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Classifying fine-scale spatial structure of riparian forests using hyperspectral high-resolution remotely sensed imagery at the Cedar River municipal watershed in western Washington, USA

Logging in the Pacific Northwest has often removed conifer trees from riparian forests, which are then colonized and dominated by red alder (Alnus rubra) and deciduous shrubs. As a result, deciduous-dominated sites are over-represented in streamside areas compared to historic conditions, causing degradation of stream habitat. A popular restoration strategy in the region is the reintroduction of conifer trees into riparian areas. An accurate characterization of streamside forest conditions at large spatial scales is required to identify disturbed areas. Traditional remote sensing imagery has not been capable of capturing the fine-scale spatial heterogeneity within riparian zones. We analyzed a MODIS–ASTER (MASTER) image collected at the Cedar River municipal watershed in western Washington to determine if this high-resolution (5 m) hyperspectral imagery (50 bands) would be better able to characterize the high level of heterogeneity common in this environment. A supervised classification procedure using a maximum likelihood algorithm was conducted delineating nine of the dominant land cover types in the study. Subsequent field observations and validation procedures indicated that the classification had an overall accuracy of roughly 80%. The results show that this technique provided a cost-effective means of mapping the fine-scale spatial heterogeneity typical of streamside forest stands in the region.