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@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 we’ll 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. nColumns nColumns (0,0) (0,0) nBands nBands nRows nRows (r,c) (r,c) Time 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/handbook/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.asp

8. Image Arithmetic • Combine multiple channels of information to enhance features • e.g. NDVI (NIR-R)/(NIR+R)

9. Image Arithmetic • Combine multiple channels of information to enhance features • e.g. Normalised Difference Vegetation Index (NDVI) • (NIR-R)/(NIR+R) 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 >500km long • examine CHANGE e.g. in land cover

14. Top left AVHRR Ch3 day 235 Top Right AVHRR Ch3 day 236 Bottomdifference 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 >> red NDVI  1 • If NIR<<red NDVI  -1 • In practice, NDVI > 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

25. saturated

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