ABSTRACT
When considering spatial or any other type of resolution, it is imperative to consider the purpose of the application; that is, the characteristics of the object one is trying to detect strongly dictate the appropriateness of a given resolution specification. For instance, an analyst looking for large marijuana fields might find 30-m Landsat data to be of sufficient spatial resolution. However, an analyst or law enforcement officer looking for local isolated patches of marijuana embedded within small forest clearings might miss these small patches using 30-m resolution data. Researchers new to remote sensing might immediately assume that one should always use the finest spatial resolution data available for any given project. However, as spatial resolution increases, so too does the size of the data set and therefore the computation power required and costs associated with acquisition and processing. Furthermore, a tradeoff always exists with coverage on the ground and spatial resolution. This is due in large part to data transfer rates.
