web-lee-stocking-island-2

The SIG option in 4SM for a 4-bands BGRNir image
(this is outdated, now treated by the RLBbg variable) Where only bands 1 and 2 exhibit bottom detection above the threshold value adopted, the computed depth can be adversely biased subject to the unknown contrast in the actual bottom reflectance signature. This is countered in 4SM through the following learning process:

in a first modeling run, where 3 bands exhibit bottom detection:
- a 3-band bottom reflectance signature is obtained, and the computed depth is optimized accordingly
- the average ratio LBS[1]/LBS[2] is computed over the whole image for each of the 200 discrete levels of average bottom brightness. This information is stored in a textfile
in a second modeling run:
- this database textfile is then available for modeling all pixels where only bands 1 and 2 exhibit bottom
- this improves the computed depth and yields some spectral contrast between LBS[1] and LBS[2], while LBS[3] is assigned the average of the other two bands.
This of course amounts to stretching the information that is available to an acceptable level,
- but it is far from perfect, as it allocates to deeper pixels the average spectral properties of the shallow bottom subatrates observed in the 0 to 4-6 m depth range.
Nevertheless, as can be seen in the illustration below, this SIG trick in 4SM improves dramatically the depths computed for a vast majority of pixels deeper than 4-6 m

In this run, two horizontal stripes have been modeled...
The bias in depths computed using only the Blue/Green pair of bands

can reach up to ~-3 m for a greenish botton substrate
can reach up to ~+3 m for a blueish bottom substrate
is quasi null for a very bright bottom substrate

... with the SIG option disabled
It is strongly correlated with the average brightness of the bottom

brighter bottoms tend to have a flatter spectral signature
darker bottoms tend to have a more contrasted signature

==> The SIG option takes advantage of the image-wide average of this trend

Channel marker buoy sudy area

Raw image, histeq enhancement	Deglinted image, further histeq enhancement
image Z stepped	image Z b&w
	Channel marker buoy study area Some bottom substrates descriptions from Louchard et al.: a Migrating tidal oolitic sand wave, extremely bright, contain 0.8 to 1.6 micrograms of chlorophyll a per cm3 (other sandy bottom substrates in less energetic environments are coarser and darker, and contain up to 6 micrograms of chlorophyll a per cm3 because of the development of algal polymer biofilms at their surface). b Sand stabilized by sparse Thalassia seagrass B Thalassia seagrass meadows on more or less bioturbated sands C Abundant brown Sargassum algae on pavement channel floor
image B the very bright oolithic sand wave ranges from ~0 to ~5-7 m in depth the dark channel floor exhibits a very uniform average reflectance	image B in b&w the variations in the coverage of Thalassia seagrass meadows reportedd by Louchard et al. are readily depicted in this image of the average bottom reflectance but variations in the sand grain size and/or in the density of biofilm at the sand surface and/or the intensity of the bioturbation might just as well be the cause
image LBS normalized *This image displays the normalized "greenness" of the spectral bottom signature obtained through shallow water modeling:* *Grey shades* where bottom signature is "flat" *Green shades* where bottom signature is more or less higher in the green band than in the blue band ========= normalization of LBS is just the last option in 4SM processing	image LBS
	The results obtained by 4SM using IKONOS data correlate well with those presented by Driersen et al, "Ocean color remote sensing of seagrass and bathymetry in the Bahamas Banks by high-resolution airborne imagery " Limnol. Oceanogr., 48(1, part 2), 2003, 444–455), which used hyperspectral PHILLS data