10/11/2015

1

Image ratios and indices Ratios … are used to enhance albedo contrasts by reducing inter-band similarities e.g. Near-IR / Red … to identify vegetation e.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:

Small satellites & big data

Normalised Difference Vegetation Index NDVI

Division compensates for differential illumination It gives a close estimate of biomass This yields values between -1 and 1, … a 32 bit channel ….. or an 8 bit channel by scaling (+1 and *255) 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)

10/11/2015

2

Special sensors for NDVI http://phenology.cr.usgs.gov/ndvi_foundation.php

SPOT 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 global cycle: https://archive.org/details/SVS-3584

10/11/2015

3

The use of NDVI to determine vegetative green-up after a forest fire Geog432

1987 2002

NDVI NDVI

The use of NDVI to determine vegetative green-up after a forest fire

NDVI difference – 1987-2002

Red - Negative Growth Range Clear - Neutral Growth Range

Yellow - Minimal Positive Growth Orange - Maximum Positive Growth

10/11/2015

4

Delineation of Grizzly Bear Habitat in Bute Inlet

Sieved maximum NDVI result

GEOG432 project

http://www.grayhawk-imaging.com/useofndviimagery.html

10/11/2015

5

http://abstracts.rangelandmethods.org/doku.php/remote_sensing_methods:normalized_burn_ratio

Similar indices: Normalised Burn Ratio (Index) (Near IR – Mid-IR) / (Near IR + Mid-IR) Landsat TM: 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) ----------------------------------------------------------------------------- Green: NDGI= (NIR-G) / (NIR+G) TM = (4-2)/ (4+2)

Snow: NDSI= (Green-MIR) / (Green+MIR) TM = (2-5) / (2+5) Water: NDWI (NIR – MIR)/ (NIR + MIR) TM = (4-5) / (4+5)

10/11/2015

6

Tasseled Cap Transformation Three new channels are created by applying coefficients to the input bands: the new channels ‘capture’ the essence of a 4-6 band data set (MSS, TM, ETM+)

Thus each pixel is assigned a new DN in 3 new created channels.

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

Tasseled Cap reduces an overlapping multispectral band dataset by linear transformation into a lower number of channels (3) which respond to particular scene characteristics.

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.

Landsat 5 TM coefficients for the Tasseled Cap Band Brightness Greenness Wetness 1 .3037 -.2848 .1509 2 .2793 . -.2435 .1973 3 .4743 -.5436 .3279 4 .5585 .7243 .3406 5 .5082 .0840 -.7112 7 .1863 -.1800 -.4572 Character: Overall reflectance NIR v Visible MIR v NVIR

10/11/2015

7

Tasseled Cap Transformation

MSS data, the 4-band dataset created channels:

Brightness, Greenness and Yellowness

TM data, the 6-band (no thermal) creates:

Brightness, Greenness and Wetness

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?) <-<- brightness (early summer)

3. Maturity: -> -> greenness (late summer)

4. Senescence (harvest) - bare field/stubble: <-<-greenness, ->-> brightness (Fall)

10/11/2015

8

Brightness – measure of soil reflectance

Greenness – vegetation

Wetness – soil and canopy moisture

tasseled cap channels

See: Thayer Watkins website

http://www.sjsu.edu/faculty/watkins/tassel.htm

NDVI v Tasseled Cap greenness both contrast NIR versus visible reflectance

TCA Greenness is similar to NDVI, with subtle differences and is used in habitat studies.

Figure from John Paczkowski MSC thesis – remote sensing and grizzly bear habitat

10/11/2015

9

Reasons to use Tassel Cap Analysis

It reduces a multi band dataset (4-6) to 3 channels – Brightness, Greenness, Wetness – each might be useful

The 3 channels could be used in classification

The coefficients are universal for each sensor

Russian tassel cap->

But –they have only been developed for some sensors… (the coefficients vary according to spectral wavelengths and radiometric resolution)

PCI Geomatica

Landsat 1-3 MSS

Landsat 5 TM

Landsat 7 ETM+

- NOT Landsat 8 OLI

Other ?:

CBERS-02B (China/Brazil)

Ikonos, Quickbird 2

ASTER / MODIS

10/11/2015

10

http://eoedu.belspo.be/en/guide/compprin.asp

PCA is a mathematical transformation that converts original data into new data channels that are uncorrelated and minimise data redundancy. Like TCA, it can also: reduce shadows and spectral correlation between bands

Principal Components Analysis (PCA)

http://geology.wlu.edu/harbor/geol260/lecture_notes/Notes_rs_PC.html

The 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)

10/11/2015

11

Principal Components Analysis (PCA)

The new channels are defined by eigenvectors / eigenvalues. In the ‘matrix’: Eigenvectors: define the contribution of each band Eigenvalues: ‘explain’ the % variance of each PCA channel PC1 and PC2 explain 95-99% and PC3 most of the rest PC1= what is explained in both bands (images) PC2= what is different between them (similar to a band ratio)

PCA channels

Eigenvectors of covariance matrix (arranged by rows): TM1 2 3 4 5 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

Component 71% Brightness 21% Greenness 3.8% Swirness / Wetness 2.3% Impact of TM6 1.6% Band 5 v 7 (MIR) 0.2% Band 1 v 3 (B v R) 0.1% Band 2 v 3 (Yellowness)

PC1: Brightness, PC2: Greenness, PC3: Swirness / Wetness

10/11/2015

12

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

Differences with Tasseled Cap (TCA) : 1. PCA transformation is scene specific -TCA coefficients are 'global‘

2. PCA generates as many as there are input channels

- TCA creates three new transformed channels e.g. for Landsat TM, there could be 7 new component channels There is a high correlation between all ‘greenness’ channels: -As they all contrast near-IR and visible bands NDVI 4/3 ratio TCA greenness PCA component 2 (usually)

10/11/2015

13

Decorrelation Stretch: Remote sensing technique to enhance images

- Based on Principal Components Analysis (PCA)

- used to Enhance Rock Art Images By Jon Harman, Ph.D.