Choosing the right type of imagery for your project
Satellite Imagery is used for a wide range of projects, but often it is tough to figure out just which type of data is needed. In the world of remote sensing, there are many different satellite sensors, all with different resolutions and multispectral capabilities. In this blog, we will explore some basics of remote sensing, what data is available, and common applications of the data.
Remote Sensing 101
As great as a natural color image can look, it is not collected in the same way as, say, the camera on your smart phone. The process of collecting imagery is most commonly referred to as remote sensing, which means objects on earth are detected and classified without direct contact. This blog will focus on passive sensing, as this is what is used for natural color and multispectral images (except radar). Passive sensors gather information via radiation that is either emitted or reflected by whatever object the satellite is trying to capture. This data is captured with whatever spectral bands a given sensor can acquire (generally at least red, green, blue, and near infrared bands are collected), then downlinked to a ground station for processing. From there, the natural color or multispectral image you might have ordered would be put together using the Red, Green and Blue bands, also known as an RGB image.
A wide variety of sensors and band combinations are available, and the best options will depend on what you are trying to detect, and the size of your project area. Below are a few situations one might face when deciding how to go about selecting the most suitable captures.
What kind of imagery do I need for a base map?
Base mapping is generally a very common request. Engineering, architecture, and similar firms will go for images to view ground conditions before building on their project areas. Essentially, they just need an accurate and high-resolution picture. In these cases, natural color is the best option.
How much resolution do I need?
The resolution needed will come down to how large your project area might be, and what features need to be viewed clearly. Say your project requires you to obtain an image of California. For this, you wouldn’t want as high of a resolution as if you needed an image of an individual airport, for example. This would be an extreme amount of detail for such a large area and would result in enormous file sizes. Additionally, higher resolution sensors typically capture smaller area sizes, so many distinct images would be required to cover such an area. Assuming full coverage exists, it would be from images accumulated over the course of many months or years. Lower resolution options will give a better overview of an area of this size, and generally fewer images (and image dates) to complete the coverage.
One great option is to obtain high resolution imagery of a project site, and lower resolution for the surrounging area. In the images below you will see captures from both the RapidEye & GeoEye-1 sensors. RapidEye captures at 5 meter resolution (lower resolution), and GeoEye captures at the much higher 0.5 meter resolution. Both show coverage of islands of Hawaii. As you can see, RapidEye still has great detail for a larger city area, but GeoEye-1 has the ability to see specific features within a city.
Figure 1: GeoEye image (0.5 meter resolution) Figure 2: RapidEye image (5 meter resolution)
When would I need the multispectral bands?
The most common uses for these are measuring crop health and doing vegetation analysis. Most sensors offered by L3Harris will be equipped with four bands, which are red, green, blue and near-infrared (or RGB & NIR). An image made from these four bands within the ENVI software environment is below. For crop health, one would measure relative reflectance along the wavelengths of these bands. When crops are not healthy, one will notice a decreased reflectance, mainly in the near-infrared reflectance plateau, but also within the blue and green regions of the spectrum.
Multispectral bands can also be used to detect different characteristics on the earth’s surface. Soil, plants, asphalt, etc. all have different spectral signatures, which would be apparent through these multispectral bands. Depending on how many bands you have available, and where they are along the light spectrum, you can start to see more differences within each surface. For example, an asphalt rooftop may appear as only one color with the standard RGB & NIR bands available. If you were working with shortwave infrared (or SWIR) as well, you would start to see different colors within your image. This is because the differences in the components of asphalt are now being detected.
While many applications of this type of data exist, we have covered some very common and reliable project types served by L3Harris. Fortunately, with an ever-growing archive of satellite imagery already in existence, and the opportunity to have more collected as needed, a solution can be found for the most unique type of geospatial project. If you need data or help figuring out what type to use for a project, contact us at firstname.lastname@example.org.
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