Biodivers Conserv (2009) 18:2521–2535
DOI 10.1007/s10531-009-9605-7
ORIGINAL PAPER

Mapping potential habitats of threatened plant species
in a moist tall grassland using hyperspectral imagery
Jun Ishii Æ Shan Lu Æ Syo Funakoshi Æ Yo Shimizu Æ Kenji Omasa Æ
Izumi Washitani

Received: 18 October 2007 / Accepted: 19 February 2009 / Published online: 28 February 2009Ó Springer Science+Business Media B.V. 2009

Abstract We examined the capability of hyperspectral imagery to map habitat types ofunder-storey plants in a moist tall grassland dominated by Phragmites australis andMiscanthus sacchariflorus, using hyperspectral remotely-sensed shoot densities of the two
grasses. Our procedure (1) grouped the species using multivariate analysis and discriminated habitat types (species groups) based on P. australis and M. sacchariflorus shootdensities, (2) used estimated shoot densities from hyperspectral data to draw a habitat typemap, and (3) analyzed the association of threatened species with habitat types. Our
identification of four habitat types, using cluster analysis of the vegetation survey coveragedata, was based on P. australis and M. sacchariflorus shoot density ratios and had an
overall accuracy of 77.1% (kappa coefficient = 0.71). Linear regression models based on
hyperspectral imagery band data had good accuracy in estimating P. australis andM. sacchariflorus shoot densities (adjusted R2 = 0.686 and 0.708, respectively). These
results enabled us to map under-storey plant habitat types to an approximate prediction
accuracy of 0.537. Among the eight threatened species we examined, four exhibited asignificantly biased distribution among habitat types, indicating species-specific habitatuse. These results suggest that this procedure can provide useful information on the statusof potential habitats of threatened species.
Keywords Floodplain wetland  Hyperspectral image  Miscanthus sacchariflorus 
Multivariate analysis  Phragmites australis  Potential habitats  Shoot density 
Spatial autocorrelation  Threatened species

Introduction
The recent loss or degradation of wetlands due to human activities is extensive, andwetland conservation and restoration have become of increasing concern worldwide
J. Ishii (&)  S. Lu  S. Funakoshi  Y. Shimizu  K. Omasa  I. 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

