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Indices and Transforms
Ratios… 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 …… < 1 = unvegetated
* RVI can create infinite values
Difference Vegetation Index (DVI) = NIR - Red …… < 0 = unvegetated
* DVI is influenced by different lighting
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Normalised Difference Vegetation Index: NDVI
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Normalised Difference Vegetation Index NDVI
Division compensates for differential Illumination and yields values between-1 and 1, … in a 32 bit channel
It gives a close estimate of biomassalso referred to as greenness
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 (~ 0.2 to 0.5),
high values indicate temperate and tropical rainforests (0.6 to 0.9)
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The difference between the
average NDVI for a particular
month of a given year (such
as August 1993, above) and
the average NDVI for the
same month over the last 20
years is the NDVI anomaly. In
1993, heavy rain in the
Northern Great Plains led to
flooding in the Missouri River.
The resulting exceptionally
lush vegetation appears as a
positive anomaly (green).
Annual and interannual chnages in NDVI
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http://phenology.cr.usgs.gov/ndvi_foundation.php
Many satellite sensors have red and Near IR bands to assess global vegetation
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Special sensors for NDVI
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 250m/1000m red /near-IR bands for NDVI
NDVI is used to measure vegetation amount or biomass, in regional and global estimates. "NDVI is directly related to photosynthesis and thus energy absorption of plant canopies"
Global NDVI change: https://archive.org/details/SVS-3584
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Delineation of Grizzly Bear Habitat in Bute InletGEOG432 project
Sieved maximum NDVI result
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http://www.grayhawk-imaging.com/useofndviimagery.html
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The use of NDVI to determine vegetative green-up after a forest fire Geog432
1987 2002
NDVI NDVI
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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
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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)
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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)
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The technique was named after the pattern of spectral change of agricultural crops during senescence, plotting brightness (visible) against greenness (NIR). The sequence is:
1. Bare fields / newly planted crops - high brightness, low greenness (spring)
2. Plant Growth - <-<- brightness (early summer)
3. Maturity: -> -> greenness (late summer)
4. Senescence (harvest) - bare field/stubble: <-<-greenness, ->-> brightness (Fall)
Tasseled Cap transformation
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Tasseled Cap transformation ArcMap 10.3
|
The Tasseled Cap (Kauth-Thomas) transformation is designed to analyze and
map vegetation and urban development changes detected by satellite sensors.
It is known as the Tasseled Cap transformation due to the data shape.
It was developed in 1976 by R.J. Kauth and G.S. Thomas of the Environmental
Research Institute of Michigan (ERIM). The researchers found the patterns in
Landsat MSS data of agricultural fields as a function of the life cycle of the crop.
Essentially, as crops grow from seed to maturity, there is a net increase in near-
infrared and decrease in red reflectance based on soil color
4:Green 5:Red 6:NIR1 7:NIR2
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Tasseled Cap Transformation
MSS data, the 4-band dataset creates channels:
Brightness, Greenness, Yellowness (non-such)
TM data, 6-band (no thermal)
Brightness
Greenness
Wetness (SWIR)
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Coastal
Band 1
Blue
Band 2
Green
Band 3
Red
Band 4
NIR
Band 5
SWIR1
Band 6
SWIR2
Band 7
Brightness 0 0.3029 0.2786 0.4733 0.5599 0.5080 0.1872
Greenness 0 -0.2941 -0.2430 -0.5424 0.7276 0.0713 -0.1608
Wetness 0 0.1511 0.1973 0.3283 0.3407 -0.7117 -0.4559
Landsat 8 OLI coefficients
Why are they different at all ?
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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 Wetness1 .3037 -.2848 .15092 .2793 -.2435 .19733 .4743 -.5436 .32794 .5585 .7243 .34065 .5082 .0840 -.71127 .1863 -.1800 -.4572
Character: Overall reflectance NIR v Visible MIR v NVIR
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Brightness – measure of soil reflectance
Greenness – vegetation
Wetness – soil / canopy moisture
tasseled cap channels 1,2,3
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NDVI v Tasseled Cap greennessboth contrast NIR versus visible reflectance
TCA Greenness is similar to NDVI, with subtle differences and is used in habitat studies.
Figure :John Paczkowski MSc thesis – remote sensing and grizzly bear habitat
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–but 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 / Sentinel 2
Other ?:CBERS-02B (China/Brazil)
Ikonos, Quickbird 2
ASTER / MODIS
Russian tassel cap
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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
I can show cute tassel photos
-known as the Tasseled Toque
in Canada (fake RS humour]
http://www.sjsu.edu/faculty/watkins/tassel.htm
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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)
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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)
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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)
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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 0.40PC2 -0.28 -0.14 -0.29 0.82 0.23 -0.25 -0.16PC3 0.51 0.31 0.43 0.49 -0.46 -0.05 -0.00PC4 -0.09 -0.09 -0.19 0.19 -0.23 0.91 -0.18PC5 0.31 0.13 0.05 -0.12 0.35 -0.00 -0.86PC6 0.69 -0.16 -0.68 -0.01 0.01 -0.04 0.19PC7 -0.19 0.90 -0.39 -0.04 0.00 0.00 0.06
Component71% Brightness21% Greenness3.8% Swirness / Wetness2.3% Impact of TM61.6% 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
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PC components PC4: TM6, PC5: TM5/7, PC6: TM1/3, PC7: TM2/3
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Differences with Tasseled Cap (TCA) :
1. PCA transformation is scene specific -TCA coefficients are 'global‘
2. PCA generates as many components 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)
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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.
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Face Recognition Using Principal Component Analysis
1: Human-ness (or lack of)
2: Gender (or skin colour)
3: Hair (colour / volume / lack of)
4: Facial hair
5: Mouth – Smile – Teeth - smirk
6-7: Age, Ears, Eyes, Nose … ?