# A channel shifting GIS toolbox for exploring floodplain dynamics through channel erosion and deposition

Miloš Rusnák, Šimon Opravil, Samuel Dunesme, Hamid Afzali, Louis Rey, Hervé Parmentier, Hervé Piégay

► To cite this version:

Miloš Rusnák, Šimon Opravil, Samuel Dunesme, Hamid Afzali, Louis Rey, et al. A channel shifting GIS toolbox for exploring floodplain dynamics through channel erosion and deposition. Geomorphology, 2025, 477, pp.109688. 10.1016/j geomorph.2025.109688. hal-05380420

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# A channel shifting GIS toolbox for exploring floodplain dynamics through channel erosion and deposition

Miloš Rusnák<sup>a,*</sup>, Šimon Opravil<sup>a</sup>, Samuel Dunesme<sup>b</sup>, Hamid Afzali<sup>a</sup>, Louis Rey<sup>b</sup>, Hervé Parmentier<sup>b</sup>, Hervé Piégay<sup>b</sup>

## ARTICLE INFO

**Keywords:**

Toolbox
River migration
Erosion
Deposition
Floodplain
River styles

## ABSTRACT

Assessing spatial patterns and the long-term evolution of river channel planforms is needed for understanding river behaviour and change. GIS tools exist to explore channel planform characters in space and time, but none yet exist to characterise channel shifting and associated floodplain dynamics. The paper presents the applications of a new toolbox that can work as a standalone (e.g., the ArcGIS Standalone channel shifting toolbox - SCS Toolbox) or linked to existing toolboxes such as the fluvial corridor toolbox (FCT). It complemented a wide palette of tools and approaches that are used for understanding and conceptualising river-style and fluvial continuum. Vectorised channel polygons are used to detect the basic morphometric processes between 2 consecutive observation dates as channel erosion, deposition and lateral movement orientation. The toolbox application supports the evaluation of spatial dynamics and lateral erosion by identifying channel migration and reach scale mobility. It also includes floodplain statistics based on combined LiDAR information, where height above the channel and vegetation canopy height are generated. The toolbox was tested on five river systems, some being sinuous (Ain and Topfa Rivers), meandering (Ondava River) or braided-wandering (Belá River). These examples illustrate how spatio-temporal analyses of river channel shifting are used for process-based understanding of channel evolution, in-channel dynamics, lateral movement and floodplain-channel interactions.

## 1. Introduction

Channel planform is defined as the spatial organisation of a river on the valley floor and represents a specific manifestation of processes that are shaped and organised by the river channel. Understanding this spatial organisation, evolution, and its long-term analyses is generally the first step in fluvial geomorphological diagnosis, which is key to assessing morphological behaviour or in-channel processes, and interpreting potential drivers of change. Geomorphological modification of channel planforms (shifting, narrowing, widening) is reflected in the floodplain erosion and accretion processes (Boothroyd et al., 2021; Kiss et al., 2008; Micheli and Larsen, 2011; Piút, 2002).

Channel migration, defined as lateral movement of the channel, has been documented over a range of meandering, braiding, anabranching or wandering channel patterns (Rinaldi et al., 2016). Shifting channels develop bends in floodplain reaches with erodible materials. Flow energy concentrates along the channel banks, erodes the floodplain, picks

up the sediment and drops it when the water slows downstream. River bank erosion is seen as part of long-term channel change, meander migration, floodplain development, and destruction (Couper, 2004).

River systems are heavily affected by anthropogenic pressures (Liébault and Piégay, 2002) such as gravel extraction (Marston et al., 2003; Rinaldi et al., 2005; Surian and Rinaldi, 2003), channelisation (Hajdukiewicz et al., 2017; Korpak, 2007; Scorpio et al., 2018), dam construction (Surian, 1999) or land cover changes (Jefferson and McGee, 2013; Liébault et al., 2005; Price and Leigh, 2006), which induced channel adjustments such as incision, narrowing or widening, but also complex change (planform transformation) in channel shifting intensity and pattern. In the past years, bank stabilisation and erosion mitigation were systematically applied.

Nowadays, the dynamics of the lateral migrating rivers play a key function in preserving and restoring the riverine ecosystem and bank erosion is usually joined with accretion processes that create new natural habitats. Active migrating rivers are interesting for their high

<footnote><sup>a</sup> Department of Physical Geography, Geomorphology and Natural Hazards, Institute of Geography, SAS, Bratislava, Slovakia</footnote>

<footnote><sup>b</sup> ENS Lyon, UMR 5600 Environnement Ville Société, CNRS, Lyon, France</footnote>

