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Some operations require data from one grid to be transferred to a grid with a different cell size or projection or coordinate system. The general term for this sort of transfer is resampling, because all of the ways to do it involve sampling one or more cell values from the original grid to compute an output cell value in the new grid; in the process there is always some loss of information. |
Resampling most commonly occurs when changing the coordinate system or projection of a dataset to match another one, or to output it in a particular regional coordinate system. For example, the SRTM elevation grids are produced in a latitude-longitude coordinate system on the WGS84 datum; to map them into a State Plane coordinate system, for example, on the NAD83 datum requires resampling.
Most often the aim is to preserve a similar cell size, although when converting between latitude-longitude and linear systems there is always some approximation because the lat-lon system does not have square cells in a linear system. Even with the same cell size, however, there are orientation and location differences of the two grids which make it necessary to use one or more values from the source grid to determine an appropriate output value in the output grid.
Resampling is usually done one of 3 ways: nearest-neighbor, in which the value of the most-overlapping cell is copied; bilinear interpolation, in which the values of the 4 closest cells are averaged; and cubic convolution, using the 16 nearest cells. Nearest-neighbor is used when categorical data like land-use codes are being resampled, as it makes no sense to average them; the other methods are used for continuous data like elevation.
It's important to note that resampling always involves some loss of fidelity, either in position (pixel shifts in nearest-neighbor resampling) and/or in value (the smoothing or extrapolating effects of bilinear and cubic methods); therefore, data should be resampled as few times as possible during processing and mapping.