial
patterns of ranked abundance of the six threatened plant
species and the two-parameter sets of parameters in

2,000 grid squares (10 × 10 m) within a 20 ha study areaof a moist tall grassland using generalized liner models
to evaluate the effectiveness of the indices.

2. Methods
2.1 Study area and species
The study was conducted in a 20 ha area within atypical moist tall grassland dominated by P. australis
and M. sacchariflorus in the eastern part of the Watarase
Wetland (Figs. 1, 2). The annual temperature and precipitation averaged over the period from 1976 to 2005,
which were recorded at the Koga Meteorological Stationnear the study area, were 14°C and 1,187 mm, respectively. The wetland has been managed by the Japanese
Ministry of Land, Infrastructure, Transport and Tourism,
and the Federation of Utility Cooperative Association of
the Watarase Wetland for flood prevention, water use,recreation and reed harvesting. Management activities
include dredging for river improvement and controlledburning for the production of commercially used reeds.
The study area was divided into 2,000 contiguous
grids of 10 × 10 m squares, in which all measurements
and analyses were done. Data of three grid squares were
not available because of missing values in hyperspectral
data.
(a)

The Watarase River
The Uzuma River
(b)

(c)

N

The Omoi River

Lake Yanaka

1km

The study area(400 × 500 m)

Fig. 1 Location of the Watarase Wetland in Japan and the study
area. The vegetation is dominated by moist tall grasses
Phragmites australis and Miscanthus sacchariflorus.

Fig. 2 Physiognomy of the moist tall grassland dominated by
Phragmites australis and Miscanthus sacchariflorus (a)and under-layer plants (b, c) at the study site of the
Watarase Wetland.

Hyperspectral Remote Sensing in a Tall Moist Grassland

2.2 Measurements
Abundance measurements of the six threatened plant
species, Apodicarpum ikenoi Makino, Carex cinerascens Kükenth., Galium tokyoense Makino, Ophioglossum namegatae Nishida et Kurita, Rumex longifolius
DC. and Thalictrum simplex var. brevipes Hara (Ministry of the Environment of Japan, 2007), all of whichwere relatively abundant in the moist tall grassland of
the Watarase Wetland, were carried out in May 2006
when spring sprouting peaked. Each grid square (10 ×10 m) was divided into four sub-squares (5 × 5 m), andthe abundance of each species in each sub-square wasrecorded by three rank orders (0: absent, 1: less than
10 ramets, 2: 10 ramets or more), and summed over thefour sub-squares to obtain grid data in nine rank orders(0-8).
We used shoot numbers of P. australis andM. sacchariflorus estimated from hyperspectral-remotesensing data with an adjusted R2 of 0.686 and 0.708,
respectively, in 2004 with a pixel resolution of 1.5 m(Ishii et al., submitted). Hyperspectral images wereobtained on 21 May 2004 from an aircraft flying at analtitude of 1,438 m by the Airborne Imaging Spectrometer for Applications (AISA), which is operated by
SPECIM LTD (Spectral Imaging LTD). AISA has
68 contiguous channels of approximately 10-nm
bandpass over 398-993-nm spectral wavelength ranges.
It also has very high radiometric resolution (16 bit).
‘Shoot density (shoot number per square meter) of eachspecies’ and ‘summed shoot density and shoot density
ratio per square meter of both species’ for each gridsquare were calculated form the shoot numbers exported
from the map using EARDAS IMAGINE 9.0 (Leica
Geosystems Geospatial Imaging, LLC., USA). The‘shoot density ratio’ was calculated as (shoot density of
M. sacchariflorus)/(shoot density of P. australis+1).
The reason for the addition of the 1 in the denominatoris that shoot density data of P. australis included zero.
2.3 Statistical analysis
The effectiveness of ‘shoot density of each of
P. australis and M. sacchariflorus’ and ‘summed shootdensity and shoot density ratio of both species’ onranked abundances of threatened species was modeledusing generalized linear models (GLMs). After investigating how non-normality would affect our results,
GLMs with a Gaussian error and negative binomial
error were applied to A. ikenoi C. cinerascens,
G. tokyoense, O. namegatae and T. simplex var. brevipes
and to R. longifolius, respectively. Considering thatranked abundances may show nonlinear responses tohabitat variables, quadratic terms were also added to themodels. All the habitat variables were standardized to amean of zero and unit variance before the analyses.
Generally, spatial autocorrelation is problematic forclassical statistical tests that assume independently
distributed errors (Lichstein et al., 2002). To evade thestatistical problem, we incorporated an autocovariate
term which accounted for spatial effects of neighbouring

