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The relationship is an inverse one. For example, if you have a 1 m error in a 30 m pixel, the percentage of error is much smaller than if you have a 1 m error in a 15 m pixel.


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For read access the easiest way to get ECW support is through the OSGeo4W installer. Do an Advanced install, and in the libs section enable the gdal-ecw library.


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Have you though of just using the free GDAL tool? http://www.gdal.org/gdaldem.html It can run in windows using OSGEO4W and can ouput .img files. For Imagine 8 it goes something like... From Spatial Modeler: This model is found in the file <.IMAGINE_HOME.>/etc/models/Slope_percent.gmd. From Image Interpreter: Select Topographic Analysis... | Slope.... ...


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There is a simple way you could do this using a python script, however it does not truly create a cloud shadow. For more information on creating a cloud shadow see this paper by Zhu and Woodcock (2014) and the associated literature. EDIT: There are most probably built-on methods to shift data in most GIS software;. If you manage to replicate the result ...


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I think what you want is called a classification. There's a decent example of the scientific workflow-methodology here: http://academic.emporia.edu/aberjame/student/banman5/perry3.html I'd suggest using a supervised classification, you'll need to identify which areas are rice yourself. This video details how to create a supervised classification in Erdas ...


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To calculate a volume, you need the 3D Analyst extension. 3D Analyst has several ways to calculate volumes. All methods require the data to be in a projected coordinate system. You can use TIN or Terrain surfaces. Method 1: Surface Volume This method assumes one of the surfaces is a flat plane and the other surface varies. It can calculate volumes ...


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The image needs to be cut apart and re-mosaiced after adjusting the colors in each piece. This can be done. As an example, I extracted the green band of the image. To make my work simple (the computing platform I am using, Mathematica 9, does not easily extract pixels along arbitrary polylines), I rotated it to make some of the image boundaries perfectly ...


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Histogram matching works by forcing the histogram of one image to match as closely as possible the histogram of a second target image. I'm afraid that it won't work on a single image. (There is the exception of using Histogram Matching to force an image to theoretical distribution, like the Gaussian, but that won't help in this case either.) Also, I'm not ...



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