[GRASS-user] value differences between landsat images

[Juan Manuel Barreneche]

Hello grass users!

This may be a very technical question, but i hope somebody knows the answer

I'm currently working with landsat 5 images, trying to identify water
bodies with an algorithm that i found on the web (*). The thing is,
many calculations are made in the process, including NDVI and
normalization of the different bands, but there are important
differences between landsat images, even with adjacent ones. This
differences in values can be > 30, for the same kind of terrain, e.g.:

r.what -f input=223_082_B east_north=502773.212142,6416651.23944
502773.212142 | 6416651.23944 || 83 |

r.what -f input=223_083_B east_north=502789.528784,6415683.11873
502789.528784 | 6415683.11873 || 49 |

(in this example i used the r.what function in both images, in some
area that is repeated but slightly moved from one image to the other)

This would obviously cause a difference in the calculations that i
want to run, and so can be a big problem for me, but that depends of
how these differences are produced, for which i imagine two scenarios:
1. In one hand, if the different values are generated by a constant
that multiplies all the values in the image (i.e., all the "real"
values), then the problem is solved, because said constant would be
eliminated in the calculations (ej. in the NDVI formula, it would be
divided by itself).
2. On the other hand, if the different values are generated by a
constant that is summed to all the values, then i'm really lost,
because i can't imagine a way around to solve the problem.

Maybe some other scenario is really ocurrying (maybe a combination of
both??). I made a small test with two adjacent images (by comparing
many points like in the example), and it looked like the first
scenario, but with great uncertainty.... (In practice i can't do the
same exploration for all the images that i have, because of the error
involved and the time that consumes)

If anyone have any clue that can help i'd be very grateful,

cheers,

Juan M. Barreneche
Zoología de Vertebrados,
Facultad de Ciencias
Universidad de la República,
Montevideo, Uruguay

(*): DETECÇÃO E DISCRIMINAÇÃO DE SOMBRAS, NUVENS E CORPOS D'ÁGUA EM
IMAGENS DE SENSORIAMENTO REMOTO. Airton Marco Polidorio, Nilton
Nobuhiro Imai, Antonio Maria Garcia Tommaselli, Franklin César Flores.
Revista Brasileira de Cartografia # 58/03, Dec 2006. (ISSN 1808-0936)