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Biodivers Conserv (2009) 18:2521–2535(e.g. Whitehead et al. 1990; Sinclair et al. 1995; Edyvane 1999; Gibbs 2000). In Japan, thetotal wetland area decreased by approximately 40% during the twentieth century, mainly
because of urbanization and agricultural development (Ministry of Land, Infrastructure andTransport of Japan 2000). A major type of Japanese lowland wetland, which was once very
common even in densely populated areas, is lowland floodplain dominated by the moist tall
grasses Phragmites australis (Cav.) Trin. ex Steud., Miscanthus sacchariflorus (Maxim.)
Benth., or both.
A long history of reclamation, especially recent riparian work, has decreased the rangeof floodplain wetlands and has deprived them of their ecological integrity and associatedindigenous plant species (Washitani 2001). Consequently, many of these plants are now
listed on the national Red List (Environmental Agency of Japan 2007), making theirconservation, as well as that of moist tall grasslands, a priority. However, conservation
plan decision-making requires detailed knowledge of both the current distribution of
threatened species and dynamic spatio-temporal habitat patterns that govern their longterm persistence and distribution.
A zonation pattern of P. australis and M. sacchariflorus from deeper water to drier areas
is common in Japanese moist tall grasslands. The relative dominances of these species are,therefore, thought to be determined primarily by groundwater level (Yamasaki and Tange
1981; Yamasaki 1990). Both natural and anthropogenic disturbances may also influencethese patterns by direct damage to plants or habitat alteration, including changes inhydrological processes, light availability, nutrient status, and other physical environment
factors (e.g. Haslam 1972). Such disturbances could be caused by flooding, water level
fluctuations, grazing, fire, mowing, ice and wave in wetlands (Keddy 2000). In particular,
flooding repeatedly rearranges the environment, destroying certain habitats and creatingothers (Arscott et al. 2002; Richards et al. 2002). Thus, this habitat complex is a dynamic,
shifting mosaic (see Denslow 1985). Spatio-temporal characterization and mapping thatreflect the degree of P. australis and M. sacchariflorus dominance are likely to be prerequisites to understanding the environmental requirements of under-storey plants,
including threatened species.
The use of remote sensing in habitat mapping provides explicit and timely spatial
information (reviewed by Mertes 2002; Ozesmi and Bauer 2002), although difficulties inmapping continuous patterns of plant species assemblages still remain (Schmidtlein and
Sassin 2004). Recent advances in remote sensors, including hyperspectral sensors, arehighly promising for analyzing cover types (including plant species) and their percentages(Turner et al. 2003), in particular dominant species abundance. In a previous study, wedemonstrated the technical possibility of using hyperspectral imagery to estimate P. australis and M. sacchariflorus shoot densities in a moist tall grassland (Lu et al. 2006).
Grouping plant species that share habitats using multivariate analysis may be useful inmapping habitat or vegetation types (Guisan and Zimmermann 2000; Ohmann and Gregory2002). Dominant species affect the distribution of non-dominant species and hence species
groups sharing environmental conditions in a community (e.g. Allen and Forman 1976;
Abul-Fatih and Bazzaz 1979; Fowler 1981; Hils and Vankat 1982; Shevtsova et al. 1995;
Aksenova and Onipchenko 1998). In a moist tall grassland, the absolute and/or relative
abundance of the dominants P. australis and M. sacchariflorus may be a good predictor of
species group habitat types.
In this study, we examined the capability of hyperspectral imagery to map the habitat
types of the under-storey plants that constitute the continuous and significant vegetation
mosaic of a moist tall grassland, based on remotely-sensed hyperspectral data of
P. australis and M. sacchariflorus shoot densities. In our previous remote sensing study

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2523(Lu et al. 2006), the use of matched filtering, a specialized type of spectral mixture analysis
for hyperspectral data, resulted in poor estimates of P. australis, due to the difficulty of
selecting a pure pixel or endmember. To overcome this technical difficulty, we avoided
selecting an endmember by using linear regression models. Shoot densities and band ratios(Ozesmi and Bauer 2002; Galvão et al. 2003) calculated from hyperspectral data were usedas the dependent and independent variables, respectively.
Our procedure was composed of the following steps and substeps: (1-1) grouping
species sharing similar microhabitat preferences using multivariate analysis and (1-2)
identifying the relationship between species group habitat types and P. australis andM. sacchariflorus shoot densities; (2-1) improving the method to estimate P. australis andM. sacchariflorus shoot densities from hyperspectral data; (2-2) drawing a habitat typemap; and (2-3) analyzing the spatial pattern in the distribution of habitat types; and finally,(3-1) analyzing the association of threatened species with habitat types.

Methods
Study area and analytical strategy
The study was performed in a typical moist tall grassland dominated by P. australis andM. sacchariflorus located in the eastern part (approximately 16 km2) of Watarase wetland
in central Japan (approximately 33 km2, 36°110 –36°170 N, 139°400 –139°420 E; Fig. 1).
Watarase wetland is a semi-artificially controlled floodplain of three tributaries of the Tone
River: the Watarase, Uzuma, and Omoi Rivers. The wetland is valued for controlling
floods and its water use in irrigated agriculture, recreational opportunities, and reed harvesting. It also supports rich biodiversity, with more than 650 plant species (Ohwada andOgura 1996), including 59 species listed on the national Red List (Environmental Agency
The Uzuma River
The Watarase RiverThe Omoi River

Lake Yanaka

N
0km 1kmFig. 1 Location of Watarase wetland in central Japan. Surrounded by urban and agricultural areas, thewetland contains Lake Yanaka and three rivers (solid gray lines) that flow into each other. The vegetation isdominated by the moist tall grasses Phragmites australis and Miscanthus sacchariflorus. Dotted black lines
represent constructed banks within the wetland

