Dataset supporting "Microtopographic and Hydrological Response to Repeated Seismic Line Disturbance in a Boreal Fen of Northern Alberta, Canada"

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Authors: Xue Yan Chan, Anna Dabros, Gregory J. McDermid
Cited by: 0

Abstract

This dataset contains raster and vector geospatial data products supporting the research article: Chan, X.Y.; Dabros, A.; McDermid, G.J. Microtopographic and Hydrological Response to Repeated Seismic Line Disturbance in a Boreal Fen of Northern Alberta, Canada. Forests 2026, 17, 489. https://doi.org/10.3390/f17040489 These data were derived from drone-based remote sensing surveys and subsequent geospatial analysis of seismic line disturbances in a boreal fen ecosystem in northern Alberta, Canada. The dataset includes environmental raster layers and vector segment units used to quantify microtopographic and hydrological characteristics and evaluate differences between seismic line disturbance classes (new disturbance, old disturbance, re-disturance) Contents The dataset includes the following files: dtm.tif – Digital terrain model (DTM) raster representing ground surface elevation (meters) dtw.tif – Depth-to-water raster (meters), where values of 0 indicate surface water microform.tif – Microform classification raster, identifying hummock, lawn, and hollow surface types segments.gpkg – Vector dataset containing seismic line segments and reference areas used as units of aggregation and analysis. This file includes attributes describing segment classification and identifiers used for statistical comparisons. An R script is included to reproduce the analysis and figures from the manuscript using these data (chan_et_al_2026_analysis.R). Coordinate Reference System All spatial data are provided in NAD83(CSRS) / UTM Zone 12N, EPSG:2955

Fields of Study & Subjects

Peatlands and Wetlands Ecology Ecology and biodiversity studies Climate change and permafrost

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
26 February 2026
Language
EN
Document type
Dataset