Deriving Planform Morphology and Vegetation Coverage From Remote Sensing to Support River Management Applications

Abstract

With the increasing availability of big geospatial data (e.g., multi-spectral satellite imagery) and access to platforms that support multi-temporal analyses (e.g., cloud-based computing, Geographical Information Systems, GIS), the use of remotely sensed information for monitoring riverine hydro-morpho-biodynamics is growing. Opportunities to map, quantify and detect changes in the wider riverscape (i.e., water, sediment and vegetation) at an unprecedented spatiotemporal resolution can support flood risk and river management applications. Focusing on a reach of the Po River (Italy), satellite imagery from Landsat 5, 7, and 8 for the period 1988–2018 were analyzed in Google Earth Engine (GEE) to investigate changes in river planform morphology and vegetation dynamics associated with transient hydrology. An improved understanding of these correlations can help in managing sediment transport and riparian vegetation to reduce flood risk, where biogeomorphic processes are commonly overlooked in flood risk mapping. In the study, two established indices were analyzed: the Modified Normalized Difference Water Index (MNDWI) for monitoring changes in the wetted river planform morphology, inferring information about sediment dynamics, and the Normalized Difference Vegetation Index (NDVI) for evaluating changes in vegetation coverage. Results suggest that planform changes are highly localized with most parts of the reach remaining stable. Using the wetted channel occurrence as a measure of planform stability, almost two-thirds of the wetted channel extent (total area = 86.4 km2) had an occurrence frequency >90% (indicating stability). A loss of planform complexity coincided with the position of former secondary channels, or zones where the active river channel had narrowed. Time series analysis of vegetation dynamics showed that NDVI maxima were recorded in May/June and coincided with the first peak in the hydrological regime (occurring in late spring and associated with snowmelt). Seasonal variation in vegetation coverage is potentially important for local hydrodynamics, influencing flood risk. We suggest that remotely sensed information can provide river scientists with new insights to support the management of highly anthropized watercourses.

Assessing the potential of Sentinel-2 data for tracking invasive water hyacinth in a river branch

“…Current RS technology provides opportunities to map and quantify the wider riverscape (i.e., water, sediment, and vegetation) at an unprecedented spatiotemporal resolution that can support several applications, such as fluvial geomorphology, riparian vegetation, and flood risk management. 68,69 Unlike the broadband multispectral data, improved spectral and spatial resolution sensors, such as the recently launched 10-m spatial resolution S2 multispectral instrument (MSI), have significantly enhanced research capabilities and the understanding of the spatial distribution of water hyacinth, especially in small freshwater bodies, which scale of interest has been so far beyond the reach of the typical larger resolution of the broadband multispectral sensors. The S2 MSI sensor is thus contributing greatly to detecting, mapping, and monitoring water hyacinth infestation and coverage at a river scale.…”

Shear layer over floodplain vegetation with a view on bending and streamlining effects

“…Vegetation introduces additional flow resistance, influencing water-surface elevations and channel conveyance capacity [21,22]. By altering the mean and turbulent flow structure, vegetation influences the transport of sediments and substances [23][24][25][26]. The key dynamic feature of vegetated flows is the additional drag force exerted by vegetation (Fig.…”

Analysis of Meandering River Morphodynamics Using Satellite Remote Sensing Data—An Application in the Lower Deduru Oya (River), Sri Lanka

“…One important aspect of the Landsat data is the availability of multispectral bands, and these bands can be combined to calculate indices that represent the relative abundances of different features, such as water, vegetation, and built-up areas [29]. Due to this capability, Landsat images are commonly used when calculating land use/land cover and vegetation properties, when identifying water bodies, and for flood mapping.…”