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of Japan 2007), including Amsonia elliptica (Thunb.) Roem. et Schult., Apodicarpum
ikenoi Makino, Arisaema heterophyllum Blume, Carex cinerascens Kükenth., Euphorbia
adenochlora Morr. et Decne., Galium tokyoense Makino, Ophioglossum namegatae
Nishida et Kurita, and Thalictrum simplex var. brevipes Hara (Masumi Ohwada, unpublished data).
The annual shoot emergence of P. australis and M. sacchariflorus usually starts in March,
after controlled burns to manage shoot production for commercial use of P. australis.
The shoots elongate rapidly, reaching maximum height in July–August. Flowering andfruiting occur from September to October and November to December, respectively. The
above-ground parts die back in winter, with the rhizomes over-wintering. The average
1976–2005 annual rainfall and air temperature, recorded at Koga Metrological Station near
the study area, were approximately 1,187 mm and 14°C.
After observing the spatial variation of P. australis and M. sacchariflorus shoot densities at several scales in a preliminary field survey, we chose 1–5 m as the measurement
and spatial analysis scale.
Figure 2 summarizes our procedure, with individual measurement and analysis steps.

Measurement
Habitat types and discriminator• Species and vegetation data
*species composition*P. australis andM. sacchariflorus shoot densities

Hyperspectral remote sensing• Species and vegetation data
*P. australis andM. sacchariflorus shoot densities• Hyperspectral data

Analysis1-1. Grouping species sharing similar
microhabitat preferences using cluster
analysis1-2. Identifying the relationshipbetween species groups and P.
australis and M. sacchariflorus shootdensities

2-1. Linear regression analyses toestimate P. australis and M.
sacchariflorus shoot densities fromhyperspectral data
2-2. Mapping habitat types based on
the association of species groups with
P. australis and M. sacchariflorus
shoot densities using hyperspectral
remote sensing
2-3. Analyzing the spatial pattern in the
distribution of habitat types mappedusing Moran’s I correlograms

Association of threatened species with
habitat types• Species and vegetation data
*geographic coordinates of field
points of threatened species

3-1. Analyzing the association of
threatened species with habitat types
mappedFig. 2 Study flow chart. Section numbers correspond to study procedure steps

