Short Paper

J. Agric. Meteorol. 67 (2): 85 – 88, 2011

Identiﬁcation of invasive vegetation using hyperspectral imagery
in the shore of the Kinu River, Japan
Shan LU*, Yo SHIMIZU**, Jun ISHII**, Izumi WASHITANI**,and Kenji OMASA**, †*School of Urban and Environmental Sciences, Northeast Normal University,
5268 Renmin Street, Changchun, 130024, China**Graduate School of Agricultural and Life Sciences, The University of Tokyo,
1–1–1 Yayoi, Bunkyo, Tokyo, 113–8657, Japan
Abstract
Weeping love grass (Eragrostis curvula) has become a well-established invasive species along theKinu River, Japan and is now considered a problematic invasive weed species. The aim of this study
was to map the probability of the establishment of this invasive grass in the shore of the Kinu River
using airborne hyperspectral imagery. Binary logistic regression analysis was used to model the probablepresence/absence of weeping love grass. This study tried entering two types of input variables, original
reﬂectance bands and MNF (Minimum Noise Fraction) transformed bands, into the regression model.
No available variable of original reﬂectance data was selected, but two bands of MNF were selectedin the regression analysis. The ﬁnal classiﬁcation, using the selected MNF bands, has distinguished
weeping love grass from pseudo-absence pixels with user’s and producer’s accuracies of 100％ and66.7％ respectively. The kappa coefﬁcient was 0.74. These results indicate that the MNF transformed
hyperspectral bands are more suitable than the original reﬂectance data to estimate the distribution ofinvasive weeping love grass in the shore of the Kinu River.
Key words: Binary logistic regression, Hyperspectral imagery, Invasive vegetation.

weeping love grass (Matsumoto et al., 2000; Muranaka
and Washitani, 2001, 2002). Therefore, monitoring andestimating the distribution of weeping love grass is ofgreat importance for planning its removal. To makeclear the expansion dynamics of weeping love grass,spatial information of the distribution of the ﬂowering
grass which produces a large number of seeds is themost important. Thus, the objective of this study
was to investigate the invasive vegetation of weeping
love grass, which were ﬂowering, using hyperspectral
remote sensing imagery on the Kinu Riverbed. We
used the binary logistic regression model to predict theoccurrence of the grass. Two types of input variables(original reﬂectance data and MNF transformed data)
were tried to obtain a reasonable indicator to predictthe potential existence of weeping love grass.
Minimum noise fraction (MNF) transformation
is used to determine the inherent dimensionality ofimage data, to segregate noise in the data, and toreduce the computational requirements for subsequent

1. Introduction
Weeping love grass is originally a grass from southern Africa that was introduced into Japan in 1959 inorder to protect soil on dikes and for greening reasons(Muranaka and Washitani, 2001). However, it is now
found along several riverbeds in Japan, such as Kinu
Riverbed and Yoshino Riverbed (Washitani et al., 2010),
and recently the grass has become a problematic plant.
This is because it’s rapid and widespread expansion
has disturbed the riverbed ecosystem. The existenceof native plants, such as Aster kantoensis Kitam. andAnaphalis margaritacea (L.) Benth. et Hook.f. subsp.
yedoensis (Franch. et Sav.) Kitam., has been threatenedsince the soil quality, light availability and the riverbed
topography has changed due to the invasion of theReceived; December 15, 2010.
Accepted; February 23, 2011.
Corresponding Author: aomasa@mail.ecc.u-tokyo.ac.jp†85

J. Agric. Meteorol. 67 (2), 2011were used to represent each 5×5m plot. The locationsof the four corners of each of these plots were recorded
on a Trimble Pathfinder ProXR GPS device. This
provided us with sub-meter accuracy.
Logistic regression models were used to predict theper-pixel probability of the occurrence of weeping
love grass. This study tried entering two types of
input variables for the regression models. One was
the original reflectance data (68 bands) and theother was the selected bands of the minimum noise
fraction (MNF) transformation of hyperspectral data.
In selecting the input MNF bands, all of the MNF
bands were visually inspected and they were discarded
if they exhibited severe ﬂightline effects and contained
a lot of noise with eigenvalues that were near 1. The
remaining bands (18 bands) were chosen throughstepwise entry into the logistic regression model. Theregression model was trained and validated using arandom sample of pixels with ＞60％ cover from theKinu Riverbed (n＝7), as well as a random sampleof pseudo-absence points (n＝18). These points werea random sample of pixels selected from the region
that was most comprehensively surveyed for weeping
love grass.