163

locations of the response variable (Augustin et al., 1996)
into the models as well as habitat variables. The spatial
patterns of ranked abundances of the six threatened species and habitat variables were analyzed by Moran’s
I correlograms (Cliff & Ord, 1981). Moran’s I was
calculated for nine distance classes: 10-20, 20-30, 30-40,
40-50, 50-60, 60-70, 70-80, 80-90 and 90-100 m using
R (package “spdep”; R Development Core Team, 2006).
The significance of Moran’s I was assessed by the
progressive Bonferroni correction (Legendre & Legendre,1998). Since all of the Moran’s I values of ranked
abundances of the six threatened species and habitat
variables were the highest at the nearest distance class
10-20 m, an autocovariate at 10 m was computed.
Candidate models for all combinations of variables
were constructed. Parsimonious models were selectedusing Akaike’s information criterion (AIC) (Burnham &Anderson, 2002). The larger the delta AIC values (∆i),
the less plausible is the fitted model i as being the best
approximating model in the candidate set (Burnham &Anderson, 2002). Therefore, the models were ranked
based on these values within 0-2. To evaluate the model
fit, we calculated R2 using the following formula(Nagelkerke, 1991):
R2 = 1 – exp[–2/n(lA – lB)]
where n is the sample size, lA is the log-likelihood of
the model of interest, and lB is the log-likelihood of thenull model containing only an intercept (Lichstein et al.,2002).

3. Results
3.1 Distributions of threatened species and theirhabitats
There were marked differences in occurrence
and abundance patterns among the threatened species(Table 1). A strong positive correlation between occurrence and abundance was also found (Pearson’s correlation coefficient: r = 0.960, P < 0.001). Galium
tokyoense and O. namegatae showed both high occurrence and abundance (more than 70% of the grid squares
and more than 6 in the average rank order of abundance).
Table 1 Distribution of the six threatened plant species in the
20 ha study area. The average rank order of abundance
was calculated for the grid squares (10 × 10 m) in which
each species was recorded.
SpeciesNumber (percentage) ofgrid squares in which
each species was

Average rankorder ofabundance

Apodicarpum ikenoi

350(17.5)

2.0

Carex cinerascens875(43.8)

4.5

Galium tokyoense

1,715(85.9)

6.0

Ophioglossum namegatae

1,439(72.1)

6.3

Rumex longifolius

98( 4.9)

1.3

Thalictrum simplex var. brevipes

871(43.6)

3.1

Total number of grid squares

1,997

164

J. ISHII and I. WASHITANIIn contrast, A. ikenoi and R. longifolius showed low(about 20% and 2) and very low values (about 5 % andless than 2), respectively. Carex cinerascens and T.
simplex var. brevipes were intermediate in the values(about 40 % and 3 to 5).
In the study area, there were more grid squares inwhich a shoot density of M. sacchariflorus was lower
than that of P. australis (Figs. 3, 4). Both correlations between shoot densities of P. australis andM. sacchariflorus (Pearson’s correlation coefficient: r =– 0.049, P = 0.028; Fig. 3) and between summed shootdensity and shoot density ratio (Pearson’s correlation
coefficient: r = 0.328, P < 0.001; Fig. 4) were significant but weak, suggesting no colinearity problems.
Abundance patterns with respect to shoot densities ofP. australis and M. sacchariflorus differed among
threatened species (Fig. 5). Galium tokyoense andO. namegatae showed high abundance over wide rangesof shoot densities of P. australis and M. sacchariflorus
in the study area (Figs. 5(c), 5(d)). In contrast,

