Remote sensing (RS) technology offers unparalleled opportunities to explore river systems using RADAR, multispectral, hyper spectral, and LiDAR data. The accuracy reached by these technologies recently has started to satisfy the spatial and spectral resolutions required to properly analyse the hydromorphological character of river systems at multiple scales. Using the River Hierarchical Framework (RHF) as a reference we describe the state-of-the-art RS technologies that can be implemented to quantify hydromorphological characteristics at each of the spatial scales incorporated in the RHF (i.e. catchment, landscape unit, river segment, river reach, sub-reach—geomorphic and hydraulic units). We also report the results of a survey on RS data availability in EU member states that shows the current potential to derive RHF hydromorphological indicators from high-resolution multispectral images and topographic LiDAR at the national scale across Europe. This paper shows that many of the assessment indicators proposed by the RHF can be derived by different RS sources and existing methodologies, and that EU countries have sufficient RS data at present to already begin their incorporation into hydromorphological assessment and monitoring, as mandated by WFD. With cooperation and planning, RS data can form a fundamental component of hydromorphological assessment and monitoring in the future to help support the effective and sustainable management of rivers, and this would be done most effectively through the establishment of multi-purpose RS acquisition campaigns and the development of shared and standardized hydromorphological RS databases updated regularly through planned resurveyed campaigns.

The use of hyperspectral (HS) and LiDAR acquisitions has a great potential to enhance mapping and monitoring practices of endangered grasslands habitats, beyond conventional botanical field surveys. In this study we assess the potentiality of recursive feature elimination (RFE) in combination with random forest (RF) classification in extracting the main HS and LiDAR features needed to map selected Natura 2000 grasslands along Polish lowland river valleys, in particular alluvial meadows 6440, lowland hay meadows 6510, and xeric and calcareous grasslands 6120. We developed an automated RFE-RF system capable to combine the potentials of both techniques and applied it to multiple acquisitions. Several LiDAR-based products and different spectral indices (SI) were computed and used as input in the system, with the aim of shedding light on the best-to-use features. Results showed a remarkable increase in classification accuracy when LiDAR and SI products are added to the HS dataset, strengthening in particular the importance of employing LiDAR in combination with HS. Using only the 24 optimal features selection generalized over the three study areas, strongly linked to the highly heterogeneous characteristics of the habitats and landscapes investigated, it was possible to achieve rather high classification results (K around 0.7–0.77 and habitats F1 accuracy around 0.8–0.85), indicating that the selected Natura 2000 meadows and dry grasslands habitats can be automatically mapped by airborne HS and LiDAR data. Similar approaches might be considered for future monitoring activities in the context of habitats protection and conservation.

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.

Recent developments in remote sensing (RS) technologies lead the way in characterizing river morphology at regional scales and inferring potential channel responses to human pressures. In this paper, a unique regional database of continuous hydromorphological variables (HyMo DB) based on areal and topographic data has been generated from RS analysis. Key riverscape units with specific geomorphic meaning have been automatically mapped for 1700 km² of river floodplains from simultaneous very‐high‐resolution (VHR) near‐infrared aerial imagery and low‐resolution LiDAR‐derived products. A multi‐level, geographical object‐based architecture (GEOBIA) was employed to integrate both spectral and topographic information and generate a regional classifier able to automatically map heterogeneous fluvial patterns in different geographical and topographical contexts of the Piedmont Region (Italy).This HyMo‐generated DB offers a unique set of tools for hydromorphologists and can be exploited for different purposes. For the first time, topographic information can be exploited regionally per riverscape unit class, allowing for quantitative analysis of their regional spatial and statistical variability. In this manner, river types can be automatically characterized and classified using objective and repeatable hydromorphological variables. We discuss the potential of quantifying functional links between riverscape units and their driving processes, a valuable source of information to start assessing and highlighting the entity of potential channel adjustments at the regional scale to human pressures. The HyMo DB can also be integrated with historical, field‐based information to better comprehend current fluvial changes at a local scale. In view of future RS acquisitions, the present approach will result in a suitable procedure for quantitative, objective and continuous monitoring of river evolutions over large scales. This type of hydromorphological characterization will allow regional trends and patterns to be highlighted through time and river management strategies to thus be implemented at both regional and local scales. Copyright © 2016 John Wiley & Sons, Ltd.