<footnote>* Corresponding author.
E-mail address: geogmilo@savba.sk (M. Rusnák).</footnote>
<table><caption>**Table 1**
List of the GIS toolboxes and spatial tools presently used for river diagnosis and fluvial features extraction and characterisation.</caption><thead><tr><th>Toolbox</th><th>Description</th><th>Type</th><th>References</th><th>Link</th></tr></thead><tbody><tr><td>BasinMaker</td><td>BasinMaker v3.0 is a set of Python-based GIS tools that support vector-based watershed delineation, rediscretization, simplification, and revision for complex watershed systems that may include thousands of stream and lakes</td><td>python package</td><td>Han et al. (2023)</td><td>https://hydrology.uwaterloo.ca/basinmaker/index.html</td></tr><tr><td>BRAT</td><td>the Beaver Restoration Assessment Tool (BRAT) is a planning tool to assess the potential for beaver as a stream conservation and restoration agent over large regions and watersheds</td><td>ArcGIS toolbox</td><td>Macfarlane et al. (2017)</td><td>https://brat.riverscapes.net/</td></tr><tr><td>Diachronix</td><td>analyses planimetric evolution over time, or its longitudinal pattern of fluvial polygon within regular units</td><td>QGIS Python Plugins Repository</td><td></td><td>https://gitlab.huma-num.fr/ppassy/diachronix</td></tr><tr><td>Fluvial Corridor Toolbox</td><td>the Fluvial Corridor Toolbox is a set of algorithms intended to describe and measure fluvial features and processes</td><td>GIS plugins (ArcGIS, QGIS)</td><td>Roux et al. (2015)</td><td>https://github.com/EVS-GIS</td></tr><tr><td>Geomorphic Change Detection (GCD)</td><td>Geomorphic Change Detection is a technique by which changes from the geomorphic processes of erosion and deposition are inferred from repeat topographic surveys</td><td>standalone program, ArcGIS toolbox</td><td>Bailey et al. (2020), Wheaton et al. (2009)</td><td>https://github.com/Riverscapes/gcd/releases/tag/7.3.0</td></tr><tr><td>GeoNet</td><td>computational tool for the automatic extraction of channel networks and channel heads from high-resolution topography</td><td>Python/Matlab executable script</td><td>Sangreddy et al. (2016), Passalacqua et al. (2010a, 2010b, 2012)</td><td>https://sites.google.com/site/geoethome/home</td></tr><tr><td>CHaMP Topo Processing Tools</td><td>the CHaMP Topo Processing Toolbar exists to take raw data from CHaMP ground-based topographic surveys</td><td>ArcGIS toolbox</td><td>Bouwes et al. (2011), Wheaton et al. (2012)</td><td>http://chamtools.northarrowresearch.com/</td></tr><tr><td>PyGUT</td><td>assessing the status of instream habitat condition</td><td>ArcGIS toolbox (dependencies)</td><td>Kramer et al. (2017), Bangen et al. (2017)</td><td>https://gut.riverscapes.net/</td></tr><tr><td>RCAT</td><td>the Riparian Condition Assessment Toolbox (RCAT) remotely assess the condition of riparian and floodplain areas using nationally available datasets (spatially limited).</td><td>ArcGIS toolbox</td><td>O'Brien and Wheaton (2015)</td><td>https://rcat.riverscapes.net/Documentation/RCAT.html</td></tr><tr><td>River Bathymetry Toolkit (RBT)</td><td>the RBT allows users to describe and measure river channels using high-resolution digital elevation models (DEM). The DEMs can be derived from airborne remote sensing, such as LiDAR, or from ground-based topographic surveys</td><td>ArcGIS toolbox</td><td>McKean et al. (2009)</td><td>http://essa.com/tools/river-bathymetry-toolkit-rbt/</td></tr><tr><td>River Tools</td><td>collection of tools for studying rivers: Segment a river polygon into boxes with a given length and width</td><td>QGIS Python Plugins Repository</td><td></td><td>https://gitlab.in2p3.fr/julie.pierson/river_tools</td></tr><tr><td>RiverGIS</td><td>create HEC-RAS flow model</td><td>QGIS Python Plugins Repository</td><td></td><td>https://rivergis.com</td></tr><tr><td>RiverMetrics</td><td>calculate the river sinuosity and river braiding index using a vector line representing the river axis and a polygon layer representing the wet channel</td><td>QGIS Python Plugins Repository</td><td></td><td>https://github.com/pierluigiderosa/RiverMetrics</td></tr><tr><td>Riverscapes Viewer</td><td>geospatial data explorer</td><td>web application, GIS plugins (ArcGIS, QGIS)</td><td></td><td>https://viewer.riverscapes.net/</td></tr><tr><td>RivMAP</td><td>RivMAP - River Morphodynamics from Analysis of Planforms.</td><td>MATLAB package</td><td>Schwenk et al. (2017)</td><td>https://uk.mathworks.com/matlabcentral/fileexchange/58264-rivmap-river-morphodynamics-from-analysis-of-planforms</td></tr><tr><td>South Rivers Toolbox</td><td>predictive model of river morphodynamics to support river management.</td><td>QGIS Python Plugins Repository</td><td>Yépez et al. (2024)</td><td>https://github.com/vosores/SouthRiversToolbox</td></tr><tr><td>STARS</td><td>the purpose of the Spatial Tools for the Analysis of River Systems (STARS) toolset is to generate and format the data needed to fit spatial statistical models in R software</td><td>ArcGIS Toolset, R package</td><td>Ver Hoef et al. (2014)</td><td>https://www.fs.usda.gov/research/rms/projects/stars-ssn</td></tr><tr><td>TauDEM</td><td>tools for the extraction and analysis of hydrologic information from topography</td><td>command line executable programs, ArcGIS toolbox</td><td>Tarboton et al. (1991)</td><td>https://hydrology.usu.edu/taudem/taudem5/</td></tr><tr><td>The Channel Migration Toolbox</td><td>tools that automate the measurement of stream channel migration. Lateral movement of stream channels is typically measured in support of Channel Migration Zone (CMZ) mapping, a requirement for Shoreline Master Programs.</td><td>ArcGIS toolbox</td><td>Legg and Olson (2014), Olson et al. (2014)</td><td>https://apps.ecology.wa.gov/publications/summarypages/1406032.html</td></tr><tr><td>ToPCAT</td><td>topographic Point Cloud Analysis Toolkit (ToPCAT) is a software package used to decimate (or thin) a point cloud and calculate meaningful statistics for grid-defined areas</td><td>ArcGIS toolbox (part of GCD)</td><td>Brasington et al. (2012), Buscombe (2016), Passalacqua et al. (2015), Rychkov et al. (2012)</td><td>https://gcd.riverscapes.net/Concepts/topcat-decimation.html</td></tr><tr><td>Top Toolbox</td><td>Top Toolbox is a suite of MATLAB functions for topographic analysis. The major aim of Top Toolbox is to offer helpful analytical GIS utilities in a non-GIS environment to support the simultaneous application of GIS-specific and other quantitative methods.</td><td>Matlab toolbox</td><td>Schwanghart and Scherler (2014), Schwanghart and Kuhn (2010)</td><td>https://github.com/wschwangart/topotoolbox</td></tr><tr><td>V-BET</td><td>the Valley Bottom Extraction Tool (VBET) is a tool used to identify the valley bottom of a riverscape,</td><td>ArcGIS toolbox (part of RCAT)</td><td>O'Brien and Wheaton (2015)</td><td>https://rcat.riverscapes.net/Documentation/Version_1.0/VBET.html</td></tr></tbody></table>
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Simplified logical structure of data input and output for the ArcGIS Standalone channel shifting toolbox - SCS Toolbox (detailed can be found in Supplementary Materials S1). Asterix in layers entered into the SCS toolbox marked data generated from the previous module.</figcaption></figure>

