When imaging data is collected using airborne remote sensing systems, it is common that the image quality (IQ) of the collected data is not uniform over the entire region of collection. This non-uniformity of IQ is often a limiting factor to the utility of collected data. It would therefore be useful to have a mechanism to predict, assess and manage the non-uniformity of the IQ of remote sensing data both before and after data collection. A mechanism is proposed to model spatially and temporally varying IQ aspects of an imaging collection as a matrix across the region of collection. Within this framework an image quality metric such as a NIIRS based IQE or other IQ predictor is applied to the matrix of parameters, thus sampling IQ such that a 'map' or 'picture' of image quality is created. This allows specific knowledge of IQ performance at particular locations in an image, allowing better resource management when multiple targets with separate collection requirements are collected in the same imaging event. Application to mission planning and optimization of system resources under contingency operations, such as when a system must operate in a degraded state, are also discussed.