C. cinerascens and T. simplex var. brevipes tended to beless abundant in grid squares with lower shoot densities
of M. sacchariflorus (Figs. 5(b), 5(f)). Abundance ofA. ikenoi and R. longifolius tended to be relatively highin more limited ranges of shoot densities of P. australis
and M. sacchariflorus (Figs. 5(a), 5(e)).
3.2 Spatial autocorrelation of ranked abundance of
threatened species and habitat variables
All of the Moran’s I values of ranked abundances ofthe six threatened species were significant (P < 0.05)
except for the distance class 90-100 m of C. cinerascens
and T. simplex var. brevipes. They showed a monotonic
decline with geographical distance, and species withhigher occurrence and abundance tended to have higher
Moran’s I values (Fig. 6).
Shoot densities, summed shoot density and shootdensity ratio of P. australis and M. sacchariflorus also
showed significant Moran’s I values (P < 0.05) up to
100 m with a monotonic decline (Fig. 7).

Shoot density of M. sacchariflorus (m-2)(a) Apodicarpum ikenoi(b) Carex cinerascens
RankedShoot density of P. australis (m-2)(c) Galium tokyoense(d) Ophioglossum namegatae(e) Rumex longifolius(f) Thalictrum simplex var. brevipes

Shoot density ratio

Fig. 3 Scatter plot of shoot densities of Phragmites
australis and Miscanthus sacchariflorus of the
1,997 grid squares in the 20 ha study area.
The blue line shows equal shoot densities ofP. australis and M. sacchariflorus.

Shoot density of M. sacchariflorus (m-2)

0 10 abundance

Shoot density of P. australis (m-2)

Summed shoot density (m-2)

Fig. 4 Scatter plot of summed shoot density and shootdensity ratio of Phragmites australis and Miscanthus
sacchariflorus of the 1,997 grid squares in the 20 hastudy area. The blue line shows equal shoot densities
of P. australis and M. sacchariflorus.

Fig. 5 Ranked abundances of Apodicarpum ikenoi (a), Carex
cinerascens (b), Galium tokyoense (c), Ophioglossum
namegatae (d), Rumex longifolius (e), Thalictrum
simplex var. brevipes (f) against shoot densities ofPhragmites australis and Miscanthus sacchariflorus in
the 1,997 grid squares of the 20 ha study area. The open
circles and solid dots represent the presence and absenceof each species, respectively. Larger open circles
represent higher abundances.

Hyperspectral Remote Sensing in a Tall Moist Grassland

Fig. 6 Moran’s I correlograms of ranked abundancesof the six threatened plant species in the 20 hastudy area. All of Moran’s I values, except forthe distance class 90-100 m in Carex
cinerascens and Thalictrum simplex var.
brevipes, are significant with 5% probability
level adjusted using the progressive
Bonferroni correction.

165

3.3 Model selection
Tables 2 and 3 show selected models of the six
threatened species for ‘shoot density of each of
P. australis and M. sacchariflorus’ and ‘summed shootdensity and shoot density ratio of both species,’ respectively, with ∆i smaller than 2, ranked using the model
selection procedure based on AIC values. All the models
for the six species included habitat variables. In addition,accuracy as measured by adj.R2 was the same amongmodels for these two parameter sets in all species, andtended to reflect their occurrence and abundance patterns (Table 1). For G. tokyoense and O. namegatae withhigh occurrence and abundance, model accuracy wassignificantly high (adj.R2 = 0.72 and 0.75, respectively),and for C. cinerascens and T. simplex var. brevipes withrelatively high occurrence and abundance that was lower(adj.R2 = 0.53 and 0.45, respectively). Although A. ikenoi
showed low occurrence and abundance, its model
accuracy was similar to that for T. simplex var. brevipes(adj.R2 = 0.43). In contrast, the model for R. longifolius
with the lowest occurrence and abundance had very
poor explanatory power of ranked abundance (adj.R2 =0.04). The estimated rank orders of abundance of
G. tokyoense and O. namegatae by the models with thelowest AIC for ‘shoot density of each of P. australis andM. sacchariflorus’ are shown in Fig. 8.
In all species examined, significant effects of habitat
variables were also detected in the models with the lowest AIC for ‘shoot density of each of P. australis andM. sacchariflorus’ and/or ‘summed shoot density andshoot density ratio of both species’ (Tables 4, 5). In themodels for C. cinerascens and O. namegatae, a quadratic term of shoot density of M. sacchariflorus andlinear terms of shoot density of each of P. australis and(a) Hyperspectral image
Estimated rankorder of abundance