habitat diversity and ecosystem value and strongly influence their structure and functions (Costanza et al., 1997; Francis et al., 2009; Gregory et al., 1991; Macfarlane et al., 2018; Rietkerk and van de Koppel, 2008). Understanding these dynamics is a necessary step in developing appropriate strategies for restoring human-modified water-courses and reaching good ecological quality in accordance with the European Water Framework Directive (2000/60/EC).

The GIS (Geographical Information System) development enables the reconstruction of past morphological dynamics and the evolution of

planform trajectory (Hohensinner et al., 2004; Michalková, 2009; Nar-dini and Brierley, 2021). Planimetric analyses quantitatively documented spatial river adjustment and enabled temporal monitoring of the chan-nel dynamics during study periods. Thus, if spatial information about channel position is surveyed in GIS, planimetric changes and their quantification can be extracted from channel characters (Gilvear and Bryant, 2003). In fluvial geomorphology, historical maps, aerial pho-tographs and satellite images are used to identify erosion and deposition areas based on the channel polygons' superposition (Hooke, 1979;
<caption>**Table 2**</caption>

Description of the code from the field [EA] based on the superposition of the channel polygons of 2 consecutive dates in the layer "EA processes_{date 1}_{date 1 + n}.shp".

<table><thead><tr><th>Process</th><th>FIELD code</th><th>Description</th></tr></thead><tbody><tr><td rowspan="2">channel-floodplain</td><td>erosion</td><td>lateral erosion of the floodplain related to the channel movement (new extent of the polygon)</td></tr><tr><td>deposition</td><td>deposition in the form of the new floodplain and stabilised by vegetation (former extent of the polygon)</td></tr><tr><td rowspan="2">in-channel</td><td>island_erosion</td><td>erosion of the island and modification of the channel in inside boundaries</td></tr><tr><td>island_deposition</td><td>deposition in the form of a new island inside the boundaries of the channel</td></tr><tr><td>-</td><td>stable</td><td>without changes</td></tr><tr><td>-</td><td>hollow</td><td>the area between 2 consecutive channel polygons bordered by channel in date 1 and date 1 + n</td></tr></tbody></table>

Larsen et al., 2006; Sloan et al., 2001). Assessment of channel migration rates, orientation and trend is common to evaluate river meandering behaviour (Güneralp and Marston, 2012; Micheli and Larsen, 2011; Rusnák et al., 2016) or cutoff description (Hooke, 1995, 2003, 2004, 2007; Labaš and Kidová, 2022). In braided rivers with gravel bars, erosion-depositional processes express sediment connectivity and long-term sediment dynamics (Lehotský et al., 2018). Moreover, planimetric analyses can describe changes in island configuration as a significant signal for understanding the behaviour of anabranching rivers (Gautier et al., 2021) or define process-based channel changes within defined morphological units (Llena et al., 2020). For altered rivers, migration analyses are applied to reconstruct past activities and allow the proposal of effective channel restoration management (Marteau et al., 2017). Nowadays, the automatization of geospatial processes and big data analyses are focus on global data series and are applied to understanding river systems (Allen and Pavelsky, 2018; Carbonneau and Bizzi, 2023; Pavelsky and Smith, 2008; Pekel et al., 2016; Schwenk et al., 2017; Yamazaki et al., 2019).

Many spatial tools developed for river assessment (see details in Table 1) are focused on catchment/drainage properties (RCAT, Basin-Maker), processing topography and elevation models (TauDEM, RBT, ToPCAT, GeoNet, GCD) or generating spatially segmented channel courses (river network) for statistical analyses of river evolution, both in space and time (Diachronix, Fluvial Corridor Toolbox, River Tools, Riv-MAP). A large group of tools are focused on DEM processing and channel morphology extraction (River Bathymetry Toolkit), geomorphic change detection (GCD) or hydraulic property processing for channel network characterisation based on topography (GeoNET, TopoToolbox, Basin-Maker, TauDEM). These tools are effective for catchment-scale stream network characterisation, hydrologic and morphologic metrics calculation or watershed delineation. A second group of tools focuses on automatic processing of riverscape characters along stream length to detect valley bottom (V-BET), or planimetric evolution (Diachronix), and morphodynamics (South Rivers Toolbox). The last group specifically focuses on specific property analyses of riparian corridors, such as habitat conditions and riparian vegetation (RCAT, PyGUT, BRAT) or preparing datasets for future processing (RiverGIS).

