The United States RPA map had extensive ancillary data available on forest cover class from the USDA Forest Service permanent forest inventory plots. Ten eastern and 12 western forest cover types were identified in the continental United States map. This level of detail is impossible to achieve in classification of satellite data for Mexico and Central America. Thus, identification of only five forest cover classes will be attempted for TM and SPOT imagery. Mangrove Supervised training site selection is being used for images with sufficient ancillary information or aerial photography available.
Unsupervised clustering is preferable otherwise.
A maximum likelihood classification is used for both types of training sets. The two TM quarter scenes of Honduras were classified using aerial photographs and recent forest cover maps as verification data. Unsupervised classifications of the Guatemala and Costa Rica TM quarter scenes, and the Nicaragua and Panama SPOT scenes are also completed and are being verified by Professor W. Frank Miller of Mississippi State University and Dr. Steven Sader of the University of Maine, with cooperators from forestry agencies and non- governmental organizations. Four of the TM scenes selected in Mexico correspond with the random sample of TM prints used in the FAO resource assessment. Forest cover types displayed on a video monitor are located on corresponding TM imagery and training samples are selected or classifications are evaluated accordingly.