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I have a task to be tackled of which I am not sure, wheter it is even possible. I need to classify single trees/clusters of trees in a forest automatically using sentinel-2 data. Supervised classification should be extremely trick in my opinion. I have some ground data, so that certain pixels are identified as a specific kind of tree in a point layer. Is there any way to get the automatic classification done? Especially when that scheme should not only work on a single image but on several images of the same area?

  • The spatial resolution of Sentinel-2 is 10 meters, see the tech docs. There is a good academic article explaining what is possible with imagery of around 15m spatial resolution. – John Powell Mar 24 '17 at 10:23
  • The article talks about tree classification with a 1m resolution. I can see that that would work. However I am asking because the 10m spatial resolution seems a bit too coarse for the task. I was hoping someone might have experience with this. (Also the article seems a bit old. A lot has happened since 2004 - thanks anyway!). – Homerun_ Mar 24 '17 at 10:48
  • I think it is way too coarse, which is why I mentioned it. You can't extract features finer than pixel res, no matter how many look angles you have. We use such data (individual tree heights and extents) and I know for a fact that the company that does it uses 25cm resolution aerial imagery. – John Powell Mar 24 '17 at 10:54
  • Hm yeah, that sounds plausible. Just what I thought. Thank you :) – Homerun_ Mar 24 '17 at 10:57
  • Out of curiosity, what are you trying to classify the trees for? – Aaron Mar 25 '17 at 16:29
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NASA offer an advanced webinar course https://arset.gsfc.nasa.gov/land/webinars/advanced-land-classification that uses the Semi-Automatic classification plugin in QGIS. It will take you through step by step with Landsat imagery. You can use the same steps for Sentinel imagery. The Semi-automatic classification plugin comes with the best user documentation I have seen for a QGIS plugin. It's well worth a look and the time to work out how to use it.

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