fatih:
I plan to determine land use/cover changes using different Landsat images
in GRASS GIS. Is there anybody who has tried this before? I am not
familiar with the GRASS GIS. So, a document/manual which includes step by
step description of image processing may help.
GRASS provides the tools to do it :-). What you really need is to decide which
methods to try and finally use (worth reading [1][2][3]).
A few more words
Among the most common change detection techniques are:
1. post-classification comparison (of a pre and a post data set): classify the
pre and the post data separately (check the wiki-page that Daniel posted in
the other reply to your post), then subtract to produce a change map.
2. PCA-based method(s) where PCA is utilised in order to "isolate" the changes
in less dimensions than the original (given we talk about multi-dimensional
data sets, e.g. multi-spectral bands of a satellite image acquisition). [4][5]
One PCA-based example is to treat all (pre and post) bands as one data set and
perform PCA. Changes are expected to appear "isolated" in a few (lower order)
components. You can use the "enhanced" components to classify the changes.
However, it is required to carefully check the numbers in order to best
identify the changes of your interest. Visually controlling the outcomes is
not enough! The eigen-vectors which "compose" the outcomes and the eigen-
values which describe how much of the original variance holds each component.
Have (also) a look here:
<http://geoinformatics.fsv.cvut.cz/gwiki/Change_Detection_with_GRASS_GIS_-
_Comparison_of_images_taken_by_different_sensors>.
Cheers, Nikos
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[1] Lu, D and Mausel, P and Brondizio, E and Moran, E, "Change detection
techniques", International Journal of Remote Sensing, vol. 25, no. 12, pp.
2365, 2003.
--%<--- PCA-based
[2] Deng, JS and Wang, K and Deng, YH and Qi, GJ, "PCA-based land-use change
detection and analysis using multitemporal and multisensor satellite data",
International Journal of Remote Sensing, vol. 29, no. 16, pp. 4823, 2008.
[3] Koutsias, N and Mallinis, G and Karteris, M, "A forward/backward principal
component analysis of Landsat-7 ETM+ data to enhance the spectral signal of
burnt surfaces", ISPRS Journal of Photogrammetry and Remote Sensing, vol. 64,
no. 1, pp. 37, 2009.
--->%--
[4] <http://grass.osgeo.org/grass64/manuals/html64_user/i.pca.html>
[5] an attempt to collect details on using PCA (in GRASS, R):
<http://grass.osgeo.org/wiki/Principal_Components_Analysis>