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4

There are other options to download Landsat7 images. Here's what I do: Go to EarthExplorer and click on register on the upper right corner to create an account. Then again go to EarthExplorer and log in with the account that you have just created. In the first tab Search Criteria, you can set the acqusition time and area of the data by different ...


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What we see on this image is a good example of exactly how broken Landsat 7 is at this point. Below is a RGB from the specific area: You have faulty values and spatial errors all across the image. Overall, using this image for any sort of analysis is going to result in lots of strange things. The line seen in the question is clearly visible, and it extents ...


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This isn't really an answer, but it is funny that I just saw an article today about this problem. Maybe it will lead you in the right direction. http://www.nextgov.com/big-data/2016/05/promise-terrapattern-visual-search-engine-satellite-imagery/128673/ Terrapattern, a visual search engine for satellite imagery, released this week by a team of artists ...


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For Windows OS, some GDAL builds from Gisinternals are compiled with the KEA driver as well, see http://www.gisinternals.com/packageinfo.php?file=release-1800-x64-gdal-2-1-0-mapserver-7-0-1.zip UPDATE The KEA driver needs a GDAL version 2.0 or later. The linked file works well with the gisinternals build of GDAL 2.1.0, and the resulting tif seems to be ...


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SPOT has similar characteristics and is often used as a substitute. ASTER has similar characteristics and is often used as a substitute. Both will have the required coverage dates. ASTER will not have the blue band. Globcover has 2005 data but is at a poorer resolution. It has 2010 as well. MERIS, MODIS and other coarse systems are used to fill gaps in ...


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The combine function (which is used in foreach) does not store the relevant components into the final randomForest object. See ?randomForest::combine: The confusion, err.rate, mse and rsq components (as well as the corresponding components in the test component, if exist) of the combined object will be NULL. But the predict method returns OOB ...


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You could try an image segmentation approach but, I would not hold my breath on usable results. As far as application of a classification algorithm to panchromatic imagery, it is quite doubtful that you will get usable results because of the lack of any spectral separability associated with your target vegetation class. The only relevant information that you ...


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Potentials that I'd suggest that you look at are: NDVI percentiles - to indicate the highest & lowest NDVI values, without having the issues associated with anomalous min & max values. Range of NDVI values in a year - to indicate variability over the year. Potentially based on the percentiles, instead of min & max values. Bi-modality, to ...


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One solution is to use the data composite available on the site Global Forest Change , but it is done on more than one year. If you are lucky, you could also find some data on the SPOT image catalog, but it was not a systematic acquisition. Finally, you can try some gap filling with coarse resolution images (MERIS or MODIS)


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Yes - you can use MODIS imagery. The MODIS sensors have been degrading a bit and as such, there are some problems there. Also, given that you don't have exactly the same bands, view angles are a bit different, vastly different spatial scale and so on, the comparison will most likely be quite noisy and as such, it may indicate that a correction that actually ...



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