Most of the GIS concepts described also apply to raster data. However, raster data has some unique properties as well. Earlier in this chapter in the history of remote sensing, the focus was on earth imaging from aerial platforms. It is important to note that raster data can come in many forms including ground-based radar, laser range finders, and other specialized devices for detecting gases, radiation, and other forms of energy within a geographic context. For the purpose of this book, we will focus on remote sensing platforms that capture large amounts of earth data. These sources included earth imaging systems but also certain types of elevation data, and some weather systems where applicable.
Raster data is captured digitally as square tiles. This means the data is stored on a computer as a numerical array of rows and columns. If the data is multispectral, the data set will usually contain multiple arrays of the same size, which are geospatially referenced together to represent a single area on the earth. These different arrays are called bands. Any numerical array can be represented on a computer as an image. In fact, all computer data is ultimately numbers. It is important in geospatial analysis to think of images as a numeric array because mathematical formulas are used to process them.
In remotely sensed images, each pixel represents both space (location on the earth of a certain size), and the reflectance captured as light reflected from the earth at that location into space. So each pixel has a ground size and contains a number representing the intensity. Because each pixel is a number, we can perform math equations on this data to combine data from different bands and highlight specific classes of objects in the image. And if the wavelength value is beyond the visible spectrum we can highlight features not visible to the human eye. Substances such as chlorophyll in plants can be greatly contrasted using a specific formula called the normalized vegetation differential index or NDVI.
By processing remotely sensed images, we can turn these data into visual information. Using the NDVI formula we can answer the question, What is the relative health of the plants in this image? But you can also create new types of digital information, which can be used as input for computer programs to output other types of information.
Computer screens display images as combinations of red, green, and blue (RGB) to match the capability of the human eye. Satellites and other remote sensing imaging devices can capture light beyond that visible spectrum. On a computer, wavelengths beyond the visible spectrum are represented in the visible spectrum so we can see them. In remote sensing, infrared light makes moisture highly visible. This phenomenon has a variety of uses such as monitoring ground saturation during a flood or finding hidden leaks in a roof or a levee.