[GRASS-user] Pre-processing LANDSAT TM Orthorectified images from
neteler at osgeo.org
Mon Jun 2 03:50:08 EDT 2008
On Mon, Jun 2, 2008 at 7:50 AM, maning sambale
<emmanuel.sambale at gmail.com> wrote:
> For a project I am involved with, we are conducting landcover
> classification from LANDSAT TM (orthorectified) data downloaded from GLCF.
> We are now in the process on pre-processing the image and then conduct
> classification using i.smap.
> Following the GRASS book, we will be conducting the pre-processing
> steps outlined:
> 1. calibration from DN to apparent radiance at sensor - gain/bias offsets
> following grass book 2nd ed. (page 222)
> r.mapcalc "band.rad = ((LMAX - (LMIN))/(255.0 - 1.0)) * (band -1.0) + (LMIN)"
i.landsat.toar is the more sophisticated approach (or, easier since you
don't need to write the formula manuallly).
> 2. correction for atmospheric effects
> use i.atcorr
> problem: the metadata supplied by GLCF does not indicate image acquisition time
> sample metadata: http://tinyurl.com/6oo428
> or use
AFAIK LANDSAT is sun-synchronous, it passes in the local morning time
(something like 10:30-11:00).
Maybe this helps:
i.atcorr is the sophisticated correction, i.landsat.dehaze only a simple
approach based on image statistics.
> 3. correction for topographic/terrain effects (most of my study area
> are in maountainous regions)
> use book 2nd ed. (page 226) "cosine correction"
> using r.sunmask, r.mapcalc
> Problem: again, no image acquisition time
(see above). "cosine correction" is a simple approach. See also
i.topo.corr (from GRASS AddOns).
> 4. removal of clouds
> use i.landsat.acca
Never tried, please report back.
> The beauty with GRASS and the GRASS book is that it has the
> tools/modules I need for this project, however, I find it difficult to
> choose which one I should use (i.atcoor or i.landsat.dahaze?). Am I
> following the steps in correct order? Or is it necessary to do all
> this? Reading from GLCF documentation, they did orthrectification for
> this image already.
> Any pointers would be very helpful.
I have collected all this now in
Feel free to further improve that Wiki page.
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