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Measurements
Species and vegetation data
A vegetation survey was performed in May–June 2004 and 2005 to obtain data for analyzing the associations between P. australis and M. sacchariflorus shoot densities and plant
species groups (Fig. 2, Sect. 1-1 and 1-2). A total of 613 quadrats (1 9 1 m) were scatteredthroughout the study area. The locations were selected in a variety of vegetation types in
the field and included the maximum ranges of P. australis and M. sacchariflorus shootdensities. Species composition, coverage (%), and P. australis and M. sacchariflorus shootnumber (=shoot density) were recorded for each quadrat.
Phragmites australis and M. sacchariflorus shoot densities for remote-sensing analysis(Fig. 2, Sect. 2-1) were measured from 22 May, the day after an aircraft flew to acquire
hyperspectral images, to 14 June 2004. Twenty-three plots (5 9 5 m) were placed within
the study area and the locations were selected to include the maximum ranges ofP. australis and M. sacchariflorus shoot densities as possible. In each plot, three quadrats(1 9 1 m) were placed 1 m apart, and data were recorded for each quadrat, with
P. australis and M. sacchariflorus shoot densities calculated as the average over three
quadrats. To identify the plot positions on the hyperspectral images, the geographic
coordinates of the four corners of each plot were recorded, using a Trimble GPS Pathfinder
ProXR (1 m maximum error).
In July 2005, we recorded the geographic coordinates in the southeastern part of thestudy area of 184 arbitrarily chosen fields points of eight plant species (an average of 43
points per species) that are relatively abundant in the area: A. elliptica, A. ikenoi,
A. heterophyllum, C. cinerascens, E. adenochlora, G. tokyoense, O. namegatae, andT. simplex var. brevipes (Fig. 2, Sect. 3-1). The national Red List (Environmental Agencyof Japan 2007) designates A. ikenoi, G. tokyoense, and O. namegatae as vulnerable and theothers as near vulnerable.
Hyperspectral data
Hyperspectral images were acquired by the airborne imaging spectrometer for applications(AISA) Eagle, which is operated by SPECIM LTD (Spectral Imaging LTD). The instrument collected images at nadir in 68 contiguous bands of reflectance data (bands 1–68),
sampled at 8.9 nm intervals in the 398–984 nm spectral wavelength ranges (visible to nearinfrared). It also has very high radiometric resolution (16 bit). AISA data were collected on
21 May 2004 at 14:37–15:35 local time under clear conditions. The imaging, conductedfrom an aircraft flying at an altitude of 1,438 m, provided 1.5 m-pixel resolution. Fourscenes (each approximately 10 9 1 km) were selected to cover the study area.
The AISA imagery was corrected radiometrically and then atmospherically to apparent
on-board reflectance using the Fiber Optic Downwelling Irradiance System (FODIS). The
reflectance image value was derived by multiplying the real value by 10,000. Four scenes
were mosaicked into one, with estimated positional error \1 pixel, using ERDASIMAGINE 9.0 (Leica Geosystems Geospatial Imaging, LLC, Norcross, GA). We thentransferred the geographic coordinates of the 23 5 9 5 m plots surveyed for P. australis
and M. sacchariflorus shoot densities to the mosaicked scene. The reflectance data of eachplot were calculated as the average obtained from all pixels in the plot. Before conducting
linear regression analyses to estimate P. australis and M. sacchariflorus shoot densities

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from the hyperspectral data (Fig. 2, Sect. 2-1), all reflectance data were divided by thereflectance value in band 1, to reduce the errors between the images.
Analyses and mapping using hyperspectral imagery
Habitat types and discriminator
To group species sharing similar microhabitats, a hierarchical, agglomerative cluster
analysis was performed on the coverage data of the under-storey species recorded in thevegetation survey (613 quadrats) using the Bray–Curtis similarity measure and the flexible
linkage method with b = -0.25 as implemented by PC-ORD for Windows (McCune andMefford 1999; Fig. 2, Sect. 1-1). P. australis and M. sacchariflorus were deleted from thedata matrix because the association between their shoot densities and species groups
identified by the cluster analysis was analyzed in the following step. Species with afrequency of 10% or less were also removed because their presence or absence in a sample
may be due to chance alone (McCune and Grace 2002), leaving 25 species for the analysis.
Prior to the analysis, the data were transformed by arcsine squareroot transformation to
decrease skewness and kurtosis for each species (McCune and Grace 2002). The differences among species groups derived from the cluster analysis were tested by MRPP (multiresponse permutation procedure) based on Bray–Curtis similarity.
The four species groups thus recognized were used as habitat types for the following
analyses (Fig. 2, Sect. 1-2). The different habitat types were well delineated on the scatterplots by the shoot-density ratios of P. australis and M. sacchariflorus but not by the shootdensity of either species alone, as shown in Fig. 4. Therefore, the shoot density ratio was
calculated as a discriminator for each quadrat as: (M. sacchariflorus shoot density)/(P. australis shoot density ? 1). The addition of 1 in the denominator reflects a shootdensity of 0 for P. australis. The best discriminating value for each habitat type pair wasdetermined as the value that maximized the area under the ROC curve (AUC) values(Hanley and McNeil 1982). The overall performance of the calculated discriminating
values was tested by a concordance test with a kappa coefficient (Rosenfield and Fitzpatrick-Lins 1986).
Mapping habitat types based on hyperspectral data
We used linear regression models to estimate P. australis or M. sacchariflorus shootdensities, based on hyperspectral imagery band data (Fig. 2, Sect. 2-1). To select optimalmodels, we calculated Akaike’s information criterion (AIC; Burnham and Anderson 2002)
for all possible candidate models with one or two combinations of variables of 68-banddata using R (R Development Core Team 2006). The models were ranked based on bothdelta AIC values (Di) and a measure of the weight of evidence of the best model or Akaike
weights (wi; Burnham and Anderson 2002).
Lu et al. (2007) found that a pre-classification process using the normalized difference
vegetation index (NDVI; Thenkabail et al. 2000) to separate the whole image into vegetated and non-vegetated areas improved classification accuracy. We calculated NDVI for
the entire study area as: (NIR - red)/(NIR ? red), where NIR = 774 nm, red = 675 nm.
The vegetation area was extracted when NDVI C 0.74 in our AISA scene.
For mapping in these vegetation pixels, P. australis and M. sacchariflorus shoot densities were estimated using the selected models, and the shoot-density ratio was calculated123

