Precision vs Resolution in elevation data
I think I can answer it for you.
If you look at the precision vs. accuracy image on the link you provided, precision refers to the repeatability of the observation. For example, if I measure the distance from one point to another and it is always vaying only by a very small amount, then I am making measurements at a high precision.
But, basically, resolution and precision are not the same. You can have a high-resolution image or elevation model that is not precise, just like you can have a high-precision elevation model that is not high resolution.
TanDEM-X in particular is aiming to be a 12 m resolution global DEM with a precision of 2 m in relative and 10 m in absolute (from Wikipedia). This implies that this elevation will be high-precision, low(ish)-accuracy and high-resolution (for a global DEM).
EDIT: just a note, the type of data used to represent the data, i.e., int, float, has nothing to do with precision. You should not kid yourself that holding lots of numbers after the decimal point means that an observation is precise!
Resolution in raster/grid context is the "cellssize", or the width/height in a certain unit (meter, feet etc) of each cell/pixel in the grid.
I have seen the term precision used in two ways with grids:
- Most of the time, the same as you referred to, the accuracy of the measurement
- Datatype being used for cell/band values, e.g float, double, integers
A DTM is an approximation from which it is possible to infer meaning about the world. Publishers of height data tend to give measurements of how closely a model fits the real world. But that does not tell you how well it will infer meaning for a particular question.
For example, if you want to calculate gradient for a very flat area of land a sparse model will be sufficient. The same model will be much less useful in terrain with very close canyons and steep cliff edges.
It is possible to use sparse data to model the world and correctly infer meaning if the data is right for that particular purpose. It is also possible to yield bad results from accurate and precise data.
Low resolution data can give accurate predictions about a specific phenomena if it is suitable for that particular use.