Flower Species Identification And
Coverage Estimation Based On
Hyperspectral Remote Sensing Data
Gai Yingying1, Fan Wenjie1, Xu Xiru1, Zhang Yuanzhen2
1. Institute of RS and GIS, Peking University, Beijing,
China
2. China Meteorological Administration Training Centre,
Beijing, China
BUSINESS
Email Address: fanwj@pku.edu.cn (Fan Wenjie)
Outline
1. Preface
2. Data
2.1 Data acquirement
2.2 Data preprocessing
3. Methodology
3.1 Flower spectral feature extraction
3.2 Mixed spectra unmixing
4. Results
5. Discussion
Preface
Causes of grassland degradation:
• overgrazing
• excess reclamation
……
Superiorities of hyperspectral remote sensing:
• provide information at different temporal and spatial scales
• high spectral resolution
……
Monitoring grass species and coverage accurately using
hyperspectral remote sensing data makes a significant
contribution to species diversity research and sustainable
development of grassland ecosystem. Hyperspectral remote
sensing becomes an important way of monitoring terrestrial
ecosystem.
Data
Data acquirement
• Study area: Hulunbeier meadow grassland, Hulunbeier City, Inner
Mongolia, China.
• Time: from July 1st to July 3rd, 2010
• Flower species: Serratula centauroides Linn., Clematis hexapetala
Pall., Artemisia frigida Willd. Sp. Pl., Galium verum Linn.,
Hemerocallis citrina Baroni, Lilium concolor var. pulchellum and
Lilium pumilum
Data
Data acquirement
• Device: ASD FieldSpec-3, with the spectral range of 350–2500
nm and the spectral resolution of 1 nm
• Data type: spectra of same kind flower canopies, spectra
quadrates contained flowers of single and multiple species
of
Data
Data prepocessing
• Wavelet filtering
Wf (a, b) 



f (t ) a,b (t )dt
Data
Data prepocessing
• Comparison of Wavelet filtering and Savitzky-Golay filtering
Signals of high frequency were more stable
dealing with wavelet filter than Savitzky-Golay
filter.

Methodology
Flower spectral feature extraction
• Spectral
Differential
--- identify Serratula centauroides Linn. and divide other flowers into three sets
1) The spectral derivatives of Serratula centauroides Linn.
between purple and blue bands are below zeros;
2) The maximum derivatives of both Clematis hexapetala Pall.
and Artemisia frigida Willd. Sp. Pl. in the range from 500 nm
to 600 nm are much smaller than others;
3) The derivatives of Galium verum Linn. and Hemerocallis
citrina Baroni reach peaks in 500-550 nm, while Lilium
concolor var. pulchellum and Lilium pumilum in 550-600 nm.

Methodology
Flower spectral feature extraction
• Spectral
Differential
--- identify Serratula centauroides Linn. and divide other flowers into three sets
(1)
(2)
(3)
(4)

Methodology
Flower spectral feature extraction
• Spectral
Reordering
---identify Clematis hexapetala Pall. and Artemisia frigida Willd. Sp. Pl.
When spectra were reordered based on
Clematis hexapetala Pall., curves of
Artemisia frigida Willd. Sp. Pl. shows
different fluctuation. It is the same the other

Methodology
Flower spectral feature extraction
• Vegetation
Index
---identify the other two sets: Galium verum Linn., Hemerocallis citrina Baroni
Lilium concolor var. pulchellum, Lilium pumilum.
NDVI 
R800  R670
R 800  R670
Flower species
NDVIs
Galium verum Linn.
0.5119-0.5985
Hemerocallis citrina Baroni
0.2145-0.3224
 
R720
R550
Flower species
γ
Lilium pumilum
2.9407-3.7834
Lilium concolor var. pulchellum
4.1446-9.0796
Methodology
Mixed spectra unmixing
• linear spectral mixture analysis
Definition:
 mixed pixel
 end-member
P
N
c e
i 1
i
i
P --- measured spectra vector
n
N --- number of end-numbers
Ci --- proportion of ei in pixels
n --- error


c  ET E

1
ET p
C --- proportional vector of end-numbers
E --- matrix of end-number vector
 quadrate spectra --- mixed spectra
 flower spectra --- end-member spectra
 range of wave bands--- 400-750 nm
Results
Accuracy analysis of flowers identification
Flower species
Not-identify error
/%
Incorrect-identify error
/%
Total error/%
Serratula centauroides Linn.
8.33
0
8.33
Clematis hexapetala Pall.
0
6.67
6.67
Artemisia frigida Willd. Sp. Pl.
6.67
0
6.67
Galium verum Linn.
5.88
3.03
8.91
Hemerocallis citrina Baroni
0
5.88
5.88
Lilium concolor var. pulchellum
0
0
0
Lilium pumilum
0
0
0
Verification results showed that when the coverage of flowers
was more than 10%, the accuracy of identification methods would
be higher than 90%.
Results
Accuracy analysis of pixel unmixing method
Flower species
Mean error
Standard deviation
Serratula centauroides Linn.
0.040
0.065
Clematis hexapetala Pall.
0.042
0.034
Artemisia frigida Willd. Sp. Pl.
0.062
0.032
Galium verum Linn.
0.029
0.073
Hemerocallis citrina Baroni
0.052
0.037
Lilium concolor var. pulchellum
0.021
0.028
Lilium pumilum
0.018
Null
Note: There are not enough data for validation of Lilium pumilum.
Results also showed that the linear unmixing model was an
effective method for estimating the coverage of flowers in
grassland with the mean error of about 4%.
Discussion
Discussion
The methods studied in the paper demonstrate
promising application in monitoring some herb plants
during florescence. More flowers will also be
distinguished with high accuracy if multi-temporal data
are available. In our study, application of field
measured hyperspectral data in vegetation monitoring
has been broaden, but species identification using
remote sensing is to some extent limited by field
observation. Admittedly, what we have observed in this
study is far from complete and it requires further
research.
Discussion
Discussion
Grasslands need protection!
Email Address: fanwj@pku.edu.cn (Fan Wenjie)
Institute of RS and GIS, Peking University, China