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4

Ok, I'm sorry to post a question and then answer it myself so quickly, but I found a nice set of course slides from Utah State University that has a lecture on opening raster image data with GDAL. For the record, here is the code I used to open the PRISM Climate Group datasets (which are in the EHdr format). def ReadBilFile(bil): import gdal ...


4

Vegetation extraction is a bit more complex than running the spatial analysis tools that you named. For better results I would suggest the following: run analysis on a 4 band image (e.g. R,G,B,NIR) change image to be symbolized as 432 for RGB not 321 create training samples that represent vegetation and run a supervised classification These steps will ...


4

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 ...


3

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 ...


3

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 ...


3

After running Raster Calculator, use the Copy Raster tool with the pixel_type parameter set to 8_BIT_UNSIGNED, as shown below.


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The first thing you will want to do is look at the Google Terms of Use and Licensing. Google is very particular on how their data and software can be used. I would look at this first as it may be a show-stopper. The second thing I would consider is that the imagery in Google isn`t raw imagery; they are chips or tiles of data saved in a web tiling format. ...


2

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 ...


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On Europe, there are: CORINE land cover (downloadable from there) LUCAS (see it there).


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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 ...


1

The GDAL utilities are command line tools of convenience for standard geoprocessing tasks but if what you want is not there then you will have to write code to call the underlying API yourself to build your own tool. Here are a set of tutorials on using GDAL which will explain how to do what you want. However, I recommend that, unless you absolutely have ...


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If by "using GDAL" you include writing code using the library as opposed to the more limited capabilities available only using the utilities from the command line, then the GDAL API tutorial walks you through all the steps to open a raster image, access its various properties (size, # of bands, rotation/skew, etc), and finally how to read and write a ...


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Just landed on this query. Hope you have found the answer by now, or if not, here I guess is the answer. For a given roi object, say 'oroi', use this command to get the vertices >> oroi -> GetProperty, data=a so, here 'a' will have the X,Y,Z values of the vertices. Actually do 'a(*)+=0.5' to get the vertices values (at the center of the pixels). ...


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You can also use GRASS for this work, I have found that it provides robust results for indices calculation when atmospheric correction is applied as per the modules.



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