processing (Boardman and Kruse, 1994；Green etal., 1988). The MNF transformation is essentially
two cascaded Principal Components Transformations.
The ﬁrst transformation, based on an estimated noise
covariance matrix, decorrelates and rescales the noisein the data. This ﬁrst step results in transformed data inwhich the noise has unit variance and no band-to-bandcorrelations. The second step is a standard Principal
Components Transformation of the noise-whitened
data. For the purposes of further spectral processing,the inherent dimensionality of the data is determined byexamination of the ﬁnal eigenvalues and the associatedimages. This data space can be divided into two parts:
one part is associated with large eigenvalues and coherent eigenimages, and the other is a complementarypart with near-unity eigenvalues and noise-dominatedimages. Noise is separated from the data by using only
the coherent portions. This then improves the spectral
processing results. The MNF transformation has been
demonstrated to be effective for mapping invasive
weeds with some hyperspectral data (Underwood et al.,
2003; Mundt et al., 2005; Hestir et al., 2008).
2. Materials and Methods
We chose a study site where weeping love grass
was spread widely along the shore of the mid andlower reaches of the Kinu River, near Ujiie Ohashi.
The center of the study area was around 36°41′21″N,139°56′23″E. The AISA (Airborne Imaging Spectrometer for Applications) hyperspectral imagery wasacquired on 26 May 2004. AISA is a hyperspectral
sensor which is manufactured by Spectral Imaging
CO., LTD. in Finland. The images that the AISA
collected had 68 contiguous spectral bands, sampled at
approximately 9 nm intervals in a range from 398 to984 nm. One pixel size was 1.5×1.5 m. The radiance
was calculated as the upwelling radiant energy received
at the sensor, and the apparent reﬂectance was deﬁned
as the ratio between the upwelling and downwellingradiant energy. The downwelling radiant energy fromthe sun was measured by the onboard fiber optic
downwelling irradiance sensor (FODIS). Images wererecorded as the apparent reﬂectance measured at theinstrument height. Detailed ﬁeld information on theinvasive grass was obtained from the end of May tothe middle of June 2004. Thirty ﬁve 5×5 m plots wereestablished, and in each plot, three 1×1 m subplots
were randomly chosen to measure the coverage ofweeping love grass. Means from the three subplots

3. Results and Discussion
No signiﬁcant original reﬂectance band was selectedby the logistic regression analysis for predicting thepresence/absence of the weeping love grass, while aprediction of the formula using the MNF band 13 and14 optimized the discrimination of weeping love grassfrom other land cover types. This model was assessedby regressing the estimated probability of weeping lovegrass occurrence (p) against the percentage coverage
estimates from the inventory data. The value of p(p＝0.68), corresponding to a 60％ cover by weeping
love grass with residual errors that were not high, wasidentiﬁed and the pixels were classiﬁed as “weeping
love grass” if their predicted value exceeded thisthreshold (Hestir et al., 2008).
The predicted p value provided an indicator of thepercentage coverage of weeping love grass within apixel (shown in Table 1). In the training samples,the user’s and producer’s accuracies for weeping lovegrass presence were 100％ and 71.4％ respectively.
The total accuracy for all the training samples was92.0％. The kappa coefﬁcient relative to the weeping
love grass was 0.78.
To estimate the prediction accuracy of the model,
86

S. Lu et al. : Identification of invasive vegetation with hyperspectral imagery
Table 1. Confusion matrix and classiﬁcation accuracy of the weeping love grass logistic regression modeltraining-validation point
Ground reference
Producer’s
User’s
Weeping PseudoTotal
accuracy
accuracy
love grass absence
Weeping love grass
505
71.4％
100％
Training Map classes
unclassiﬁed
2
1820
100％
90.0％
18257Total
Kappa＝0.78
92.0％
Weeping love grass
202
66.7％
100％
Validation Map classes
unclassiﬁed
178100％
87.5％
Total
3710
90.0％
Kappa＝0.74Fig. 1. The potential presence of the weeping love grass (White color) overlaid on the hyperspectral imagery.
was mapped according to the regression model madeby logistic regression analysis. Figure 1 shows thepotential presence of the weeping love grass overlaid
on one band of hyperspectral imagery. To obtain aclear distribution of the invasive weeping love grass,
we have masked all the detected weeping love grass
pixels which were located in the non-vegetation area
by giving a threshold of 0.2 for the NDVI image.
The ﬁgure shows that most of the weeping love grass
occurred along the riverbed where our surveying was
conducted to the south of the bridge. Another mainarea for the distribution of weeping love grass wason the south area of the image, where we saw very

the remaining 10 inventory samples were assessed. The
ﬁnal classiﬁcation distinguished weeping love grassfrom pseudo-absence pixels with user’s and producer’s
accuracies of 100％ and 66.7％ respectively. The total
accuracy for all the testing samples was 90.0％. The
kappa coefﬁcient relative to the weeping love grass was
0.74. The MNF process has reduced the redundancyof the hyperspectral data, and it has also extracted
the useful information in the data. The information
extraction of the MNF meant it performed well inidentifying the invasive weeping love grass (O’Neilland Ustin, 2000).
The potential presence of the weeping love grass87