Channel shifting is commonly detected from a sequence of historical maps, airborne photos or satellite images from which channel polygons or centerlines are delineated at each of the observed dates and overlayed using GIS procedures (Alber and Piégay, 2017; Greco et al., 2007; Roux et al., 2015). Erosion and deposition areas are identified from the channel polygons between two dates (Bertalan et al., 2018; Ghoshal et al., 2010; Gurnell, 1997; Hohensinner et al., 2004; Kidová et al., 2016; Michalková et al., 2011; Micheli and Kirchner, 2002). Subsequently, GIS software automatically identifies river reaches, and qualitative information is statistically processed to extract a set of indicators for interpreting remotely sensed data (Kidová et al., 2017; Nardini and Brierley, 2021). In most cases, the main source of channel transformation and

evolution is the digitised polygon of the channel, created by manual vectorisation or automatic classification. The spatio-temporal configuration is extracted from the channel polygon, and the basic morphometric parameters, such as channel width, erosion and deposition of channel-floodplain area, are detected (Anaïs et al., 2022). However, no fully automatic toolbox procedure still exists that uses direct channel polygons. At the same time, spatio-temporal analysis of active polygons is a fundamental method used in fluvial geomorphology to identify channel patterns, channel width, gravel bar area and erosion processes. Usually, the automatization process uses a centreline to detect lateral shifts (The Channel Migration Toolbox) or raster layers imported as masks (RivMAP). This automatization process often requires at least a basic programming knowledge, whether in Python or MATLAB. The application of longitudinal segmentation creates disaggregated statistical units that are quite well standardized to evaluate bias and explore channel shifting rates and trends, but they are usually based on a set of individual tools implanted manually in a chain of tasks, and none are fully automatized. An automatic processing chain for river segmentation and feature extraction from the network scale (Roux et al., 2015; Yépez et al., 2024) would accelerate data extraction and would provide standardized maps such as floodplain chronology detection and floodplain age mapping (Greco et al., 2007), where floodplain surfaces are dated based on the cartographic representation of the river channel over different periods.

Therefore, our aim is to introduce a new Standalone channel shifting toolbox (SCS Toolbox) developed in the ArcGIS environment to extract the erosion and deposition from vectorised polygons. The SCS toolbox is focused on generating spatial layers whose parameters accelerate analytical parts of work and detect basic signatures for river migration, enabling complex assessment of the river channel segments. The conceptual toolbox is designed to provide i) generation of basic statistical layers for centerline generation and longitudinal segmentation; ii) identified erosion and depositional areas for every longitudinal segment with detection of shift orientation of individual banks and separation bank-related or in-channel processes; iii) dating floodplain surfaces that are created by the lateral shift of channel and identified vegetation height and height above the channel for statistical assessment of this new surfaces on the floodplain based on additional LiDAR data when available. The SCS toolbox is built on the channel polygon datasets, supplemented by LiDAR information. The toolbox is tested on four river systems with different planform characteristics for illustrating its practical application, e.g., sinuous (Ain and Topl'a Rivers), and meandering single-thread planform (Ondava River) as well as braided-wandering multi-channel planform (Belá River).

## 2. Standalone channel shifting toolbox description

Toolbox has been developed based on the principle of the FluvialCorridor Toolbox (Alber and Piégay, 2011; Roux et al., 2015) that generates disaggregated spatial units with basic morphometric statistics of the river corridor within a hydrographic network. Generally, the Standalone channel shifting toolbox is constructed on the principle that only channel polygons are mandatory to generate toolbox outputs. More detailed statistics can be obtained by adding an optional layer of the digital terrain model (DTM) and digital surface model (DSM). The input layer of channels should be a single polygon representing an active channel (merged water and bar area). Polygons can contain "hollows" that represent a stable area of islands. This is the same approach where stable floodplains along banks are stored as "NODATA" information. The channel date of origin (date) must be stored in the attribute table with the same field name for all channel polygons. As an optional selection in Module 4, a digital elevation model representing ground topography (DEM), vegetation envelope (DSM) and flowpath generated directly from the elevation model can be entered into the calculation.

Overall, the SCS toolbox consists of two main blocks (detailed technical information can be found in Supplementary Materials S1). The
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Process of calculation channel planimetric changes between 2 channel polygons from 1949 and 1961 (a); identification erosion and accumulation areas from the attribute table field [EA] (b); and calculation orientation of the lateral movement between 2 consecutive dates stored in the field [direction] (c) and [migration] (d).</figcaption></figure>

first block creates basic supporting layers, as are centerlines and segments directly from channel polygons (Modules 1 and 2; Fig. 1). These layers are used for the next data processing in the second block and can also be created outside of the SCS Toolbox or by manual digitising. In Module 3 (Fig. 1), the toolbox automatically identifies erosion and deposition areas and separates in-channel and bank (or floodplain)

erosion. Moreover, the orientation of the banks for channel migration is calculated. The last Module (M4) exports the zonal polygons with basic statistics about floodplain age, height above the channel and vegetation height (more information can be found in the ArcGIS help manual in Supplementary Materials S2).
<caption>**Table 3**</caption>

Description of the code from the field [direction] and [migration] based on the superposition of the channel polygons of 2 consecutive dates in the layer "EA processes_{date}_{date+1}.shp".

<table><thead><tr><th>FIELD</th><th>FIELD code</th><th>Description</th></tr></thead><tbody><tr><td rowspan="4">[direction]</td><td>RIGHT</td><td>lateral movement to the right side of the centerline orientation</td></tr><tr><td>LEFT</td><td>lateral movement to the left side of the centerline orientation</td></tr><tr><td>in-channel process</td><td>process related to the channel (without lateral dimension)</td></tr><tr><td>deposition_LEFT/ RIGHT</td><td>migration of the river caused by abandoned with side identification</td></tr><tr><td rowspan="2">[migration]</td><td>erosion_LEFT/ RIGHT</td><td>migration of the river channel by active lateral movement with identification of the channel side; hollows between channel polygons are included.</td></tr><tr><td>in-channel process</td><td>process related to the channel (without lateral dimension)</td></tr></tbody></table>

