Vectors and Rasters
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(N.b. this is a refresher from the BAS QGIS Tutorial)
Vector data provide a way to represent real world features within the GIS environment. A feature is anything you can see on the landscape. Imagine you are standing on the top of a mountain. Looking down you can see rock outcrop, crevasses, lakes, tents and so on (imagining you are in Antarctica!). Each one of these things would be a feature when we represent them in a GIS Application. This kind of data is often known as discrete data (as opposed to continuous, which we will cover later). Vector features have attributes, which consist of text or numerical information that describe the features.
A vector feature has its shape represented using geometry. The geometry is made up of one or more interconnected vertices. A vertex describes a position in space using an X, Y and optionally Z axis.
Vectors are most commonly thought of as points, (poly)lines and polygons.
Where there is only a single vertex, it is referred to as a point. Examples are station locations, spot heights and ice core locations
Where there are two or more vertices that do not join up at the start and end, a polyline is created. Examples are contours, ship tracks and rivers.
Where three or more vertices are present, and the first and last vertex join up, an enclosed polygon is created. Examples include land cover, sea ice extent and rock outcrop.
Shapefiles
the most common form of vector data within GIS
developed and regulated by Esri - proprietary
although the term is 'a shapefile', the format actually consists of many different files (between ~3-10), as seen below. You need all of the files for the shapefile to work properly.
GeoPackages
a newer and less widespread format for vector data, but with many advantages over shapefiles
as seen in the screenshot below, they only consist of one .gpkg file
it is defined by the Open Geospatial Consortium (OGC) - therefore open and non-proprietary
Geodatabases, GeoJSON, GPX, KML, CSV, and many more...
Rasters represent data in a different way to vectors. They are a grid of pixels, and each pixel ( sometimes called cells) contains a value that represents the conditions for the area covered by that cell. Some examples of typical raster data include satellite imagery, sea ice concentration and digital elevation models (DEMs).
Raster data is used in a GIS application when we want to display information that is continuous across an area and cannot easily be divided into vector features. The examples of vector data that we discussed before (station locations, contours, coastline) would be very difficult to represent as a raster. They could be saved as a raster, but it would be a big waste of space for all of the pixels that would contain null data.
It is sometimes sensible to convert data from vector to raster or from raster to vector, but not very often
Rasters should always be georeferenced (they should know their place on the earth's surface). This usually consists of a coordinate for the top left pixel in the image, the size of each pixel in the X direction and the size of each pixel in the Y direction. From this information, the file knows the location of each of its pixels.
Formats for georeferenced rasters can vary. A GeoTiff is a very common format for GIS rasters that has many advantages, but there are many others as well.
You can sometimes work with non-georeferenced images, for example, old aerial photographs or paper maps that have been scanned. You can add these to your GIS and then manually 'georeference' them by locating common points in the image and in a known dataset.
Every raster layer in a GIS has pixels/cells of a fixed size that determine its spatial resolution. This becomes apparent when you look at an image at a small scale (first image below) and then zoom in to a large scale (second image).
(image source: Landsat 8 image, 5th Jan. 2020, courtesy of USGS)
The cell size of the above image is 30 m. The current highest resolution freely available satellite imagery is 10 m. Very high resolution imagery can be purchased with a cell size of 30 cm!
Our eyes can only see RGB wavelengths, but electronic sensors in cameras are able to detect wavelengths that our eyes cannot. Raster images that include data for non-visible parts of the light spectrum are often referred to as multi-spectral images. In GIS, recording the non-visible parts of the spectrum can be very useful. For example, measuring infra-red light can be useful in identifying water bodies.
Because having images containing multiple bands of light is so useful in GIS, raster data are often provided as multi-band images. Each band in the image is like a separate layer. The GIS will combine three of the bands and show them as red, green and blue so that the human eye can see them. The number of bands in a raster image is referred to as its spectral resolution.
The bands in a Landsat 8 satellite image are shown below. A normal 'natural colour' image will consist of bands 4, 3 and 2. Different combinations of bands can highlight different things, such as snow or fire. An example of a 'false colour' band combination highlighting a lava lake inside a volcano is shown below. In a natural colour image, the lake is not generally visible.
Bands
Wavelength (micrometers)
Spatial resolution (meters)
Band 1 - Coastal aerosol
0.43-0.45
30
Band 2 - Blue
0.45-0.51
30
Band 3 - Green
0.53-0.59
30
Band 4 - Red
0.64-0.67
30
Band 5 - Near Infrared (NIR)
0.85-0.88
30
Band 6 - SWIR 1
1.57-1.65
30
Band 7 - SWIR 2
2.11-2.29
30
Band 8 - Panchromatic
0.50-0.68
15
Band 9 - Cirrus
1.36-1.38
30
Band 10 - Thermal Infrared (TIRS) 1
10.6-11.19
100
Band 11 - Thermal Infrared (TIRS) 2
11.50-12.51
100
Table from: https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites​A false colour composite image of Saunders Island, highlighting the lava lake. Source: BAS.
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