Abstract
In this paper, we present a new, semi-automated methodology for mapping hydromorphological indicators of rivers at a regional scale using multisource remote sensing (RS) data. This novel approach is based on the integration of spectral and topographic information within a multilevel, geographic, object-based image analysis (GEOBIA). Different segmentation levels were generated based on the two sources of Remote Sensing (RS) data, namely very-high spatial resolution, near-infrared imagery (VHR) and high-resolution LiDAR topography. At each level, different input object features were tested with Machine Learning classifiers for mapping riverscape units and in-stream mesohabitats. The GEOBIA approach proved to be a powerful tool for analyzing the river system at different levels of detail and for coupling spectral and topographic datasets, allowing for the delineation of the natural fluvial corridor with its primary riverscape units (e.g., water channel, unvegetated sediment bars, riparian densely-vegetated units, etc.) and in-stream mesohabitats with a high level of accuracy, respectively of K = 0.91 and K = 0.83. This method is flexible and can be adapted to different sources of data, with the potential to be implemented at regional scales in the future. The analyzed dataset, composed of VHR imagery and LiDAR data, is nowadays increasingly available at larger scales, notably through European Member States. At the same time, this methodology provides a tool for monitoring and characterizing the hydromorphological status of river systems continuously along the entire channel network and coherently through time, opening novel and significant perspectives to river science and management, notably for planning and targeting actions.
Highlights
The characterization of riverscape units requires monitoring water channels, in-channel morphological habitats, and riparian corridor structures and their interaction with the surrounding floodplain
Features, respectively groups 2 and 3 for a total of 6 features), the Kappa accuracy is significantly lower (0.59 for SVM and 0.60 for RF). This is an expected result since spectral information is required for distinguishing Sparsely-Vegetated units from Unvegetated Sediment bars and Water Channel, which could be characterized by similar topographic characteristics but surely different spectral values
The multilevel GEOBIA framework presented in this paper proves to be a new powerful tool for semi-automated classification of essential geomorphic features, in particular for the characterization of the natural fluvial corridor, the delineation of the active river channel, and for in-stream mesohabitat mapping
Summary
The characterization of riverscape units requires monitoring water channels, in-channel morphological habitats, and riparian corridor structures and their interaction with the surrounding floodplain. These surveys require resource-demanding field campaigns supported, where available, by manual interpretation of aerial imagery [1,2]. Field surveys are often based on categorical information and expert-based opinions that may be biased by operators’ subjectivity and inconsistency These issues limit de-facto their operative implementation to a limited number of rivers (rarely extended to the entire river network scale), and may call into question their suitability for monitoring purposes, which demand an objective and repeatable assessment method. This is especially the case for modern river management in Europe, where the Water Framework Directive (WFD) [4] acknowledges the major importance of fluvial geomorphology, requiring Member States to evaluate and monitor these types of forms
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