Converting 0.1 ha pixels in a raster classified image into polygons with a 100 ha minimum mapping unit, as required by national Gap Analysis standards, is not a trivial task. Following polygonization, coverages were generalized to the 100 ha MMU level by a customized program written in a combination of ARC Macro Language and C to subsume the smaller polygons into the larger polygons iteratively with increasing minimum size thresholds. The elimination of the targeted polygons into larger adjacent polygons was done by weighting for all possible combinations of adjacent cover types so that aggregation made ecological sense. The cartographer has to decide whether to simply label the derived polygon with the most abundant vegetation class or to preserve the composition of all the major types within it. California Example Satellite imagery can also be interpreted using the same principles developed for aerial photo interpretation.
Humans have some advantages over computers.
We detect spatial patterns, while computers typically operate one pixel at a time. As with computer-assisted classification, it may be useful to segment the state into ecological regions prior to interpretation. Two approaches to creating digital coverages of these boundaries have been used in Gap Analysis, depending on the facilities available. The latter approach has the advantages that mapping can be continuous rather than by sections the size of the photographic prints, and especially that other data layers can be displayed simultaneously to guide interpretation. Also, the image contrast can be enhanced by the analyst to bring out features of interest, whereas you are limited to a single representation with photographic prints.