BUSINESSBUSINESS

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

Email Address: fanwj@pku.edu.cn (Fan Wenjie)

Outline

1. Preface

2. Data2.1 Data acquirement2.2 Data preprocessing

3. Methodology3.1 Flower spectral feature extraction3.2 Mixed spectra unmixing

4. Results

5. Discussion

Preface

Causes of grassland degradation:• overgrazing• excess reclamation ……

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.

Superiorities of hyperspectral remote sensing:• provide information at different temporal and spatial scales • high spectral resolution ……

DataData 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

DataData acquirement• Device: ASD FieldSpec-3, with the spectral range of 350–2500 n

m and the spectral resolution of 1 nm

• Data type: spectra of same kind flower canopies, spectra of quad

rates contained flowers of single and multiple species

DataData prepocessing• Wavelet filtering

dtttfbaWf ba )()(),( ,

Data prepocessing• Comparison of Wavelet filtering and Savitzky-Golay filtering

Data

Signals of high frequency were more stable dealing with wavelet filter than Savitzky-Golay filter.

Flower spectral feature extraction• Spectral Differential

--- identify Serratula centauroides Linn. and divide other flowers into three sets

Methodology

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.

Flower spectral feature extraction• Spectral Differential

--- identify Serratula centauroides Linn. and divide other flowers into three sets

Methodology

(1)

(2)

(3)

(4)

Flower spectral feature extraction• Spectral Reordering

---identify Clematis hexapetala Pall. and Artemisia frigida Willd. Sp. Pl.

Methodology

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 way round.

Flower spectral feature extraction• Vegetation Index

---identify the other two sets: Galium verum Linn., Hemerocallis citrina Baroni

Lilium concolor var. pulchellum, Lilium pumilum.

Methodology

670800

670800

RR

RRNDVI

550

720

R

R

Flower species γ

Lilium pumilum 2.9407-3.7834

Lilium concolor var. pulchellum 4.1446-9.0796

Flower species NDVIs

Galium verum Linn. 0.5119-0.5985

Hemerocallis citrina Baroni 0.2145-0.3224

Mixed spectra unmixing• linear spectral mixture analysis

Methodology

necPN

iii

1

P --- measured spectra vector

N --- number of end-numbers

Ci --- proportion of ei in pixels

n --- error

quadrate spectra --- mixed spectra flower spectra --- end-member spectra range of wave bands--- 400-750 nm

pEEEc TT 1

C --- proportional vector of end-numbers

E --- matrix of end-number vector

Definition: mixed pixel end-member

Accuracy analysis of flowers identification

Results

Verification results showed that when the coverage of flowers was more than 10%, the accuracy of identification methods would be higher than 90%.

Flower speciesNot-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

Accuracy analysis of pixel unmixing method

Results

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%.

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.

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