Greenness 2

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Colour Composites and classification with Ratios Any 3 channels can be used to create a colour composite or as input for a classification: below a color composite in Utah made from 1/7 = blue; 4/2 = green; 3/1 = red

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Transcript of Greenness 2

  • Colour Composites and classification with Ratios

    Any 3 channels can be used to create a colour composite or as input for a classification: below a color composite in Utah made from 1/7 = blue; 4/2 = green; 3/1 = red

  • Image ratios and indices

    Ratiosused to enhance albedo contrasts by reducing inter-band similaritiese.g. Near-IR / Red to identify vegetatione.g. Red / Mid-IR to identify snow / ice

    Ratio Vegetation Index (RVI) = Near IR / Red if < 1 = unvegetated

    * RVI can create infinite values

    Difference Vegetation Index (DVI) = NIR - Red if < 0 = unvegetated

    * DVI is influenced by different lighting

    Combining these two creates the most common vegetation index:

  • Normalised Difference Vegetation Index NDVI

    Division compensates for differential lighting

    It gives a close estimate of biomass

    This yields values between -1 and 1, in a 32 bit channel

    .. or a 8 bit channel by scaling (+1 and *127)

    Negative values of NDVI (values approaching -1) correspond to water. Values close to zero (-0.1 to 0.1) = barren areas of rock, sand, or snow. low, positive values represent shrub and grassland (approximately 0.2 to 0.4), high values indicate temperate and tropical rainforests (values approaching 1)

  • Normalised Difference Vegetation Index NDVI

  • NDVI Canada (from AVHRR)NDVI origins: J. W. Rouse, R. H. Haas. J. A. Schell, et al., 1974. Monitoring vegetation systems in the Great Plains with ERTS. In. Proceedings of Third Earth Resources Technology Satellite-1 Symposium, Greenbelt, NASASP-351: 310-317W.

  • Special sensors for NDVISPOT 5 has extra bands / wide sensor in visible/NIR with 1 km resolution to capture a repeat 2400 km swath for global coverage

    MODIS and NOAA-AVHRR have red /near-IR bands for NDVI

    NDVI is used measure vegetation amount or biomass, in regional and global estimates."NDVI is directly related to the photosynthetic capacity and hence energy absorption of plant canopies"

  • annual NDVI change, see:


  • Wyoming Normalised Burn Ratio NBR = (4-7) / (4+ 7)

  • Other indices include:Soil-adjusted Vegetation Index (SAVI) = 1.5 * (NIR - R) / (NIR + R + 0.5)

    Optimised Soil-adjusted Vegetation Index (OSAVI) = (NIR - R) / (NIR + R + 0.16) NDGI = (NIR-G) / (NIR+G)

    NDSI= (G-MIR) / (G+MIR) (S = Snow) TM = (2-5) / (2+5)

    NDWI (water) : (NIR MIR)/ (NIR + MIR) TM = (4-5) / (4+5)

  • The technique was named after the pattern of spectral change of agricultural crops during senescence, plotting brightness against greenness. The sequence is:

    1. Bare fields / newly planted crops - high brightness, low greenness (spring)2. Plant Growth - (slight?) greenness (late summer)4. Senescence (harvest) - bare field/stubble: brightness (Fall) Tasseled Cap reduces an overlapping multispectral dataset by linear transformation into a lower number of channels (3) which respond to particular scene characteristics. Tasseled Cap Transformation

  • Tasseled Cap Transformation Kauth, R. J. and Thomas, G. S., 1976, The tasseled cap --a graphic description of the spectral-temporal development of agricultural crops as seen in Landsat, in Proceedings on the U.S. Department of the Interior 9 U.S. Geological Survey Symposium on Machine Processing of Remotely Sensed Data, West Lafayette, Indiana, June 29 -- July 1, 1976, 41-51. Three new channels are created by applying coefficients to the input bands:TC1,2,3 (Landsat MSS) = a * MSS1 + b* MSS2 + c * MSS3 + d * MSS4

