Satellite Imagery is an excellent way to display crucial data in a clear and concise way. Satellites capture far more data than just the visible spectrum of light that we can see. This allows for many different types of imagery analysis beyond just pretty pictures. This creates a large problem for new users of satellite imagery. What is the best way to display the information for your project?
In the field of remote sensing, there are numerous kinds of payloads and sensors equipped in different satellites. These sensors vary on what are the missions and purpose of each satellite upon deployment into outer space. Sensors have different image data type, resolution and multispectral capabilities. Through this article users will be able to learn the basics of remote sensing and common application of the data.
Remote sensing 101: Feeling without touching
Remove Sensing provides users with a way to view an area without having to specifically visit that area. This is done by sensing and recording reflected energy from the surface and processing, analyzing gathered information.
There are two kinds of remote sensing: passive remote sensing, and active remote sensing.
Passive Remote Sensing – the satellite detects when naturally occurring energy is available, thus producing natural color, multispectral and / or hyperspectral images. If there is no reflected energy available from the sun at night, thermal or infrared imaging can be detected.
Active Remote Sensing – active sensor satellites provide its own energy source for illumination to record imagery. Active satellite sensors emit radiation such as microwaves which is reflects off the ground and back to the satellite sensor. The advantage of using an active sensor is the ability to obtain measurements regardless of the time of the day whether it could be day or night. The most common type of active sensors for satellite imagery is synthetic aperture radar (SAR). This type of sensor can provide useful data regardless of time of day and cloud cover.
This picture illustrates an example on how a passive and active satellite sensor is able to generate satellite image data.
Comparison between passive remote sensing (Optical imagery) vs Active remote sensing (Synthetic Aperture Radar Image). The image on left is Kompsat-3 optical imagery while the image on the right is Sentinel-1A Synthetic Aperture Radar Image captured over Canberra Australia.
What spatial resolution of satellite imagery data I need?
Spatial resolution in remote sensing refers to the size of one pixel (the smallest dot that makes up an optical image) on the ground and describes how much detail in a satellite image is visible to the human eye or how details a picture is.
Proimagery offers satellite images with varying resolutions from 3-meter medium resolution satellite imagery up to sub-meter high-resolution satellite images. Choosing the resolution of images may come down to how large your project area might be and how important ground features need to be observed and analyzed.
To help you know the difference between various resolutions of satellite images data, look at these two satellite images captured over Woden Valley, Australia. The left image is a 70cm high-resolution satellite image from Kompsat-3. At this spatial resolution, customers allow seeing buildings, roads, streets, the distinction between property boundaries, and even properties that have swimming pools. While the image on the left is a medium resolution Dove satellite imagery which provides a much coarser view and detail of the ground features.
Comparison between the details of high resolution and medium resolution satellite imagery
How can I utilize multispectral imagery?
Most satellites are capable of imaging beyond the visible light spectrum and come equipped with sensors that can view four spectral bands of imagery (blue, green, red, and near-infrared bands). With the use of multispectral imagery, users can extract various information not visible to the human eye since multispectral bands can be used to identify various characteristics of ground features such as vegetation, water extent, soil moisture, roads, etc. which have different spectral signatures.
Here are some common uses of multispectral imageries Proimagery can provide to users:
Using vegetation index on assessing vegetative analysis and monitoring crop health using time-series analysis from planting to harvesting
Extracting flood extent using Normalized Difference Water index.