Remote Sensing and Image Processing 4 Dr Mathias

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

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 2

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

Aside: data volume? • Size of digital image data easy (ish) to calculate – size = (n. Rows * n. Columns * n. Bands * n. Bits. Per. Pixel) bits – in bytes = size / n. Bits. Per. Byte – typical file has header information (giving rows, cols, bands, date etc. ) (0, 0) n. Columns (0, 0) n. Bands n. Rows n. Bands n. Columns (r, c) Time 4

Aside • Several ways to arrange data in binary image file – Band sequential (BSQ) – Band interleaved by line (BIL) – Band interleaved by pixel (BIP) 5 From http: //www. profc. udec. cl/~gabriel/tutoriales/rsnote/cp 6 -4. htm

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 ~ 237 MB • actually 226. 59 MB as 1 MB 1 x 106 bytes, 1 MB actually 220 bytes = 1048576 bytes • see http: //www. matisse. net/mcgi-bin/bits. cgi – Landsat 7 has 375 GB on-board storage (~1500 images) Details from http: //ltpwww. gsfc. nasa. gov/IAS/handbook_htmls/chapter 6. htm 6

Data volume: examples • MODIS reflectance 500 m 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 = 77 MB – 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 250 m • Roughly 4800 * 36 * 2 ~ 1. 6 GB per tile, so 100 s GB data volume per day! Details from http: //edcdaac. usgs. gov/modis/mod 09 a 1. asp and http: //edcdaac. usgs. gov/modis/mod 09 ghk. asp 7

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

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 9

Image Arithmetic • Common operators: Ratio topographic effects visible in all bands FCC 10

Image Arithmetic • Common operators: Ratio (cha/chb) apply band ratio = NIR/red what effect has it had? 11

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

Image Arithmetic • Common operators: Subtraction An active burn near the Okavango Delta, Botswana NOAA-11 AVHRR LAC data (1. 1 km pixels) September 1989. Red indicates the positions of active fires NDVI provides poor burned/unburned discrimination Smoke plumes >500 km long • examine CHANGE e. g. in land cover 13

Top left AVHRR Ch 3 day 235 Top Right AVHRR Ch 3 day 236 Bottom difference pseudocolur scale: black - none blue - low red - high Botswana (approximately 300 * 300 km) 14

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

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

Monitoring using. Vegetation Indices (VIs) • Basis: 17

Why VIs? • empirical relationships with range of vegetation / climatological parameters · f. APAR – 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. ) 18

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

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

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! 21

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

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 23

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saturated 25

Practical 2: image arithmetic · Calculate band ratios · What does this show us? · NDVI · Can we map vegetation? How/why? 26

MODIS NDVI Product: 1/1/04 and 5/3/04 27