The goal of this study was to determine the value of including landscape ecology patterns and structure metrics extracted from high-resolution, remotely-sensed imagery in the development of a Shoreline Environmental Impact Index (SEII). Methods of combining landscape ecology metrics to create a meaningful Shoreline Environmental Impact Index included multiple linear regression, multiple discriminant analysis, genetic neural networks, and feed-forward, backpropagation neural networks. The landscape ratings produced by the SEII’s generated using these methods were then compared to landscape ratings by experts. There was very little difference in the performance of several SEII’s generated despite differences in metrics and their weighting chosen by the different methods. The ratings from all methods showed their ability to reflect the expert ratings with moderate accuracy: � 84 percent agreement. Conclusions indicate that the contributions of landscape metrics to the ability of an SEII to discriminate between levels of shoreline degradation are variable, dependent upon the method of combination. Any of the current forms of the SEII is suitable for generating general indication of shoreline health.