0
0<
2<
4<
6<
(b) Galium tokyoense

Fig. 7 Moran’s I correlograms of shoot densities (a)and summed shoot density and shoot density
ratio (b) of Phragmites australis andMiscanthus sacchariflorus in the 20 ha study
area. All of Moran’s I values are significant
with 5% probability level adjusted using the
progressive Bonferroni correction.

(c) Ophioglossum namegatae

Fig. 8 Hyperspectral image (a) and maps showing estimatedrank orders of abundance of Galium tokyoense (b) andOphioglossum namegatae (c) at the study site of the
Watarase Wetland.

166

J. ISHII and I. WASHITANI

M. sacchariflorus were significantly negative and positive, respectively, and a quadratic term of summed shootdensity was significantly negative for both species. For
G. tokyoense, a quadratic term of shoot density of
P. australis had a significant negative effect, and linear
terms of both summed shoot density and shoot density
ratio had significant positive and negative effects, respectively. As in C. cinerascens, T. simplex var. brevipes
showed a significant negative effect of a quadratic termof shoot density of M. sacchariflorus, but in this species,
a significant positive effect of a linear term of summed
shoot density only was detected. The models with thelowest AIC of both A. ikenoi and R. longifolius for thetwo parameter sets included habitat variables related toshoot density of P. australis and summed shoot density,
but only a significant positive effect of a linear term ofshoot density of P. australis and a significant negative
effect of a quadratic term of summed shoot density,
respectively, were detected.
In addition to habitat variables, the autocovariate at
10 m was always included in the models for the six
threatened species (Tables 2, 3). It showed a significant
effect which explained a larger part of explainablevariation in the model with the lowest AIC value
for each species except for T. simplex var. brevipes(Tables 4, 5).

Table 2 Model selection statistics for the analyses of effects of shoot density of each of Phragmites australis
and Miscanthus sacchariflorus on ranked abudances of the six threatened plant species in the Watarase
Wetland.
Phra: shoot density of P. australis, Mis: shoot density of M. sacchariflorus, ACV: autocovariate (values
in parentheses represent the distance at which the autocovariate was calculated).

Species / Model*

AIC

⊿i

wi

adj.R 2

4,573.63

0.00

0.27

0.43

4,574.83

1.19

0.15

0.43

4,575.54

1.91

0.11

0.43

4,575.61

1.98

0.10

0.43

Apodicarpum ikenoi
Phra, Phra2, ACV (10 m)
Phra, ACV (10 m)
2

2

Phra, Phra , Mis , ACV (10 m)
2

Phra, Phra , Mis, ACV (10 m)
Carex cinerascens
Mis, Mis2, ACV (10 m)

8,185.71

0.00

0.37

0.53

Phra , Mis, Mis , ACV (10 m)

8,187.45

1.73

0.16

0.53

Phra, Mis, Mis2, ACV (10 m)

8,187.46

1.75

0.16

0.53

7,545.09

0.00

0.28

0.72

7,545.50

0.41

0.22

0.72

7,545.96

0.87

0.18

0.72

Phra, Mis, ACV (10 m)

7,783.01

0.00

0.20

0.75

Phra, Phra2, Mis, ACV (10 m)

