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The result from NDVI will be continuous (i.e. decimal) values between -1 to +1, therefore the raster must be able to store these values, and will use signed pixel depth. If you truly want 8-bit unsigned, you will need to adjust the expression in the raster calculator by linearly scaling to values between 0-255 and then applying the int() function on the ...

I would recommend calculating soil moisture indices from Landsat TM bands. MTRI has an interesting article on creating soil moisture index (SMI) from Landsat TM 5. Also, I would recommend exploring soil moisture estimates using TM band 6 (Thermal IR). Attached is a good tutorial on calculating indices from Landsat TM bands using ArcGIS 9.x (as you ...

Here is one possible workflow using ModelBuilder and Spatial Analyst tools that works for me: You supply the input raster, an XY coordinate for the location at which to sample for the region to be reclassified, and the new value of the classification. The output is a new raster (it won't let you overwrite the input raster), but that can be overcome ...

There seems to be two camps about this one. Some prefer to mosaic before classification, others prefer to classify the images before mossaicking. Personally, I would classify the images first, then mosaic them. Have a look at the discussions on this page and you'll find arguments for and against both methods. Generally, they state that you should ...

Changes in vegetation over the month between your scenes could be part of the issue. It is also possible that there is some haze over areas of your scene outside of your dark object location(s), and therefore this haze is not being removed during your atmospheric correction. Another reason that you see contrast between the two scenes could be due to ...