From the comments this question seems less about "resolving an object" than identifying individual objects from imagery. Here's a general approach.
Get a small sample of high-resolution imagery with which your identification algorithm works really well.
Next, reduce its resolution by subsampling. Your identification algorithm will probably work less well. Try different subsampling sizes to create different simulated source resolutions. Compute the accuracy of your algorithm at these resolutions. Keep track of false positives and false negatives or any other error measure appropriate to your scientific need. This subsampling is probably a best-case scenario, since lower resolution data might come from higher up and have more distortions than a purely subsampled high resolution image of the same area. But we're talking ball-parks here.
Now you have a rough measure of how good a particular resolution is at applying your algorithm, and can then decide whether data at a given resolution is good enough for you.
You can use this is a plan for further data collection - such as deciding whether to fly a balloon high and cover more area at a lower resolution or fly lower and cover less area but at a higher resolution. These efficiency trade-offs are your choice.