Abstract
Recent developments in the fields of geographical object-based image analysis (GEOBIA) and ensemble learning (EL) have led the way to the development of automated processing frameworks suitable to tackle large-scale problems. Mapping riverscape units has been recognized in fluvial remote sensing as an important concern for understanding the macrodynamics of a river system and, if applied at large scales, it can be a powerful tool for monitoring purposes. In this study, the potentiality of GEOBIA and EL algorithms were tested for the mapping of key riverscape units along the main European river network. The Copernicus VHR Image Mosaic and the EU Digital Elevation Model (EU-DEM)—both made available through the Copernicus Land Monitoring Service—were integrated within a hierarchical object-based architecture. In a first step, the most well-known EL techniques (bagging, boosting and voting) were tested for the automatic classification of water, sediment bars, riparian vegetation and other floodplain units. Random forest was found to be the best-to-use classifier, and therefore was used in a second phase to classify the entire object-based river network. Finally, an independent validation was performed taking into consideration the polygon area within the accuracy assessment, hence improving the efficiency of the classification accuracy of the GEOBIA-derived map, both globally and by geographical zone. As a result, we automatically processed almost 2 million square kilometers at a spatial resolution of 2.5 meters, producing a riverscape-units map with a global overall accuracy of 0.915, and with per-class F1 accuracies in the range 0.79–0.97. The obtained results may allow for future studies aimed at quantitative, objective and continuous monitoring of river evolutions and fluvial geomorphological processes at the scale of Europe.
Highlights
Hydromorphological pressures affect 40% of European water bodies, hampering the achievement of their good ecological status [1]
When comparing the Overall Accuracy (OA) of the various ensemble learning (EL) results, we found that random forest (RF) is significantly outperforming other algorithms, highlighting the potentiality of the bagging technique against boosting and voting in handling such a challenging big-data classification problem
The analysis presented here could be replicated as well at the level of individual river basin districts, arguably providing a cost-effective way to monitor the evolution of river landscapes and to analyze catchment scale effects of human impacts, enormously magnifying the capacity of data gauging compared to traditional field surveys and visual image interpretation
Summary
Hydromorphological pressures affect 40% of European water bodies, hampering the achievement of their good ecological status [1]. An emerging discipline joining the river science and remote sensing (RS) [5], offers new possibilities for monitoring river processes at high spatial and temporal resolution, thanks to the exploitation of objective, repeatable and continuous information alongside the entire river network [6]. An essential aspect in river science—both for fluvial processes assessment and for river management purposes—is the delineation of the main riverscape units that characterize the fluvial landscape in the vicinity and within the so-called active river channel [7]. Mapping riverscape units is important for the understanding of the macrodynamics of a river system and if repeated through time it can be a powerful tool for different purposes, such as the assessment of evolutionary trajectories of river reaches [11], stream fragmentation [12] or human pressures [13,14], just to mention a few
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