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 --- [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> _______________________________________________ grass-user mailing list [email protected] http://lists.osgeo.org/mailman/listinfo/grass-user
