Stream classifications and indicators that are capable of summarizing the complex information of ecological communities provide useful tools in river ecosystem management. However, these tools have not yet achieved practical application here in Japan due to the fact that in many studies, the criteria for selecting stream classifications and indicators are neither objective nor quantitative. Combining model-based clustering and indicator values (IndVal) makes it possible to assess the appropriateness of stream classifications based on the indexability from multiple classifications. Additionally, by establishing threshold values, indicators can be objectively selected. Using these techniques, we examined appropriate data acquisition methods for determining a stream classification system for vegetation at the catchment scale. Additionally, we studied the suitability of methods that were derived solely from either biological or environmental data as well as those that combined the two. We prepared three sets of data that utilized different research zone and scope-setting methods and then created three types of stream classifications from each, ultimately evaluating the appropriateness by the quality of selected indicators and the level of indexability. The most appropriate methods involved those derived from biological and environmental data exhibiting consistently high indexability as well as those that created vegetation maps in certain areas of the distribution over the entire catchment area. Furthermore, the quality of the selected indicators was consistent with the indexability results.