Coastal changes with DEP Coastlines

In this lesson, we’ll be looking at coastal erosion (retreat) and accretion (growth) over time in Vanuatu, based on DEP’s Pacific Coastlines (Beta) product. This product consists of annual shorelines at mean sea level, created by combining Landsat collection 2 surface reflectance data with tidal modelling (Bishop-Taylor et al., 2021). It also comes with a rates of change point dataset that provides coastal change statistics at every 30 metres along shorelines.

Links:

• DEP map interface: https://maps.digitalearthpacific.org/#share=s-tIricadkEWrHOQy58RD1

• Data download: https://data.digitalearthpacific.org/#dep_ls_coastlines/

• Tileserver endpoint (WMTS, XYZ, etc): https://tileserver.prod.digitalearthpacific.io/

References:

• Bishop-Taylor, R., Nanson, R., Sagar, S., Lymburner, L. (2021). Mapping Australia’s dynamic coastline at mean sea level using three decades of Landsat imagery. Remote Sensing of Environment, 267, 112734. Available: Bishop-Taylor et al. (2021).

• https://knowledge.dea.ga.gov.au/notebooks/Real_world_examples/Coastal_erosion/

Data filtering¶

The DEP Coastline (v0.7.0.55, released July 2025) covers the entire Pacific and comes in a geopackage file. The 1.97 GB file can be downloaded from:

• https://s3.us-west-2.amazonaws.com/dep-public-data/dep_ls_coastlines/dep_ls_coastlines_0-7-0-55.gpkg

The geopackage file contains several layers. We will be using just two today:

• shorelines_annual (line): represents the median shoreline (edge-of-water) location at approximately mean sea level for each year.

• rates_of_change (point): provides rates of coastal change (in metres per year) at every 30 m along shorelines. These rates are calculated by performing a linear regression between annual shoreline positions and time (year), using the most recent shoreline as the baseline

Let’s stream the file using obstore, and read the shorelines_annual layer into a geopandas.GeoDataFrame. We will set a filter to just Vanuatu (VUT)'s Exclusive Economic Zone (EEZ).

The annual_shoreline data consists of the following columns:

• year: The year represented by the corresponding shoreline

• certainty: “good” to indicate high quality data, “insufficient data” to indicate that not enough data was available for that year to ensure high quality output, or “unstable data” to indicate the data was highly variable within that year

• eez_territory: A 3-letter country code to indicate the economic exclusion zone within which the shoreline falls

• geometry: MultiLineString geometry for the corresponding row.

Next, let’s read the rates_of_change layer, also filtering to just VUT, plus an additional condition to choose only certainty == 'good' points.

The rates_of_change data consists of the following columns:

• rate_time: Annual rates of change (in metres per year) calculated by linearly regressing annual shoreline distances against time (excluding outliers). Negative values indicate retreat and positive values indicate growth

• sig_time: Significance (p-value) of the linear relationship between annual shoreline distances and time. Small values (e.g. p-value < 0.01) may indicate a coastline is undergoing consistent coastal change through time.

• se_time: Standard error (in metres) of the linear relationship between annual shoreline distances and time. This can be used to generate confidence intervals around the rate of change given by rate_time (e.g. 95% confidence interval = se_time * 1.96).

• outl_time: Individual annual shorelines are noisy estimators of coastline position that can be influenced by environmental conditions (e.g. clouds, breaking waves, sea spray) or modelling issues (e.g. poor tidal modelling results or limited clear satellite observations). To obtain reliable rates of change, outlier shorelines are excluded using a robust Median Absolute Deviation outlier detection algorithm, and recorded in this column.

• sce: Shoreline Change Envelope (SCE). A measure of the maximum change or variability across all annual shorelines, calculated by computing the maximum distance between any two annual shoreline (excluding outliers). This statistic excludes sub-annual shoreline variability.

• nsm: Net Shoreline Movement (NSM). The distance between the oldest and most recent annual shoreline (excluding outliers). Negative values indicate the coastline retreated between the oldest and most recent shoreline; positive values indicate growth. This statistic does not reflect sub-annual shoreline variability, so will underestimate the full extent of variability at any given location.

• max_year, min_year: The year that annual shorelines were at their maximum (i.e. located furthest towards the ocean) and their minimum (i.e. located furthest inland) respectively (excluding outliers). This statistic excludes sub-annual shoreline variability.

• dist_<year>: The distance between the shoreline position in the last year (2024) and the indicated year. Positive values indicate seaward movement of shorelines, negative values landward.

• certainty: “good” to indicate high quality data, “insufficient observations” to indicate less than 15 years of observations were available, “extreme value” to indicate the rates of change are greater than 200-m / year, and “high angular variability” to indicate the standard deviation of the angle between the last year and other years is >30 degrees.

Analysis¶

We now have coastline data for the whole of Vanuatu. Let’s zoom into one small island as a case study.

Here, we can see that the Northern shoreline of the island is retreating, whereas the Southern part of the island is expanding.

Export for whole country¶

Let’s now save the Vanuatu coastlines data to a geopackage (GPKG) file, which can hold both the shorelines_annual and rates_of_change layers, plus QGIS styles.