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2

I would imagine that the source of the error is that the names of your raster object "satImage" do not match the variables used in the random forests model. You can check this by using match() or %in% on the names of the two objects. x = c("t.1","t.2","t.3","t.4") y = c("t1","t.2","t.3","t.4") x %in% y If you would like to get very specific you could ...


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I would suggest classifying the shadows separately from the rest of the image. If you can find a distinct shadow class, mask out the "shadow" pixels and then stretch them and reclassify (be careful how you stretch). I am not an expert in image classification, but I would definitely validate any results with other imagery.


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You should use the pie chart styling, for more info look at the docs here


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For an analysis like this, you need to atmospherically correct the images and convert to surface reflectance. You will also want to make sure the images were acquired at approximately the same time period to take into account seasonal variability. There are a variety of resources available for atmospheric correction. ENVI has a Atmospheric Correction Module ...


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Maybe you would like to try a different way. First you can generate an empty vector grid like a fishnet (Vector > Research Tools > Vector Grid (Output Grid as Polygons!)), then you can make a spatial join (Vector > Data Management Tools > Join Attributes by Location (Take summary of intersecting features! > you will need the COUNT Field in the result shape ...


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Interpolate each layer to a raster and then add the 3 layers together. I don't know what your data looks like but I will give an example and you can change it for your values. Layers Clay has the attribute values of 1, 2, 3. Make the values of the raster 100, 200, 300. If you are starting off with 1, 2, 3 just multiply this layer by 100. Sand has the ...


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You can edit the range manually by double clicking on the Value you want to change


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Besides the good answer of Mapperz, you may also find the explanations in the ArcGIS Help of some use. It refers to ArcGIS, but the underlying principles are the same in QGIS: Classifying numerical fields for graduated symbology http://resources.arcgis.com/en/help/main/10.2/index.html#//00s50000001r000000


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Equal_Interval "the range is then divided by the number of classes" http://wiki.gis.com/wiki/index.php/Equal_Interval_classification Quantiles " for visualizing continuous data that is not distributed normally" http://wiki.gis.com/wiki/index.php/Geometric_Interval_Classification Natural Breaks "method designed to optimize the arrangement of ...


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One place to look for imagery sources is OpenStreetMap's "editor imagery index", which is basically a machine readable list of imagery provider URLs and extents. https://github.com/osmlab/editor-imagery-index The needs of your project aren't exactly the same as OSM's but there is overlap. In this case, in the Nairobi region it shows Bing and Mapbox ...


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Not exactly "high-resolution," but the price is right. Esri's World Imagery map service presents satellite imagery for the world and high-resolution imagery for the United States and other areas around the world.


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I assume you mean to reclassify the data into categories based on specific ranges, thus creating a discrete raster from a continuous raster. Do this... Since you already have the Spatial Analyst extension, go to Spatial Analyst in your toolbox. Click on Reclass, then Reclassify. Here you can add specific ranges and map them each to a new value, then export ...



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