Channel planform geometry and slopes from freely available high-spatial resolution imagery and DEM fusion: Implications for channel width scalings, erosion proxies, and fluvial signatures in tectonically active landscapes

Abstract

The rapid expansion of high-spatial resolution optical sensors and imagery over the last decade presents exceptional opportunities for quantifying visible attributes of geomorphic systems. In this study, we detail a simple, robust methodology (ChanGeom) to extract continuous channel width and centerline datasets for single-thread channels using freely available high-spatial resolution imagery currently available in Google Earth and Bing Maps. Comparisons with a global dataset of field and lidar-derived channel widths indicate minimal errors associated with the imagery and ChanGeom methodology (<1% overall), while examples from the Goriganga River (Indian Himalaya) and the Yakima River (WA, USA) emphasize the benefit of empirical width values over established channel width scalings in deciphering fluvial responses to complex landscape forcings in tectonically active regions. Additionally, accurate centerline delineation from the ChanGeom methodology provides improved sinuosity measurements, and when fused with coarse resolution digital elevation models (DEMs), removes along-profile shortening and coincident increases in reach-scale channel slope. Lastly, comparisons of ASTER GDEM V2, SRTM V4.1, and lidar channel profiles extracted in moderate to high-relief regions demonstrate the inferiority of the ASTER GDEM for channel slope calculations, despite the apparent spatial resolution advantages (9×). The methodology presented here will facilitate new discoveries in the fluvial environment that have historically been difficult due to access and imagery resolution issues, and provide greater perspective on channel signatures and responses to a host of landscape forcings, especially in tectonically active bedrock and lower order drainage systems.