1. Environmental Remote Sensing GEOG 2021 Lecture 3 Spectral information in remote sensing

2. visualisation/analysis spectral curves –spectral features, e.g., 'red edge’ scatter plot –two (/three) channels of information colour composites –three channels of information principal components analysis enhancements –e.g. NDVI

3. visualisation/analysis spectral curves –reflectance (absorptance) features –information on type and concentration of absorbing materials (minerals, pigments) e.g., 'red edge': increase Chlorophyll concentration leads to increase in spectral location of 'feature' e.g., tracking of red edge through model fitting or differentiation

4. visualisation/analysis

5. http://envdiag.ceh.ac.uk/iufro_poster2.shtm

6. REP moves to shorter wavelengths as chlorophyll decreases Red Edge Position point of inflexion on red edge

7. REP correlates with ‘stress’, but no information on type/cause Measure REP e.g. by 1st order derivative See also: Dawson, T. P. and Curran, P. J., "A new technique for interpolating the reflectance of red edge position." Int. J. Remote Sensing, 19, (1998), 2133-2139.

8. Consider red / NIR ‘feature space’ Soil line vegetation

9. visualisation/analysis Colour Composites choose three channels of information –not limited to RGB –use standard composites e.g. false colour composite (FCC) learn interpretation Vegetation refl. high in NIR on red channel, so veg red and soil blue

10. visualisation/analysis Std FCC - Rondonia

11. visualisation/analysis Std FCC - Swanley TM data - TM 4,3,2

12. visualisation/analysis Principal Components Analysis –PCA (PCT - transform) may have many channels of information –wish to display (distinguish) –wish to summarise information Typically large amount of (statistical) redundancy in data

13. visualisation/analysis Principal Components Analysis redNIR See: http://rst.gsfc.nasa.gov/AppC/C1.html

14. red NIR Scatter Plot reveals relationship between information in two bands here: correlation coefficient = 0.137

15. visualisation/analysis Principal Components Analysis –show correlation between all bands TM data, Swanley: correlation coefficients : 1.000 0.927 0.874 0.069 0.593 0.426 0.736 0.927 1.000 0.954 0.172 0.691 0.446 0.800 0.874 0.954 1.000 0.137 0.740 0.433 0.812 0.069 0.172 0.137 1.000 0.369 -0.084 0.119 0.593 0.691 0.740 0.369 1.000 0.534 0.891 0.426 0.446 0.433 -0.084 0.534 1.000 0.671 0.736 0.800 0.812 0.119 0.891 0.671 1.000

16. visualisation/analysis Principal Components Analysis –particularly strong between visible bands –indicates (statistical) redundancy TM data, Swanley: correlation coefficients : 1.000 0.927 0.874 0.069 0.593 0.426 0.736 0.927 1.000 0.954 0.172 0.691 0.446 0.800 0.874 0.954 1.000 0.137 0.740 0.433 0.812 0.069 0.172 0.137 1.000 0.369 -0.084 0.119 0.593 0.691 0.740 0.369 1.000 0.534 0.891 0.426 0.446 0.433 -0.084 0.534 1.000 0.671 0.736 0.800 0.812 0.119 0.891 0.671 1.000

17. visualisation/analysis Principal Components Analysis –PCT is a linear transformation –Essentially rotates axes along orthogonal axes of decreasing variance red NIR PC1 PC2

18. visualisation/analysis Principal Components Analysis –explore dimensionality of data % pc variance : –PC1PC2PC3PC4PC5PC6PC7 –79.0 11.9 5.2 2.3 1.0 0.5 0.1 96.1% of the total data variance contained within the first 3 PCs

19. visualisation/analysis Principal Components Analysis –e.g. cut-off at 2% variance –Swanley TM data 4-dimensional first 4 PCs = 98.4% –great deal of redundancy TM bands 1, 2 & 3 correlation coefficients : 1.000 0.927 0.874 0.927 1.000 0.954 0.874 0.954 1.000

20. visualisation/analysis Principal Components Analysis –display PC 1,2,3 - 96.1% of all data variance Dull - histogram equalise...

21. visualisation/analysis Principal Components Analysis –PC1 (79% of variance) Essentially ‘average brightness’

22. visualisation/analysis Principal Components Analysis stretched sorted eigenvectors PC1+0.14 +0.13 +0.28 +0.13 +0.82 +0.12 +0.43 PC2-0.44 -0.27 -0.60 +2.23 +0.47 -0.49 -0.77 PC3+1.68 +1.35 +2.45 +1.34 -1.49 -0.67 +0.05 PC4+0.29 +0.10 -1.22 +1.90 -1.83 +4.49 +2.30 PC5+0.03 -0.39 -2.81 +0.70 -1.78 -5.12 +6.52 PC610.42 +1.10 -6.35 -0.70 +1.64 -0.23 -2.39 PC7-8.77 28.50 -8.37 -1.43 +1.04 -0.40 -1.75

23. visualisation/analysis Principal Components Analysis shows contribution of each band to the different PCs. –For example, PC1 (the top line) almost equal (positive) contributions (‘mean’) PC1+0.14 +0.13 +0.28 +0.13 +0.82 +0.12 +0.43 –PC 2 principally, the difference between band 4 and rest of the bands (NIR minus rest) PC2-0.44 -0.27 -0.60 +2.23 +0.47 -0.49 -0.77

24. visualisation/analysis Principal Components Analysis –Display of PC 2,3,4 Here, shows ‘spectral differences’ (rather than ‘brightness’ differences in PC1)