## 2.1. Create channel centrelines (Module 1)

The module 1 creates a channel centerline for all channel polygons entered into the analyses. It is possible to create the centerline as a unified centerline from the polygon's union and apply it to the segmentation layer. In both cases, an algorithm for centerline creation uses the Thiessen polygon approach for centerline delineation (Lewandowicz and Flisek, 2020a). In this module, as output is created layer: "centro_{date}.shp" for individual polygon centerline and "SegCenterline.shp" for segmentation centerline. In both cases, the orientation of the centerline downstream and centerline topology need to be checked for further processing. The cleaning process in module 1 uses small line noise removal, vertices density unification and extended final centerline to the polygon boundary. Channel and segmentation centerlines can also be vectorised manually or can be applied from different methodological approaches outside of the toolbox, such as the centerline skeletonization applied in the Medial Axis Transformation, triangulation, Voronoi diagrams or the Straight skeleton algorithm (Aigner et al., 2015; Haunert and Sester, 2008; Lewandowicz and Flisek, 2020b; Li et al., 2019; Lu and Ai, 2018; Meijers et al., 2016; Smogavec and Zalik, 2012; Zeng et al., 2015). The centerline generated in an external environment needs to

<caption>**Table 4**</caption>

Overview of required input data for calculation zonal statistical layer with different combinations of the input data and description of the field values.

<table><caption>**Table 5**</caption><thead><tr><th colspan="4">Input layer</th><th rowspan="2">Output</th><th rowspan="2">Field</th><th rowspan="2">description</th></tr><tr><th>Polygons</th><th>DEM</th><th>DSM</th><th>flowpath</th></tr></thead><tbody><tr><td>☑</td><td></td><td></td><td></td><td>famLayer.shp</td><td>["FAM"]</td><td>a spatial layer union among all-time horizons with ["FAM"] field stored information about the year of channel position</td></tr><tr><td></td><td>☑</td><td></td><td>☑</td><td>DED.tif</td><td>value</td><td>detrended elevation model representing height above the channel</td></tr><tr><td></td><td>☑</td><td>☑</td><td></td><td>veget_CHM.tif</td><td>value</td><td>reclassify vegetation canopy height model</td></tr><tr><td>☑</td><td>☑</td><td>☑</td><td>☑</td><td>M4st Statistics_all.shp</td><td>["FAM"]<br/>["Distance"]<br/>["ID SEQ"]<br/>["e_{MIN, MAX, RANGE, MEAN, STD, SUM}]"<br/>["v_{MIN, MAX, RANGE, MEAN, STD, SUM}]"</td><td>the date of the channel polygon<br/>the field with interval value in meters<br/>spatial sequence upstream (upstream from centerline orientation)<br/>zonal statistics for floodplain elevation (DED)<br/>zonal statistics for vegetation height from the Canopy Height Model (CHM)</td></tr><tr><td>☑</td><td>☑</td><td></td><td>☑</td><td>M4st Statistics_DED.shp</td><td>["FAM"]<br/>["Distance"]<br/>["ID SEQ"]<br/>["e_{MIN, MAX, RANGE, MEAN, STD, SUM}]"</td><td>the date of the channel polygon<br/>the field with interval value in meters<br/>spatial sequence upstream (upstream from centerline orientation)<br/>zonal statistics for floodplain elevation (DED)</td></tr><tr><td>☑</td><td></td><td></td><td></td><td>M4st Statistics_FAM.shp</td><td>["FAM"]<br/>["Distance"]<br/>["ID SEQ"]</td><td>the date of the channel polygon<br/>the field with interval value in meters<br/>spatial sequence upstream (upstream from centerline orientation)</td></tr></tbody></table>

Basic parameters for the studied river system.

<table><thead><tr><th rowspan="2">river</th><th rowspan="2">planform</th><th rowspan="2">country</th><th rowspan="2">Studied length (km)</th><th rowspan="2">Average active channel width (m)</th><th rowspan="2">No. of dates</th><th rowspan="2">time span</th><th rowspan="2">data sources</th><th rowspan="2">source</th><th colspan="2">SCS module</th></tr><tr><th>M1, M2, M3</th><th>M4</th></tr></thead><tbody><tr><td>Belá</td><td>braided-wandering</td><td>Slovakia</td><td>21</td><td>31</td><td>11</td><td>1949-2018</td><td>manual vectorised aerial images</td><td>Kidová and Lehotský (2012), Kidová et al. (2016, 2022), Rusnák et al. (2024)</td><td>☑</td><td>☑</td></tr><tr><td>Ondava</td><td>meandering</td><td>Slovakia</td><td>11</td><td>30</td><td>11</td><td>1820-2019</td><td>manual vectorised historical map and aerial images</td><td>Rusnák et al. (2019), Rusnák and Lehotský (2014)</td><td>☑</td><td></td></tr><tr><td>Ain</td><td>sinuous (gravel-bed)</td><td>France</td><td>41</td><td>110</td><td>4</td><td>2000-2021</td><td>combination of manual vectorisation and automatic classification of aerial images</td><td>Dufour and Piégay (2010), Marston et al. (1995)</td><td>☑</td><td></td></tr><tr><td>Topľa</td><td>sinuous (gravel-bed)</td><td>Slovakia</td><td>39</td><td>37</td><td>6</td><td>1949-2009</td><td>manual vectorised aerial images</td><td>Rusnák et al. (2016, 2022)</td><td>☑</td><td></td></tr></tbody></table>
<figure><img src="image_5.png" /><figcaption>Fig. 3. Localisation of studied rivers (a) and detail to the planform evolution of the braided-wandering river system of the Belá River (b), meandering Ondava River (c), sinuous rivers Ain (d) and sinuous gravel-bed river Topľa (e).</figcaption></figure>

fulfil basic rules:

i) centerline is a single line and does not contain multipart,

ii) oriented downstream,

iii) end and start point touch channel polygon boundaries,

iv) completely within channel polygons,

v) individual centerline contains an attribute field with the date type value matched to the polygon's date field.

