“FluvialCorridor”: A new ArcGIS toolbox package for multiscale riverscape exploration

Roux, C.; Alber, Adrien; Bertrand, Mélanie; Vaudor, Lise; Piégay, Hervé

doi:10.1016/j.geomorph.2014.04.018

Cited by 146 publications

(109 citation statements)

References 26 publications

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“…The authors used the DTM to calculate the Slope (6, Figure 4) using the Zevenbergen-Thorne method [41]. The "Fluvial corridor" toolbox described in Roux et al [42] was adopted by this study for the delineation of the Valley Bottom (8, Figure 4), defined as the modern alluvial floodplain by Alber and Piégay [9]. It is a crucial fluvial unit in the bio-geomorphic characterization of stream networks [43] because it represents the deposition zone of alluvium, including both riverbed and floodplain areas.…”

Section: Remote Sensing and Gis Input Data Preparationmentioning

confidence: 99%

Hierarchical Object-Based Mapping of Riverscape Units and in-Stream Mesohabitats Using LiDAR and VHR Imagery

2016

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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.

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Section: Remote Sensing and Gis Input Data Preparationmentioning

confidence: 99%

Hierarchical Object-Based Mapping of Riverscape Units and in-Stream Mesohabitats Using LiDAR and VHR Imagery

2016

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“…Because of the typical linear and narrow shape of the riparian zones, fieldbased monitoring involves sampling, high labor costs, and time-consuming travels (Debruxelles et al 2009;Myers 1989). The continuous improvement of the spatial resolution of remote sensing data combined with more powerful computer capacity and new geomatic procedures to extract information make the remote sensing approach more competitive (Alber and Piégay 2011;Carbonneau and Piégay 2012;Johansen et al 2010;Michez et al 2013;Roux et al 2014). The use of this very-high-resolution (VHR) imagery in a multitemporal approach is an emerging topic (Ardila et al 2012;Champion 2012;Lasaponara et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system

Michez

Piégay

Lisein

et al. 2016

Environ Monit Assess

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Riparian forests are critically endangered many anthropogenic pressures and natural hazards. The importance of riparian zones has been acknowledged by European Directives, involving multi-scale monitoring. The use of this very-high-resolution and hyperspatial imagery in a multi-temporal approach is an emerging topic. The trend is reinforced by the recent and rapid growth of the use of the unmanned aerial system (UAS), which has prompted the development of innovative methodology. Our study proposes a methodological framework to explore how a set of multi-temporal images acquired during a vegetative period can differentiate some of the deciduous riparian forest species and their health conditions. More specifically, the developed approach intends to identify, through a process of variable selection, which variables derived from UAS imagery and which scale of image analysis are the most relevant to our objectives.The methodological framework is applied to two study sites to describe the riparian forest through two fundamental characteristics: the species composition and the health condition. These characteristics were selected not only because of their use as proxies for the riparian zone ecological integrity but also because of their use for river management.The comparison of various scales of image analysis identified the smallest object-based image analysis (OBIA) objects (ca. 1 m 2 ) as the most relevant scale. Variables derived from spectral information (bands ratios) were identified as the most appropriate, followed by variables related to the vertical structure of the forest. Classification results show good overall accuracies for the species composition of the riparian forest (five classes, 79.5 and 84.1 % for site 1 and site 2). The classification scenario regarding the health condition of the black alders of the site 1 performed the best (90.6 %).The quality of the classification models developed with a UAS-based, cost-effective, and semi-automatic approach competes successfully with those developed using more expensive imagery, such as multi-spectral and hyperspectral airborne imagery. The high overall accuracy results obtained by the classification of the diseased alders open the door to applications dedicated to monitoring of the health conditions of riparian forest. Our methodological framework will allow UAS users to manage large imagery metric datasets derived from those dense time series.

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“…With the emerging study of 'digital' rivers, with large scale continuous datasets and increasingly less reliance on field-based analyses of point data, stream power calculations are thus becoming ever more achievable and cost-effective in the assimilation of basin-wide databases to inform river management. Integrated tools are now available, such as the ArcGIS toolboxes FluvialCorridor [201] and RESonate [202] and the IDRAIM framework for fluvial characterisation [203,204], and they will continue to be developed and refined to reflect emerging technologies, the growth of data sources and widening applications. Although costly, limited in coverage and associated with operational issues, field-based methods are still invaluable in providing complementary and supplementary data [141,205], notably for calibrating remotely sensed and image-processed datasets.…”

Section: Discussionmentioning

confidence: 99%

Quantifying River Channel Stability at the Basin Scale

Soar

Wallerstein

Thorne

2017

Water

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This paper examines the feasibility of a basin-scale scheme for characterising and quantifying river reaches in terms of their geomorphological stability status and potential for morphological adjustment based on auditing stream energy. A River Energy Audit Scheme (REAS) is explored, which involves integrating stream power with flow duration to investigate the downstream distribution of Annual Geomorphic Energy (AGE). This measure represents the average annual energy available with which to perform geomorphological work in reshaping the channel boundary. Changes in AGE between successive reaches might indicate whether adjustments are likely to be led by erosion or deposition at the channel perimeter. A case study of the River Kent in Cumbria, UK, demonstrates that basin-wide application is achievable without excessive field work and data processing. However, in addressing the basin scale, the research found that this is inevitably at the cost of a number of assumptions and limitations, which are discussed herein. Technological advances in remotely sensed data capture, developments in image processing and emerging GIS tools provide the near-term prospect of fully quantifying river channel stability at the basin scale, although as yet not fully realized. Potential applications of this type of approach include system-wide assessment of river channel stability and sensitivity to land-use or climate change, and informing strategic planning for river channel and flood risk management.

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“FluvialCorridor”: A new ArcGIS toolbox package for multiscale riverscape exploration

Cited by 146 publications

References 26 publications

Hierarchical Object-Based Mapping of Riverscape Units and in-Stream Mesohabitats Using LiDAR and VHR Imagery

Hierarchical Object-Based Mapping of Riverscape Units and in-Stream Mesohabitats Using LiDAR and VHR Imagery

Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system

Quantifying River Channel Stability at the Basin Scale

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