GEOG2021 Environmental Remote Sensing Lecture 3 Spectral Information in Remote Sensing
Aim Mechanisms variations in reflectance - optical/microwave Visualisation/analysis Enhancements/transforms
Mechanisms
Reflectance Reflectance = output / input (radiance) measurement of land complicated by atmosphere input solar radiation for passive optical input from spacecraft for active systems RADAR Strictly NOT reflectance - use related term backscatter
Mechanisms
Optical Mechanisms
Reflectance causes of spectral variation in reflectance (bio)chemical & structural properties chlorophyll concentration soil - minerals/ water/ organic matter
Optical Mechanisms vegetation
Optical Mechanisms soil
Optical Mechanisms soil
RADAR Mechanisms See: http://southport.jpl.nasa.gov/education.html
RADAR Mechanisms
RADAR Mechanisms
RADAR Mechanisms
Vegetation amount consider change in canopy cover over time varying proportions of soil / vegetation (canopy cover)
Vegetation amount Bare soil Full cover Senescence
Vegetation amount 1975 Rondonia See: e.g. http://earth.jsc.nasa.gov/lores.cgi?PHOTO=STS046-078-026 http://www.yale.edu/ceo/DataArchive/brazil.html
Vegetation amount 1986 Rondonia
Vegetation amount 1992 Rondonia
Uses of (spectral) information consider properties as continuous e.g. mapping leaf area index or canopy cover or discrete variable e.g. spectrum representative of cover type (classification)
Leaf Area Index See: http://edcdaac.usgs.gov/modis/dataprod.html
See: http://www.bsrsi.msu.edu/rfrc/stats/seasia7385.html Forest cover 1973
Forest cover 1985
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
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
visualisation/analysis
visualisation/analysis
http://envdiag.ceh.ac.uk/iufro_poster2.shtm
point of inflexion on red edge Red Edge Position point of inflexion on red edge REP moves to shorter wavelengths as chlorophyll decreases
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.
Consider red / NIR ‘feature space’ vegetation Soil line
visualisation/analysis Colour Composites choose three channels of information not limited to RGB use standard composites (e.g., FCC) learn interpretation
visualisation/analysis Std FCC - Rondonia visualisation/analysis
visualisation/analysis Std FCC - Swanley TM data - TM 4,3,2 visualisation/analysis
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
visualisation/analysis Principal Components Analysis red NIR See: http://rst.gsfc.nasa.gov/AppC/C1.html
Scatter Plot reveals relationship between information in two bands red NIR Scatter Plot reveals relationship between information in two bands here: correlation coefficient = 0.137
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
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
visualisation/analysis Principal Components Analysis PCT is a linear transformation Essentially rotates axes along orthogonal axes of decreasing variance red NIR PC1 PC2
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 96.1% of the total data variance contained within the first 3 PCs
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
visualisation/analysis Principal Components Analysis display PC 1,2,3 - 96.1% of all data variance Dull - histogram equalise ...
visualisation/analysis Principal Components Analysis PC1 (79% of variance) Essentially ‘average brightness’
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
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
visualisation/analysis Principal Components Analysis Display of PC 2,3,4 Here, shows ‘spectral differences’ (rather than ‘brightness’ differences in PC1)
Enhancements Vegetation Indices reexamine red/nir space features
Enhancements Vegetation Indices define function of the two channels to enhance response to vegetation & minimise response to extraneous factors (soil) maintain (linear?) relationship with desrired quantity (e.g., canopy coverage, LAI)
Enhancements Vegetation Indices function known as ‘vegetation index’ Main categories: ratio indices (angular measure) perpendicular indices (parallel lines)
RATIO INDICES Enhancements Vegetation Indices
Enhancements Vegetation Indices Ratio Vegetation Index RVI Normalised Difference Vegetation Index NDVI
RATIO INDICES Enhancements Vegetation Indices NDVI
RATIO INDICES Enhancements Vegetation Indices NDVI
RATIO INDICES NDVI over Africa (AVHRR-derived) Tucker et al. - A: April; B: July; C: Sept; D: Dec 1982
PERPENDICULAR INDICES Enhancements Vegetation Indices
Enhancements Vegetation Indices Perpendicular Vegetation Index PVI Soil Adjusted Vegetation Index SAVI
PERPENDICULAR INDICES And others ...
Summary Scattering/reflectance mechanisms monitoring vegetation amount visualisation/analysis spectral plots, scatter plots, PCA enhancement VIs