## 2.2. Longitudinal segments (Module 2)

The second module generates longitudinal segments from channel polygons. This module uses the segmentation centerline from Module 1 and disaggregates the centerline based on the spatial interval in meters defined by the user. In general, this approach is based on the principles of the Fluvial Corridor Toolbox disaggregation scheme (Roux et al., 2015). Optional, during segment delineation, the user can define the smoothing parameter (in meters) for the centerline in sinuous and meandering channels to generalise the centerline in a defined distance. For further processing in the modules M3_EAcalcualtion and M4_FloodplainStat, segments from external sources such as the

FluvialCorridor Toolbox can also be used. Segments from external sources must cover the area of the polygon's union, and the attribute table needs to define the segments field with the sequence information (IDSeq).

## 2.3. Detection erosion and deposition processes (Module 3)

Module 3 assesses channel planimetric changes and identifies erosion (E) or accumulation (A) areas for the left and right banks. The methodology of the script arises from the concept of the erosion corridor (Piégay et al., 1997, 2005), where the spatial position of the channel from historical aerial images and maps is used for detecting bank erosion (Rusnák et al., 2016) and applied as script (Lehotský et al., 2013; Rusnák and Lehotský, 2014). In this module, channel polygons and individual centerline layers are used, and both of them must have defined the field date in the attribute table. The data type for the date field is set to data type and should have datetime isoformat (YYYY-MM-DD). Script uses a datetime Python library for sorting algorithms, dating and matching polygons and centerlines in consecutive dates and calculating erosion and accumulation areas between them (date 1 and date 1 + n).

Based on the superposition (Table 2), the script algorithm divides the
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Spatial-temporal evolution of erosion-accumulation areas with examples for Ain (a), Belá (b), Ondava (c), and Topľa (d) Rivers.</figcaption></figure>

erosion/accumulation areas between the channel and floodplain and also calculate processes that emerge inside the channel polygon based on the work of Lehotský et al. (2018). Attribute “hollow” is an area between 2 consecutive horizons that can result from reworking the floodplain in the case of the meandering river or untouched floodplain after channel avulsion or meander cutoff (Fig. 2).

The orientation mask is calculated by combining the channel polygons and oriented individual centerlines for two consecutive periods. Information about orientation is stored in the **[direction]** field (Table 3). Combination of the information form field [EA] and **[direction]** creates a field **[migration]** where the erosion and deposition process is identified on both sides of the channel. In contrast to direction, this field pointed to the boundary channel migration to the right and left side (this parameter also incorporates hollows between channels) and expressed overall channel movement. If the segmentation layer is used, the toolbox automatically recalculates the area of the migration per day (EA_rate_A) and lateral movement in meters per segment (EA_rate_m).

## 2.4. Calculation of floodplain statistics (Module 4)

The module is used to generate a zonal statistical layer related to the historical evolution of the lateral movement of the polygons. This module generates a floodplain age map (FAM) in the basic settings and intersects this layer with channel segments. Adding the digital elevation model of floodplain topography (DEM from ground point cloud) and flowpath, Module 4 calculates the detrended elevation model (dedDEM) and its zonal statistics for segments. The vegetation canopy height model (CHM) is calculated if the digital surface model is added. A complete summary of layers and statistics parameters is stated in Table 4.

This module detects horizontal migration and long-term planform

dating (Bertalan et al., 2019; Greco et al., 2007; Liro, 2015; Marchese et al., 2017). Combined with LiDAR data, this toolbox can be used for the vertical floodplain evolution assessment, and if the point cloud is classified into vegetation classes, vegetation height statistics can also be detected (Lallias-Tacon et al., 2017; Rusnák et al., 2024; Svetozarov and Gallay, 2024). Module 4 used a raster elevation model of the ground point cloud (DEM) to calculate the detrended elevation model. Trend surface is calculated from a flowpath line that should be vectorised directly from DEM and express channel water surface height if an elevation model without bathymetry is used. Vegetation envelope raster (DSM) represents an elevation model where reclassified vegetation is stored as raster data.

# 3. Toolbox application

## 3.1. Rivers and data description

Standalone channel shifting Toolbox was tested and validated on four river systems with different planform configurations (Table 5). Channel polygons were manually vectorised from historical aerial images and old maps or generated by automatic aerial image extraction. Centerline generation and segmentation were tested for all river systems. The SCS toolbox was verified for the sinuous (Ain and Topľa River), meandering (Ondava River) and braided-wandering planforms of the Belá River with multiple flow channels and well-developed in-channel islands (Fig. 3). In all cases, the polygon layer entered for calculation of results from the first three toolbox modules. Manual vectorisation was applied for the Bela, Ondava and Topľa Rivers. Data from the Ain River was mixed with manual vectorisation and automatic extraction from the aerial images. The last module, FloodplainStat, was
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Temporal evolution of left and right bank migration with examples for Ain (a), Belá (b), Ondava (c), and Topl'a Rivers (d).</figcaption></figure>

tested for the Bela River, where we used a digital terrain and surface model (DTM and DSM). Elevation data from LiDAR were provided by the Geodesy, Cartography and Cadastre Authority of the Slovak Republic in a LAZ point cloud with an average density of the last reflection points of 18 points/m². Ground points were used to generate a digital terrain model (DTM) with a resolution of 1 m, and ground and vegetation points were used to create a surface model (DSM). The original point cloud classification from the provider was used. Final data analyses and data mining were processed in RStudio, where direct.dbf files were uploaded to data frames by the Foreign library.

