What does resampling modify in a raster dataset?

Resampling in a raster dataset predominantly involves changing the cell size of the raster. This process is essential when aligning raster datasets that have different resolutions or when adjusting a dataset for analytical purposes.

When resampling, algorithms will take the pixel values from the original raster and assign them to a new grid with a different cell size. This may involve averaging pixel values, taking the maximum, or using other methods to create a new representation that fits the adjusted scale. For example, if you have a high-resolution raster and need to reduce it to a coarser resolution, resampling will produce a new raster with larger pixel dimensions that encompasses information from multiple original pixels.

In contrast, modifying original pixel values directly is not typically the focus of resampling; rather, any change in pixel values is a result of this redistribution during the resampling process. Additionally, the color schema refers to how data is visually represented and is not inherently altered by the resampling process itself. The geographic coordinate system defines how the raster's spatial data is situated geographically, and while this can be affected in broader processes, resampling itself focuses specifically on the size and structure of the raster cells.