CoastCR is an open source package for computing coastline time series and trend analysis using the baseline and transect approach. This tool is a part of ODSAS (Open Digital Shoreline Analysis System v1.0) which uses both CoastCR and SAGA GIS [1, 2]. CoastCR main input is the points representing the intersections along transects perpendicular to a baseline, of a set of date-stamped coastlines (obtained during mapping stage using SAGA GIS) and calculates the standard metrics of coastline change and rate of change similarly to the metrics produced using Digital Shoreline Analysis System (DSAS) [3, 4]. Package website: https://alejandro-gomez.github.io/CoastCR/
CoastCR 1.2.0.
This version incorporates a change in the Date format. Currently, the user can select more than one shoreline for each day. For this, the user should be introduced to the table with the dates and associated uncertainty in a column named "Day" in format (YYYYY-mm-dd). If the information about the acquisition hour is available, the user should introduce a column called "Hour" in format (HH:MM:SS). Now the column names should be "Day", "Hour" and "Uncertainty". If the data does not include the hour information, remove the column to avoid possible errors.
Contents:
Many researchers and practitioners interested on Shoreline Change Analysis often separate the mapping stages (mostly done within a GIS) from the time series and trend analyses, which can be undertaken in a programming environment [1]. This package is intended to be used for the time series and trend analyses using R programming environment which is an open-source software.
The main functions implemented in, CoastCR are used to:
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Compute the coastline change using the most common change metrics used on transects to a baseline method. Two main functions are included in the current version to filter and estimate the parameters:
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Baseline filter: This function is part of the pre-processing stages and is used to ensure that for each baseline-transect, only one crossing with each date-stamped coastline is used. If for a given transect and date-stamped coastline, more than one crossing is found (e.g., as might happen for very wiggly coastlines), the filter selects the crossing that is closest to the baseline and neglects the others.
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Coast rates: This function estimates the main five key measures for each individual transect and also produce aggregates for all transects identically as within the DSAS tool [2]. This five metrics are:
- NSM: Net Shoreline Movement (m).
- EPR: End Point Rate (m yr-1).
- SCE: Shoreline Change Envelope (m).
- LRR: Linear Regression Rate (m yr-1).
- WLR: Weighted Linear Regression Rate (m yr-1).
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- Coastline Variations is the main function that runs the aforementioned functions. First, filters the intersection points and then calculates the key measures of change.
# To install the latest version from Github:
# install.packages("remotes")
remotes::install_github("alejandro-gomez/CoastCR")# Load libraries
library(sf)
library(CoastCR)
# Intersections shapefile
shp <- st_read(system.file("./extdata/intersect.shp", package = "CoastCR"))
# Normal lines shapefile
normals <- st_read(system.file("./extdata/normals.shp", package = "CoastCR"))
# Table with dates and associated uncertainty
table <- read.csv(system.file("./extdata/table_coastlines.csv", package = "CoastCR"))
# Define baseline position. Offshore = OFF; Onshore = ON; Mixed = MIX.
position = "OFF"
# Define outputs names
out_points <- "./int_filter.shp"
out_name <- "./normals_rates.shp"
coast_var(shp, normals, table, position, out_points, out_name)Using CoastCR for research publication? Please cite it! I am an early career scientist and every citation matters.
Gómez-Pazo, A., Payo, A., Paz-Delgado, M.V., Delgadillo-Calzadilla, M.A. (2022). Open Digital Shoreline Analysis System: ODSAS v1.0. Journal of Marine Science and Engineering, 10, 26. DOI: https://doi.org/10.3390/jmse10010026
Alejandro Gómez-Pazo, Andres Payo and M. Victoria Paz-Delgado
Contributors: M.A. Delgadillo-Calzadilla
Alejandro Gómez-Pazo: a.gomez@usc.es
[1] Gómez-Pazo, A., Payo, A., Paz-Delgado, M.V., Delgadillo-Calzadilla, M.A., 2022. Open Digital Shoreline Analysis System: ODSAS v1.0. Journal of Marine Science and Engineering, 10, 26. DOI: https://doi.org/10.3390/jmse10010026
[2] Paz-Delgado, MV., Payo, A., Gómez-Pazo, A., Beck, A-L., Savastano, S., 2022. Shoreline Change from Optical and Sar Satellite Imagery at Macro-Tidal Estuarine, Cliffed Open-Coast and Gravel Pocket-Beach Environments. Journal of Marine Science and Engineering, 10(5), 561. DOI: https://doi.org/10.3390/jmse10050561
[3] Himmelstoss, E.A., Henderson, R.E., Kratzmann, M.G., Farris, A.S., 2018. Digital Shoreline Analysis System (DSAS) version 5.0 user guide. US Geological Survey Open File Report 2018-1179, 110 pp
[4] Burningham, H.; Fernandez-Nunez, M. 19 - shoreline change analysis. In Sandy beach morphodynamics, Jackson, D.W.T.; Short, A.D., Eds. Elsevier: 2020; pp 439-460.
Paz-Delgado, MV., Payo, A., Gómez-Pazo, A., Beck, A-L., Savastano, S., 2022. Shoreline Change from Optical and Sar Satellite Imagery at Macro-Tidal Estuarine, Cliffed Open-Coast and Gravel Pocket-Beach Environments. Journal of Marine Science and Engineering, 10(5), 561. DOI: https://doi.org/10.3390/jmse10050561
Ankrah, J.; Monteiro, A.; Madureira, H., 2022. Bibliometric Analysis of Data Sources and Tools for Shoreline Change Analysis and Detection. Sustainability, 14(9), 4895. DOI: https://doi.org/10.3390/su14094895
Vallarino Castillo, R.; Negro Valdecantos, V.; Moreno Blasco, L., 2022. Shoreline Change Analysis Using Historical Multispectral Landsat Images of the Pacific Coast of Panama. Journal of Marine Science and Engineering, 10(12), p.1801. DOI: https://doi.org/10.3390/jmse10121801
Tsiakos, C-A.D.; Chalkias, C., 2023. Use of Machine Learning and Remote Sensing Techniques for Shoreline Monitoring: A Review of Recent Literature. Applied Sciences 13 (5), 3268. DOI: https://doi.org/10.3390/app13053268
Hossen, M.F.; Sultana, N., 2023. Shoreline change detection using DSAS technique: Case of Saint Martin Island, Bangladesh. Remote Sensing Applications: Society and Environment, 30, p.100943. DOI: https://doi.org/10.1016/j.rsase.2023.100943
Gómez-Pazo, A., 2023. El empleo de metodologías de código abierto para las investigaciones costeras: comparativa de las técnicas de detección de cambios. Boletín de la Asociación de Geógrafos Españoles, (96). DOI: https://doi.org/10.21138/bage.3318