7,783.17

0.16

0.19

0.75

Phra, Mis, Mis2, ACV (10 m)

7,783.22

0.21

0.18

0.75

7,783.51

0.50

0.16

0.75

827.38

0.00

0.23

0.04

2

2

Galium tokyoense
Phra, Phra2, ACV (10 m)
2

Phra, Phra , Mis, ACV (10 m)
2

2

Phra, Phra , Mis , ACV (10 m)
Ophioglossum namegatae

22Phra, Phra , Mis, Mis , ACV (10 m)
Rumex longifolius
Phra, Phra2, ACV (10 m)
2

Phra , ACV (10 m)
Mis, ACV (10 m)
2

829.01

1.64

0.10

0.04

829.23

1.86

0.09

0.04

829.24

1.87

0.09

0.04

829.24

1.87

0.09

0.04

829.32

1.94

0.09

0.04

829.36

1.98

0.08

0.04

7,360.85

0.00

0.24

0.45

7,361.36

0.51

0.19

0.45

Phra, Phra , Mis, Mis , ACV (10 m)

7,361.72

0.87

0.16

0.45

Phra2, Mis, Mis2, ACV (10 m)

7,362.22

1.37

0.12

0.45

Mis , ACV (10 m)
Phra, ACV (10 m)
2

2

Phra, Phra , Mis , ACV (10 m)
2

Phra, Phra , Mis, ACV (10 m)
Thalictrum simplex var. brevipes
Mis, Mis2, ACV (10 m)
2

Phra, Mis, Mis , ACV (10 m)
2

2*Only models with ⊿i < 2 for each species are shown.

Hyperspectral Remote Sensing in a Tall Moist Grassland

167

Table 3 Model selection statistics for the analyses of effects of summed shoot density and shoot density
ratio of Phragmites australis and Miscanthus sacchariflorus on ranked abudances of the six
threatened plant species in the Watarase Wetland.
Sum: Summed shoot density, Ratio: shoot density ratio, ACV: autocovariate (values inparentheses represent the distance at which the autocovariate was calculated).

Species / Model*

AIC

⊿i

wi

adj.R 2

4,576.34

0.00

0.17

0.43

4,577.16

0.81

0.11

0.43

4,577.23

0.88

0.11

0.43

4,577.53

1.19

0.09

0.43

4,578.30

1.95

0.06

0.43

4,578.30

1.96

0.06

0.43

Apodicarpum ikenoi
Sum, ACV (10 m)
2

Sum, Sum , ACV (10 m)
2

Sum , ACV (10 m)
Sum, Ratio, ACV (10 m)
Sum, Ratio2, ACV (10 m)
Ratio, ACV (10 m)
Carex cinerascens
Sum, Sum2, Ratio2, ACV (10 m)

8,188.41

0.00

0.16

0.53

Sum, Sum2, ACV (10 m)

8,188.91

0.50

0.12

0.53

Ratio2, ACV (10 m)

8,189.34

0.93

0.10

0.53

Sum , Ratio , ACV (10 m)

8,189.38

0.96

0.10

0.53

Sum2, ACV (10 m)

8,189.93

1.52

0.07

0.53

2

Sum, Sum , Ratio, ACV (10 m)

8,190.06

1.64

0.07

0.53

Sum, Sum2, Ratio, Ratio2, ACV (10 m)
Ratio, ACV (10 m)

8,190.24

1.83

0.06

0.53

8,190.31

1.90

0.06

0.53

Sum, Ratio, ACV (10 m)

7,544.83

0.00

0.22

0.72

Sum, Sum2, Ratio, ACV (10 m)

7,545.53

0.70

0.16

0.72

2

2

Galium tokyoense

27,546.16

1.33

0.12

0.72

2

7,546.58

1.75

0.09

0.72

2

7,546.76

1.93

0.09

0.72

Sum, Sum2, ACV (10 m)

7,778.27

0.00

0.37

0.75

Sum, Sum2, Ratio, ACV (10 m)

