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Environmental Remote Sensing GEOG 2021

Environmental Remote Sensing GEOG 2021. Lecture 3 Spectral information in remote sensing. visualisation/analysis. spectral curves spectral features, e.g., 'red edge’ scatter plot two (/three) channels of information colour composites three channels of information

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Environmental Remote Sensing GEOG 2021

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

  7. Measure REP e.g. by 1st order derivative REP correlates with ‘stress’, but no information on type/cause 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. vegetation Soil line Consider red / NIR ‘feature space’

  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. Std FCC - Rondonia visualisation/analysis

  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 red NIR See: http://rst.gsfc.nasa.gov/AppC/C1.html

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

  18. 96.1% of the total data variance contained within the first 3 PCs visualisation/analysis Principal Components Analysis • explore dimensionality of data % pc variance : • PC1 PC2 PC3 PC4 PC5 PC6 PC7 • 79.0 11.9 5.2 2.3 1.0 0.5 0.1

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

  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 PC6 10.42 +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)

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