## 3.2. Lateral dynamics, erosion and deposition

The first stage of the Channel Toolbox application was the generation of downstream-oriented centerline and longitudinal segments with a spacing of 100 m. Based on the field [EA], it was possible to identify stable areas of active channel, erosion and depositional processes between the active channel and floodplain (interaction outside of the channel) or erosion and deposition of the island (inner dynamics of the system). In-channel island erosion and deposition processes are significant only in the braided-wandering river system Bela (Fig. 4) up to the

year 2002. After this year, there have been dominant outside floodplain erosion/depositional processes following the transition of the river system to a wandering planform. For the meandering and gravel-bed rivers (Ain, Ondava, Topl'a), prevailing channel-floodplain interaction with a significant decrease in the intensity of erosion-depositional processes from the beginning of the study period (for Ondava and Topl'a River). This points to the decreasing lateral dynamics and prevailing depositional processes over erosional ones, leading to channel narrowing.

Lateral migration in meters (Fig. 5) does not copy the course of the erosion-depositional processes. The toolbox enables to identification of the movement of the river bank for the erosion process (active) and particularly for the second opposite bank that movement is caused by sediment accumulation. For both cases, the toolbox distinguished the left and right sides or the process can be analysed together to determine side orientation or type of movement.

Adding an optional segmentation layer with regular spacing (in this case, 100 m) algorithm disaggregates data to a longitudinal segment labelled by field [ID_SEQ] based on the defined spacing value in the upstream orientation. Plotting erosion and depositional or migration rates in meters (Fig. 6) identified different lateral behaviours or active
<figure><img src="image_8.png" /><figcaption>Fig. 6. Longitudinal behaviour of lateral erosion and deposition for the study period 1820–2019 with examples for Ondava River.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Final product of the FloodplainStat module for the Belá River (a), which creates detrended elevation (DEM) model (b); vegetation height as CHM - canopy height model (c); dating floodplain surfaces as FAM - floodplain age map (d) and statistical layer with statistics from detrended DEM and CHM for all longitudinal segments and polygon - example of mean elevation above channel (e).</figcaption></figure>

areas in a longitudinal direction.

### 3.3. FloodplainStat module application

This module created three types of outputs based on the input layers. Firstly, the module created a zonal statistic layer by combining the channel floodplain age layer (FAM) and disaggregating polygons based

on the segments (Fig. 7). If DTM and DSM are used, zonal statistics of the height above the river channel were obtained from detrended DTM. Finally, information from the FAM (dating) was combined with dedDEM (vertical positioning) or vegetation canopy height (CHM) within 100 m segments (longitudinal positioning). The spatial and temporal evolution of floodplain elevation above the channel pointed to the higher position of channel surfaces in older periods (Fig. 8c). Plotting maximal or mean
<figure><img src="image_10.png" /><figcaption>**Fig. 8.** General statistics generated for the Belá River from the FloodplainStat module express floodplain elevation height above river channel for different dates of used channels polygons (a); vegetation canopy height dated by used channel polygons (b) and longitudinal evolution of floodplain heights above the channel for all channel surfaces (c) and for channel surfaces dated only for the year 1949 (d).</figcaption></figure>
values in a longitudinal direction based on the channel segmentation can be used to detect channel incision (Fig. 8d). Especially if analyses compare only channel surfaces from the year 1949, the system detects the maximal position of the oldest surfaces compared to the present position of the channel. One limitation of this incision analysis is natural bank height estimation. Only surface hung higher than natural bank heights can be estimated as incised. Also, different channel types can have different bank heights and contribute to the uncertainty of incision estimation. This is visible in Fig. 8d, where higher values upstream of the Bela River between 15 and 16.5 km result from planform changes of headwater stream with a significant increase in interactions with glacial moraine sediments with higher grain size and steeper channel.

The canopy height model (CHM) creates a robust statistical database on both spatial and temporal scales. Vegetation height is detected in the longitudinal direction and dated by year of imaging of the channel polygon (Fig. 8b). A combination of this spatial and temporal dataset, together with erosion-depositional processes, can be used to assess vegetation-morphologic interaction within the river channel.

## 4. Discussion and conclusion

The toolbox application supports the evaluation of spatial dynamics and lateral erosion with the identification of channel migration and reaches scale mobility as well as the potential changes in dynamics activity. This is shifting increase versus shrinking due to vegetation encroachment or human constraints. Lateral dynamics also describe the good aquatic status of natural gravel bed rivers and drivers to preserve habitat diversity (Ward et al., 2002). Identification of lateral mobility and erodible corridors, together with the definition of freedom space (Piégay et al., 2005), identify the functioning of the river and improve the ecological status (Dépret et al., 2023). Spatial disaggregation (Alber and Piégay, 2011) is used for linear referencing the erosion/depositional processes from Module 3 and identified vegetation canopy and height of the channel surfaces above the present river channel in Module 4. Spatial layers in the shapefile format are the results of the toolbox and define the spatial location of all parameters.

The SCS toolbox is designed for robust analysis of complex river shifting dynamics and provides floodplain statistics based on polygon topology rules and zonal statistics. The toolbox identifies the erosion and accumulation patches of the floodplain and planform transformation inside of the active channel (islands and inner erosion in multi-channel systems). The main limitation of river calculation is the computing capacity of the workstation. While processing large river systems and large raster DEM models, the toolbox has limited internal or virtual RAM memory. The toolbox enables the automatization of all channel polygons with statistical analyses of erosion and accumulation areas and their manifestation for the left and right sides of the channel. Moreover, the EA module recognizes erosional and depositional areas between the channel and floodplain and the inner dynamic of the system related to the island transformation without channel migration. This process is linked to sediment connectivity and lateral input from a floodplain or channel system bar-to-bar and island-bar connectivity (Lehotský et al., 2018). The rate of lateral movement enables the detection of the lateral mobility of the rivers or bank erosion analyses and helps to understand the geodiversity of the river system, which is necessary for good ecological status or river management and river recovery (Dépret et al., 2017). Testing this algorithm for different river styles and planforms confirms its application and suitability for assessing the whole range of river planform conditions. Several GIS toolboxes exist for characterizing fluvial features but few for informing lateral shifting. The SCS toolbox aimed directly at active channel properties, floodplain age dating and extraction erosion/depositional areas directly from polygons. Similarly, to the RivMAP project (Schwenk et al., 2017), the SCS toolbox detects lateral changes in the river system, but topologically, it is based on polygon processing. Moreover, combined with the Fluvial corridor toolbox segmentation algorithm, riparian zone canopy height and

