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Flower Species Identification And Coverage Estimation Based On Hyperspectral Remote Sensing Data

Flower Species Identification And Coverage Estimation Based On Hyperspectral Remote Sensing Data. Gai Yingying 1 , Fan Wenjie 1 , Xu Xiru 1 , Zhang Yuanzhen 2 1. Institute of RS and GIS, Peking University, Beijing, China 2. China Meteorological Administration Training Centre, Beijing, China.

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Flower Species Identification And Coverage Estimation Based On Hyperspectral Remote Sensing Data

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  1. 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)

  2. 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

  3. 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.

  4. 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

  5. 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 of quadrates contained flowers of single and multiple species

  6. Data • Data prepocessing • Wavelet filtering

  7. 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.

  8. Methodology • Flower spectral feature extraction • SpectralDifferential • ---identify Serratula centauroides Linn. and divide other flowers into three sets • The spectral derivatives of Serratula centauroides Linn. between purple and blue bands are below zeros; • 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; • 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.

  9. Methodology • Flower spectral feature extraction • SpectralDifferential • ---identify Serratula centauroides Linn. and divide other flowers into three sets (1) (2) (3) (4)

  10. Methodology • Flower spectral feature extraction • SpectralReordering • ---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 way round.

  11. Methodology • Flower spectral feature extraction • VegetationIndex • ---identifythe other two sets: Galium verum Linn., Hemerocallis citrina Baroni • Lilium concolor var. pulchellum, Lilium pumilum.

  12. Methodology • Mixed spectra unmixing • linear spectral mixture analysis • Definition: • mixed pixel • end-member P --- measured spectra vector N --- number of end-numbers Ci --- proportion of ei in pixels n --- error 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

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

  14. Results Accuracy analysis of pixel unmixing method 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%.

  15. 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.

  16. Discussion Discussion Grasslands need protection!

  17. Thanks ! Email Address: fanwj@pku.edu.cn (Fan Wenjie) Institute of RS and GIS, Peking University, China

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