The watershed includes 37 physiographic provinces, and just as many different farming practices and land use management strategies. Choosing and implementing a land cover mapping strategy for an area of this size and diversity is a daunting task. The mapping challenge Most macroscale land cover maps produced from mosaiced Landsat scenes use an unsupervised or supervised classification methodology. Unsupervised classification requires significant manual interpretation and is therefore impractical for applications that aim for near real-time updating capabilities. Nor are they sufficiently flexible to tailor individual applications.
Supervised classification requires a-priori knowledge of the landscape.
This too can be difficult, as most examples of Landsat based classifications depend on either ancillary data for training and validation or in-situ field validation. In the case of the Chesapeake Bay, there are few spatially consistent data sets available for comparison. Of the few data sets that exist, most are either coarse in spatial resolution, out of date, or are error prone. Obtaining field data conincident with Landsat image acquisitions can also be a challenge. Interannual variation in vegetation, urban development, and row crop rotations all vary at less than a 5-year time interval.