Who invented Remotes Sensing

What is Remotes Sensing

The technology of modern remote sensing began with the invention of the camera more than 150 years ago. Remote sensing is the natural or artificially-enhanced perception of objects or occurrences from a significant distance. Remote sensing in its most basic and naturally-occurring form is eyesight. Barring obstructions, eyesight allows us to experience and understand phenomena and things not in our immediately accessible frame of reference. In the same vein, hearing is a form of remote sensing, allowing us to experience the audio component of articifical and naturally-occurring phenomena at a distance from our own location. Granted that our hearing, with few exceptions, fails to offer us the same perceptive advantages as our eyesight, so too our own eyesight bears perceptive limitations, particularly where our focal abilities and perceptions of light and dark are concerned. More debilitating still are our own physical limitations for achieving ceratin vantage points necessary for properly observing specific objects and occurrences and collecting specific data thereabout. To this extent, limitations inherent in such human perception in no way implies a limitation in human creativity and design. Beyond the limits of human perception, sometimes called the “naked eye”, technology may assist through the use of peripherals (e.g. binoculars and telephones), either simple or sophisticated, which enhance those things and phenomena we would not otherwise be able to perceive on our own. Similarly, such peripherals may be placed, either manually or by design, in those observation spots normally inaccessible to human eyes and ears (e.g. a high ceiling).

The Quality of Remote Sensing Data

Spatial resolution: The size of a pixel that is recorded in a raster image – typically pixels may correspond to square areas ranging in side length from 1 to 1,000 meters.

Spectral resolution: The wavelength width of the different frequency bands recorded – usually, this is related to the number of frequency bands recorded by the platform. Current Landsat collection is that of seven bands, including several in the infra-red spectrum, ranging from a spectral resolution of 0.07 to 2.1 μm. The Hyperion sensor on Earth Observing-1 resolves 220 bands from 0.4 to 2.5 μm, with a spectral resolution of 0.10 to 0.11 μm per band.

Radiometric resolution: The number of different intensities of radiation the sensor is able to distinguish. Typically, this ranges from 8 to 14 bits, corresponding to 256 levels of the gray scale and up to 16,384 intensities or “shades” of colour, in each band. It also depends on the instrument noise.

Temporal resolution: The frequency of flyovers by the satellite or plane, and is only relevant in time-series studies or those requiring an averaged or mosaic image as in deforesting monitoring. This was first used by the intelligence community where repeated coverage revealed changes in infrastructure, the deployment of units or the modification/introduction of equipment. Cloud cover over a given area or object makes it necessary to repeat the collection of said location.

Remote Sensing Roots

Remote sensing as a practical application has its initial roots in national security. The advent of aviation and its application to espionage for the purposes of aerial reconaissance photography exemplifies the use of remote sensing peripherals in acquisition otherwise unattainable by eyesight alone. Introduced most prominently with the U2 spy plane in the mid-1950s, aerial military reconaissance became the predecessor of the remote sensing satellites born of the space age nearly two decades later. As the Cold War between the United States and the Soviet Union ran its tense course during the 1950s and 1960s, both super powers engaged in a race to conquer the orbit that lay just beyond Earth’s realm. The National Aeronautics and Space Administration (NASA) of the United States successfully undertook an initiative to establish remote sensing satellites in Earth’s orbit. These particular satellites were equipped with analog cameras capable of taking snapshots of specific areas of Earth’s surface on standard celluloid film. The film was returned to Earth via a special device and then retrieved for developing and analysis.

Subsequent to 1972, images from remote sensing satellites began to be transmitted electronically, as is the case today. Analog imaging was eventually replaced by digital imaging, with applications stretching beyond reconaissance to include cartography, land surface contours, and natural resource monitoring. In this regard, remote sensing satellites have become equipped with a myriad of cameras and sensors conducive to picking up electromagnetic radiation emitted naturally from Earth’s surface. In addition, remote sensing satellites are equipped with devices for emitting radiation to specific areas of Earth’s surface, which may then be picked up by sensors upon reflection back. The wavelengths of electromagnetic radiation perceived by these satellites may indicate specific geologic conditions or the presence of natural resources below ground. For this reason, remote sensing satellites are equipped with sensors of different resolutions, for different wavelengths of electromagnetic radiation. Images of different wavelengths provide different types of information about the object or phenomenon under consideration. For instance, the cameras aboard early reconaissance satellites had a resolution of roughly two meters, while later satellites used in earth science research contain imaging sensors with resolutions of between five meters and sixty meters, depending on the wavelength and image color-type.

Data Processing Levels

To facilitate the discussion of data processing in practice, several processing “levels” were first defined in 1986 by NASA as part of its Earth Observing System[3] and steadily adopted since then, both internally at NASA and elsewhere:

Level 0: Reconstructed, unprocessed instrument and payload data at full resolution, with any and all communications artifacts removed.

Level 1a: Reconstructed, unprocessed instrument data at full resolution, time-referenced, and annotated with ancillary information, including radiometric and geometric calibration coefficients and geo-referencing parameters computed and appended but not applied to the Level 0 data.

Level 1b: Level 1a data that have been processed to sensor units; not all instruments have Level 1b data; level 0 data is not recoverable from level 1b data.

Level 2: Derived geophysical variables at the same resolution and location as Level 1 source data.

Level 3: Variables mapped on uniform spacetime grid scales, usually with some completeness and consistency.

Level 4: Model output or results from analyses of lower level data.

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