1. Remote Sensing and Image Processing: 4 Dr. Hassan J. Eghbali

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 Dr. Hassan J. Eghbali

3. Basic image characteristics pixel - DN pixels - 2D grid (array) rows / columns (or lines / samples) dynamic range –difference between lowest / highest DN Dr. Hassan J. Eghbali

4. 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.) Aside: data volume? (0,0) nColumns nRows (r,c) nBands (0,0) nColumns nRows (r,c) nBands Time Dr. Hassan J. Eghbali

5. Several ways to arrange data in binary image file –Band sequential (BSQ) –Band interleaved by line (BIL) –Band interleaved by pixel (BIP) Aside Dr. Hassan J. Eghbali

6. 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  1x10 6 bytes, 1MB actually 2 20 bytes = 1048576 bytes see http://www.matisse.net/mcgi-bin/bits.cgi –Landsat 7 has 375GB on-board storage (~1500 images) Data volume: examples Dr. Hassan J. Eghbali

7. 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! Data volume: examples Dr. Hassan J. Eghbali

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

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 Dr. Hassan J. Eghbali

10. Image Arithmetic Common operators:Ratio topographic effects visible in all bands FCC Dr. Hassan J. Eghbali

11. Image Arithmetic Common operators:Ratio (ch a /ch b ) apply band ratio = NIR/red what effect has it had? Dr. Hassan J. Eghbali

12. Image Arithmetic Common operators:Ratio (ch a /ch b ) Reduces topographic effects Enhance/reduce spectral features e.g. ratio vegetation indices (SAVI, NDVI++) Dr. Hassan J. Eghbali

13. Image Arithmetic Common operators:Subtraction examine CHANGE e.g. in land cover 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 Dr. Hassan J. Eghbali

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) Dr. Hassan J. Eghbali

15. Image Arithmetic Common operators:Addition –Reduce noise (increase SNR) averaging, smoothing... –Normalisation (as in NDVI) + = Dr. Hassan J. Eghbali

16. Image Arithmetic Common operators:Multiplication rarely used per se: logical operations? –land/sea mask Dr. Hassan J. Eghbali

17. Monitoring usingVegetation Indices (VIs) Basis: Dr. Hassan J. Eghbali

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.) Dr. Hassan J. Eghbali

19. Why VIs?  tracking of temporal characteristics / seasonality  can  can reduce sensitivity to:  topographic effects  (soil background)  (view/sun angle (?))  (atmosphere)  whilst maintaining sensitivity to vegetation Dr. Hassan J. Eghbali

20. Some VIs RVI (ratio) DVI (difference) NDVI NDVI = Normalised Difference Vegetation Index i.e. combine RVI and DVI Dr. Hassan J. Eghbali

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! Dr. Hassan J. Eghbali

22. why NDVI?  continuity (17 years of AVHRR NDVI) Dr. Hassan J. Eghbali

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 Dr. Hassan J. Eghbali

25. saturated Dr. Hassan J. Eghbali

26. Practical 2: image arithmetic  Calculate band ratios  What does this show us?  NDVI  Can we map vegetation? How/why? Dr. Hassan J. Eghbali

27. MODIS NDVI Product: 1/1/04 and 5/3/04 Dr. Hassan J. Eghbali