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When to use resampling and upsampling in rasterio?
Resampling refers to changing the cell values due to changes in the raster cell grid. This can occur during reprojection. Even if the projection is not changing, we may want to change the effective cell size of an existing dataset. Upsampling refers to cases where we are converting to higher resolution/smaller cells.
Which is an example of downsampling in rasterio?
Downsampling is resampling to lower resolution/larger cellsizes. By reading from a raster source into an output array of a different size or by specifying an out_shape of a different size you are effectively resampling the data. Here is an example of upsampling by a factor of 2 using the bilinear resampling method.
What is the process of reprojection in rasterio?
Rasterio can map the pixels of a destination raster with an associated coordinate reference system and transform to the pixels of a source image with a different coordinate reference system and transform. This process is known as reprojection.
How is the default transform calculated in rasterio?
Rasterio provides a rasterio.warp.calculate_default_transform () function to determine the optimal resolution and transform for the destination raster. Given a source dataset in a known coordinate reference system, this function will return a transform, width, height tuple which is calculated by libgdal.
When to resample raster to coarser resolution?
On execution, the input raster will first be resampled to the coarser resolution, then the tool is applied. When performing analysis, make sure you are asking appropriate questions of the cell size. For example, it is unlikely you will study mouse movement when the cell size is 5 kilometers.
Do you need the same cell resolution for all raster datasets?
Different raster datasets do not need to be stored using the same cell resolution. But when you are processing between multiple datasets, the cell resolution, like the registration, ideally should be the same.