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Matplotlib has many backends In my Mac OS X matplotlib (1.4.2) installation, using the pure Python script in List of all available matplotlib backends the result is: print backends ['agg', 'cairo', 'cocoaagg', 'gdk', 'gtk', 'gtk3', 'gtk3agg', 'gtk3cairo', 'gtkagg', 'gtkcairo', 'macosx', 'mixed', 'nbagg', 'pdf', 'pgf', 'ps', 'qt4', 'qt4agg', 'qt5', ...


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Matt Hansen's team has a paper published on forest cover change in Eastern Europe that goes back to 1985 - see Eastern Europe's forest cover dynamics from 1985 to 2012 quantified from the full Landsat archive http://www.sciencedirect.com/science/article/pii/S0034425714004817 I'm also checking with colleagues on whether Matt Hansen's algorithm is available ...


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The general name for the operation that will allow you to compare two classified images is cross tabulation or what is sometimes called a contingency table. This will allow you to calculate change in class values. In the SAGA toolbox of QGIS there is a tool called Cross-classification and tabulation that will perform this operation.


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Not a complete answer, but I don't have enough reputation to comment on your post: The Supplementary Materials (SM) for the Science article provides references to a number of different journal-articles that outline various parts of the methodology. The SM can be found here Extending the time-series to include Landsat-5 (and potentially Landsat-8 to make ...


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I have found this scientific article for forest/non-forest mapping using Landsat but unfortunately it is not free to read (15 $). Wentao Ye; Xi Li; Xiaoling Chen and Guo Zhang A spectral index for highlighting forest cover from remotely sensed imagery", Proc. SPIE 9260, Land Surface Remote Sensing II, 92601L (November 8, 2014); ...


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Just to add a visual to the answer by F_Kellner


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As far as I know, there is no way to change this globally. The added decimals are the results of your raster data having a too big precision value. Nevertheless, removing the decimals from the labels manually per raster layer is quite simple. Click on the Label header, then select the Format Labels.. item. There round down to 0 the number of decimals ...


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Responding directly to your question: applying the same signature file / spectral response to each image is somewhat risky, due to the potential for variation in how the clear-cut area looks, and how the health forest looks, due to phenological variation. As such, I'd classify each image on its own, and then go on to do change analysis on the output. Note ...


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I can't explain why your original raster does not have a populated count field - I thought it appeared by default for integer rasters. In any case, you can get those values by: Create a new integer raster, from your original, using Int. This will create a copy of the raster with Value and Count fields. Join the new raster back to the original raster based ...


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You can calculate natural break values using the PySAL library, then Reclassify or use those values as you choose. import arcpy, pysal from pysal.esda.mapclassify import Natural_Breaks as nb myArray = arcpy.RasterToNumPyArray(<PATH TO RASTER HERE>) breaks = nb(myArray.ravel(),k=<NUMBER OF CLASSES HERE>,initial=20)


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I believe there is a more programmatic way, however it only automates the steps you have already described. I don't readily know of a more efficient way to automate for this effect. First, my gut reaction says to use arcpy.mapping to grab a reference to your target layer object, access the layer.symbology object and then extract the ...


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As best I can tell it would make no difference aside from annoyance as the table indicates 0 pixels are classified with these "ghost values". Hence the term. It appears that the pixel values are continuous so if you leave a gap --say a class for 3 and a class for 5-- then it will fill the gap with zeros in the table. In this example class 4.



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