Remote Sensing

Types of Remotely Sensed Data

When it comes to classifying remotely sensed data, there are many different ways to do so. The most common of these are by the type of platform (aircraft or satellite); the region of spectrum used to image the earth's surface (optical, infrared, microwave); the platform trajectory, where sun-synchronic or geostationary satellites are recognized; the number of spectral bands (e.g., panchromatic, multispectral, or hyperspectral); the spatial resolution (high; medium, also known as “Landsat-like”; or low); the temporal resolution (e.g., hourly, daily, weekly, or revisiting frequency); the radiometric resolution (e.g., 8, 12, or 16 bits); and the application (meteorological, land resources).

Optical multispectral systems on-board satellites, like the Landsat, SPOT, Quickbird, IKONOS, IRS, and Terra, are referred to as passive systems, because they rely on sunlight reflected off the earth to collect images. Since data are collected at frequencies roughly equivalent to the human eye, these sensors are unable to acquire data independently of solar illumination or wherever conditions such as cloud cover, haze, dust, or smoke prevail. To overcome these problems, the socalled active sensors (because they can send their own microwave signals down to earth and process the signals that are received back) were developed. Earlier satellite and shuttle missions, like the SIRs and Seasat and, recently, sensors on-board the Radarsat, ERS, ALOS, and Envisat, belong to the active sensor category of synthetic aperture radars (SARs). LiDAR (light detection and ranging) is another example of an airborne sensor that collects data using laser pulses within the visible and infrared ranges.

The SAR capability of acquiring images regardless of cloud coverage provides the users with significant advantages when it comes to viewing under conditions that preclude observations made by optical sensors. For instance, it was only in the 1960s that images of the Amazon Basin in Brazil could be produced for the first time, thanks to the radar-based project RADAM. Complete air photo coverage could never be obtained because of the almost constant cloud cover over much of the region. Likewise, a study published in Indonesia reported that during 25 years of optical (passive) remote-sensing data acquisition in [Page 366]that country, only 3% of 1,000 scenes acquired contained less than 10% cloud cover.

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