Basics of Satellite Imagery
I am doing my second project with Omdena which involves assessing the impact of desert locusts in Kenya. This is my first time working with satellite imagery, and as a newbie there were so many new terms that I had to wrap my head around. As usual, I spent a lot of time Googling stuff up and finally decided to write this piece down as a reference for other newbies in satellite imagery such as myself. Without much ado, let us get started.
Landstat and Sentinel
These will be the first of the words that come up when you do something related to satellite images. These are nothing but the names of satellites that orbit the earth. So if you see a “Landstat image”, it means that image was captured by the landstat satellite. Now what is the difference between the two?
Landsat satellites provide high-quality, multi-spectral imagery of the surface of the Earth. These moderate-resolution, remotely sensed images are not just pictures, but contain many layers of data collected at different points along the visible and invisible light spectrum. Landsat satellites have the optimal ground resolution and spectral bands to efficiently track land use and to document land change due to climate change, urbanization, drought, wildfire, biomass changes (carbon assessments), and a host of other natural and human-caused changes.
Sentinel-2 multispectral sensor allows quality pictures of the terrain that can be implemented for land management, monitoring agriculture, and forestry. Sentinel-2 is an optical satellite.
Bands
The sensors aboard each of the Landsat satellites were designed to acquire data in different ranges of frequencies along the electromagnetic spectrum. These ranges are called bands. Many raster files, including color digital photos, are made up of multiple bands or layers. You can think of image bands (also called channels or layers) as a collection of images taken simultaneously of the same place. Many sensors on earth observing satellites measure the amount of electromagnetic radiation (EMR) that is reflected or emitted from the Earth’s surface. These sensors, known as multispectral sensors, simultaneously measure data in multiple regions of the electromagnetic spectrum. The range of wavelengths measured by a sensor is known as a band and are commonly described by the name (Red or Near-IR for example) and the wavelength of the energy being recorded. (Source)
Raster images
In its simplest form, a raster consists of a matrix of cells (or pixels) organized into rows and columns (or a grid) where each cell contains a value representing information, such as temperature. Rasters are digital aerial photographs, imagery from satellites, digital pictures, or even scanned maps. (Source)
TIFF
Tag Image File Format, abbreviated TIFF or TIF, is a computer file format for storing raster graphics images, popular among graphic artists, the publishing industry, and photographers.TIFF files can be saved in a variety of color formats and in various forms of compression. TIFFs use lossless compression to maintain image integrity and clarity and are often used for professional photography. GIF (short for Graphics Interchange Format) is a file format for storing graphical images up to 256 colors.
TFW
TIFF world file used by GIS applications; contains the location, scale, and rotation of a map that is stored as a TIFF image; often used for saving the world location of an aerial satellite photograph; uses the same format as JPEG World files. TFW files are saved in a plain text format. They store a series of lines that specify the X and Y pixel size, rotational information, and the world coordinates for the top-left corner of the image. (Source)
PRJ File
A file with the PRJ extension has become the industry standard for containing information related to a coordinate system. (Good source)
GeoJSON
GeoJSON is an open standard geospatial data interchange format that represents simple geographic features and their nonspatial attributes. Based on JavaScript Object Notation (JSON), GeoJSON is a format for encoding a variety of geographic data structures.