J. Agric. Meteorol. 67 (2), 2011
data in terms of image quality with implications fornoise removal. IEEE Trans. Geosci. Remote Sens.,
26, 65–74.
Hestir, E. L., Khanna, S., Andrew, M. E., Santos, M.
J., Viers, J. H., Greenberg, J. A., Rajapakse, S. S.,
and Ustin, S. L., 2008: Identiﬁcation of invasive
vegetation using hyperspectral remote sensing in theCalifornia Delta ecosystem. Remote Sens. Environ.,112, 4034–4047.
Matsumoto, J., Muraoka, H., and Washitani, I., 2000:
Whole plant carbon gain of an endangered herbaceous species Aster kantoensis and the inﬂuencing ofshading by an alien grass Eragrostis curvula in its
gravelly ﬂoodplain habitat. Ann Bot., 86, 787–797.
Mundt, J. T., Glenn, N. F., Weber, K. T., Prather,T. S., Lass, L. W., and Pettingill, J., 2005: Discrimination of hoary cress and determination of its
detection limits via hyperspectral image processing
and accuracy assessment techniques. Remote Sens.
Environ., 96, 509–517.
Muranaka, T., and Washitani, I., 2001: Alien plant invasions and gravelly ﬂoodplain vegetation of the Kinu
River. Ecology and Civil Engineering., 4, 121–132.
Muranaka, T., and Washitani, I., 2002：Eragrostis curvula：Invasion and inﬂuence on gravelly ﬂoodplainof the Kinu River. In Hand Book of Alien Speciesin Japan. ed. by Ecological Society of Japan (ed. by
Ecological Society of Japan). Chijinshokan, Tokyo,
pp. 199.
O’Neill, M., Ustin, S. L., Hager, S., and Root, R.,
2000: Mapping the distribution of leafy spurge at
Theodore Roosevelt National Park using AVIRIS.
In Proceedings of the Ninth JPL Airborne Earth
Science Workshop, JPL Pasadena, CA, pp. 00–18:
339–348.
Underwood, E., Ustin, S., and DiPietro, D., 2003: Mapping nonnative plants using hyperspectral imagery.
Remote Sens. Environ., 86, 150–161.
Washitani, I., Amano, K., Ishii, N., Ishii, M., Takemon,Y., Hoshino, Y., Hosoya, K., and Murakami, O.,
2010: Guideline and case studies for alien species
management in Japanese riparian habitats. Foundation for Riverfront Improvement and Restoration,
Tokyo, pp. 100–107.

dense weeping love grass growing, but the river had
blocked us making any surveys there.
4. Conclusions
We used hyperspectral imagery to detect and map
invasive weeping love grass in the shore of the Kinu
River, Japan. A binary logistic regression analysis wasconducted to model the probable presence/absenceof weeping love grass. This study tried to enter twotypes of input variables, original reﬂectance data andMNF transformed bands, into the regression model.
No reasonable original reﬂectance band was selected toestimate the presence/absence of the invaded weeping
love grass, but two bands of MNF were selected.
The ﬁnal classiﬁcation using the selected MNF bands
distinguished weeping love grass from pseudo-absence
pixels with user’s and producer’s accuracies of 100％and 66.7％ respectively. The kappa coefﬁcient was0.74. These results indicate that the MNF transformed
hyperspectral bands were more suitable than that of theoriginal reﬂectance data to estimate the distribution ofthe invasive weeping love grass in the shore of the Kinu
River. The estimated distribution map of the weeping
love grass would be able to help the regional planningof the invaded vegetation removal and protection ofnative plants.
Acknowledgments
We would like to thank Dr. Muranaka of theGraduate School of Agricultural and Life Sciences,
The University of Tokyo for his kind help during theﬁeld surveys. This research was supported in part by
the River Fund in charge of the Foundation of Riverand Watershed Environment Management (FOREM),Japan.
References
Boardman, J. W., and Kruse, F. A., 1994: Automatedspectral analysis: A geologic example using AVIRIS
data, north Grapevine Mountains, Nevada. In Proceedings, Tenth Thematic Conference on Geologic
Remote Sensing, Environmental Research Institute ofMichigan, Ann Arbor, MI, pp. I-407-I-418.
Green, A. A., Berman, M., Switzer, P., and Craig, M.
D., 1988: A transformation for ordering multispectral88