High resolution, basin extent observations and implications for understanding river form and process

Abstract

Fifty years of fl uvial studies have posited a variety of conceptual frameworks for characterizing river forms and processes throughout entire basins, including hydraulic geometry, the river continuum concept, self-organized criticality, and sediment links. This article uses basin-extent, high resolution observations of fl uvial forms in the Nueces River basin, Texas, and Yellowstone National Park to evaluate the ability of these frameworks to characterize system behavior across a multitude of scales. The Nueces data were collected with remote sensing methods and the Yellowstone data were collected through extensive fi eld surveys. The data resolution, spatial extent, and quality of these data sets allow direct comparison between the two areas.The 'hyperscale' comparison supports using of each these frameworks at specifi c scales, but also indicates an irreducible amount of variation in both datasets across many different scales that is not captured by the conceptual frameworks. Moreover, the scales and locations where one framework, such as hydraulic geometry, works well are often not the same scales and locations where another framework, such as the river continuum concept, works well.Because the conceptual frameworks appear to operate at scales and locations distinct from one another, the measurement approaches necessary to observe them must also be at different scales and locations. For example, 'seeing' self-organized criticality in a river system is diffi cult without an extensive survey through space, whereas the recognition of sediment links requires quite intense sampling in specifi c river regions. We suggest that these separations between measurement scales represent an incommensurability issue in river studies, making it very diffi cult to both communicate among and test between two or more competing theories. Making simultaneous hyperscale observations of the river is one approach to minimizing the theory-ladeness of observation, as deviations from different predictions can be plotted at every scale.

Using Unmanned Aerial Vehicles (UAV) for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments

“…Areas characterized by "smooth" surfaces (snowy, sandy, or rocky areas) may be failure-prone because of possible difficulties by the matching algorithms to extract corresponding features over uniform surfaces [19,27]. It is the case for geomorphic studies applying proximity sensing for coastal geomorphology studies.…”

“…By taking the GNSS data as reference for this area, the mentioned issues seem to be related to the TLS survey. Examples of quantitative applications of the SfM methodology to UAV images in literature are limited and a quality assessment is still a requirement for different geomorphic environments [24,27,31]. This may be accomplished through comparative studies between SfM-derived topographic datasets and datasets from other high-resolution methods, or from the less expensive GNSS survey of natural points or artificial targets evenly distributed on the area of interest.…”

“…Mathews and Jensen [23] and d'Oleire-Oltmanns et al [24] addressed issues related to vineyard and soil erosion, whilst Wallace et al [25] and Hunt et al [26] used UAV data for studies of forestry and agriculture. Fonstad and Marcus [27], Houser et al [7], and Turner et al [28] report applications to topography whereas Harwin and Lucieer [29] assess the quality of results on coastal environments. A complete comparative study on performances, achieved by traditional photogrammetry and image-based approaches, is provided by Rosnell and Honkavaara [30].…”

Tributary connectivity, confluence aggradation and network biodiversity

“…Fonstad and Marcus, 2010). Early progress toward automated estimation of bed material grain size at bar-scales (Chandler et al, 2004;Verdu et al, 2005) and along entire rivers (Carbonneau et al, 2004(Carbonneau et al, , 2005 has been extended by using imagery to accomplish the necessary grain-size calibration (Dugdale et al, 2010), by avoiding the need for calibration at all (Buscombe and Masselink, 2009;Buscombe et al, 2010) and by using hyperspatial data (<100mm resolution) to map sub-pixel grain sizes (Black et al, 2014).…”

Vegetation in Drylands: Effects on Wind Flow and Aeolian Sediment Transport

“…In turn, 'structure-from-motion' (SfM) photogrammetry can easily convert drone images of an element or landscape into a georeferenced digital elevation model (DEM) [182][183][184]. This technique can produce centimetre-scale horizontal and vertical precision that is comparable with airborne LiDAR [185] and terrestrial laser scanning [184], allowing landscape change before and after geomorphic events to be accurately quantified.…”