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2527(Fig. 2, Sect. 2-2). The habitat type in each pixel was determined by referring to the habitat
type discriminator values.
Using the habitat type map obtained in this manner, we analyzed the spatial pattern of
habitat types, using Moran’s I correlograms (Cliff and Ord 1981; Fig. 2, Sect. 2-3), toinvestigate the effects of our measurement and spatial analysis scale on analyzing andmapping the habitat types. Because habitat types were based on the P. australis andM. sacchariflorus shoot-density ratio, I was calculated for it using R. Four investigation
areas (100 9 100 m), comprising two nearly pure P. australis or M. sacchariflorus stands
and two areas with large variations in P. australis and M. sacchariflorus shoot-density
ratios, were chosen to include a variety of shoot-density-ratio spatial patterns. We exported
the shoot-density ratio data for each area and calculated I for five distance classes: 0–3,
3–6, 6–9, 9–12, and 12–15 m. For each distance class, a randomization test with 9,999
permutations (one-tailed test) was performed and the significance of I was assessed by aprogressive Bonferroni correction (Legendre and Legendre 1998).
Association of threatened species with habitat types
Using geographic coordinates, we layered the locations of the field points of eight threatened species onto the habitat type map we had created and for each species counted thenumber of points in each habitat type (Fig. 2, Sect. 3-1). To analyze the association of
threatened species with habitat type, we performed a goodness of fit test using R. For thistest, the original chi-squared value was calculated (v2ori). All possible contingency tables
were generated, and the chi-squared values were calculated (v2all). The probability ofrejecting the null hypothesis was calculated as: (number of v2all C v2ori)/(number of v2all).
The expected value was defined as the number of each habitat type occupied by the 184
data points.

Results
Characterization of habitat types
As noted in the ‘‘Methods’’ section, through cluster analysis of species occurrences within
the 613 quadrats, we identified four species groups (Fig. 3). This level of grouping retained
Fig. 3 Dendrogram of theresults of hierarchical,
agglomerative cluster analysis,grouping 613 quadrats into thefour species groups i, ii, iii and
iv. The dendrogram is scaled by
Wishart’s objective function,
expressed as the percentage of
information remaining at eachlevel of grouping (McCune andGrace 2002). n number ofquadrats in each group

Information remaining (%)
100

75

50

25

0

species group i(n = 201)

species group ii(n = 205)
species group iii(n = 96)

species group iv(n = 111)