7,779.53

1.26

0.20

0.75

7,779.95

1.68

0.16

0.75

7,780.24

1.97

0.14

0.75

825.71

0.00

0.33

0.04

Sum, Ratio , ACV (10 m)
2

Sum, Sum , Ratio , ACV (10 m)
Sum, Ratio, Ratio , ACV (10 m)
Ophioglossum namegatae22Sum, Sum , Ratio, Ratio , ACV (10 m)
2

2

Sum, Sum , Ratio , ACV (10 m)
Rumex longifolius
Sum, Sum2, ACV (10 m)
2

2

Sum, Sum , Ratio , ACV (10 m)
2

Sum, Sum , Ratio, ACV (10 m)

827.68

1.97

0.12

0.04

827.69

1.98

0.12

0.04

7,355.52

0.00

0.44

0.45

Thalictrum simplex var. brevipes
Sum, Sum2, ACV (10 m)
2

2

Sum, Sum , Ratio , ACV (10 m)
2

Sum, Sum , Ratio, ACV (10 m)*Only models with ⊿i < 2 for each species are shown.

7,357.00

1.48

0.21

0.45

7,357.49

1.97

0.16

0.45

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J. ISHII and I. WASHITANI

Table 4 Summary of the GLM models with the lowest value of
AIC for the parameter set, shoot density of each of
Phragmites australis and Miscanthus sacchariflorus of
the six threatened plant species in the Watarase Wetland.
See Table 2 for abbreviations of variables.
Species / Variables

Coefficients

95% confidence interval
Lower-0.0100.133-0.1051.031-0.0480.018-0.220
0.979

0.028
0.248
0.010
1.084

Carex cinerascens(intercept)
Mis
Mis2
ACV (10 m)-0.0550.287-0.3451.027-0.1750.033-0.594
0.982

Rumex longifolius(intercept)
Phra
Phra2
ACV (10 m)

Coefficients

95% confidence interval
Lower

Upper

Apodicarpum ikenoi(intercept)
Phra
Phra2
ACV (10 m)

Ophioglossum namegatae(intercept)
Phra
Mis
ACV (10 m)

Species / Variables

Upper

Apodicarpum ikenoi

Galium tokyoense(intercept)
Phra
Phra2
ACV (10 m)

Table 5 Summary of the GLM models with the lowest value of
AIC for the parameter set, summued shoot density andshoot density ratio of Phragmites australis andMiscanthus sacchariflorus of the six threatened plant
species in the Watarase Wetland. See Table 3 forabbreviations of variables.

-0.2720.363-0.2851.053-0.149
0.087
0.174
1.031
-3.4460.867-0.941
4.665

Thalictrum simplex var. brevipes(intercept)
0.930
Mis
0.826
-0.440
Mis2
ACV (10 m)
0.220-0.4370.120-0.5291.024-0.298
0.013
0.095
1.003
-3.711-0.074-1.962
3.803
0.806
0.565
-0.6970.175(intercept)
Sum
ACV (10 m)-0.0140.025
1.042-0.052-0.009
0.989

0.024
0.059
1.095

0.065
0.540
-0.096
1.071

Carex cinerascens(intercept)
Sum
Sum2
Ratio2
ACV (10 m)-0.0640.274-0.322-0.0661.031-0.182-0.038-0.634-0.148
0.988

0.054
0.586
-0.0100.016
1.074-0.1060.606-0.041
1.082

Galium tokyoense(intercept)
Sum
Ratio
ACV (10 m)-0.2530.125-0.0811.050-0.4220.049-0.1551.021-0.0840.201-0.0071.079-0.000
0.162
0.253
1.060

Ophioglossum namegatae(intercept)
Sum
Sum2
ACV (10 m)-0.1680.507-0.3351.035-0.3130.227-0.6151.008-0.0230.787-0.0551.062-3.181
1.809
0.080
5.526

