Remote sensing concepts

Most of the GIS concepts we’ve described also apply to raster data. However, raster data has some unique properties as well. Earlier in this chapter, when we went over 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 to detect gases, radiation, and other forms of energy in a geographic context.

For this book, we will focus on remote sensing platforms that capture large amounts of Earth data. These sources include Earth imaging systems, certain types of elevation data, and some weather systems, where applicable.

Images as data

Raster data is captured digitally as square tiles. This means that the data is stored on a computer as a numerical array of rows and columns. If the data is multispectral, the dataset 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. In geospatial analysis, it is important 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 (the 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. Since each pixel is a number, we can perform mathematical equations on this data to combine data from different bands and highlight specific classes of objects in the image. If the wavelength value is beyond the visible spectrum, we can highlight features that aren’t visible to the human eye. Substances such as chlorophyll in plants can be greatly contrasted using a specific formula called the Normalized Difference Vegetation Index (NDVI).

By processing remotely sensed images, we can turn this data into visual information. Using the NDVI formula, we can answer the question, what is the relative health of the plants in this image? You can also create new types of digital information, which can be used as input for computer programs to output other types of information.

Remote sensing and color

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 this visible spectrum. On a computer, wavelengths beyond the visible spectrum are represented in the visible spectrum so that we can see them. These images are known as false color images. In remote sensing, for instance, 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 levee.