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about 18% of the information in the dendrogram. MRPP showed significant differencesamong all species groups (chance-corrected within-group agreement A = 0.342,
P \ 0.001). Habitat types corresponding to species groups i, ii, iii, and iv were designatedas habitat types I, II, III, and IV, respectively.
The best discriminating shoot-density ratio values between habitat types IV and I, I andII, and II and III were 2.5, 0.7, and 0.2, respectively. Based on these values, the four habitat
types were discriminated with an overall accuracy of 77.1% (kappa coefficient = 0.71,
P \ 0.001; Table 1). Most omission errors occurred in habitat type II and were associatedwith the most commission errors in habitat type III (Table 1; Fig. 4).
Habitat types IV, I, II, and III were discriminated as ‘‘nearly pure M. sacchariflorus’’,‘‘M. sacchariflorus-dominated’’, ‘‘P. australis-dominated’’, and ‘‘nearly pure P. australis’’
stands, respectively (Fig. 4).
Mapping habitat types based on hyperspectral data
Model selection results using an information-theoretical approach showed that one modeland four models with Di \ 4 and with Akaike weights [0.1 could predict P. australis andM. sacchariflorus shoot densities, respectively (Table 2). The model with the lowest AIC
value for P. australis was:
Shoot density ¼ 25:487  ðband58=band1)  9:611  ðband66=band1Þ:ð1Þ

2

The adjusted coefficient of determination (adjusted R ) was 0.686. The best fitted model
for M. sacchariflorus density was:
Shoot density ¼ 129:347  ðband19=band1Þ  53:078  ðband36=band1Þ  234:769:
ð2Þ
The adjusted R2 was 0.708. The central wavelengths of band1, 19, 36, 58 and 66 are
397.79, 550.11, 701.42, 901.52 and 974.95 nm, respectively.
Using the shoot-density value estimated by the models to distinguish between the four
habitat types, we mapped the ranges of the habitat types (Fig. 5). The overall accuracy of
Table 1 Confusion matrices for accuracy of the discriminating values between the habitat types for species
groups identified by cluster analysis (see text and Fig. 3)
Observed habitat
types

Accuracy (%)aNumber of sites

Estimated habitat types
I

II

III

IV

I

75.4

155

117

16

4

18

II

58.7

155

24

91

31

9

III

100

96

0

0

96

0

IV

85.5

110

10

4

2

94

516

151

111

133

121

77.4

82.0

72.2

77.7

Total
Reliability accuracy (%)b

The left-hand side (y axis) is labeled with the habitat types on the verification sites; the upper edge (x axis) islabeled with the same habitat types which refer to those on the verification sites to be evaluatedOverall accuracy (398/516) 9 100 = 77.1%
Kappa coefficient = 0.71
a(100—percent omission error): also called producer’s accuracyb(100—percent commission error): also called user’s accuracy

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Fig. 4 Scatter plot of Phragmites australis and Miscanthus sacchariflorus shoot densities in species groups
i, ii, iii, and iv (see Fig. 3)
Table 2 Model selection statistics and adjusted R2 for the analyses of the relationships between both shootdensities of Phragmites australis and Miscanthus sacchariflorus and hyperspectral reflectance data in
Watarase wetland
Modela

AIC

Di

wi

Adjusted R2

167.660.00

0.23280.686

550.11/397.79, 701.42/397.79 nm

181.170.00

0.23280.708

541.43/397.79, 701.42/397.79 nm

181.980.81

0.15560.697

532.87/397.79, 576.58/397.79 nm

182.601.43

0.11410.689

558.93/397.79, 701.42/397.79 nm

182.831.66

0.1014

0.686

P. australis
901.52/397.79, 974.95/397.79 nm
M. sacchariflorus

In hyperspectral reflectance data: band 1, 397.79 nm; band 17, 532.87 nm; band 18, 541.43 nm; band 19,
550.11 nm; band 20, 558.93; band 22, 576.58 nm; band 36, 701.42 nm; band 58, 901.52 nm; band 66,
974.95 nma

Only models with Di smaller than 4 and with Akaike weights greater than 0.1 are shown

the habitat type map was 0.537. This was approximately calculated as: (overall accuracy of
discriminating shoot-density-ratio values among habitat types) 9 (average value ofadjusted P. australis and M. sacchariflorus coefficients of determination) = (77.1/
100) 9 [(0.686 ? 0.708)/2]).
Moran’s I correlograms indicated that P. australis and M. sacchariflorus shoot-density
ratio data, which were exported from the habitat map, showed significant spatial patterns,
with I decreasing steadily with geographical distance in all four investigation areas(Fig. 6). Overall, I was higher in areas of nearly pure P. australis or M. sacchariflorus

