Resolution The resolution of an image refers to the potential detail provided by the imagery. In remote sensing we refer to three types of resolution: spatial, spectral and temporal. Spatial Resolution refers to the size of the smallest feature that can be detected by a satellite sensor or displayed in a satellite image. It is usually presented as a single value representing the length of one side of a square.
For example, a spatial resolution of m means that one pixel represents an area by meters on the ground. To better understand multispectral remote sensing, you need to know some basic principles of the electromagnetic spectrum. A spectral remote sensing instrument collects light energy within specific regions of the electromagnetic spectrum. Each region in the spectrum is referred to as a band. Remote sensing data can be collected from the ground, the air using airplanes or helicopters or from space.
You can imagine that data that are collected from space are often of a lower spatial resolution than data collected from an airplane. The tradeoff however is that data collected from a satellite often offers better up to global coverage. For example the Landsat 8 satellite has a 16 day repeat cycle for the entire globe. This means that you can find a new image for an area, every 16 days. It takes a lot of time and financial resources to collect airborne data. Thus data are often only available for smaller geographic areas.
Also, you may not find that the data are available for the time periods that you need. For example, in the case of NAIP, you may only have a new dataset every years. When talking about spectral data, you need to understand both the electromagnetic spectrum and image bands. Spectral remote sensing data are collected by powerful camera-like instruments known as imaging spectrometers. Displaying multiband satellite images Landsat Explorer web app introduction Working with satellite imagery in ArcMap The image analysis window in ArcMap Downloading Landsat 8 data Adding Landsat 8 images to ArcMap Visually interpreting satellite images Satellite image classification Taught By.
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