Vegetation indices and the red-edge index

1. Vegetation indices and the red-edge index Jan Clevers Centre for Geo-Information (CGI)

2. Quantitative Remote Sensing: The Classification • Signatures: Spectral, Spatial, Temporal, Angular, and Polarization • Statistical Methods • Correlation relationships of land surface variables and remotely sensed data • + Easy to develop, effective for summarizing local data • - Models are site-specific, no cause-effect relationship • Example: WDVI (Clevers, 1999), GEMI (Pinty and Verstraete, 1992) • Physical Methods • Inversion of [snow | canopy | soil] reflectance models • + Follow a physical law, improvement through iteration • - Long development curve, potentially complex • Example: MODIS LAI (Myneni, 1999) • Hybrid Methods • Combination of Statistical and Physical Models • Example: EO-1 ALI LAI (Liang, 2003) Source: Liang, S., 2004 Centre for Geo-information

3. Vegetation Indices • strengthening the spectral contribution of green vegetation • minimizing disturbing influences of: • soil background • irradiance • solar position • yellow vegetation • atmospheric attenuation mostly utilizing a red (R) and NIR spectral band Centre for Geo-information

4. Ratio-based Vegetation Indices 1.0 0.6 NIR 0.8 0.4 0.2 0 • NIR/R ratio (RVI) • NDVI = (NIR-R)/(NIR+R) • (Normalized Difference VI) NDVI 1 R 0 LAI 2 à 3 Centre for Geo-information

5. NIR soil line (PVI = 0) R Orthogonal-based Vegetation Indices • Perpendicular VI (PVI): • 1/(a2+1) (NIR – a × R) • Weighted Difference VI (WDVI): • NIR – a × R • Difference VI (DVI): • NIR – R a = slope soil line Centre for Geo-information

6. Simplified reflectance model R = Rv × B + Rs × (1 – B) R : measured reflectance Rv : reflectance vegetation Rs : reflectance soil B : apparent soil cover Centre for Geo-information

7. Calculate WDVI Red: R = Rv × B + Rs × (1 – B)NIR: NIR = NIRv × B + NIRs × (1 – B) Assume: a = NIRs / Rs (slope soil line) The NIR signal coming from the vegetation only can be approximated by the WDVI: WDVI = NIR – a × R Centre for Geo-information

8. NIR R l2 l1 Hybrid Vegetation Indices • Soil Adjusted VI (SAVI): • (1 + L) × (NIR – R)/(NIR +R + L) L = l1 + l2  0.5 Broge & Leblanc, Remote Sens. Environ. 76 (2000): 156-172 Centre for Geo-information

9. Enhanced Vegetation Index (EVI)for use with MODIS data • C1 = atmospheric resistance red correction coefficient [6.0] • C2 = atmospheric resistance blue correction coefficient [7.5] • L = canopy background brightness correction factor [1.0] http://tbrs.arizona.edu/project/MODIS/evi.php Centre for Geo-information

10. Use of vegetation Indices Estimation of: • Leaf Area Index (LAI) • Vegetation cover • Absorbed Photosynthetically Active Radiation (APAR) • Chlorophyll or nitrogen content • Canopy water content • Biomass • Carbon • Structure of the canopy Centre for Geo-information

11. Use of vegetation Indices Estimation of: • Leaf Area Index (LAI) • Vegetation cover • Absorbed Photosynthetically Active Radiation (APAR) • Chlorophyll or nitrogen content • Canopy water content • Biomass • Carbon • Structure of the canopy Centre for Geo-information

12. Red Edge Index Determining vegetation condition using RS:e.g. blue shift of the red edge as a result of stress reflectance (%) 1 2 60 healthy with stress 40 20 0 0.4 0.5 0.6 0.7 0.8 wavelength (µm) Centre for Geo-information

13. Calculation REIP Red edge inflection point (REIP) = Red edge position (REP) = Maximum of the first derivative. is maximum. Centre for Geo-information

14. PROSPECT – SAIL simulation Centre for Geo-information

15. Soil background influence Centre for Geo-information

16. Atmospheric influence Centre for Geo-information

17. Inverted Gaussian function Rs = shoulder reflectanceRo = minimum reflectanceo = wavelength at Ro = Gaussian shape parameter Centre for Geo-information

18. Linear interpolation method Centre for Geo-information

19. Linear interpolation method Centre for Geo-information

20. REP image for MERIS Each digital number represents a wavelength value (being the REP) Centre for Geo-information

21. Chlorophyll Index (CI) CIred_edge = (RNIR / Rred_edge) – 1 = (R780 nm / R710 nm) – 1 As estimator of chlorophyll content Gitelson et al., Geophysical Research Letters 33 (2006), 5 pp. http://www.calmit.unl.edu/people/gitelson/ Centre for Geo-information

22. Photochemical Reflectance Index (PRI) PRI = (R531 nm – R570 nm) / (R531 nm + R570 nm) As estimator of photosynthetic activity Gamon et al., Remote Sensing of Environment 41 (1992), 35 – 44. Centre for Geo-information

23. Use of vegetation Indices Estimation of: • Leaf Area Index (LAI) • Vegetation cover • Absorbed Photosynthetically Active Radiation (APAR) • Chlorophyll or nitrogen content • Canopy water content • Biomass • Carbon • Structure of the canopy Centre for Geo-information

24. Estimating Canopy Water Content (CWC) 970 nm 1200 nm Centre for Geo-information

25. Estimators for Canopy Water Content • Reflectances • Continuum removal: MBD, AUC, ANMB • Water band indices: WI, NDWI WI = R900/R970 NDWI = (R860 – R1240) / (R860 + R1240) • Derivatives Centre for Geo-information

26. Results: PROSPECT-SAILH simulation CWC Centre for Geo-information

27. Results: Millingerwaard 2004 - FieldSpec Centre for Geo-information

28. Summary Centre for Geo-information

29. Continuum removal (1) Use Continuum Removal to normalize reflectance spectra to allow comparison of individual absorption features from a common baseline. The continuum is a convex hull fit over the top of a spectrum utilizing straight line segments that connect local spectra maxima. The first and last spectral data values are on the hull and therefore the first and last bands in the output continuum-removed data file are equal to 1.0. (Source: ENVI online help) Convex hull Centre for Geo-information

30. Continuum removal (2) http://speclab.cr.usgs.gov/PAPERS.refl-mrs/ Centre for Geo-information

31. Continuum removal (3) Centre for Geo-information

32. Continuum removal (3) MBD = Maximum Band Depth Centre for Geo-information

33. Continuum removal (3) AUC = Area Under Curve ANMB = Area Normalized by the Maximum Band depth Centre for Geo-information

34. Spectral unmixing Spectral unmixing aims at finding the fractions or abundances of end-members, which are spectrally pure by deconvolving them from a mixed spectrum Reflectance spectra Centre for Geo-information

35. Mathematics of linear unmixing Ri = reflectance of the mixed spectrum of a pixel in image band i ¦j= fraction of end-member j Reij = reflectance of the end-member spectrum j in band i i = the residual error n = number of end-members Constraining assumptions: and Centre for Geo-information

36. Alunite Calcite Kaolinite Silica Zeolite RMS image Geologic map from unmixing Spectral unmixing at Cuprite Centre for Geo-information

37. Problems with unmixing • How to select the end members? • Do these describe the data spectrally? • Are these of interest? • Is mixing a linear process? Spectral unmixing Centre for Geo-information

38. Spectral field measurements Centre for Geo-information

39. Questions ? www.scopus.com/home.url www.isiknowledge.com © Wageningen UR