elevation above the channel are assessed for each of the floodplain age classes. Statistical evaluation provides comprehensive information about historical channel evolution. In comparison to the step-by-step sequential manual processing, it enables fast and automatic generation of spatial statistics.

The limitation of the automated procedures is the quality of input layers and basic topological rules: channel polygons have to be single polygons, the centerline is a single line, does not contain multipart, and is oriented downstream with end and start point touch channel polygon boundaries. For calculation, it can also be used as a segmentation and centerline layer manually vectorised or generated outside of the SCS script, as is the FluvialCorridor toolbox (Roux et al., 2015). Characterizing channel morphodynamics often lies in polygon data processing. The SCS toolbox is applicable for automatic diagnosis at the reach scale with the availability of historical sources about channel position. It is valuable to biologists, ecologists, managers of river authorities or government agencies, and engineers, providing qualitative information expressing the intensity of the change through polygon analyses (erosional and depositional processes) or historical floodplain evolution (floodplain elevation history and canopy height). Future applications are dedicated to river restoration decisions, conservation projects or the impact of anthropogenic modifications. Furthermore, improvements are planned, and external dataset segmentation layers can be combined with external data sources. Moreover, channel width can be calculated from the active channel area by dividing it by the length of segments. The challenge for the future is to create a system of universally accepted metrics that describe riverscape and a general workflow model with final report metrics that can be automatically processed to detect and assess river typology that reflects river changes.

Standalone channel shifting toolbox analyses channel dynamics by assessing erosional and depositional processes. Furthermore, these processes are linked within module 4 and floodplain evolution. Channel segments are connected with a floodplain age map (FAM) created based on the channel position intersection (Greco et al., 2007). This approach facilitated the process of floodplain dating and tracking floodplain evolution. Combined with the floodplain height model and vegetation canopy height, it allowed for an understanding of direct floodplain evolution linked with channel transformation. The application of this module was tested for the Belá River, where the floodplain channel surfaces from historical aerial images are hung above the channel, which pointed to channel degradation and incision starting in 1949 (Rusnák et al., 2024). This approach is similar to the methodology applied to analyse the elevation differences between the active channel and the abandoned channel using a detailed topographic survey for small pre-Alpine tributaries (Liébault and Piégay, 2002) or GNSS survey (Calle et al., 2017). The vegetation canopy height model connected with historical channel positions can create a complex model for the spatiotemporal evolution of a channel's lateral and vertical dynamics (Lallias-Tacon et al., 2017).

Nevertheless, the limitations of complex channel assessment from aerial images and remote sensing data are the spatial accuracy of channel vectorisation, initial data referencing, and topology (Swanson et al., 2011). Finally, the Standalone channel shifting toolbox complemented a wide palette of tools and approaches for understanding and conceptualising river-style and fluvial continuum. In combination with other available tools, it is a useful and important piece of the toolbox supporting fluvial management, restoration analyses and decision-making in the river landscapes. During the last century, rivers underwent a significant transformation expressed by channel narrowing, incision and associated planform metamorphosis. Planimetric changes are significant, resulting in channel simplification, bar shrinking, and a decline in lateral shifting. The geomorphic recovery is commonly based on floodplain reprofiling and erosional-depositional process-based restoration through bank resistance decreases or gravel replenishment. Automatic toolboxes that process polygon data can be effective tools for evaluating the historical evolution of river channels and even for
assessing natural recovery or restoration effects.

### CRediT authorship contribution statement

**Miloš Rusnák:** Writing – original draft, Methodology, Data curation, Conceptualization. **Simon Opravil:** Writing – original draft, Data curation. **Samuel Dunesme:** Data curation. **Hamid Afzali:** Data curation. **Louis Rey:** Data curation. **Hervé Parmentier:** Data curation. **Hervé Piégay:** Writing – review & editing, Methodology, Conceptualization.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgement

The study was supported by the Science Grant Agency (VEGA) of the Ministry of the Education, Science, Research and Sport of the Slovak Republic and the Slovak Academy of Sciences [Grant Nr. 02/0016/24] and by the Slovak Research and Development Agency under the Contract no. APVV-23-0265. The corresponding authors are grateful to the Government of the Slovak Republic, which provided a mobility grant from the National Scholarship Programme of the Slovak Republic for a research stay in Lyon (France). This research was carried out with the framework of the Graduate School H20'Lyon (ANR-17-EURE-0018) of the Université of Lyon (UdL), which is part of the program "Investissements d'Avenir" operated by Agence Nationale de la Recherche (ANR). The authors thank the Geodesy, Cartography and Cadastre Authority of the Slovak Republic (ÚGKK SR) for providing LiDAR data. We thank the two anonymous reviewers for their constructive comments and feedback that helped improve the manuscript.

### Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j geomorph.2025.109688.

### Data availability

LiDAR data are freely available from the Geodesy, Cartography and Cadastre Authority of the Slovak Republic website (LiDAR: https://www.geoportal.sk/en/zbgis/als/). The SCS Toolbox is freely available from the GitHub website https://github.com/milosrusnak. The example data are available with the permission of the third party upon reasonable request to the corresponding author.

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