FastIceCovars

Scripts to generate the fast ice covariate data for the Seals from Space project.
For more information, contact Dennis Jongsomjit [djongsomjit@pointblue.org] or
Leo Salas [lsalas@pointblue.org]

Overall approach

We start with a raster dataset of bathymetric data. From this raster we construct a grid of the desired resolution
(in our case 5 km cells). This grid is then used to sample NIC data. The following attributions are treated here
as immutable: mean depth, distance to shore, distance to the 300 m depth isobath, mean slope, distance to ADPE
and EMPE colonies, distance to glacier. Only the ice data attributions vary, and these depend on the NIC ice date
chosen to attribute the data.

We provide a function to query the NIC database and then choose a specific date. Once chosen, the function filters
the grid for only points on fast ice in the chosen date, and retrieves distance to the edge, fast ice width and a
simple metric of how good the fast ice is around the point.

Preliminary work in ArcGIS

A bathymetric grid of the southern ocean was obtained at a 500m resolution (IBCSO v1.0; Arndt et al. 2013).
Within ArcMap a land and ice shelf layer (ADD v2.0; British Antarctic Survey 1998) was used to mask out these
areas from the bathymetric grid.

From this layer then:

• A bathymetry line shapefile at -300 m was created using the Contour tool of ArcGIS.
• Slope was calculated in degrees using the Slope tool of ArcGIS.
• To derive the mean depth, and mean slope, the bathymetric grid and slope grid were averaged across 10 x 10 500-m cells using the Aggregate tool of ArcGIS. The aggregate tool allows for the creation of grids at
  different resolutions. Because we aggregated by 10 x 10 cells, the resulting grid is of 5-km resolution.
• The 5-km grids was then used to create the 5-km sampling location shapefile (i.e. the cell centroids from the raster)
  using the Raster to Point tool of ArcGIS. These sampling locations can be filtered to locations of interest.
  For the purpose of this work, we used areas where fast ice is potentially present.
• We limited the points to a minimum latitude of 60 degees South, and a maximum determined by the land-plus-ice shelf mask.
• The points were thus attributed with mean slope and bathymetry. We then calculated distance to the 300-m isobath
  using the XX tool of ArcGIS. (Dennis to complete)

The resulting shapefile is ready for use in the R environment.

Filtering and attribution of points using R

The attributed shapefile is read into the R environment. Within R, each sampling location was further attributed with
spatially overlapping grid values or distances to shapefile features. The following R script files should be run in
the order in which they are described here - starting with getLandEdge.R and ending with getNICice.R
Although the scripts could be combined into a single file, these files represent each a break point in a process
that together can take many hours to execute. By splitting the process in these individual files we sought to help
the user split the attribution process into smaller and hopefully more convenient time intervals.

Step 1: The file getLandEdge.R uses a default NIC ice date to retrieve the land/ice shelf edge for the entire continent.
This edge dataset is saved in the git directory automatically and used by the ice attribution script (nic_ice_v3.R)

Step 2: The file attributeStudyAreaPoints.R creates the first attribution of the immutable features: bathymetry, slope, etc.
The output is the spatial points data.frame that is used by createNearestLandPoint.R to find the nearest land point
for each point in the grid.

Step 3: The file createNearestLandPoint.R only does the attribution of the nearest land point, and must be run before
the ice attribution script.

Step 4: attribute the points with fast ice data with the script getNICice.R

Projection

For processing, all objects were in or re-projected to the Antarctic Polar Stereographic WGS84 projection

Proj4 = "+proj=stere +lat_0=-90 +lat_ts=-71 +lon_0=0 +k=1 +x_0=0 +y_0=0 +datum=WGS84 +units=m
+no_defs +ellps=WGS84 +towgs84=0,0,0"

Citations

• Arndt, J.E., H. W. Schenke, M. Jakobsson, F. Nitsche, G. Buys, B. Goleby, M. Rebesco, F. Bohoyo, J.K. Hong, J. Black,
  R. Greku, G. Udintsev, F. Barrios, W. Reynoso-Peralta, T. Morishita, R. Wigley, "The International Bathymetric Chart
  of the Southern Ocean (IBCSO) Version 1.0 - A new bathymetric compilation covering circum-Antarctic waters",
  Geophysical Research Letters, doi: 10.1002/grl.50413

• British Antarctic Survey. 1998. Antarctic Digital Database, Version 2.0. Manual and bibliography. Scientific Committee
  on Antarctic Research, Cambridge. 74 pp. https://www.add.scar.org/