When talking about spectral data, you need to understand both the electromagnetic spectrum and image bands. Notice that spaceborne data are often of lower resolution, however, because a satellite rotates continuously around the earth, the spatial coverage may be better than airborne data. For example, in the case of NAIP, you may only have a new dataset every 2-4 years. Also, you may not find that the data are available for the time periods that you need. Thus data are often only available for smaller geographic areas. It takes a lot of time and financial resources to collect airborne data. This means that you can find a new image for an area, every 16 days. The tradeoff however is that data collected from a satellite often offers better (up to global) coverage.įor example the Landsat 8 satellite has a 16 day repeat cycle for the entire globe. You can imagine that data that are collected from space are often of a lower spatial resolution than data collected from an airplane. Remote sensing data can be collected from the ground, the air (using airplanes or helicopters) or from space. Key Attributes of Spectral Remote Sensing Data Space vs Airborne Data Each region in the spectrum is referred to as a band.Ībove: A video overview of spectral remote sensing.Ībove: Watch the first 8 minutes for a nice overview of spectral remote sensing. A spectral remote sensing instrument collects light energy within specific regions of the electromagnetic spectrum. The electromagnetic spectrum is composed of a range of different wavelengths or “colors” of light energy. To better understand multispectral remote sensing you need to know some basic principles of the electromagnetic spectrum. RIGHT: Active sensors emit their own energy from a source on the instrument itself. LEFT: Remote sensing systems which measure energy that is naturally available are called passive sensors. This means that the sensor is measuring light energy from an existing source - in this case the sun. ![]() Multispectral remote sensing is a passive remote sensing type. This week you will work with multispectral imagery or multispectral remote sensing data. This means that the instrument emits energy actively rather than collecting information about light energy from another source (the sun). If you recall, a lidar instrument is an active remote sensing instrument. In the previous weeks of this course, you learned about lidar remote sensing. You will need a computer with internet access to complete this chapter. Describe the spatial and temporal tradeoffs between data collected from a satellite vs an airplane.Describe at least 3 differences between NAIP imagery, Landsat 8 and MODIS in terms of how the data are collected, how frequently they are collected and the spatial and spectral resolution. ![]() Define multispectral (or multi-band) remote sensing data.Define spectral and spatial resolution and explain how they differ from one another.Learning ObjectivesĪfter completing this chapter, you will be able to: In this chapter, you will learn about various options for multispectral remote sensing data and the advantages and disadvantages of these data options. Intermediate-earth-data-science-textbook HomeĬhapter Seven - Intro to Multispectral Remote Sensing Data Use Data for Earth and Environmental Science in Open Source Python Home.Chapter 12: Design and Automate Data Workflows. ![]()
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