    TC1,2,3 (Landsat TM) = e *TM1 + f*TM2 + g*TM3 + h*TM4 + j*TM5 + k*TM7

    For MSS data, the 4-band dataset creates channels: Brightness, Greenness and Yellowness

    For TM / ETM+ data, the 6-band (no thermal) creates: Brightness, Greenness and Wetness

  • tasseled cap channelsLandsat 5 TM coefficients Band BrightnessGreenness Wetness 1 .3037 -.2848 .1509 2 .2793 . -.2435 .1973 3 .4743 -.5436 .3279 4 .5585 .7243 .3406 5 .5082 .0840 -.7112 7 .1863 -.1800 -.4572see Thayer Watkins website

  • NDVI v Tasseled Cap greennessTCA Greenness is similar to NDVI, with subtle differences and is used in habitat studies.

  • Principal Components Analysis (PCA)PCA is a mathematical transformation that converts original data into new data channels that are uncorrelated and minimize data redundancy.

    Differences with TCA :

    PCA transformation is scene specific (TCA coefficients are 'global)

    2. TCA creates three new transformed channels, PCA generates as many as there are input channels e..g for Landsat TM, there could be 7 new component channels

    There is a high correlation between all greenness channels:

    NDVI, 4/3 ratio, TCA greenness and PCA component 2 (usually)

  • bands can be reduced to their respective 'components', by an 'axial rotation' The main axis through the points is a 'component'; if all points were on it, correlation=1, the first component (PC1) would 'explain' all the variation.The 2nd component (PC2) is normal to PC1, uncorrelated and hence two bands are converted to two components, but most variation is explained by the first (the 2nd is always smaller) PC1= what is explained in both bands (images) PC2= what is different between them (similar to a band ratio)

  • PCA channelsEigenvectors of covariance matrix (arranged by rows): TM1 2 3 45 6 7 PC1 0.22 0.15 0.29 0.16 0.75 0.33 0.40 PC2 -0.28 -0.14 -0.29 0.82 0.23 -0.25 -0.16 PC3 0.51 0.31 0.43 0.49 -0.46 -0.05 -0.00 PC4 -0.09 -0.09 -0.19 0.19 -0.23 0.91 -0.18 PC5 0.31 0.13 0.05 -0.12 0.35 -0.00 -0.86 PC6 0.69 -0.16 -0.68 -0.01 0.01 -0.04 0.19 PC7 -0.19 0.90 -0.39 -0.04 0.00 0.00 0.06 ComponentBrightnessGreennessSwirness / WetnessImpact of TM6Band 5 v 7 (MIR)Band 1 v 3 (B v R)Band 2 v 3 (Yellowness)PC1: Brightness, PC2: Greenness, PC3: Swirness / Wetness

  • PC components PC1: TM6, PC2: 5/7, PC3: 1/3, PC4: 2/3

    *************As a rule, the first components contain most of the information, and therefore the four bandsof the Landsat MSS images or the six Landsat TM bands maybe reduced to the first three principal components. In some specialty works, in the case of Landsat TM data a fourth component, namely fog is introduced and coefficients are different. Unlike many other linear transformations used in remote sensing, the coefficients used for operating the tasseled cap transformation are predetermined and not derived from the set of data. *Principle Components Analysis:Different bands inmultispectral imageslike those from Landsat TM havesimilar visual appearances since reflectances for the same surface cover types are almost equal. Principle Components Analysis is astatistical proceduredesigned toreduce the data redundancyand put as much information from the image bands into fewest number of components. The intent of the procedure is to produce an image which is easier to interpret than the original.Principal components analysis is a method in which original data is transformed into a new set of data which may better capture the essential information. Often some variables are highly correlated such that the information contained in one variable is largely a duplication of the information contained in another variable. Instead of throwing away the redundant data principal components analysis condenses the information in intercorrelated variables into a few variables, called principal components.Principal components analysis is a special case of transforming the original data into a new coordinate system. If the original data involves n different variables then each observation may be considered a point in an n-dimensional vector space. The change of coordinate system for a two dimensional space is shown below.