In its simplest form, a raster consists of a matrix of pixels (or cells) organized into rows and columns (or a grid) in which each pixel contains a value representing information, such as image reflectance or temperature. Rasters are digital images collected by aircraft, drones, satellites, ground and water-based sensors, digital pictures, and scanned maps.
Data stored in a raster format represents real-world phenomena in the following ways:
- Continuous data represents spectral data, such as satellite, aerial, and drone images, and physical and environmental data such as elevation and temperature.
- Thematic data (also known as discrete data) represents features such as land use and soils data.
- Pictures can include scanned maps or drawings and building photographs.
Continuous images can be displayed as data layers along with other geographic data on a map but are often used as the source data for image analysis. Thematic and digital pictures are often used as attributes in tables. They can be displayed with geographic data and are used to convey additional information about map features.
Image and raster data is useful for a wide range of applications. In a GIS, image and raster data is typically used for the following:
- Images as basemaps
A common use of image data in a GIS is as an image background for other feature layers. For example, orthoimages that are displayed under other GIS layers allow map users to confirm that map layers are spatially aligned and represent real objects, as well as provide additional contextual information. The main sources of raster basemaps are orthorectified images from aerial, drone, and satellite imagery, and scanned maps. The following image is used as a basemap for road data.
- Rasters as surface maps
Rasters are well suited for representing data that changes continuously across a landscape (surface). They provide a method of storing the continuity as a surface. They also provide a regularly spaced representation of surfaces. Elevation values measured from the earth's surface are a common application of surface maps, but other values, such as rainfall, temperature, salinity, magnetic properties, and density of materials can also define surfaces that can be spatially analyzed. The raster in the image below displays elevation—using green to show lower elevation and red, pink, and white cells to show higher elevation.
- Rasters as thematic maps
Rasters representing thematic data can be derived from analyzing other data. A common analysis application is classifying a satellite image into land-cover categories. This groups the values of multispectral data into classes (such as vegetation type) and assigns a categorical value. Thematic maps can also result from geoprocessing operations that combine data from various sources, such as vector, raster, and terrain data. For example, you can process data using a geoprocessing model to create a raster dataset that maps suitability for a specific activity. The image below is an example of a classified raster dataset showing land use.
Advantages of storing data as a raster
Sometimes you don't have the choice of storing data as a raster; for example, imagery is only available as a raster. However, many other features (such as points) and measurements (such as rainfall) can be stored as either a raster or a feature (vector) data type.
Advantages of storing data as a raster include the following:
- A simple data structure—A matrix of pixels with values having a coordinate location that can be linked to an attribute table
- A format for advanced spatial and statistical analysis
- Represent continuous image data, surfaces, and perform scientific analysis
- Uniformly store points, lines, polygons, and surfaces
- Perform fast overlays with complex datasets
In some cases, storing data as a raster is not suitable, and you may want to use a vector-based storage option. The following are examples:
- Spatial inaccuracies exist due to the limits imposed by the raster dataset cell dimensions
- Raster datasets can be very large. Resolution increases as the size of the pixel decreases; however, typically cost also increases in both disk space and processing speeds. For a given area, changing pixels to one-half the current size requires as much as four times the storage space, depending on the type of data and storage techniques used. Increased storage and processing times can be mitigated using raster functions and on-the-fly processing.
- Loss of geometric precision accompanies restructuring data to a regularly spaced raster-cell boundary.
General characteristics of raster data
In image datasets, each pixel has a value. The pixel values represent the phenomenon portrayed by the raster dataset, such as a spectral value, category, magnitude, or height. The category can be a land-use class such as grassland, forest, or road. Spectral values are used in satellite and aerial imagery to represent light reflectance and color. A magnitude may represent gravity, noise pollution, or percent of rainfall. Height (distance) can represent surface elevation above mean sea level, which can be used to derive slope, aspect, and watershed properties.
Pixel values can be either positive or negative, integer, or floating point. Integer values are best used to represent categorical (discrete) data and floating-point values are well suited to represent continuous surfaces. Pixels can also have a NoData value to represent the absence of data. For information about NoData values, see NoData in raster datasets.
Rasters are stored as an ordered list of pixel values—for example, 80, 74, 62, 45, 45, 34, and so on.
The area (or surface) represented by each pixel consists of the same width and height and is an equal portion of the entire surface represented by the image. For example, an image representing elevation—a digital elevation model (DEM)—may cover an area of 100 square kilometers. If there are 100 pixels in this image, each pixel represents 1 square kilometer of equal width and height (that is, 1 km by 1 km).
The dimension of the pixels can be as large or as small as needed to represent the surface conveyed by the raster dataset and the features within the surface, such as a square kilometer, square foot, or square centimeter. The pixel size determines how coarse or fine the patterns or objects in the image appear. The smaller the pixel size, the smoother or more detailed the image. If a pixel size is too large, information may be lost or subtle patterns may be obscured. For example, if the pixel size is larger than the object of interest, that object may not exist in the raster dataset. In the diagram below, a simple polygon feature is represented by a raster dataset at various pixel sizes.
The location of each pixel is defined by the row or column where it is located in the raster matrix. The matrix is represented by a Cartesian coordinate system in which the rows of the matrix are parallel to the x-axis and the columns to the y-axis of the Cartesian plane. Row and column values begin with 0. In the example below, if the raster is in a Universal Transverse Mercator (UTM) projected coordinate system and has a pixel size of 100, the pixel location at 5,1 is 300,500 East, 5,900,600 North.
When you need to specify the extent of an image, the extent is defined by the top, bottom, left, and right coordinates of the rectangular area covered by the image, as shown below.
Geographic properties of image data
Four geographic properties are typically recorded for all image datasets. These are useful for georeferencing and help explain how image data files are structured. This concept is important to understand: it helps explain how images are stored and managed in the geodatabase.
Image datasets have a unique way of defining geographic location. Once the pixels are accurately georeferenced, an ordered list of the pixel values in an image or raster is available. This means that each raster dataset typically has a header record containing its geographic properties, and the body of the content is an ordered list of pixel values.
The four geographic properties of image datasets are the following:
- A coordinate system
- A reference coordinate or x,y location (typically the upper left or lower left corner of the image)
- A pixel size
- The count of rows and columns
This information can be used to find the location of any specific pixel. When this information available, the raster data structure lists the pixel values in order from the upper left pixel along each row to the lower right pixel, as illustrated below.