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Fig. 5 Habitat type map of moist tall grasslands in Watarase wetland, based on hyperspectral imagery.
Pixel size = 1.5 m. Habitat types IV, I, II, and III were identified as ‘‘nearly pure Miscanthus
sacchariflorus’’, ‘‘M. sacchariflorus-dominated’’, ‘‘Phragmites australis-dominated’’, and ‘‘nearly pure P.
australis’’ stands, respectively (see text and Fig. 4)123

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Fig. 6 Moran’s I correlograms of Phragmites australis and Miscanthus sacchariflorus shoot-density ratios
in four areas: nearly pure P. australis stand (a), nearly pure M. sacchariflorus stand (b) and two areas withlarge shoot-density ratio variations of both species (c and d). Asterisks indicate I values that are significant
with 5% probability level adjusted using progressive Bonferroni correction

stands than in areas with large variations of P. australis and M. sacchariflorus shootdensity ratios.
Association of threatened species with habitat types
The threatened species showed fairly typological distribution patterns among the habitat
types (Table 3). Four species, A. elliptica, A. ikenoi, E. adenochloa, and G. tokyoense,
exhibited significantly biased distributions among habitat types. Amsonia elliptica,
E. adenochloa, and G. tokyoense were significantly associated with habitat types IV, I, andII, while A. ikenoi occurred in habitat type III at significantly higher frequency than
expected. No significant associations were detected in four species, Arisaema heterophyllum, Carex cinerascens, Ophioglossum namegatae and Thalictrum simplex var.
brevipes, although each showed a specific trend.

Discussion
Remote sensing of dominant species and habitat mapping
The classification and mapping of a habitat mosaic within a plant community with continuous transitions among plant species assemblages using remotely sensed data is

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Table 3 The association of threatened species with four habitat types of a moist tall grassland in Watarase
wetlandn

Habitat types
IV

I

II

v2

P

III

Amsonia elliptica

24

16.7

25.0

50.0

8.3

16.965**

Apodicarpum ikenoi

83

1.2

9.6

25.3

63.9

9.269

*

Arisaema heterophyllum

40

2.5

5.0

32.5

60.0

5.053

NS

Carex cinerascens

9

0

11.1

11.1

77.8

3.047

NS

Euphorbia adenochlora

54

11.1

24.1

33.3

31.5

8.441

*

Galium tokyoense

44

18.2

20.5

36.4

25.0

14.153**

Ophioglossum namegatae

72

5.6

16.7

33.3

44.4

1.857

NS

Thalictrum simplex var. brevipes

17

0

29.4

41.2

29.4

6.362

NS

All species

184

7.6

14.7

27.7

50.0

Percentages of the frequencies in which each species was recorded in four habitat types were shown. Habitat
types were arranged in order of relative dominance of Phragmites australis and Micanthus sacchariflorus(see Fig. 4)* P \ 0.05; ** P \ 0.01; NS not significant

