CliffDelineaTool v1.1.0: an algorithm for identifying coastal cliff base and top positions

Correct quantification of coastal cliff erosion requires accurate delineation of the cliff face bounded by the cliff top and base lines. Manual mapping is time consuming and relies on mapper’s decisions and skills. Existing algorithms are generally site specific and may be less suitable for areas with diverse cross-shore cliff geometry. Here we describe CliffDelineaTool (v1.1.0), a MATLAB-based algorithm that identifies cliff base and top positions on complex cliffs using cross-shore transects extracted from digital elevation models. Testing on four 750-1200 m cliffed 10 coastlines shows that the model performance is comparable to manual mapping and provides some advantages over existing models but provides poor results for cliff sections with ambiguous cliff top edges. The results can form the basis for a range of analyses including coastal inventories, erosion measurements, spatio-temporal erosion trends, and coastline evolution modelling.

applied terrain filters (1 st and 2 nd derivative of elevation) sensitive to slope change to identify the cliff base line for a simple cliff morphology. Several authors identified cliff base positions as inflection points along cross-shore transects.
35 Liu et al. (2009)  top points were defined as locations along the transects with the largest vertical distance between the cliff profile and trendline, with the cliff base located below and cliff top above the trendline. Transect lengths were manually adjusted to ensure proper cliff top selection (Palaseanu-Lovejoy et al., 2016). Payo et al. (2018) developed the CliffMetrics algorithm using the model of Palaseanu-Lovejoy et al. (2016) combined with automated extraction of the shoreline and transects for coasts with complex alongshore geometry where small bays and headlands alternate. They used a 45 constant transect length with decrease in model performance, but considerable time gain (Payo et al., 2018).
CliffMetrics performs well for simple cross-shore cliff morphology, but it is less suitable for more complex cliff profiles, where rotational landslides, within-cliff flattening, roads, etc. are present .
Here, we build on previous models to develop a new MATLAB-based algorithm, CliffDelineaTool (v1.1.0; Swirad, 2021) that identifies cliff base and top positions on cross-shore transects for a range of complex cliff geometries. The 50 model parameters are calibrated using four cliff sections that encompass a range of geomorphic settings, and then tested on four different cliff sections with topography ranging from simple to complex. The results are compared to manually mapped cliff lines and CliffMetrics.

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The model (Figure 1) uses eight user-defined parameters (Table 1) and an input text file containing rows of point ID, transect ID, distance from the seaward end of the transect, and elevation (Swirad et al., 2016). A single input file includes multiple ordered transects representing an entire coastal section.       (Table 2). Local slope angle distributions were summarized in statistical terms (Figure 4), and outliers were defined as points greater than q3 + 1.5 × (q3 -q1) or less than q1 -1.5 × (q3 -q1), where q1 and q3 are the 25 th and 75 th percentiles (red pluses in Figure 4). NVert = 20 was selected for AOIs #1-2 and NVert = 30 was 130 selected for AOIs #3-4 to minimize the number of outliers while maintaining a relatively narrow and normal slope angle distribution. Threshold slope angles were picked as minimum or maximum values excluding outliers (black whisker ends in Figure 4;   Table 1). The modelled cliff base and top locations were converted to polylines using ArcGIS and intersected with transects to define cliff base and top locations on skipped transects. Overall, these steps improved automated cliff mapping performance for the more complex cliff sections and usually exhibited lower RMS compared to CliffMetrics (Table 3; Figure 6).  (Table 1).     (Table 4), and visual assessment on initial model runs (Table 1). RMS between true and modelled cliff base and top locations was used to assess model performance on all transects (including skipped transects).

3 Results
Consistency of the manual, and performance of the automated cliff mapping varied between the four evaluation AOIs.
The simple cliff geometry and unambiguous location of the cliff base and top of AOI #5 resulted in low (≤2.7 m) RMS for all mapping methods (Table 5; Figure 7a CliffMetrics sometimes placed cliff base at beach cusps (C in Figure 7d).  Figure 7c). However, both models also picked sections of the landslide head scarp (D in Figure   7d). In AOI #7 two mappers interpreted an elevated section between two separate landslide scars as cliff top (E in Figure 7e), differing from most other mappers and both models that opted for a simpler cliff top shape (Figure 7e-f).
The cliff top position was the most diverse for AOI #8, with mappers' interpretation ranging from the top of the coastal mountain slope (F in Figure 7g) to the mid-slope (G in Figure 7g    The input parameters have a varied impact on model performance ( Figure 6). MaxBaseElev is easily selected using a slightly conservative cliff base elevation estimated from the general site settings. The optimal NVert parameter depends on DEM resolution and cross-shore cliff extent. Calibration showed that in general the greater the NVert 210 value the narrower the distribution of the four threshold slopes (BaseSea, BaseLand, TopSea and TopLand) and the higher the number of outliers. That relationship holds until NVert value (30 for AOI #1, 40 for #3 and 70 for #4, Figure   4) over which the q1 -q3 box becomes very wide or the distribution is dominated by outliers. Figure 8  The modified version did not include stage 2 (shifting cliff top landwards for within-cliff flattening areas) and stage 3 (removal of outliers) but did include a Laplacian topographic filter (Richter et al., 2013). The automated results were visually inspected and some (10% of the cliff base and 29% of the cliff top positions) required manual modification 230 to correct positions. Young et al. (2021) used the present CliffDelineaTool model to identify the cliff base in 155 0.25 m resolution DEMs along a 2.5 km coastal section at 1 m alongshore transect spacing. Quality control showed that cliff base misplacement was negligible, while the total processing time was ~30 min . These studies demonstrate the tool's applicability for both large space and time datasets, and over a range of DEM resolutions.

Conclusions
Building on previous studies, we developed a new algorithm (CliffDelineaTool) to delineate coastal cliffs from DEMs.
The model identifies cliff base and top positions along cross-shore transects using elevation and slope characteristics.
It considers complex cliff morphology and removes alongshore cliff top outliers. CliffDelineaTool provides results comparable to manual mapping and outperforms existing models for the complex cross-profiles analyzed. The

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automated results have known errors and should be inspected visually. The method has been applied successfully on two large datasets Young et al., 2021), greatly reducing processing time. With calibration and quality control CliffDelineaTool can be used on a wide variety of coastal setting facilitating a range of scientific and managerial applications but has limited application where the cliff top is ambiguous.