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Change the resampling option for the rendering. Here is an example with default display: The blocky nature is due to the way hillshades are sampled within cells. The Layer Styling panel has Resampling options. Change Nearest Neighbour to Bilinear or Cubic. This can be done when zoomed in or out or both: The result is a smoother raster:


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One approach is to create polygons from the cliff areas eg Raster Calculator with Slope > 60, then Polygonize. Then, largely follow the steps outlined in this answer to split the polygons into roughly equal areas. This allows you to break up long/large cliff areas into smaller chunks. Because cliff areas tend to be skinny, I found the centroids often lay ...


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A pixel represents a point. It has no length or width. Slope comes from the relationship between the height of the point and that of neighbouring points, and the distance between them. It is the inverse tangent of the quotient of the difference in height and difference in lateral position slope = arctan(dy/dx) In practice, it can be an average or other ...


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That seems to be a low level error from libtiff library. By the error message the libtiff function cannot read data from a certain part of the TIFF file. Directory here means the internal directory that TIFF file has and offset means the number of bytes from the beginning of the file. Either your copy of the TIFF file is corrupted or you have a different ...


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He is a suggested workflow for you to follow: Make sure your blue lines and green points share a common ID value (integer) that links them Convert your line to raster based upon that ID, use the Polyline to Raster tool Extract from the DEM the cells under the raster line using Extract by Mask Identify the elevation under your green point for that line, you ...


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v.drape and v.to.3d in the Processing Toolbox support point features. So, load your .csv x,y data into the map, copy all the features and paste them as a temporary scratch layer (so you can have processing tools to interact with the points) and then make sure the units on your DEM are what you want to get attached to the points. Then run one of those two ...


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Use numpy.gradient import numpy as np # Example 2D array of elevation, and constant gridsize elev = np.array( [[2., 3., 2.], [3., 4., 4.]]) cellsize = 10. # Evaluate gradient in two dimensions px, py = np.gradient(elev, cellsize) slope = np.sqrt(px ** 2 + py ** 2) # If needed in degrees, convert using slope_deg = np.degrees(np.arctan(slope)) ...


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