Remote Sensing and Image Processing: 4 - PowerPoint PPT Presentation

View by Category
About This Presentation
Title:

Remote Sensing and Image Processing: 4

Description:

Office: 301, 3rd Floor, Chandler House. Tel: 7670 4290. Email: mdisney_at_geog.ucl.ac.uk ... MODIS reflectance 500m tile (not raw swath... – PowerPoint PPT presentation

Number of Views:190
Avg rating:3.0/5.0
Slides: 28
Provided by: ple67
Learn more at: http://www2.geog.ucl.ac.uk
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Remote Sensing and Image Processing: 4


1
Remote Sensing and Image Processing 4
  • Dr. Mathias (Mat) Disney
  • UCL Geography
  • Office 301, 3rd Floor, Chandler House
  • Tel 7670 4290
  • Email mdisney_at_geog.ucl.ac.uk
  • www.geog.ucl.ac.uk/mdisney

2
Image display and enhancement
  • Purpose
  • visual enhancement to aid interpretation
  • enhancement for improvement of information
    extraction techniques
  • Today well look at image arithmetic and spectral
    indices

3
Basic image characteristics
  • pixel - DN
  • pixels - 2D grid (array)
  • rows / columns (or lines / samples)
  • dynamic range
  • difference between lowest / highest DN

4

Aside data volume?
  • Size of digital image data easy (ish) to
    calculate
  • size (nRows nColumns nBands
    nBitsPerPixel) bits
  • in bytes size / nBitsPerByte
  • typical file has header information (giving rows,
    cols, bands, date etc.)

5

Aside
  • Several ways to arrange data in binary image file
  • Band sequential (BSQ)
  • Band interleaved by line (BIL)
  • Band interleaved by pixel (BIP)

From http//www.profc.udec.cl/gabriel/tutoriales/
rsnote/cp6/cp6-4.htm
6

Data volume examples
  • Landsat ETM image? Bands 1-5, 7 (vis/NIR)
  • size of raw binary data (no header info) in
    bytes?
  • 6000 rows (or lines) 6600 cols (or samples) 6
    bands 1 byte per pixel 237600000 bytes
    237MB
  • actually 226.59 MB as 1 MB ? 1x106 bytes, 1MB
    actually 220 bytes 1048576 bytes
  • see http//www.matisse.net/mcgi-bin/bits.cgi
  • Landsat 7 has 375GB on-board storage (1500
    images)

Details from http//ltpwww.gsfc.nasa.gov/IAS/handb
ook/handbook_htmls/chapter6/chapter6.htm
7

Data volume examples
  • MODIS reflectance 500m tile (not raw swath....)?
  • 2400 rows (or lines) 2400 cols (or samples) 7
    bands 2 bytes per pixel (i.e. 16-bit data)
    80640000 bytes 77MB
  • Actual file also contains 1 32-bit QC (quality
    control) band 2 8-bit bands containing other
    info.
  • BUT 44 MODIS products, raw radiance in 36 bands
    at 250m
  • Roughly 4800 4800 36 2 1.6GB per tile, so
    100s GB data volume per day!

Details from http//edcdaac.usgs.gov/modis/mod09a1
.asp and http//edcdaac.usgs.gov/modis/mod09ghk.as
p
8
Image Arithmetic
  • Combine multiple channels of information to
    enhance features
  • e.g. NDVI
  • (NIR-R)/(NIRR)

9
Image Arithmetic
  • Combine multiple channels of information to
    enhance features
  • e.g. Normalised Difference Vegetation Index
    (NDVI)
  • (NIR-R)/(NIRR) ranges between -1 and 1
  • Vegetation MUCH brighter in NIR than R so NDVI
    for veg. close to 1

10
Image Arithmetic
  • Common operators Ratio

topographic effects visible in all bands FCC
11
Image Arithmetic
  • Common operators Ratio (cha/chb)
  • apply band ratio
  • NIR/red
  • what effect has it had?

12
Image Arithmetic
  • Common operators Ratio (cha/chb)
  • Reduces topographic effects
  • Enhance/reduce spectral features
  • e.g. ratio vegetation indices (SAVI, NDVI)

13
Image Arithmetic
  • Common operators Subtraction

An active burn near the Okavango Delta,
Botswana NOAA-11 AVHRR LAC data (1.1km pixels)
September 1989. Red indicates the positions
of active fires NDVI provides poor
burned/unburned discrimination Smoke plumes
gt500km long
  • examine CHANGE e.g. in land cover

14
Top left AVHRR Ch3 day 235 Top Right AVHRR
Ch3 day 236 Bottom difference pseudocolur
scale black - none blue - low red -
high Botswana (approximately 300 300km)
15
Image Arithmetic
  • Common operators Addition
  • Reduce noise (increase SNR)
  • averaging, smoothing ...
  • Normalisation (as in NDVI)



16
Image Arithmetic
  • Common operators Multiplication
  • rarely used per se logical operations?
  • land/sea mask

17
Monitoring usingVegetation Indices (VIs)
  • Basis

18
Why VIs?
  • empirical relationships with range of vegetation
    / climatological parameters
  • fAPAR fraction of absorbed photosynthetically
    active radiation (the bit of solar EM spectrum
    plants use)
  • NPP net primary productivity (net gain of
    biomass by growing plants)
  • simple (understand/implement)
  • fast (ratio, difference etc.)

19
Why VIs?
  • tracking of temporal characteristics /
    seasonality
  • can reduce sensitivity to
  • topographic effects
  • (soil background)
  • (view/sun angle (?))
  • (atmosphere)
  • whilst maintaining sensitivity to vegetation

20
Some VIs
  • RVI (ratio)
  • DVI (difference)
  • NDVI

NDVI Normalised Difference Vegetation Index
i.e. combine RVI and DVI
21
Properties of NDVI?
  • Normalised, so ranges between -1 and 1
  • If ?NIR gtgt ?red NDVI ? 1
  • If ?NIR ltlt ?red NDVI ? -1
  • In practice, NDVI gt 0.7 almost certainly
    vegetation
  • NDVI close to 0 or slightly ve definitelyy NOT
    vegetation!

22
why NDVI?
  • continuity (17 years of AVHRR NDVI)

23
limitations of NDVI
  • NDVI is empirical i.e. no physical meaning
  • atmospheric effects
  • esp. aerosols (turbid - decrease)
  • direct means - atmospheric correction
  • indirect means atmos.-resistant VI (ARVI/GEMI)
  • sun-target-sensor effects (BRDF)
  • MVC ? - ok on cloud, not so effective on BRDF
  • saturation problems
  • saturates at LAI of 2-3

24
(No Transcript)
25
(No Transcript)
26
Practical 2 image arithmetic
  • Calculate band ratios
  • What does this show us?
  • NDVI
  • Can we map vegetation? How/why?

27
MODIS NDVI Product 1/1/04 and 5/3/04
About PowerShow.com