challenging, due to the low spatial and spectral resolution limitations of remote sensing(Schmidtlein and Sassin 2004). In this study, we sought to map a habitat mosaic of understorey plants within a moist tall grassland using hyperspectral imagery.
We demonstrated that P. australis and M. sacchariflorus shoot density ratios wereeffectual in discriminating habitat types (species groups) with high overall accuracy(77.1%). This enabled us to map the discrete habitat types for the under-storey plants that
constituted the continuous vegetation mosaic.
The use of linear regression models resulted in good accuracy in estimating bothP. australis and M. sacchariflorus shoot densities (adjusted coefficients of determination = 0.686 and 0.708, respectively) using hyperspectral imagery and improved theestimator capability of P. australis from that in our previous study, which used matchedfiltering analysis (Lu et al. 2006). Based on these results, we successfully mapped
the habitat types of under-storey plants in a moist tall grassland, with an approximate
prediction accuracy of 0.537.
Models estimating P. australis and M. sacchariflorus shoot densities basedon hyperspectral data
In the model selections for P. australis and M. sacchariflorus shoot densities, near-infrared(band 58, 901.52 nm; band 66, 974.95 nm) spectra and both green (band 19, 550.11 nm)
and red (band 36, 701.42 nm) spectra were chosen as variables for P. australis (Eq. 1) andM. sacchariflorus (Eq. 2), respectively. These results are consistent with those of ourprevious study (Lu et al. 2006), in which spectral differences between P. australis andM. sacchariflorus occurred in both the 500–560 and of 750–920 nm ranges. Such associations of green, red, and near-infrared spectra with plants are common (e.g. Blackburn
1998; Cochrane 2000; Thenkabail et al. 2000; Kokaly et al. 2003; Schmidt and Skidmore
2003; Mutanga et al. 2004). Our results demonstrate the capability of these spectra to
identify plant species.

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Spatial patterns of habitat types and their association with threatened species
Significant positive spatial autocorrelations, which recognized lag distances of up to 15 m
(equal to the maximum distance in the analyses) for P. australis and M. sacchariflorus
shoot-density ratios in all four investigation areas (two nearly pure P. australis
and M. sacchariflorus stands and two areas with large variations in P. australis andM. sacchariflorus shoot-density ratios), with relatively high Moran’s I values within 6 m,suggest that our measurement scale of 1–5 m provided enough spatial resolution to analyze
and map the habitat mosaic in the study area. The 6 m2 maximum quadrat size would work
well in assessing the spatial heterogeneity of a moist tall grassland habitat mosaic. Thus,
spatial data that retain spatial variations can be acquired through airplane imaging.
However, hyperspectral data acquired by satellite sensors, such as Hyperion (30 m resolution), lack sufficient resolution for our vegetation analysis.
The habitat type map with high spatial resolution enabled us to clarify the habitat
characteristics of a number of threatened species. Considering that most errors in identifying habitat types were omission errors for habitat type II, leading to habitat type III
commission errors, we interpreted the habitat types of the eight threatened species asfollows. Whereas both nearly pure M. sacchariflorus and M. sacchariflorus-dominatedstands (habitat types IV and I) were suitable habitat for A. elliptica, E. adenochlora, and
G. tokyoense, nearly pure P. australis stands (habitat type III) were the favorable type for
A. ikenoi. These results strongly suggest species-specific habitat use that should be considered in habitat management.
Application of the procedure for mapping potential habitats of threatened species
in moist tall grasslands and future research
Our results demonstrated that hyperspectral imagery is capable of mapping the habitat
types of the under-storey plants that constitute the continuous vegetation mosaic of moist
tall grasslands. Our procedure can be useful for obtaining information on the current statusof potential habitats of threatened species and designing strategies to increase survey
efficiency and reduce sampling costs of field sampling rare species over larger conservation areas, leading to more cost-effective conservation practices (Guisan et al. 2006; seealso Ferrier 2002). It could also be employed to model metapopulation dynamics(Jäkäläniemi et al. 2006; see also Moilanen and Hanski 1998; Löbel et al. 2006). Further
investigations are needed to reduce map errors and determine the effectiveness of the same
bands of hyperspectral images among different years and sites. With further improvements,
our approach may aid in the successful mapping of habitat mosaics in other plant communities as well, and may also contribute to periodical data collection to monitor dynamic
spatio-temporal habitat patterns of under-storey species, including threatened species.
Acknowledgments We thank Dr. Miho (Ajima) Nishihiro, Dr. Taku Kadoya, Dr. Shin-ichi Takagawa,Dr. Jun Nishihiro, and Mr. Akira Yoshioka of the University of Tokyo and Mr. Masumi Ohwada for fieldassistance and advice on data analyses. We also thank two anonymous reviewers for valuable comments onan earlier version of the manuscript.

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