Abstract
Stream classification at catchment scale is important in river ecosystem management; in recent years, it has often been used in conservation planning. Stream classification at catchment scale is often based on biological data obtained at discrete survey locations. Obtaining continuous biological data as vegetation maps on rivers is labor intensive. In this study, we divided the river into many segments of equal length (500 m) and recorded vegetation types that appeared in each segment through fieldwork. Obtained data that recorded the presence and absence of vegetation type for each segments were ordinated into two-dimensional space using a non-metric multidimensional scaling method, and segments were classified through non-hierarchical cluster analysis using the k-means method based on the scores obtained by the non-metric multidimensional scaling. Using index indicators (IndVal), we obtained indicator vegetation types specific to each cluster. We discussed the characteristics of each cluster on the basis of the river topography and composition of vegetation types. Clusters obtained using the present method represent a good classification that reflects characteristics of river segments. The labor effective stream classification at catchment scale become possible by this method and it is a useful tool in river ecosystem management. Our approach should be applied and evaluated in other river systems with various topography, surrounding land uses, and climates regime.
