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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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Erdas Imagine has an add-on called DeltaCue that is designed for this type of analysis. Some of the highlights include: Multiple change detection algorithms: Magnitude, TC, Primary Color, Single Band, Band Slope. Automatic percent change thresholding. Change filtering based on spectral class, material type, area of change, and shape of change. Automatic ...


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You cite ArcGIS and Erdas. Any of those software could help, but I would rather use Erdas than ArcGIS for remote sensing application (and if you want to use ArcGIS, you need the spatial analyst extension). For other softwares, your should consider QGIS, with GRASS or OTB (free and open source) as well as ENVI or Definiens.


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I developed a software to do change detection using Landsat 8 Imagery: https://github.com/ibamacsr/indicar-tools It was tested only in GNU/Linux yet.


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In my company we are using Definiens Developer http://developer.definiens.com/ for checking changes in time, ie Open Lands, woodland losses (illegal fellings, windblows, etc), new plantings… This software is also being used in oncology so don’t be surprised. Please check it out maybe you will find it any useful.


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you can use 6s in the VNIR-SWIR bands. You need some information from the metadata. Then you get the equations to apply atmospheric correction. It is free!.


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AAIC is an autonomous atmospheric correction application that works with most commercial mulitspectral sensors. The process was developed by Applied Analysis Inc. (AAI) and is distributed as an add-on by BAE Systems for SOCET GXP and Intergraph for IMAGINE. Both companies versions will support LandSat 8 imagery with their 2015 releases. Although the ...


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The logical place to start would be doing some tutorials on Erdas Imagine. There are several online, including quite a few which include Supervised and Unsupervised classifications. I.e. - University of Washington - http://sal.ocean.washington.edu/tutorials/erdas/index.html - exercise 2. There are also a number of questions on here that may help, though it ...


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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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Though I can't find any definitive reference at the moment, from all appearances, the file is modified because the statistics are actually stored within it. I doubt that calculating statistics would change image values. Saving statistics in the file is likely just a convenience to provide faster access next time they are needed. This link from GDAL, for ...


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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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