Rumex longifolius(intercept)
Sum
Sum2
ACV (10 m)-3.454
1.114-1.177
4.662-3.7210.138-2.202
3.807-3.1872.090-0.152
5.517

1.054
1.087
-0.183
0.266

Thalictrum simplex var. brevipes(intercept)
0.844
Sum
0.394
-0.139Sum2
ACV (10 m)
0.264

0.721
0.069
-0.464
0.219

0.967
0.719
0.186
0.309

4. Discussion
In floodplain wetlands, disturbance by flooding rearranges habitat complexes, destroying certain habitats
and creating others. In such a dynamic, shifting mosaicof habitat patches, the continued existence of a threatened species depends on the presence of sufficient
potential habitats, including occupied and unoccupied
ones, within the range of its dispersal. Therefore, inorder to conserve such threatened species, ecologicalcharacterization and mapping of potential habitats aswell as their actual distribution are important.
Remote sensing can aid large-scale documentationand monitoring of potential habitats of threatened species embedded in the plant habitat complexes constituting the shifting mosaics of floodplains.
In our habitat modeling of six threatened plant spe-

cies inhabiting in the under-layer of a moist tall grassland, effects of hyperspectral-remote sensing parameters
related to shoot densities of P. australis andM. sacchariflorus on species abundances were significant for all species. Two parameter sets examinedyielded closely similar values of accuracies of models
for individual species. The models for G. tokyoense andO. namegatae yielded high accuracy (adj.R2 > 0.7), enabling us to map their potential habitats. The model
accuracies were closely related to species occurrence
and abundance, which were highly correlated mutually.
It is the case for G. tokyoense and O. namegatae, theoccurrence and abundance of which were more than
70% of the study grid squares and more than six in average rank order of abundance, respectively. Generally,
species prevalence influences the accuracy of models
analyzing species-environment relationships (e.g.,Hyperspectral Remote Sensing in a Tall Moist Grassland

Zimmermann et al., 2007). This implies that an increasing number of occurrence data would improve model
accuracy for less abundant species. In addition, increasing the accuracy to estimate shoot numbers of
P. australis and M. sacchariflorus from hyperspectralremote sensing data (adj. R2 of 0.686 and 0.708, respectively) would also be one of the principal factors for
improvement of the models.
Until now, few studies have focused on habitat mapping of threatened plant species using remote-sensing
techniques (e.g., Luoto et al., 2002; Zimmermann et al.,2007). Recent advances in habitat modeling techniques
increase the possibility of analyzing and mapping theirpotential habitats based on remote-sensed dominant
plant covers. In this study, we used abundance of dominant grass species estimated from hyperspectral-remotesensing data for habitat modeling of the threatened species, since these dominant species can be distinguishedby a hyperspectral remote sensing technique (Lu et al.,2006; Ishii et al., submitted). Plant species associationsof dominant and non-dominant species have been well
documented in various plant communities (e.g., Austin,1985). Therefore, our approach may be applicable toother plant communities as well.
In the present study, the autocovariate at 10 m wasshown to be an important variable positively affecting
the modeled abundance of all species examined. One
reason for this is that a strong spatial structure of dominant P. australis and M. sacchariflorus due to clonal
growth might affect spatial distribution of threatened
species in the under-layer, although the importance offactors such as dispersal limitation or other autocorrelated environmental factors cannot be denied.
In conclusion, hyperspectral-remote sensing of shootdensities of P. australis and M. sacchariflorus is effective in habitat modeling of certain threatened species inmoist tall grasslands.

Acknowledgements
We thank the Tone River Upstream Office of the
Ministry of Land, Infrastructure and Transport of Japanfor allowing us to conduct this study, and the Foundation for Riverfront Improvement and Restoration forsupport in fieldwork. We would like to express special
thanks to Dr. Taku Kadoya, Dr. Mayura Takada andDr. Hiroshi Ishii of The University of Tokyo for theiradvice on data analyses and comments on an earlymanuscript. This research was partly funded by the
ESPEC Foundation for Global Environment Research
and Technology (a charitable trust).
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