Seismic Line Disturbance Analysis

Compares seismic line segments with the undisturbed peatland ("matrix") beside them, by disturbance class. It is adapted from the analysis script of Chan, Dabros & McDermid (2026), Microtopographic and Hydrological Response to Repeated Seismic Line Disturbance in a Boreal Fen of Northern Alberta, Canada, Forests 17, 489. On the paper's published data it gives the same tables as the original script. It needs only the terra and ggplot2 R packages; the statistical tests are written in base R.

For each segment, the tool summarises the rasters under it. Then, for each line segment, it subtracts the mean of its matrix segments:

Attribute	From	Per segment	Deviation
`dtm`	DTM	Mean elevation	Line − matrix (m)
`hummock`	Microform raster	Hummock cells / classified cells	Line − matrix
`dtw`	Depth-to-water raster	Mean DTW over ground cells (DTW ≠ 0)	Line − matrix (m)
`surfacewater`	Depth-to-water raster	Cells with DTW = 0 / all cells	Line − matrix

For each attribute it compares the disturbance classes with:

a Kruskal–Wallis test, and pairwise Wilcoxon rank-sum tests with Benjamini–Hochberg (BH) correction. The mean_group letters come from these.
a Brown–Forsythe test, and pairwise Brown–Forsythe tests with BH correction. The bf_group letters come from these. This is the Brown–Forsythe test for equal means under unequal variances (the statistic of onewaytests::bf.test, which the original script uses).

Classes that share a letter don't differ significantly at the chosen significance.

Parameter	Type	Required	Description
`in_segments`	vector (polygon)	Yes	Segment polygons, as `file.gpkg\\|layer` for a GeoPackage. Fields: `new_id` links a line segment to its matrix segments, `note` is `original` (line) or `matrix`, and `Class` is the disturbance class.
`in_dtm`	raster	Yes	DTM, for example from Generate DTM from LiDAR Data.
`in_microform`	raster	Yes	Hummock (`1`), lawn (`0`) and hollow (`-1`) raster, for example the `HH_` or `Merged_HH_` output of Detect Hummock and Hollow from LiDAR Data.
`out_folder`	directory (output)	Yes	Folder for the `tables/` and `plots/` outputs.
`in_dtw`	raster	No	Depth to water in metres, with `0` for surface water. Without it, `dtw` and `surfacewater` are skipped.
`significance`	float	No	Significance level for the letter groupings. The default is `0.05`.

The segments are projected to the DTM's coordinate system if they differ. Classes named New Disturbance, Old Disturbance and Re-disturbance keep the paper's order and colours; any other class names are sorted alphabetically.

Output	Contents
`tables/segment_values.csv`	Per-segment summaries for every segment.
`tables/segment_deviations.csv`	Per-line-segment deviations from the matrix.
`tables/<attribute>_deviation_summary.csv`	By class: n, mean, median, min, max, quartiles, SD, variance, `mean_group` and `bf_group`.
`tables/<attribute>_kruskal_wilcoxon.csv`	Kruskal–Wallis result and BH-adjusted pairwise Wilcoxon p-values.
`tables/<attribute>_brown_forsythe.csv`	Brown–Forsythe result and pairwise p-values (raw and BH-adjusted).
`tables/class_microform_proportions.csv`, `tables/class_surfacewater_proportions.csv`	Cell proportions for the matrix and each class.
`plots/<attribute>_violin.png`	Deviation distribution per class, with the class mean (dot) and `mean_group` letters.
`plots/microform_barplot.png`, `plots/surfacewater_barplot.png`	Stacked proportions for the matrix and each class.

from beratools.tools.rpy_seismic_line_analysis import rpy_seismic_line_analysis

rpy_seismic_line_analysis(in_segments="segments.gpkg|segments",
                          in_dtm="dtm.tif", in_microform="microform.tif", in_dtw="dtw.tif",
                          out_folder="analysis")

Reproducing Chan et al. (2026)

The paper's workflow maps onto the PC2Products tools like this:

Paper step	PC2Products tool	Settings
Ground classification (LAStools in the paper)	Ground Classify Using CSF	Defaults
10 cm DTM with `knnidw` (k = 10, p = 2)	Generate DTM from LiDAR Data	`style = idw` (lidR's `knnidw()` defaults are k = 10, p = 2). The cell size comes from the point density and can't be set.
Microforms: DTM minus its 3 m focal mean, ±6 cm	Detect Hummock and Hollow from LiDAR Data	`Smooth_by = mean`, `Min_ws = 3`, `lawn_range = 0.06`, `cell_size = 0.1`. The tool builds its own DTM with TIN.
Surface water: rpart classification of the orthomosaic's RGB values, CHM mask (> 0.2 m), patches under 20 × 20 cm removed	Surface Water Mapping	Defaults, with the CHM as `in_chm`
Water table: minimum water elevation per 10 m cell, ordinary kriging (Matern, 10 nearest), then DTW = DTM − water table	Depth-to-Water from Surface Water	Defaults, with the upland knoll as `in_mask`
Segments along mapped centrelines (Forest Line Mapper)	None	Not available; supply `in_segments` from elsewhere.
Comparison and statistics	Seismic Line Disturbance Analysis	`significance = 0.05`

The paper's published data (dtm.tif, dtw.tif, microform.tif and segments.gpkg, on Zenodo) can be used directly as the inputs to Seismic Line Disturbance Analysis.

Command line

bt line_analysis   # show the parameters
bt line_analysis --in_segments "segments.gpkg|segments" --in_dtm dtm.tif --in_microform HH.tif --in_dtw dtw.tif --out_folder <out_dir>