I need to reproject a DEM (raster file) in order to have the same CRS as all my other project data – I have different shapefiles for the same DEM's location but they have a different CRS.
Which resampling method would you recommend? I am using QGis.
[GIS] Best Resampling Method for DEM reprojection in QGis
demqgisresampling
Related Solutions
To load the imported PostGIS raster into QGIS:
First, you need to create a connection to your PostGIS database containing the raster.
Then, You can go to Database -> DB Manager -> DB Manager from the system menu.
The DB Manager Dialog Box will show. You can expand your database; browse to the raster and right-click on it. Choose "Add to Canvas". This works with QGIS 2.6 and 2.4 (I don't have 2.2 to test with).
Hope this helps.
If I understand the question correctly, this paper could be interesting. It discusses the process of reducing spatial resolution of satellite imagery. There is a procedure explained to degrade Landsat MSS data to a 125×125 m resolution. I stumbled upon both, your question and this paper, by chance, so I didn't go through the effort of reading everything they wrote in the paper in detail. I hope it helps, though. There is a scheme of the process on page 10. Maybe the technique is not applicable (without certain adaptations) to your particular situation, but then at least you know.
Best Answer
All resampling methods have advantages and inconvenients.
Nearest neighbours preserves the pixel values but might duplicate or remove some of them. Therefore you will probably end up with some discontinuities on your profiles (slope, etc).
Cubic convolution, on the opposite will yield smooth slopes that are nice for hydrological model, but will not preserve the pixel values. For DEM this is what I use most of the time.
Bilinear resampling is some kind of compromise between pixel value preservation and smoothing (if you can't choose).
BUT, what I recommend in your case is to keep the original CRS of your raster and reproject your vector layers. Contrary the raster projection, you do not lose information when you project your vector data, so you can return to their original CRS after running your analysis. (The only thing to care about is to densify your lines if you have very large extents, but this is usually not a problem for local analysis.)